| Project Title | : | VIRTUAL LABS |
| Project Id | : | 16CC-A1 |
| Guide Details | : | Dr RAJANIKANTH ALUVALU Associate Professor |
| Team Members | : | DANDALA ANJAN KUMAR REDDY ,DANDALA ANJAN KUMAR REDDY,GANGIDI SAI KIRAN REDDY,KADAVERGU ANUDEEP |
| Domain | : | Cloud / IoT |
| Abstract | In recent years, we have seen a significant number of new technological ideas appearing in literature discussing the future of education. For example, E-learning, cloud computing, social networking, virtual laboratories, virtual realities, virtual worlds, massive open online courses (MOOCs), and bring your own device (BYOD) are all new concepts of immersive and global education that have emerged in educational literature. One of the greatest challenges presented to e-learning solutions is the reproduction of the benefits of an educational institutions physical laboratory. For a university without a computing lab, to obtain hands-on IT training with software, operating systems, networks, servers, storage, and cloud computing similar to that which could be received on a university campus computing lab, it is necessary to use a combination of technological tools. In a college laboratory the main actions to be performed are looking up the schedule, executing the scheduled programs and then the evaluation process. In this project a virtual environment is created for each and every student thus he/she can login through their login credentials provided. In each and every city there is huge number of colleges and they are following the traditional method of laboratory process. Through this project we can make all the colleges digital. It is cost efficient where colleges can use it and integrate in their college platform. It increases the productivity as the various cloud computing algorithms are designed to perform certain operations. It reduce the paper work so all the colleges that can use this application to achieve the objectives of virtual labs. | |
| Project Title | : | WE CONNECT (maman.ga) |
| Project Id | : | 16CC-A2 |
| Guide Details | : | Mr M A Ranjith Kumar Assistant Professor |
| Team Members | : | G LAXMI PRASAD, KARRI SRI SATYA LOKESH, MUNUGOTI PAVAN KUMAR, PALADUGULA WIHAR |
| Domain | : | Cloud / IoT |
| Abstract | These days when we are posting some media (photo/video) on social media But we don’t know whom it is reaching even though private profiles are there. So to overcome this we need a social network where only we(people we trust or whom the data is to be shared) are present which can be customized according to one’s need. Thus, we can use the application as we intended to use it without any restrictions. |
|
| Project Title | : | SMART GATE : GATEWAY FOR SMART LIVING |
| Project Id | : | 16CC-A3 |
| Guide Details | : | Mr Gouse Baig Mohammad Assistant Professor |
| Team Members | : | KOMMINENI SAISIDDHARDHA, GOPIREDDY VINEEL REDDY, PASPUNOORI VINEETH,THOTA VIGNESH |
| Domain | : | Cloud / IoT |
| Abstract | : | The main purpose of the project is to manage the problems which are facing in the Community living areas. Generally, in Society all the work is decided through meetings and maintenance bills, contact no of members are recorded on the papers. There is no qualified model for the simplified experience so far. We are proposing a community cloud deployable model for addressing all kinds of needs, services and maintenance. People in the community can raise the maintenance complainants like supply of the water, plumbing, welding works, internet connectivity, electricity, carpentry, Air Conditioner, Water cooler, Refrigerator etc. More extensive services like availing workforce when required, booking the community halls, maintenance bill payments, Event Notifications and information regarding selling and renting the houses in the community. Vivid billing with detailed information, tracking the visitor moments with digital register backups. Apart from the regular usage users can conduct events in the community like pool parties, lady’s night, modelling, new year eve and they can advertise their events on the website. With this model, we can connect the entire community in fingertips for the better and beneficial social living within the cities. |
| Project Title | : | SILAYIAI |
| Project Id | : | 16CC-A4 |
| Guide Details | : | Dr Raman Dugyala Professor |
| Team Members | : | BHUKYA SAIBHARGAVI, DEVARASETTY PRANAY, KASOJU BHARATH, PANTHAGANI TEJASWINI |
| Domain | : | Cloud / IoT |
| Abstract | People find it difficult to alter their clothes. Many fashion designers don’t alter their clothes. Tailor do this job. Tailors stay in our nearby lane but their location is not known to many people. So, this project provides online platform for tailors and customers to interact with each other. Cloud storage is being used to store the details of the tailors and customers.
This application provides the nearby tailor’s details along with the rating given to tailor. Customer can even contact tailor through chat box provided in our application. Tailors can login using their authentication details and can display their designs. The interested customers can contact the tailor and can interact with them. |
|
| Project Title | : | CLOUD BASED HOSPITAL MANAGEMENT SYSTEM |
| Project Id | : | 16CC-A5 |
| Guide Details | : | Ms S Laxmi Sunaina Assistant Professor |
| Team Members | : | MIRGUDE SACHIN, MUTHYAPU SHIVA KUMAR, RUTHVIK REDDY KAWKUNTLA |
| Domain | : | Cloud / IoT |
| Abstract | Hospital needs an online system whereby it can accommodate the patients comfortably and avoid any confusion to the doctors regarding their work. There should be a system where the patients are categorized under insurance policy and non policy holders and the hospital management to claim the bills from concerned insurance company all these needs can be fulfilled by our project.
Proper maintenance of patient and doctor database and schedule appointments. Properly maintain details like rooms availability and in patients and outpatient details. And also to generate bills automatically at the time of discharge. So our platform ensures smooth operation of the hospital management tasks as well as offering facility to patient. |
|
| Project Title | : | DISEP-DISEASE SEVERITY PREDICTOR |
| Project Id | : | 16DS-A6 |
| Guide Details | : | Dr. K. Prabhakar Assistant Professor |
| Team Members | : | BOJJI SREEHITHA, AKARAPU SAMYUKTHA, MADDHURI PARTHASARADHI, VINTHA HEMANTH REDDY |
| Domain | : | Data Science / IoT |
| Abstract | In today’s world we are on a express train to a cashless society. The most accepted payment mode is credit card for both online and offline, it provides a cashless shopping in all countries. It has became a more conventional way to do online shopping, paying bills etc. Hence the risk of fraud transactions has been increasing day-by-day. Implementation of efficient fraud detection system thus became important for all credit card issuing banks to minimize the losses. In real life, fraudulent transactions are intermixed with genuine transactions and simple pattern matching is not often sufficient to detect this problem accurately. There is a need of past history of transactions like credit card holders spending pattern from the previous transactional database, and other inputs like income, location, living expense etc can be compared with the current transaction details to detect credit card frauds, if there is deviation on transactions it indicates as fraud. To detect this fraud we are using various machine learning algorithms like k-nearest neighbor, logistic regression etc classifiers to detect the fraud more accurately. |
|
| Project Title | : | INIQUITY INTERPRETER |
| Project Id | : | 16DS-A7 |
| Guide Details | : | Mr S Venu Gopal Assistant Professor |
| Team Members | : | MINNAM REDDY NAIMISHA REDDY, KILARU SAI SUSHMITHA, MALLOJI RAJ DATTA MANOHAR, M PRAGNYA REDDY |
| Domain | : | Data Science / IoT |
| Abstract | Crime is one of the biggest and dominating problems in society. As the crime rate is increasing day by day, our major concern is to reduce the Crime rate to a possible extent. Our project can solve this problem by analyzing and evaluating previous datasets and thus preparing a report on crimes within different aspects such as region, backgrounds, etc. This project analyzes datasets that consist of numerous crimes and help police officers to patrol regions easily and also speed up the process of solving crimes. | |
| Project Title | : | DETECTING FRAUD SCAM WEBSITES |
| Project Id | : | 16DS-A8 |
| Guide Details | : | Dr H Venkateswara Reddy Professor |
| Team Members | : | MOHAMMAD AYESHA TASMEEM, DEVATH INDUMATHI, LENKAPOTHULA SANDEEP GOUD, MADIPADIGE ABHISHEK |
| Domain | : | Data Science / IoT |
| Abstract | There are number of users who purchase products online and make payment through various websites. Multiple websites ask the users to provide sensitive data such as username, password or credit card details etc. often for malicious reasons. In order to detect and predict fraud website, we proposed an intelligent, flexible and effective system that is based on using classification Data mining algorithm. To extract the data sets, we are implementing classification algorithms and techniques. The website can be detected based on some important characteristics like URL and Domain Identity. Once user makes transaction through online, when he makes payment through the website our system will use data mining algorithm to detect whether the website is phishing website or not. This application can be used by many E-commerce enterprises in order to make the whole transaction process secure. |
|
| Project Title | : | Optimize traffic flow using traffic signals |
| Project Id | : | 16DS-A9 |
| Guide Details | : | Dr H Venkateswara Reddy Professor |
| Team Members | : | GONGALLA HARIKA, SOMA RAKSHITHA, KANKANALA CHETAN RAJA, MALAPATI RAVI TEJA REDDY |
| Domain | : | Data Science / IoT |
| Abstract | The higher risk of passenger safety, loss of productivity, increase in fuel consumption, and pollution is all effects of urban traffic congestion. Efficient traffic management will reduce congestion, improve performance measurements for seamless traffic flow, and proficiently manage current roadway assets. Government organizations and administrative authorities are implementing coordinated traffic signals and variable messages to manage traffic network congestion. By implementing big data solutions, administrators can leverage historical trends, a combination of real-time information, and new-age algorithms to improve and traffic networks in urban areas. The growing focus on the development of intelligent network systems and the use of big data analytics will assist the traffic management and result in reduced congestions and roadblocks. The big data analytics by Quantizing helps service providers in the mobile services industry to analyze the efficiency of the current transportation system, estimate the transport models, and predict future network scenarios. These systems are designed to help reduce network congestion and act as alerts that notify traffic authorities of potential roadblocks. | |
| Project Title | : | ENTERTAINMENT TRENDS |
| Project Id | : | 16DS-A10 |
| Guide Details | : | Mr K K Swamy Assistant Professor |
| Team Members | : | AVULA PRUTHVI, THUMUKUNTLA GNANI PRASAD REDDY, BOYIKAR UDAYKIRAN, PRODDATURI SHANTHAN |
| Domain | : | Data Science / IoT |
| Abstract | This project is about analyzing the user generated data to give a clear understanding of a selected city preferences to the media moguls. The media industry has tremendous scope for growth in all the segments due to rising incomes and evolving lifestyles. The world of business is spreading all over the planet and it is very important for a city to accommodate the changes of the public interests and bring in the content that is being streamed more. In the process of analyzing the public interests we intelligently analyze and visualize various user interests in terms of music, movies. This analyzed data will help the media moguls to be established and accommodate the user interests. | |
| Project Title | : | EDU ANALYZER |
| Project Id | : | 16DS-A11 |
| Guide Details | : | Mr K K Swamy Assistant Professor |
| Team Members | : | GADDAM NIHARIKA, CHINTALACHERUVU SRAVANTH KUMAR, DANEPALLI VINAY, MACHARLA LIKITHA |
| Domain | : | Data Science / IoT |
| Abstract | There are considerable differences in the practices followed in public and private educational sector. Some of the considerable factors are equipment, labs, curriculum, student dropout rates, failure rates and success rates. Using this project, we can measure the impact of the factors on the outcomes of the educational sectors. This model is used to take necessary steps to improve and stabilize the educational aspects. | |
| Project Title | : | FACE DETECTION BASED ATTENDANCE SYSTEM FOR UNIVERSITY |
| Project Id | : | 16DS-A12 |
| Guide Details | : | Ms S Shobarani Assistant Professor |
| Team Members | : | DUDDU VAMSHI, GUDIPATI PRAVALLIKA, BANDARU SAKETH, KANCHARLA NITHESH |
| Domain | : | Data Science / IoT |
| Abstract | Maintaining the attendance is essential in every Foundation for checking the performance of students as well as employee. Each organization has its own technique. Traditionally student’s attendance is taken physically on attendance register or sheet, given by the professor in class. These stamping techniques are repetitive and tedious. Physically recorded participation can be effortlessly controlled. Besides, it is exceptionally hard to confirm one by one student in a substantial classroom environment with disseminated branches whether the verified students are really reacting or not. | |
| Project Title | : | PREDICTIVE ANALYSIS FOR DETERMINING SAFETY OF A CITY |
| Project Id | : | 16DS-A13 |
| Guide Details | : | S Shobarani Assistant Professor |
| Team Members | : | TORPUNURI ROHIT SIMHA, ABHISHEK SRINIVAS SUNKARA, JETTY RAJIV CHANDRA, KATIPALLY VIGHNESHWAR REDDY |
| Domain | : | Data Science / IoT |
| Abstract | CT: Why do people feel safer in some cities than others? Is it a function of actual crime or of other factors that shape the way we perceive cities. There could be various factors responsible for calling a smart city as a safe city. In our point of view, analyzing data on such factors and predicting the safety and security of the cities would be a delightful solution for both migrants and immigrants. Our main focus is to intelligently analyze and visualize some of the constraints like crime rate, pollution, no of accidents in the city and psychology of the people. Using some of the techniques of data science and data mining we can thoroughly serialize and merge such data and present it to the people who are in a dilemma for transmigration. | |
| Project Title | : | AIR QUALITY PREDICTION |
| Project Id | : | 16DS-A14 |
| Guide Details | : | Dr Y Vijay Bhaskar Reddy Assistant Professor |
| Team Members | : | PONNAGANTI SAI BABU, RUDRA SATWIK, BAKKI SANKEERTH, MOHAMMAD AFROZ |
| Domain | : | Data Science / IoT |
| Abstract | Nowadays we can observe many parts of India are deadly affected with pollution. The harmful gases such as NO2 and SO2 content in the air have been increasing rapidly. This could be predicted with pollution sensors, although they can be expensive to deploy at scale. Our goal was to design a reliable and inexpensive air quality estimation solution, accessible to everyone through a web interface. In this project, we are going to analyze the dataset which consists of all the measured parameters and compared to the Air Quality Index (AQI). Based on the values in AQI the respective prediction is made on that particular city. Hence, people living in that city will understand the quality of the air they are breathing. | |
| Project Title | : | BS-Helper |
| Project Id | : | 16DS-A15 |
| Guide Details | : | Dr Y Vijay Bhaskar Reddy Assistant Professor |
| Team Members | : | NIDAMANURU SRAVANI CHOWDARY, GUMMADAVELLY SAIDEEP, PEDARAGALLA ASHOKA CHAKRAVARTHI |
| Domain | : | Data Science / IoT |
| Abstract | The platform BS-Helper reduces the work of maintaining the system and generates the report that provide details and suggestions for optimal use of available bike resources. | |
| Project Title | : | Generating Sales Document Report Using Hierarchical ALV |
| Project Id | : | 16SAP-A16 |
| Guide Details | : | Ms T. Madhuri Assistant Professor |
| Team Members | : | P KALYAN BHUSHAN, SHREYA SRIVASTAVA, E AKHILESH |
| Domain | : | SAP |
| Abstract | SAP report is an executable program that reads data from the database and generates output based on the filter criteria selected by the end user. The common desired features of any report are sorting, filtering, subtotals, totals etc. To implement these from scratch, a lot of coding effort is to be put. To avoid that we can use a concept called ABAP List Viewer (ALV). SAP provides a set of ALV function modules, which can be put into use to embellish the output of report. This set of ALV functions is used to enhance the readability and functionality of any report output. Hierarchical ALV List is a way to display data in form of hierarchy. Hierarchical display is used for displaying data that are related, like sales order and item details. Here sales order details can be the header data whereas them items in the sales order can be the item data. | |
| Project Title | : | GENERATING CUSTOMER, PURCHASE AND MATERIAL REPORT USING CLASSICAL REPORTS |
| Project Id | : | 16SAP-A17 |
| Guide Details | : | Dr K. Prabhakar Assistant Professor |
| Team Members | : | THANUGULA PAVAN KUMAR, NUNE SRI HARSHINI PATEL, PATWARI ROHIT KUMAR |
| Domain | : | SAP |
| Abstract | The validation process represents the whole procedure ranging from the application’s registration to the final confirmation and verification process is the act of demonstrating that design outputs match design inputs. Web Dynpro is a standard SAP UI technology that allows you to develop web applications using graphical tools and development environment integrated with ABAP workbench. Using graphical tools reduces the implementation effort and you can better reuse and maintain components in ABAP workbench. Our project here deals with the validation and verification of a company’s sales application using SAP ABAP BDC (Batch Data Communication) and Module pool SAP ABAP. |
| Project Title | : | POLLUTION MONITORING SYSTEM |
| Project Id | : | 16CC-B1 |
| Guide Details | : | Ms G. Madhavi Assistant Professor |
| Team Members | : | LAGISHETTY VASAVI, KATTA NEEHARIKA, RAMAVATH SHIVA, ROYYA AJAY KUMAR, CHALLA AKHILA |
| Domain | : | Cloud / IoT |
| Abstract | Pollution affects our day to day activities and quality of life. It poses a threat to the ecosystem and the quality of life on the planet. People need to know the extent to which their activities affect the quality of atmosphere. This project proposes pollution monitoring system. It is used to monitor the pollution at multiple locations. The system was developed using the Iot software. The pollution monitoring system was designed to monitor and analyze quality of atmosphere in real-time. We take the data from the database which is stored in the cloud. We will update the data over the internet. We can take the data from the cloud for further analysis. The result was displayed on the designed hardware’s display interface and could be accessed via the cloud on any smart mobile device. | |
| Project Title | : | AUTO-SCALING WORKLOADS BASED ON RESOURCE USAGE |
| Project Id | : | 16CC-B2 |
| Guide Details | : | Mr M A Ranjith Kumar Assistant Professor |
| Team Members | : | POTHULAPATI TARUN REDDY, DEEKSHITULA SREETEJA, CHALLA MANASA REDDY, SHREYA DODLA |
| Domain | : | Cloud / IoT |
| Abstract | Most Applications that are deployed on the cloud are over provisioned causing unnecessary resource wastage. This is made to handle sudden spikes of traffic which can happen for both planned and un-planned scenarios. In this project, we provide an application agnostic way of scaling workloads based on a configured metric using kubernetes, which is a Cloud Native, Container Orchestration platform and Prometheus. A Time Series Database. Using Kubernetes, we configure Horizontal Pod Auto-Scaler, to scale the application based on the different metrics I.e CPU, Memory, Queue Length, etc which are stored in Prometheus. With relevant configuration, Kubernetes automatically adds more instances/scales the application when the load or resource utilization is high, causing user requests to not fail and also improve latency. As Load on the application decreases, Kubernetes also notices that and removes instances of the application that are not necessary, thereby improving resource usage, and decreasing over-provisioning. The system shown in the project with a sample application agnostic (any type of workload) and metric agnostic (any metric based on the workload). | |
| Project Title | : | GOVERNMENT SERVICE BOT |
| Project Id | : | 16CC-B3 |
| Guide Details | : | Ms V Uma Maheswari Assistant Professor |
| Team Members | : | GUMMADI PRIYANKA, TADAKAMALLA SHIVA KUMAR, RAMINI SANJANA, VEMPALLY NISHAR\ |
| Domain | : | Cloud / IoT |
| Abstract | This project helps the people all over India to find suitable website or web service or other service regarding the government schemes, government websites, government services across India. Many people are unaware of government services and schemes. Even if they know about the schemes and services, they are not knowing about the exact procedure to avail the schemes and services of government. Unfortunately, there are a lot of other sites in the internet which misleads the people. There are so many phishing sites and duplicate sites in the internet today. The goal of this project is to give proper guidance to people from being targeted to duplicated and fishy websites and also to make their process easy. Our project provides a common interface which helps people in all the ways to avail the government scheme. We provide a bot (A chat bot which interact with humans),which will be available in all chat application like messenger, whatsapp, slack etc. So,we can direct them to proper solution with the help of our site and bot. | |
| Project Title | : | HealthVault – A Smart healthcare platform |
| Project Id | : | 16CC-B4 |
| Guide Details | : | Mr Pallati Narsimhulu Assistant Professor |
| Team Members | : | GANNEPAGA SRI DEVI, MOHD AZHAR, MD BURHANUDDIN SHAIKH, ALIMINETI RAKSHA REDDY |
| Domain | : | Cloud / IoT |
| Abstract | The goal behind developing a smart city is to improve the lives of its citizens and smart healthcare plays a significant role in achieving this objective. As per a report from Transparency Market Research, the global smart healthcare product market is expected to reach $57.85 billion by 2023. Health Vault is an independent cloud application which stores a patient’s medical records, prescriptions, diagnostic reports and vital’s history. A Patient’s medical history is a very sensitive information which only a patient should have control over and Health Vault empowers a patient to control his health data, he/she can choose who can view it and to what extent. Today multi specialty hospitals have their own application suite to store their patients record but the control of this sensitive and private data lies in the hands of the hospital. The patient can only access it, he/she cannot share the same health data directly through the application, hence there isa need for an independent platform which gives control of such personal and sensitive data in the hands of the user. Privacy. That’s Health Vault. | |
| Project Title | : | DROWSY DRIVER ALERT SYSTEM |
| Project Id | : | 16DS-B5 |
| Guide Details | : | Ms Tanukonda Jagadeeswari Assistant Professor |
| Team Members | : | ANKATI PRAVALIKA, SHANKARPANDYAN BHAVANA, VEMULA SAI KRISHNA VAMSHI, CHADA RADHEESH REDDY |
| Domain | : | Data Science / IoT |
| Abstract | The main idea behind this project is to develop a detection system which can detect fatigue of any human and can issue a timely warning. Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough. According to the expert’s studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. This system will monitor the driver eyes using a camera and by developing an algorithm we can detect symptoms of driver fatigue early enough to avoid the person from sleeping. So, this project will be helpful in detecting driver fatigue in advance and will give warning output in form of alarm. It will detect drowsiness of the driver and warn him before fall into sleep. It helps to prevent accidents that are caused by drivers who fell asleep while driving. | |
| Project Title | : | SARAMSH |
| Project Id | : | 16DS-B6 |
| Guide Details | : | Ms Tanukonda Jagadeeswari Assistant Professor |
| Team Members | : | KURUGANTI CHIRANJEEVI KARTHIK, CHOPPARA NAGASATHVIK SURAM, GOGINENI SREERAM PRUDHVI, PAGIDI MUNTHALA VIVEK |
| Domain | : | Data Science / IoT |
| Abstract | Our project aims at developing an API which takes a text and generates a summary out of it. The tools that are required for the project are built from scratch to provide flexibility and customizability. Text summarization is achieved by extractive text summarization using TF-IDF. Where summaries are generated by identifying the important sections of original text and extracting them. This API could be used anywhere which promote operational efficiency. By using this API one could summarize huge chunks of text or emails or even a text file. | |
| Project Title | : | HAND WRITTEN DIGIT RECOGNITION |
| Project Id | : | 16DS-B7 |
| Guide Details | : | Mr V Vijaya bhaskarareddy Assistant Professor |
| Team Members | : | AKARAM KIRAN MAI, ALISHALA NAVYA SRI, PILLI BILWAKSHI, KAYITHI SHIVAPRASAD REDDY, MURIMADUGULA SHIVA PRASAD |
| Domain | : | Data Science / IoT |
| Abstract | Neural networks and deep learning are two success stories in modern artificial intelligence. They’ve led to major advances in image recognition, automatic text generation, and even in self-driving cars. Handwriting recognition is one of the compelling and fascinating works because every individual in this world has their own style of writing. Sometimes it is not possible to recognize the digits written by the people. In real-time applications like bank check processing, number plate recognition. The capability of the computer is to recognize and identify and understand handwritten digits automatically. The accuracy and correctness are very crucial in handwritten digit recognition. Even small error may lead to inappropriate results in real-time applications. Handwritten digit recognition helps to provide the accurate results. | |
| Project Title | : | Accident Alert System |
| Project Id | : | 16DS-B8 |
| Guide Details | : | Mr V Vijaya bhaskarareddy Assistant Professor |
| Team Members | : | NAMA ALEKHYA, V HEMANTH KUMAR, RAMANCHA MOHANAKRISHNA, KONTHAM BHARATH CHANDRA |
| Domain | : | Data Science / IoT |
| Abstract | The main aim of the project is, to analyze the road accidents in India and predict the accident-prone areas. This analysis helps the users to know about the accident-prone areas and can take preventive measures to avoid accidents. The application is developed in such a way that it locates the current position of the vehicle while traveling and then calculates the accident percentage in that area. If the current location is very prone to the accident then it alerts the user about the location so, the user can be safe while driving at those areas. | |
| Project Title | : | PROCESS PIPELINE TO SOLVE EMPLOYEE ATTRITION AND THEIR JOB PERFORMANCE AND PREDICTION WITH AI |
| Project Id | : | 16DS-B9 |
| Guide Details | : | Mr. Mallu Praveen Assistant Professor |
| Team Members | : | SALLA SHIVATEJA VAISHNAVA DEEPA YAMSANI, JAMMULA NITHYA SRI REDDY, CHAVAN NAVEEN |
| Domain | : | Data Science / IoT |
| Abstract | Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. Now a day’s data science predictions are used in IT industries, for the improvement in market investment, employee management etc. Retention of valuable employees within an organization has become an important issue as it is hard to find out the reasons that why employees are leaving an organization and keep them satisfied is a big challenge, for this a report is made to predict the retention of an employee in an organization using the python programming with data science methods. The main idea of this report is to find out that which valuable employee will leave the company and the features which are affecting him/her to making this decision like salary level, no. of hours spending in week, promotion, no. of work accident etc. The application was developed in python programming and are made with the help of data science and machine learning models. |
|
| Project Title | : | SALARY PACKAGE PREDICTOR |
| Project Id | : | 16DS-B10 |
| Guide Details | : | Ms V Uma Maheswari Assistant Professor |
| Team Members | : | POOJITHA D NAIDU, PADAKANTI SUDHEER KUMAR, V SAI TEJA, K M ANUDEEP YADAV |
| Domain | : | Data Science / IoT |
| Abstract | : | The main idea of the project is to develop a website which takes inputs(i. e dept, cgpa) as the testing data and predict the results, for training the data(i.e dept, cgpa, salary) we take previous years records and choose the best suitable algorithm(i.e the one which gives efficient accuracy) and train the model. The predicted results will be displayed on the screen with some suggestions in it. Finally the website will be hosted so that everyone can predict and prepare accordingly. |
| Project Title | : | GENERATING BON VOYAGE REPORTS USING STANDARD PACKAGE |
| Project Id | : | 16SAP-B11 |
| Guide Details | : | Mr Para Upendar Assistant Professor |
| Team Members | : | PENDYALA SRI DIVYA, VUPPALA SOUMYA, HEMANTH KUMAR E, GUMMADI NISSY JOY |
| Domain | : | SAP |
| Abstract | Now-a-days, Due to increase in technology and demand of transport which also includes transport by air. Transport by air is mainly about flights, there are many airlines. Every airline has their own prices, timings, destinations, currencies, types, capacity, occupied seats etc. This report gives you complete details about different airlines and their features. This report gives detailed flight data in each and every aspect. The database is being maintained with the flight data and report is generated using standard packages for example SLIS and display in ALV form (ABAP List Viewer). SLIS is the type library for ALV grid and also contains many inbuilt modules. ALV gives us a standard List format and user interface to all our ABAP reports. ALV is created by a set of standard function modules provided by SAP. This also allows the user to sort the data in the report according to their priorities or requirements. | |
| Project Title | : | Multiple reports using ALV |
| Project Id | : | 16SAP-B12 |
| Guide Details | : | Mr V N L N Murthy Assistant Professor |
| Team Members | : | VELUMULA SURAJ KUMAR, KOLLIPARA DIVYA SREE, SEELAM NITHIN REDDY, DODDA SRI SAI BHARATH REDDY |
| Domain | : | SAP |
| Abstract | A SAP report is an executable program that reads data from the database and generates output based on the filter criteria selected by the end user. Execution of a SAP report almost never leads to an update of the database. The common desired features of any report are “column alignment”, sorting, filtering, subtotals, totals etc. To implement these from scratch, a lot of coding effort is to be put. To avoid that we can use a concept called ABAP List Viewer (ALV). List Viewer is a generic tool that outputs data in a table form (rows and columns), with integrated functions to manipulate output (sort, totals, filter, column order, hide, etc.) and export it (Excel, Crystal report, CSV files, etc.) Simple report has features of sorting and filtering only but this is used to generate a single report if you have to display more than one report on the output. Technically speaking if you have multiple internal tables with data to be displayed as separate blocks then we go for block report of ALV. | |
| Project Title | : | GENERATING EMPLOYEE’S LEAVE SANCTION CERTIFICATE USING SAP |
| Project Id | : | 16SAP-B13 |
| Guide Details | : | Mr. Shrawan Kumar Assistant Professor |
| Team Members | : | C SANKHYA REDDY, ALLADA MAYUKA, SAKINALA NAVEEN, KODIGANTI POOJA REDDY |
| Domain | : | SAP |
| Abstract | Usually the application of leave in education institutes and workplace is a long process. This refers to applying for leave through an email to the manager, waiting for them to respond and this process is inconvenient to both the manager and the applicant. To make this an easy process we suggest the generation of employee’s leave sanction certificate which lessens the burden. This includes generation of a pdf form that responds to the leave application. We propose that the generation of the Pdf form is done using SAP tools since it can be beneficial. The usage of the pdf forms for sanctioning leave can save a lot of paperwork and makes it easier to apply for a leave |
|
| Project Title | : | GENERATE MATERIAL DOCUMENT REPORT USING INTERNAL TABLE EVENTS |
| Project Id | : | 16SAP-B14 |
| Guide Details | : | Mr Shrawan Kumar Assistant Professor |
| Team Members | : | KUNDURU MADHAV REDDY, MARNENI KUSHAL RAO, GUJJARLAMUDI AJITH KUMAR, R S LOKESH |
| Domain | : | SAP |
| Abstract | Material documents in SAP are issued or generated from or received into inventory. The documents are created only by materials management in each firm and not by the end users. The material document review obtains the analysis and results from the firms data using the internal tables. The generation includes update and control field display and type of transaction. The logistics management can be formed using SAP easy access menu. We can generate material documents which involve issuing of goods and also the reference. The internal table events include AT START and AT END OF among other events that we implement. We use pre-defined and internal table events that we use to implement the generation of the material documents that use | |
| Project Title | : | TO DO VERIFICATION AND VALIDATION ON SALES APPLICATION USING DYNPRO ABAP |
| Project Id | : | 16SAP-B15 |
| Guide Details | : | Mr. Shrawan Kumar Assistant Professor |
| Team Members | : | GADDAM SIDDARTH REDDY, M PAVAN NAIK, CHANDA NAGENDRA RISHI RAJ, DUBYALA SAI RAHUL |
| Domain | : | SAp |
| Abstract | He validation process represents the whole procedure ranging from the application’s registration to the final confirmation and verification process is the act of demonstrating that design outputs match design inputs. Web Dynpro is a standard SAP UI technology that allows you to develop web applications using graphical tools and development environment integrated with ABAP workbench. Using graphical tools reduces the implementation effort and you can better reuse and maintain components in ABAP workbench. Our project here deals with the validation and verification of a company’s sales application using SAP ABAP BDC (Batch Data Communication) and Module pool SAP ABAP. |
| Project Title | : | POLLUTION MONITORING SYSTEM |
| Project Id | : | 16CC-B1 |
| Guide Details | : | Ms G. Madhavi Assistant Professor |
| Team Members | : | LAGISHETTY VASAVI, KATTA NEEHARIKA, RAMAVATH SHIVA, ROYYA AJAY KUMAR, CHALLA AKHILA |
| Domain | : | Cloud / IoT |
| Abstract | Pollution affects our day to day activities and quality of life. It poses a threat to the ecosystem and the quality of life on the planet. People need to know the extent to which their activities affect the quality of atmosphere. This project proposes pollution monitoring system. It is used to monitor the pollution at multiple locations. The system was developed using the Iot software. The pollution monitoring system was designed to monitor and analyze quality of atmosphere in real-time. We take the data from the database which is stored in the cloud. We will update the data over the internet. We can take the data from the cloud for further analysis. The result was displayed on the designed hardware’s display interface and could be accessed via the cloud on any smart mobile device. | |
| Project Title | : | AUTO-SCALING WORKLOADS BASED ON RESOURCE USAGE |
| Project Id | : | 16CC-B2 |
| Guide Details | : | Mr M A Ranjith Kumar Assistant Professor |
| Team Members | : | POTHULAPATI TARUN REDDY, DEEKSHITULA SREETEJA, CHALLA MANASA REDDY, SHREYA DODLA |
| Domain | : | Cloud / IoT |
| Abstract | Most Applications that are deployed on the cloud are over provisioned causing unnecessary resource wastage. This is made to handle sudden spikes of traffic which can happen for both planned and un-planned scenarios. In this project, we provide an application agnostic way of scaling workloads based on a configured metric using kubernetes, which is a Cloud Native, Container Orchestration platform and Prometheus. A Time Series Database. Using Kubernetes, we configure Horizontal Pod Auto-Scaler, to scale the application based on the different metrics I.e CPU, Memory, Queue Length, etc which are stored in Prometheus. With relevant configuration, Kubernetes automatically adds more instances/scales the application when the load or resource utilization is high, causing user requests to not fail and also improve latency. As Load on the application decreases, Kubernetes also notices that and removes instances of the application that are not necessary, thereby improving resource usage, and decreasing over-provisioning. The system shown in the project with a sample application agnostic (any type of workload) and metric agnostic (any metric based on the workload). | |
| Project Title | : | GOVERNMENT SERVICE BOT |
| Project Id | : | 16CC-B3 |
| Guide Details | : | Ms V Uma Maheswari Assistant Professor |
| Team Members | : | GUMMADI PRIYANKA, TADAKAMALLA SHIVA KUMAR, RAMINI SANJANA, VEMPALLY NISHAR\ |
| Domain | : | Cloud / IoT |
| Abstract | This project helps the people all over India to find suitable website or web service or other service regarding the government schemes, government websites, government services across India. Many people are unaware of government services and schemes. Even if they know about the schemes and services, they are not knowing about the exact procedure to avail the schemes and services of government. Unfortunately, there are a lot of other sites in the internet which misleads the people. There are so many phishing sites and duplicate sites in the internet today. The goal of this project is to give proper guidance to people from being targeted to duplicated and fishy websites and also to make their process easy. Our project provides a common interface which helps people in all the ways to avail the government scheme. We provide a bot (A chat bot which interact with humans),which will be available in all chat application like messenger, whatsapp, slack etc. So,we can direct them to proper solution with the help of our site and bot. | |
| Project Title | : | HealthVault – A Smart healthcare platform |
| Project Id | : | 16CC-B4 |
| Guide Details | : | Mr Pallati Narsimhulu Assistant Professor |
| Team Members | : | GANNEPAGA SRI DEVI, MOHD AZHAR, MD BURHANUDDIN SHAIKH, ALIMINETI RAKSHA REDDY |
| Domain | : | Cloud / IoT |
| Abstract | The goal behind developing a smart city is to improve the lives of its citizens and smart healthcare plays a significant role in achieving this objective. As per a report from Transparency Market Research, the global smart healthcare product market is expected to reach $57.85 billion by 2023. Health Vault is an independent cloud application which stores a patient’s medical records, prescriptions, diagnostic reports and vital’s history. A Patient’s medical history is a very sensitive information which only a patient should have control over and Health Vault empowers a patient to control his health data, he/she can choose who can view it and to what extent. Today multi specialty hospitals have their own application suite to store their patients record but the control of this sensitive and private data lies in the hands of the hospital. The patient can only access it, he/she cannot share the same health data directly through the application, hence there isa need for an independent platform which gives control of such personal and sensitive data in the hands of the user. Privacy. That’s Health Vault. | |
| Project Title | : | DROWSY DRIVER ALERT SYSTEM |
| Project Id | : | 16DS-B5 |
| Guide Details | : | Ms Tanukonda Jagadeeswari Assistant Professor |
| Team Members | : | ANKATI PRAVALIKA, SHANKARPANDYAN BHAVANA, VEMULA SAI KRISHNA VAMSHI, CHADA RADHEESH REDDY |
| Domain | : | Data Science / IoT |
| Abstract | The main idea behind this project is to develop a detection system which can detect fatigue of any human and can issue a timely warning. Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough. According to the expert’s studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. This system will monitor the driver eyes using a camera and by developing an algorithm we can detect symptoms of driver fatigue early enough to avoid the person from sleeping. So, this project will be helpful in detecting driver fatigue in advance and will give warning output in form of alarm. It will detect drowsiness of the driver and warn him before fall into sleep. It helps to prevent accidents that are caused by drivers who fell asleep while driving. | |
| Project Title | : | SARAMSH |
| Project Id | : | 16DS-B6 |
| Guide Details | : | Ms Tanukonda Jagadeeswari Assistant Professor |
| Team Members | : | KURUGANTI CHIRANJEEVI KARTHIK, CHOPPARA NAGASATHVIK SURAM, GOGINENI SREERAM PRUDHVI, PAGIDI MUNTHALA VIVEK |
| Domain | : | Data Science / IoT |
| Abstract | Our project aims at developing an API which takes a text and generates a summary out of it. The tools that are required for the project are built from scratch to provide flexibility and customizability. Text summarization is achieved by extractive text summarization using TF-IDF. Where summaries are generated by identifying the important sections of original text and extracting them. This API could be used anywhere which promote operational efficiency. By using this API one could summarize huge chunks of text or emails or even a text file. | |
| Project Title | : | HAND WRITTEN DIGIT RECOGNITION |
| Project Id | : | 16DS-B7 |
| Guide Details | : | Mr V Vijaya bhaskarareddy Assistant Professor |
| Team Members | : | AKARAM KIRAN MAI, ALISHALA NAVYA SRI, PILLI BILWAKSHI, KAYITHI SHIVAPRASAD REDDY, MURIMADUGULA SHIVA PRASAD |
| Domain | : | Data Science / IoT |
| Abstract | Neural networks and deep learning are two success stories in modern artificial intelligence. They’ve led to major advances in image recognition, automatic text generation, and even in self-driving cars. Handwriting recognition is one of the compelling and fascinating works because every individual in this world has their own style of writing. Sometimes it is not possible to recognize the digits written by the people. In real-time applications like bank check processing, number plate recognition. The capability of the computer is to recognize and identify and understand handwritten digits automatically. The accuracy and correctness are very crucial in handwritten digit recognition. Even small error may lead to inappropriate results in real-time applications. Handwritten digit recognition helps to provide the accurate results. | |
| Project Title | : | Accident Alert System |
| Project Id | : | 16DS-B8 |
| Guide Details | : | Mr V Vijaya bhaskarareddy Assistant Professor |
| Team Members | : | NAMA ALEKHYA, V HEMANTH KUMAR, RAMANCHA MOHANAKRISHNA, KONTHAM BHARATH CHANDRA |
| Domain | : | Data Science / IoT |
| Abstract | The main aim of the project is, to analyze the road accidents in India and predict the accident-prone areas. This analysis helps the users to know about the accident-prone areas and can take preventive measures to avoid accidents. The application is developed in such a way that it locates the current position of the vehicle while traveling and then calculates the accident percentage in that area. If the current location is very prone to the accident then it alerts the user about the location so, the user can be safe while driving at those areas. | |
| Project Title | : | PROCESS PIPELINE TO SOLVE EMPLOYEE ATTRITION AND THEIR JOB PERFORMANCE AND PREDICTION WITH AI |
| Project Id | : | 16DS-B9 |
| Guide Details | : | Mr. Mallu Praveen Assistant Professor |
| Team Members | : | SALLA SHIVATEJA VAISHNAVA DEEPA YAMSANI, JAMMULA NITHYA SRI REDDY, CHAVAN NAVEEN |
| Domain | : | Data Science / IoT |
| Abstract | Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data.
Now a day’s data science predictions are used in IT industries, for the improvement in market investment, employee management etc. Retention of valuable employees within an organization has become an important issue as it is hard to find out the reasons that why employees are leaving an organization and keep them satisfied is a big challenge, for this a report is made to predict the retention of an employee in an organization using the python programming with data science methods. The main idea of this report is to find out that which valuable employee will leave the company and the features which are affecting him/her to making this decision like salary level, no. of hours spending in week, promotion, no. of work accident etc. The application was developed in python programming and are made with the help of data science and machine learning models. |
|
| Project Title | : | SALARY PACKAGE PREDICTOR |
| Project Id | : | 16DS-B10 |
| Guide Details | : | Ms V Uma Maheswari Assistant Professor |
| Team Members | : | POOJITHA D NAIDU, PADAKANTI SUDHEER KUMAR, V SAI TEJA, K M ANUDEEP YADAV |
| Domain | : | Data Science / IoT |
| Abstract | : | The main idea of the project is to develop a website which takes inputs(i. e dept, cgpa) as the testing data and predict the results, for training the data(i.e dept, cgpa, salary) we take previous years records and choose the best suitable algorithm(i.e the one which gives efficient accuracy) and train the model. The predicted results will be displayed on the screen with some suggestions in it. Finally the website will be hosted so that everyone can predict and prepare accordingly. |
| Project Title | : | GENERATING BON VOYAGE REPORTS USING STANDARD PACKAGE |
| Project Id | : | 16SAP-B11 |
| Guide Details | : | Mr Para Upendar Assistant Professor |
| Team Members | : | PENDYALA SRI DIVYA, VUPPALA SOUMYA, HEMANTH KUMAR E, GUMMADI NISSY JOY |
| Domain | : | SAP |
| Abstract | Now-a-days, Due to increase in technology and demand of transport which also includes transport by air. Transport by air is mainly about flights, there are many airlines. Every airline has their own prices, timings, destinations, currencies, types, capacity, occupied seats etc. This report gives you complete details about different airlines and their features. This report gives detailed flight data in each and every aspect. The database is being maintained with the flight data and report is generated using standard packages for example SLIS and display in ALV form (ABAP List Viewer). SLIS is the type library for ALV grid and also contains many inbuilt modules. ALV gives us a standard List format and user interface to all our ABAP reports. ALV is created by a set of standard function modules provided by SAP. This also allows the user to sort the data in the report according to their priorities or requirements. | |
| Project Title | : | Multiple reports using ALV |
| Project Id | : | 16SAP-B12 |
| Guide Details | : | Mr V N L N Murthy Assistant Professor |
| Team Members | : | VELUMULA SURAJ KUMAR, KOLLIPARA DIVYA SREE, SEELAM NITHIN REDDY, DODDA SRI SAI BHARATH REDDY |
| Domain | : | SAP |
| Abstract | A SAP report is an executable program that reads data from the database and generates output based on the filter criteria selected by the end user. Execution of a SAP report almost never leads to an update of the database. The common desired features of any report are “column alignment”, sorting, filtering, subtotals, totals etc. To implement these from scratch, a lot of coding effort is to be put. To avoid that we can use a concept called ABAP List Viewer (ALV). List Viewer is a generic tool that outputs data in a table form (rows and columns), with integrated functions to manipulate output (sort, totals, filter, column order, hide, etc.) and export it (Excel, Crystal report, CSV files, etc.) Simple report has features of sorting and filtering only but this is used to generate a single report if you have to display more than one report on the output. Technically speaking if you have multiple internal tables with data to be displayed as separate blocks then we go for block report of ALV. | |
| Project Title | : | GENERATING EMPLOYEE’S LEAVE SANCTION CERTIFICATE USING SAP |
| Project Id | : | 16SAP-B13 |
| Guide Details | : | Mr. Shrawan Kumar Assistant Professor |
| Team Members | : | C SANKHYA REDDY, ALLADA MAYUKA, SAKINALA NAVEEN, KODIGANTI POOJA REDDY |
| Domain | : | SAP |
| Abstract | Usually the application of leave in education institutes and workplace is a long process. This refers to applying for leave through an email to the manager, waiting for them to respond and this process is inconvenient to both the manager and the applicant. To make this an easy process we suggest the generation of employee’s leave sanction certificate which lessens the burden. This includes generation of a pdf form that responds to the leave application. We propose that the generation of the Pdf form is done using SAP tools since it can be beneficial. The usage of the pdf forms for sanctioning leave can save a lot of paperwork and makes it easier to apply for a leave |
|
| Project Title | : | GENERATE MATERIAL DOCUMENT REPORT USING INTERNAL TABLE EVENTS |
| Project Id | : | 16SAP-B14 |
| Guide Details | : | Mr Shrawan Kumar Assistant Professor |
| Team Members | : | KUNDURU MADHAV REDDY, MARNENI KUSHAL RAO, GUJJARLAMUDI AJITH KUMAR, R S LOKESH |
| Domain | : | SAP |
| Abstract | Material documents in SAP are issued or generated from or received into inventory. The documents are created only by materials management in each firm and not by the end users. The material document review obtains the analysis and results from the firms data using the internal tables. The generation includes update and control field display and type of transaction. The logistics management can be formed using SAP easy access menu. We can generate material documents which involve issuing of goods and also the reference. The internal table events include AT START and AT END OF among other events that we implement. We use pre-defined and internal table events that we use to implement the generation of the material documents that use | |
| Project Title | : | TO DO VERIFICATION AND VALIDATION ON SALES APPLICATION USING DYNPRO ABAP |
| Project Id | : | 16SAP-B15 |
| Guide Details | : | Mr. Shrawan Kumar Assistant Professor |
| Team Members | : | GADDAM SIDDARTH REDDY, M PAVAN NAIK, CHANDA NAGENDRA RISHI RAJ, DUBYALA SAI RAHUL |
| Domain | : | SAp |
| Abstract | He validation process represents the whole procedure ranging from the application’s registration to the final confirmation and verification process is the act of demonstrating that design outputs match design inputs. Web Dynpro is a standard SAP UI technology that allows you to develop web applications using graphical tools and development environment integrated with ABAP workbench. Using graphical tools reduces the implementation effort and you can better reuse and maintain components in ABAP workbench. Our project here deals with the validation and verification of a company’s sales application using SAP ABAP BDC (Batch Data Communication) and Module pool SAP ABAP. |
| Project Title | : | AUTOMATED STREET LIGHTING SYSTEM |
| Project Id | : | 16CC-C1 |
| Guide Details | : | Mr . S K Prashanth Assistant Professor |
| Team Members | : | VANAMA NAMITHA, KOTIKA LEELAVATHI, PASHAM SANTHOSHA, MOLUGURI SRAVAN KUMAR |
| Domain | : | Cloud / IoT |
| Abstract | The project aims to provide concerned person to switch on or off through the application. This will be integrated with cloud computing. In cloud server there will be a set of rules and regulations such as first light and last light and when to switch on/off those street light. As the traffic decreases slowly during late-night hours, the intensity gets reduced progressively till morning to save energy and thus, the street lights switch on at the dusk and then switch off at the dawn, automatically. | |
| Project Title | : | VAYU: A REAL-TIME POLLUTION MONITORING DASHBOARD AND ALERTING SYSTEM: |
| Project Id | : | 16CC-C2 |
| Guide Details | : | Dr S. Shitharth Assistant Professor |
| Team Members | : | KUKKADAPU SREEJA, PALLE ANIKETH REDDY, PATURI SRI PRAVAN, MOHAMMED KHALID ROSHAN |
| Domain | : | Cloud / IoT |
| Abstract | A cloud-based system that monitors urban pollution statistics and generates alerts about the real-time pollution statistics and the AQI (Air Quality Index) based on the geolocation of the user at any given place or time. Air pollution has always been one of the major urban problems that Indian cities face today. A smart city is all about being data driven in tackling common urban problems; this includes collecting data, computing metrics over the collected data and gathering insights to make useful decisions. Hence, to compute metrics and gather insights, it uses Prometheus, open-source Time Series Database that allows us to periodically pull data from a source (provided by the Central Pollution Control Board, Govt. of India) and compute metrics. To gather useful insights over this data, it uses Grafana, an open-source analytics and monitoring solution that plugs into Prometheus and provides attractive and useful visual representations of this data. Both these cloud platforms are Cloud Native Computing Foundation graduated projects which speaks volumes about their utility and robustness. It also leverages Prometheus’ alerting manager that allows us to alert users if the pollution levels in their area exceed the warning range, thereby enabling common citizens and at-risk groups suffering from respiratory ailments to stay vigilant about their health. | |
| Project Title | : | HATEFUL SPEECH ON TWITTER |
| Project Id | : | 16DS-C3 |
| Guide Details | : | Ms G. Madhavi Assistant Professor |
| Team Members | : | VANKA NIHARIKA, BUDIDI BHAVYA SREE RAMYA, MARRI BADRINATH REDDY, THUGUDAM NARESH |
| Domain | : | Data Science / IoT |
| Abstract | With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds became more direct, resulting in more and more “cyber” conflicts between these people. Consequently, hate speech is used more and more, to the point where it became a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion, etc. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content.Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. | |
| Project Title | : | SEGREGATING SPAMMERS AND UNSOLICITED BLOGGERS FROM GENUINE EXPERTS ON TWITTER |
| Project Id | : | 16DS-C4 |
| Guide Details | : | Ms B. Mahalakshmi Assistant Professor |
| Team Members | : | VORUGANTI VISHWANATHA, SHANMUKHA SHASTRY, BANDA SRIHARSHA, RATNA KUMAR, ANDHE GOUTHAM SAI KUMAR |
| Domain | : | Data Science / IoT |
| Abstract | Online Social Networks (OSNs) have not only significantly reformed the social interaction pattern but have also emerged as an effective platform for recommendation of services and products. The upswing in use of the OSNs has also witnessed growth in unwanted activities on social media. On the one hand, the spammers on social media can be a high risk towards the security of legitimate users and on the other hand some of the legitimate users, such as bloggers can pollute the results of recommendation systems that work alongside the OSNs. The polluted results of recommendation systems can be precarious to the masses that track recommendations. Therefore, it is necessary to segregate such type of users from the genuine experts. We propose a framework that separates the spammers and unsolicited bloggers from the genuine experts of a specific domain. The proposed approach employs modified Hyperlink Induced Topic Search (HITS) to separate the unsolicited bloggers from the experts on Twitter on the basis of tweets. The approach considers domain specific keywords in the tweets and several tweet characteristics to identify the unsolicited bloggers. Experimental results demonstrate the effectiveness of the proposed methodology as compared to several state-of-the-art approaches and classifiers. | |
| Project Title | : | PARKCROWD |
| Project Id | : | 16DS-C5 |
| Guide Details | : | Mr Adavelli Ramesh Assistant Professor |
| Team Members | : | JANGILI SHIRISHA, BALURI SUCHITH REDDY, MYADAM SAISANKEERTH, DEVA JAYANTH |
| Domain | : | Data Science / IoT |
| Abstract | With the increasing numbers of automobiles in cities, finding a parking space which is close to one’s driving destination is costly, time-consuming, and contributes to traffic congestion in urban areas . These problems are especially extreme in the case of on-demand parking in metropolitan centers and densely populated areas around the world. Therefore, there is a strong need to disseminate availability information of vacant parking spaces.
The scarcity of parking spaces in cities leads to a high demand for timely information about their availability. In this paper, we propose a crowd sensed parking system, namely Park Crowd, to aggregate on-street and roadside parking space information reliably, and to disseminate this information to drivers in a timely manner. Our system not only collects and disseminates basic information, such as parking hours and price, but also provides drivers with information on the real time and future availability of parking spaces based on aggregated crowd knowledge. Moreover, to incentive crowd workers’ participation as well as to encourage them to provide more reliable information, we build an incentivization scheme into Park-Crowd to reward workers based on the reliability of contributed knowledge. We design Park-Crowd, an MCS system for collecting and disseminating vacant parking space information based on crowd workers’ knowledge. We build models to evaluate the reliability of the crowd workers’ knowledge and estimate future parking space Availability based on workers’ knowledge using location dependent POI questions. We also propose a scheme to reward workers based on the reliability of contributed knowledge. |
|
| Project Title | : | ONLINE CRIME REPORTING SYSTEM |
| Project Id | : | 16DS-C6 |
| Guide Details | : | Mr Adavelli Ramesh Assistant Professor |
| Team Members | : | KUNCHALA SUCHARITHA REDDY, MAREPALLY NIKHITHA, TAPSI PAVAN KUMAR, KARRE SHARATH CHANDRA KARTHIK |
| Domain | : | Data Science / IoT |
| Abstract | Crime is a part of illegal activities in human life. It is quite obvious that the rate of crimes is increasing day by day in all societies across the world, but we do believe that there is a lot which can be done by both the governments and the individuals to reduce the crimes in communities. The rise of population and complex society rises the range of anti-social conducts that must be restricted by the government through the military and different organizations particularly the Police Force. There are many current crime management systems which faces several difficulties, as there is no means to report crime instantly other than phone calls, messaging or face-to-face compliant filing. Hence, we have proposed an online crime reporting system which allows the user to file complaints or missing reports and keep a track of it. There are 3 categories that a user can file; Complaint, Crime Report and Missing Report and can see all the status of what action has been taken by the admin. To file any of the above 3 complaints, the user should register into the system and provide his right credentials to file them. The crime reporting system project also allows other users who do not want to register but can check the crimes happening at his/her or any other area, has just to provide the pin code and in return the system displays the list of crimes if any filed. The offline that is the unregistered user can also take advantage of checking the missing person details, but he/she is refrained from getting complaints done by the users. |
|
| Project Title | : | SMART TRAFFIC MANAGEMENT SYSTEM |
| Project Id | : | 16DS-C7 |
| Guide Details | : | Mr Muralidhar Mourya Assistant Professor |
| Team Members | : | VANGAVETI YASHWANTH, JOOLURI RAJU, D PRABHU SHANKAR, M JESSICA |
| Domain | : | Data Science / IoT |
| Abstract | Learning-based traffic control algorithms have recently been explored as an alternative to existing traffic control logic. The reinforcement learning (RL) algorithm is being spotlighted in the field of adaptive traffic signal control. However, no report has described the implementation of an RL-based algorithm in an actual intersection. Most previous RL studies adopted conventional traffic parameters, such as delays and queue lengths to represent a traffic state, which cannot be exactly measured on-site in real time. Furthermore, the traffic parameters cannot fully account for the complexity of an actual traffic state. The present study suggests a novel artificial intelligence that uses only video images of an intersection to represent its traffic state rather than using handcrafted features. In simulation experiments using a real intersection, consecutive aerial video frames fully addressed the traffic state of an independent four-legged intersection, and an image-based RL model outperformed both the actual operation of fixed signals and a fully actuated operation. In existing system, manually we need to control traffic by which side is having heavy traffic. Then that side put for green signal for long time. However, for every time not controlling traffic by manually due to timing (i.e. midnight), to overcome we use artificial intelligence for automatically the traffic will be controlled. To overcome the above problem in this system we are using object detection technique for detect vehicles. We are giving the input image which is containing heavy traffic then performing the detection technique on that image. It first calculates the percentage of vehicles from that image then it can control the traffic depends on threshold value (percentage). | |
| Project Title | : | SMART FARMING |
| Project Id | : | 16DS-C8 |
| Guide Details | : | Mr Muralidhar Mourya Assistant Professor |
| Team Members | : | GADDAM APOORVA, KADIYALA SAI, AMBARLA ABHISHEK, DAMALA GREESHMA |
| Domain | : | Data Science / IoT |
| Abstract | Agriculture is one of the major revenue producing sectors of India and a source of survival. Numerous seasonal, economic and biological patterns influence the crop production but unpredictable changes in these patterns lead to a great loss to farmers. These risks can be reduced when suitable approaches are employed on data related to soil type, temperature, atmospheric pressure, humidity and crop type. Whereas, crop and weather forecasting can be predicted by deriving useful insights from these agricultural data that aids farmers to decide on the crop they would like to plant for the forthcoming year leading to maximum profit. This paper presents a survey on the various algorithms used for weather, crop yield, and crop cost prediction. | |
| Project Title | : | IDENTIFYING ALERT ZONES USING REGRESSION ALGORITHMS |
| Project Id | : | 16DS-C9 |
| Guide Details | : | Mr A Bhanu Prasad Assistant Professor |
| Team Members | : | VODELA SAISWAPNIL GUPTA, V PHALGUNI, G AKIL SAGAR, MOHAMMAD IMRAN |
| Domain | : | Data Science / IoT |
| Abstract | Daily we see many accidents occur on roads, as an average 400 accidents occur in india per day. The reasons might be driver negligence or road problems. So we took a initiative to reduce the road accidents. To perform the action we gather data from different online sites (historical data) and perform cleaning and do apply the algorithms to train the data and test it to check the efficiency. Here when the user enters the accident prone area we send him a alert message so he may drive carefully before he enters the zone. | |
| Project Title | : | COST PREDICTION AND DISEASE ANALYSIS IN HEALTHCARE |
| Project Id | : | 16DS-C10 |
| Guide Details | : | Mr A Bhanu Prasad Assistant Professor |
| Team Members | : | KADEM SAI TEJA, DUGGARAJU KEERTHANA, SRIVILLA VISHNU, SYED SOHAIL AHMED |
| Domain | : | Data Science / IoT |
| Abstract | The healthcare market has become competitive and complex and is increasing everyday. Healthcare Analytics is the process of getting, cleaning, analyzing and visualizing data collected from different organizations by which patients can be provided with enhanced health outcomes. The main objectives are: 1. To scrape data from websites and store them in Pandas dataframes, 2.Use classification algorithms like Decision tree and min-max normalization to predict total expense along with the type of disease and medicine, 3.Store the visualized data in a database and give user access through authorization. The main aim of this project is to provide predicted costs to people beforehand as well as give them easy access to their visualized health output in the form of a webpage interaction. There are three roles involved namely Data Analyst who collects and analyzes data, Web Developer who creates an interface for users and Database Administrator who maintains and gives permissions to users to access their final outputs. The technologies used are Python Libraries (Pandas, Matplotlib, Seaborn,Scikit-learn),HTML5,CSS,PHP and Localhost Server. | |
| Project Title | : | SMART CITY TRAVELLING |
| Project Id | : | 16DS-C11 |
| Guide Details | : | Dr Ramu Kuchipudi Assistant Professor |
| Team Members | : | B ABHISHEK, BALAGONOLLA KARUNAKAR,BALNE AJAY, B KIRANKUMAR |
| Domain | : | Data Science / IoT |
| Abstract | Smart City Travelling by the Name indicated smartly makes it way in analyzing the user’s likes and dislikes and the time period the user is willing to explore a place and gives him with Amazing results in the form of different paths to utilize the time. This System is basically used to help a traveler new to the city or anyone who wants to explore a city in the given time period, the system makes use of the “Foursquare” API to get all the locations and places with all their information to sort and place it before the user in different paths to make his choice. The Places are sorted and selected based on the top raking’s by the foursquare. During the user Registration the user is asked some questions helping them to filter out in searching the places, the places are displayed on the maps giving a clear idea of the location and giving the paths from one place to another from the start location to the end location. ”It will be asking the particular time to visit the single place so that automatically the end time may also predicted. Same for the remaining places also. So completely the plan start time decided by user. Based on the time allocated by the user for all the places, the complete time will be shown by system”. Since the Traveler may be new to the city not knowing any place, in the map view if the user clicks on the marker he can see the ratings and reviews which are recorded from the Foursquare itself. The System requires An Working Internet Connection all the time for the app to work. The frontend of the System makes use of Android Studio while SQL Server as the Backend. | |
| Project Title | : | PARKING LOT SYSTEM USING IMAGE PROCESSING |
| Project Id | : | 16IMG-C12 |
| Guide Details | : | Mr Ganesh Deshmukh Assistant Professor |
| Team Members | : | KILARU SAI CHANDANA, KANDAGATLA MEGHANA, KONDU MANIKAR RAO, ANUGULA SREEVANTH REDDY |
| Domain | : | Image Processing / IoT |
| Abstract | Now a days, people are facing problem to find an available parking space in parking lot due to the tremendous increase of occupancy of cars. When a driver want to enter a parking lot, the driver takes long time just to find an available parking space. This is used to detect the existence of the car and also provide information about the available parking space and the location of that parking and gives this information before they enter that particular parking lot , for example if there are four parking lots near by then driver gets the information about in which lot they have to park. It is used to design a smart parking system that is sensor free and functions as an indicator of the vacant spaces to the oncoming users of the parking lot. An image processing algorithm is used to detect empty parking areas from images of the parking space. The algorithm processes the image, extracts occupancy information concerning spots, and their positions thereof and sends information to the driver. The system also reports if individual parking spots are occupied or not. Occupancy information is made available to drivers, so they can enter that particular space easily in less time. The smart parking lot management system reduces the stress and time wastage associated with car parking and makes management of such areas less costly. | |
| Project Title | : | TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING |
| Project Id | : | 16IMG-C13 |
| Guide Details | : | Mr B Suresh Kumar Assistant Professor |
| Team Members | : | KOTHA RAVI MRUDULA, ANIKAPALLI DURGA PRASAD, T ADARSH CHOUHAN |
| Domain | : | Image Processing / IoT |
| Abstract | As the urban traffic congestion increased there is a pressing need of new traffic technology because of our poor infrastructure and resources Which cannot control the traffic congestion, to overcome this problem based on the volume of population giving time stamp to respective phase is a solution to control traffic congestion this can be done by using image processing methods. | |
| Project Title | : | ATTENDANCE REGISTER USING IMAGE PROCESSING |
| Project Id | : | 16IMG-C14 |
| Guide Details | : | Mr M Y Babu Assistant Professor |
| Team Members | : | GADDAM SANKETH, GANDRA SAI MANOJ KUMAR REDDY, CHAKALI SWETHA |
| Domain | : | Image Processing / IoT |
| Abstract | Image Processing Attendance Register is a system which detects human faces, recognizes it and provides attendance. There are many ways to record attendance such as manual entry, biometric, access card and login entries. The proposed method is to record attendance through image using face detection and recognition. This method will be implemented in four steps like face detection, labelling the detected faces, training classifier based on labelled dataset and face recognition. The database is divided into various datasets and furthur, processed by a classifier to recognize the faces of the people. The final step is to take the attendance using face recognition technique in which the input will be given in the form of face images, and faces of the given images will be detected and recognized. Other system such as biometric takes a lot of time to attendance because each entry should be done one after the other and in Iris scanner the memory required is very large and difficult to maintain. This system is a better way to record attendance than the later systems. This system can be further extended and can be used in any workplace to record the attendance of the employees. | |
| Project Title | : | SMART FILE SHARING |
| Project Id | : | 16CS-C15 |
| Guide Details | : | Mr S Venu Gopal Assistant Professor |
| Team Members | : | GOGULA SAI BHAVANA, LOKA SUCHITHRA REDDY, VALLEM SRICHARAN |
| Domain | : | Cyber Security / IoT |
| Abstract | The project is a brief introduction and implementation for the RSA Algorithm which helps to establish a trusted ecosystem between an organization and it’s customers. RSA algorithm is an example of asymmetric cryptography where it uses two keys. Our email is an example of an asymmetric cryptosystem where we have a username as a public key and password as a private key. The private key can’t be derived from the public key. So RSA which can resist almost all known password attacks. The RSA algorithm security depends on the decomposition of large numbers. This algorithm uses the Diffie-Hellman key exchange algorithm method for securely exchanging cryptographic keys over a public communications channel. The advantage of RSA is to crack since it involves the factorization of prime numbers which are difficult to factorize. Moreover, the RSA algorithm uses the public key to encrypt data and the key is known to everyone, therefore, it is easy to share the public key. | |
| Project Title | : | CLASSIFICATION OF INTRUSION DETECTION ATTACKS |
| Project Id | : | 16CS-C16 |
| Guide Details | : | PENDIMUKULLA NISHANTH GOUD SRI SATYA PREETAM A THORMAMIDI MOUNIKA |
| Team Members | : | PENDIMUKULLA NISHANTH GOUD, SRI SATYA PREETAM A, THORMAMIDI MOUNIKA |
| Domain | : | Cyber Security / IoT |
| Abstract | A novel supervised machine learning system is developed to classify network traffic whether it is malicious or benign. To find the best model considering detection success rate, combination of supervised learning algorithm and feature selection method have been used. Through this study, it is found that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperform support vector machine (SVM) technique while classifying network traffic. To evaluate the performance, NSL-KDD dataset is used to classify network traffic using SVM and ANN supervised machine learning techniques. Comparative study shows that the proposed model is efficient than other existing models with respect to intrusion detection success rate. | |
| Project Title | : | GENERATING CUSTOMER INVOICE WITH BARCODE WITH THE HELP OF SAP SMART FORMS |
| Project Id | : | 16SAP-C17 |
| Guide Details | : | Mr B Suresh Kumar Assistant Professo |
| Team Members | : | GUNTUPALLI BHANU SAI, MELIMI SAI KALYAN CHAKRAVARTHY, LAVOORI DINESH |
| Domain | : | SAP |
| Abstract | Generating customer invoice using bar code is the new revolution to track all the entities, though they existing large quantities and redundant items .The barcode uses some decoding algorithms like data matrix and also QR-code, unique identification smart forms format allows to faster interaction and optimise the results. |
| Project Title | : | LIVE STREAMING AND DATA STORAGE IN CLOUD |
| Project Id | : | 16CC-D1 |
| Guide Details | : | Mr G S Prasada Reddy Assistant Professor |
| Team Members | : | SURYADEVARA KARPOORAVALLY, DUDAM MANISH KUMAR, B K ARJUN, KOUKUTLA RAHUL REDDY |
| Domain | : | Cloud / IoT |
| Abstract | Main idea of the project is to create a live video streaming application. The application is expected to be able to transfer live video data. The live video streaming technology can be used with additional user interface technologies to design an application that can be used in different ways as sharing live events, hosting online interviews, demonstrating things online, conduct webinar sessions etc. Out of many possible applications using live video streaming technology, this application targets sharing live events. This application is expected to share live events online to multiple devices at a time. To design the application, AWS cloud technologies can be used, which provides services like Cloud Front and S3 for streaming live videos and storing data in efficient ways. |
|
| Project Title | : | PRIVACY PRESERVING MECHANISM FOR CLOUD ENVIRONMENT |
| Project Id | : | 16CC-D2 |
| Guide Details | : | Mr C. Satya Kumar Assistant Professor |
| Team Members | : | KORAM HARI PRASAD, BADKOLIYA LAXMAN PRAJAPATH, P NAGASAI HIMAVANTH |
| Domain | : | Cloud / IoT |
| Abstract | The main idea of the project is to provide security for the cloud environment. The data which has been uploaded to the cloud will be encrypted with AES encryption. The key will be sent to the intended users to access the data. Secured sharing of data and files over the cloud can be guaranteed by this mechanism. | |
| Project Title | : | AIR POLLUTION ANALYSIS AND PREDICTION USING MACHINE LEARNING |
| Project Id | : | 16DS-D3 |
| Guide Details | : | Dr Ashu A Assistant Professor |
| Team Members | : | SARDARNI JAGPREETH KAUR RAMGADIYA, MADHU MANIDEEP, YEDLA RAJARAM, P DIVYA |
| Domain | : | Data Science / IoT |
| Abstract | Industrialisation has the potential to help achieve a variety of social objectives such as employment, technoligical advancements, etc. At the same time, industrialization causes negative impacts on environment, causing climate change, loss of natural resources, air and water pollution. These threaten the global environment as well as economic and social welfare. To provide better environment for the future generations, researchers proposed many approaches to reduce the negative impacts caused by industrilization. One the approaches is the analysis of air pollution level within a smart city scenario. Real time monitoring of air pollution data enables the metropolitans to analyse current situation of city. The air quality is affected by multi – dimensional factors including location, time, and uncertain variables. Hence, the data to be monitored for the air quality can be categoriezed as big data (based on the volume, varity, velocity, and varacity). As the air pollution data are characterized as big data, we will use machine learning approaches such as time series data monitoring algorithms in this project. The aim of this project is to investigate big-data and machine learning based techniques for air quality forecasting. In this project, we will use the AirQuality.xlsx, a public dataset obtained from kaggle repository. The extracted information is visualized using Hill Winter algorithm. This projects reviews the published research results relating to air quality evaluation using time series, hill winter, mean square algorithms of machine learning. | |
| Project Title | : | PREDICTION OF HEART STROKE CHANCES BASED ON DATA PROVIDED USING MACHINE LEARNING ALGORITHM. |
| Project Id | : | 16DS-D4 |
| Guide Details | : | Dr Ramu Kuchipudi Assistant Professor |
| Team Members | : | K MANNOJ, NUNE ARUN SAGAR, TIPPIRISHETTY BHANU PRAKASH, BAZEQUA FATIMA |
| Domain | : | Data Science / IoT |
| Abstract | The main goal of this project is to predict the chances of Heart strokes by using data which is already available. We divide this data set into 2 parts, The first part of data set consists of 67% of data and is used to train model. The second part of data which is 33% of data is used to test the model. We prepare the classifier model using machine learning algorithm like Decision Trees and predicting the chances of Heart stroke based on various factors like age, chest pain, blood pressure, cholesterol levels, blood sugar levels and maximum heart rate achieved. The fields of the data set mostly represent and have the values which highly contribute for the heart stroke possibilities. The data set has to be cleaned by using various data cleaning techniques and removing null or incorrect values from the dataset after the data set has been cleaned the data set is provided to machine learning algorithms which classify the data. Similarly we can also diagnose several diseases by collecting the data based on the primary causes of diseases and predicting the possibilities of this diseases in patients.By predicting the possibilities of heart stroke by using basic medical test data we can save the time as well as money on highly costly diagnosis. | |
| Project Title | : | FAKE PROFILE IDENTIFICATION |
| Project Id | : | 16DS-D5 |
| Guide Details | : | Ms S Laxmi Sunaina Assistant Professor |
| Team Members | : | NENNURU LEELA PRASAD REDDY, MANIKONDA MEGHANA, VUMMAREDDY PHANEENDER REDDY, GOLLAMUDI S V N YASASHWI |
| Domain | : | Data Science / IoT |
| Abstract | In the present generation, the social life of everyone has become associated with the online social networks. These sites have made a drastic change in the way we pursue our social life. Making friends and keeping in contact with them and their updates has become easier. But with their rapid growth, many problems like fake profiles, online impersonation have also grown. There are no feasible solution exist to control these problems. In this project, we came up with a framework with which automatic detection of fake profiles is possible and is efficient. This framework uses classification techniques like Support Vector Machine, Decision trees and random forest to classify the profiles into fake or genuine classes. As, this is an automatic detection method, it can be applied easily by online social networks which has millions of profile whose profiles cannot be examined manually. SVMs are among the best (and many believe are indeed the best) “off-the-shelf” supervised learning algorithms. Random forest or random decision forest are an ensemble training method for classification, regression and other tasks that operates by constructing a multitude of decision tress training time and outputting the class that is the more of classes or mean prediction of the individual trees. | |
| Project Title | : | ALZHEIMER’S DISEASE PREDICTION USING NAIVE BAYESIAN CLASSIFIER |
| Project Id | : | 16DS-D6 |
| Guide Details | : | Mr Pallati Narsimhulu Assistant Professor |
| Team Members | : | MANKAL ROHITH KUMAR, GUNNA SAI SACHIN REDDY, CHAVA SANTHOSH, SASANAPURI PRANEETH |
| Domain | : | Data Science / IoT |
| Abstract | In that project, we propose checking the patient for Alzheimer’s Disease using Naive Bayes classification. Alzheimer’s Disease is a progressive disease that demolishes brain’s memory and its regular functioning as well. There isn’t any single test till date to diagnose this disease and brain scans alone can’t determine whether the person is possessed by it. Currently, the physician believes that a person is affected by Alzheimer’s based on the reports of the family members about the behavioral tendencies and observations of the past medical history. AI combined with Machine Learning algorithms might now be able to change this situation. Big Data processing has become significant as the information comes from multiple, heterogeneous, autonomous sources with complex and evolving relationships, and keeps growing. We prepare the dataset from previous historical data based on the percentage of patients that get this disease considering some crucial features. | |
| Project Title | : | PHISHING WEBSITE FEATURES IN URL AND PREDICTION USING ML(MACHINE LEARNING) |
| Project Id | : | 16DS-D7 |
| Guide Details | : | Mr S K Prashanth Assistant Professor |
| Team Members | : | KONJARLA MOUNISHA, N PRANAYA REDDY, MEDURI MONICA, MUNIGANTI SRIKANTH |
| Domain | : | Data Science / IoT |
| Abstract | OBJECTIVES1. Main objective is to identify the malicious urls sent by the attackers through e-mails or websites. 2. To implement two supervised learning algorithms, they are random-forest and support vector machine algorithms to achieve better performance. 3. Considering the site popularity and traffic ranks are considered as important features to extract the phishing features of the web sites.OUTCOMES1. Identification of phishing URLs and domains exhibit characteristics that are different from other URLs and domains. 2Phishing URLs and domains exhibit characteristics that are different from other URLs and domains. 2. To warn the customers before entering the websites. Phishing URLs and domains exhibit characteristics that are different from other URLs and domains. • Phishing URLs and domain names have very different lengths compared to other URLs and domain names in the Internet. • Many of the phishing URLs contained the name of the brand they targeted Phishing URLs and domains exhibit characteristics that are different from other URLs and domains. • Phishing URLs and domain names have very different lengths compared to other URLs and domain names in the Internet. • Many of the phishing URLs contained the name of the brand they targetedABSTRACT: As a result of rapid increase in mobile usage, internet availability has increased. So web sites are providing their services online to their customers. At the same time, attackers are misusing these services by developing malicious websites. These malicious websites largely promote the growth of internet criminal activities. Therefore, there is a need of protecting customers or users from the attackers. So we propose a learning based approach to classify web sites into 3 classes. They are : 1. Benign: safe websites with normal services. 2. Spam: performs the flooding of user with ads and fake surveys, etc. 3. Malicious: created by attackers to gather sensitive information and to gain access to private computer systems. Our mechanism analyses the URLs without accessing the website content. Thus, it eliminates the run-time latency and the possibility of exposing users to the browser based vulnerabilities. By employing learning algorithms, our scheme achieves better performance on generality and coverage compared with blacklisting service. |
|
| Project Title | : | Student Performance Analysis |
| Project Id | : | 16DS-D8 |
| Guide Details | : | Dr S. Shitharth Assistant Professor |
| Team Members | : | RISHIKA REDDY KOTA, MANCHI REDDY RITHESH REDDY, TANNIRU MADHUSUDHAN, KETHAVATH PRAVEEN KUMAR |
| Domain | : | Data Science / IoT |
| Abstract | Each and every student in a school/college has different level of performance. Analyzing a student performance based only on their grade level and suggesting improvement in the next year would be not very useful in this fast moving pace of talent. A student can be analyzed based on his identity, course in which he/she is majoring, behavioral demographics, parent responsible (mother/ father) and the data from surveys from student and their parents. The data from such demographics can be used to classify a student as an average or a bad or a good performer. Such classification helps faculty of institution to take steps to change a bad performer as an average or a good student. The dataset used for such analysis is collected from surveys and observations. The data visualization can be applied to get an idea of shape of the data and to identify possible outliers. Since most of the data is categorical rather than quantitative, classifiers (Support Vector Machines) are applied to classify students in to mainly three classes ‘L’,’M’,’B’. Based on outputs students can be altered to improve their results. |
| Project Title | : | APPLICATION FOR DETECTING CYBER THREATS AT PUBLIC CROWDED PLACES BY USING DATA ANALYTICS |
| Project Id | : | 16DS-D9 |
| Guide Details | : | Mr G S Prasada Reddy Assistant Professor |
| Team Members | : | BACHU SHIVA SAI, PENTAMSETTY NIHARIKA, KOUKUNTLA VISHNUVARDHAN, K YASHWANTH |
| Domain | : | Data Science / IoT |
| Abstract | In today’s world WIFI has become an important factor in the present generation. The growth of WIFI has increased. Due to this network security attacks are also increasing. We will collect the data from different resources about security attacks. With the help of this, we will prepare the dataset. After preparing the dataset we will classify the data based on different factors. We use different machine learning algorithms. By using this application we can find different security attacks and we will provide a description of attacks | |
| Project Title | : | HUMAN ACTIVITY RECOGNITION USING MACHINE LEARNING |
| Project Id | : | 16DS-D10 |
| Guide Details | : | Ms Gandham Swetha Assistant Professor |
| Team Members | : | KUMBAM ANVESH, MEKAPOTULA VAISHNAVI, NIMMANENI CHANAKYA, SANAM SUNAYANA |
| Domain | : | Data Science / IoT |
| Abstract | Human Activity is a kind of data mining of human actions with respective to the emotions. Human Activity recognition is a device which predicts the future of a person using machine learning techniques. In which the device predict the kind of movement being carried out by the person. It is used in the health care systems, initial detection of diseases, rehabilitation centers. We can use the sensor technology such as accelerometers, gyroscopes from the modern devices a man use daily life to predict his emotions and future activities. Examples of activities detected are swimming , running , walking , standing etc. | |
| Project Title | : | ESTIMATION OF GROUND WATER LEVEL THROUGH (USING) SOIL MOISTURE |
| Project Id | : | 16DS-D11 |
| Guide Details | : | Mr C. Satya Kumar Assistant Professor |
| Team Members | : | MUDAVATH SHIVA KUMAR, CHOULAPALLY SHIVA KUMAR REDDY, NAINI RISHITHA REDDY, GURRALA RASHIK REDDY |
| Domain | : | Data Science / IoT |
| Abstract | In recent times, it became difficult to predict the underground water levels because of transient underground conditions. Generally the underground soil is divided into two zones, saturated and unsaturated. In this model, prediction of underground soil moisture in saturated zone will be implemented. By the prediction of soil moisture, underground water levels can be determined. The model will be developed based on Artificial Neural Networks (ANNs) . ANNs have the capability of prediction when the availability of the data is less for prediction. The model will be trained with different hydrological parameters which includes daily, weekly, monthly and bi-monthly data. Factor analysis along with time series forecasting will be incorporated for increased accuracy and high consistency. | |
| Project Title | : | TRAFFIC-ANALYSIS
|
| Project Id | : | 16DS-D12 |
| Guide Details | : | Dr M. A Jabbar Professor |
| Team Members | : | KAMATHAM SANDEEP, AMBARLA SRIRAM, PATCHA RAM MOHAN, RAVELLA KARAN KUMAR |
| Domain | : | Data Science / IoT |
| Abstract | In the present world traffic is the major problem for every individual. It’s hard to analyse the traffic as the vehicles are in moving state. It’s hard to track the desired vehicle from the large number of vehicles. As all the vehicles are not of the same shape and size, it will be hard to detect them. To bring up a solution for this problem we use the video footage recorded by the cameras placed at traffic signals. It doesnt require any additional cost as it uses the existing systems. We use Frame detection and edge detection algorithms to detect the vehicles. With the help of Kalman filter we can get the vehicles exact positions. Experimental results can be implemented using python code with Open-CV development kits, indicate that the proposed method can detect, track, and count moving vehicles accurately | |
| Project Title | : | CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING |
| Project Id | : | 16DS-D13 |
| Guide Details | : | Dr M. A Jabbar Professor |
| Team Members | : | GUDLA RAHUL, MOHAMMAD KARISHMA, GOURISHETTY SRUJAN, GANGISHETTY RUSHIKESH |
| Domain | : | Data Science / IoT |
| Abstract | In today’s world we are on a express train to a cashless society. The most accepted payment mode is credit card for both online and offline, it provides a cashless shopping in all countries. It has became a more conventional way to do online shopping, paying bills etc. Hence the risk of fraud transactions has been increasing day-by-day. Implementation of efficient fraud detection system thus became important for all credit card issuing banks to minimize the losses. In real life, fraudulent transactions are intermixed with genuine transactions and simple pattern matching is not often sufficient to detect this problem accurately. There is a need of past history of transactions like credit card holders spending pattern from the previous transactional database, and other inputs like income, location, living expense etc can be compared with the current transaction details to detect credit card frauds, if there is deviation on transactions it indicates as fraud. To detect this fraud we are using various machine learning algorithms like k-nearest neighbor, logistic regression etc classifiers to detect the fraud more accurately. | |
| Project Title | : | SMART SEARCH WITH PRIVACY |
| Project Id | : | 16DS-D14 |
| Guide Details | : | Mr Ganesh Deshmukh Assistant Professor |
| Team Members | : | SATLA RAHUL, SYAMANABOYENA KRISHNA CHAITANYA, KUMMARI RAJESH |
| Domain | : | Data Science / IoT |
| Abstract | Using search engines in today’s environment may lead to stealing of informal from user, so in order to avoid such loss we create smart crawler which helps as a private search engine which helps the user to search in private environment without any stealing of information. On web we can see that web pages are not indexed, it was developed many crawlers to locate web interfaces, due to the large amount of resources in the network, the better result is a challenging problem. To solve this problem, we propose mainly crawler. Crawler performs the reverse search that matches the user’s query in the URLs. In the next step, the Site Prioritize is performed in which the content of the query in the form matches. Then, according to frequency matching, sort relevant pages and rank this page using keywords. Ranking pages are displayed on the results page. We will propose a customized search to improve performance by considering how long we keep the log file. Before viewing the query before entering the query in the search box that is the focus, enter the search box | |
| Project Title | : | A SECURE DATA TRANSMISSION MECHANISM FOR VEHICULAR COGNITIVE CAPABILITY HARVESTING NETWORKS |
| Project Id | : | 16CS-D15 |
| Guide Details | : | Dr Sai Krishna Mothku Assistant Professor |
| Team Members | : | THUMMA ARAVIND, YERASI VENKAT REDDY, KOLLAREDDY DILEEP KUMAR REDDY |
| Domain | : | Cyber Security / IoT |
| Abstract | In smart city applications, communication is increasing day-by-day using wireless traffic networks. Ding et.al [1] have proposed an intelligent data transportation system which works with communicating the data between two vehicles. The existing system [1] has designed the data transportation platform for vehicular cognitive capability harvesting network (V-CCHN) using Radio Access Networks (RAN’s) The major limitation of the existing system is lack of providing security in the data transportation. Therefore, it is vulnerable for data theft and data modification. It leads to misguide the user and even allows the attackers to perform the criminal activities, such as delay in data delivery which leads to accidents, false data transmission. Hence, in this project, a secure data communication mechanism will be proposed for the data transportation system that prevents reply attack and man-in-the-middle attack. | |
| Project Title | : | AUTOMATIC MOTORCYCLIST SAFETY HELMET DETECTION |
| Project Id | : | 16IMG-D16 |
| Guide Details | : | Mr V N L N Murthy Assistant Professor |
| Team Members | : | CHINCHETI SRUTHI, GADE SRITEJA RAO, GOLI SREEJA REDDY, KOPPARTHI SAI VENKAT REDDY |
| Domain | : | Image Processing / IoT |
| Abstract | The Project titled “Motor cyclist safety helmet detection” is developed with intention to improve and enhance the safety measurements for well-being of society. The major task that need to be handled is illumination and poor video quality of surveillance traffic camera problem and dynamic changing weather conditions. In this project we aimed at proposing a well-developed framework for achieving this task by taking into consideration of all the advantages of various techniques proposed in various papers on this topic. The helmet is considered to be the motorcyclist’s main protection. Most of the countries across the globe require the use of helmets by motorcyclists, but many people fail to obey the law for various reasons. We are presenting the development of an automatic system using image processing and deep convolutional neural networks (CNNs) for finding motorcyclists who are violating helmet laws. The system comprises of detection of motorcyclists with helmet and without helmet. This helps in taking necessary actions by the police officials. Charging fines to those people who got detected will alert the society and mainly individual’s safety. This system can be deployed at several cities of India. | |
| Project Title | : | TRAFFIC CONGESTION CONTROL |
| Project Id | : | 16IMG-D17 |
| Guide Details | : | Dr Ashwani Kumar Associate Professor |
| Team Members | : | BHEEMARAPU MANEESHA, KOTLA SIDDARTHA, SOLLU PRANAY KUMAR |
| Domain | : | Image Processing / IoT |
| Abstract | The project aim is to design traffic management system. Now a day’s everyone are using a vehicle and we can see many traffic congestion in many areas .So to control this traffic jams we are designing a traffic management systems using vehicle counting. We can design this system by using image processing algorithms on video streams taken from cameras. There is a increased demand for the smart cities, so we need to use some techniques to analyze the traffic density. So traffic management system should identify the vehicle and count the vehicles passing from the cameras view field from one traffic light to another and analyze the next congestion going to be occur in advance to the nearest traffic police and clear the area. |