Abstract:This paper contains the useful aspects of smart farming. How the smart farming can be implemented in Indian farms? Smart farming is the combination of big data analysis, robotics, The Internet of Things, Wireless sensor networks, data analysis.Smart farming in Indian farms can make the Indian farmers more efficient, they could know the exact soil moisture, humidity level, climatic conditions, fertility of the soil, and the exact use the fertilizers and urea to the soil, this can help the farmer to use the fertilizers in the right amount and hence it can prevent from wastage of money too. Big data analysis will be one of the important aspects of the paper on smart farming. The review of this paper is to check the socio-economic aspects and challenges to be addressed. The primary big data analytics can go beyond primary productions; it is influencing the entire food chain. This paper has a structured approach that can be used for future studies on this topic. In India, how the smart farming technique can be implemented is also covered in this paper.

Keywords:Smart farming, Big data, IoT, Artificial Intelligence, Robotics, Wireless Sensor Networks, Data Analysis

Abstract:MOOC is an ultimate way to give educational content in higher studies settings by providing good-quality educational material to the studentts throughout the world. If we take in the differences between traditional learning paradigm and MOOCs, a trend focusing on predicting and explaining dropping out of students and low completion rates in MOOCs has emerged. The quantity of students taking these courses is very high, still, the completion rate is very low. Factors affecting the completion of a course by a student such as interest in the subject, reason for taking the course, if the teacher is able properly make students understand or not. Nevertheless, because of varying problems specifications and evaluation metrics, undertaking a high level evaluation of state-of-the-art machine learning architectures isn't easy. This paper provides a complete rundownn of MOOCs student's dropout probability with the help of machine learning techniques. Moreover, we have highlyghted a few answers being used to solve the dropout problem, provide an examinationof the challenge of prediction models, and give some important overview and suggestions that may pave the way to develop useful ML solutions for overcoming the MOOCs dropout problem. Essentially, we got features from every learning behaviors and formed multi-view behavior features. Also, examining these features, we proposed a new multi-view semi-supervised learning model to use a large number of raw data to help inadequate labeled data to improve prediction. We have used KDD Cup 2015 dataset, from the results we see that our proposed model attains better prediction of a student leaving a course than state-of-the-art methods. Long Short-Term Memory neural network (LSTM) prediction model makes use of time-control units, the unit has the ability to model early learning behaveiour with varying time intervals. Formulated from the LSTM model, we designed time-controlled gates for capturing a good long- and short term info and use the learning process info toget better forecast performance

Keywords:MOOCs, Dropout Prediction, dropout problem, completion, Long Short-Term Memory, LSTM

Abstract:We are Living in a 21st Century where everything around us has become dependent on technology to make our life much easier and to work faster. People often use technology to complete the daily task . To the best of our knowledge, the process of recording attendance at the schools, universities, offices is still manual. In schools and universities the Attendance sheet is passed to all the students to sign on it and record attendance. Where as in office the employee should sign on the record book to make sure that he is present in office. This is slow, inefficient and time-consuming. This project is to offer system that can automate the process of recording and tracking the attendance using Facial Recognition Technology using Deep Learning. Facial Recognition Technology is becoming much popular in different areas such as Airports, Banks, Military, etc. Best example is our Mobile phone where we can unlock device using Face Recognition Technology. We will use Deep Learning techniques to detect, recognize and verify the captured faces. We aim to provide a system that will make the attendance process faster and more precisely.

Keywords:Deep Learning; Face Recognition; Attendance

Abstract:We all live in the Internet era in which millions of computers are interconnected to each other to share the digital or binary data. But in the current time, not only computers but the number of digital devices are connected and share data with little or no human interference. These interconnected digital devices are called the Smart devices and this perception is called IoT (Internet of Things). IoT is the new technology that develops very rapidly in a computing World. With the help of sensors it connects the number of devices globally in the cloud. On the IoT platform number of devices are connected to each other, sensed through sensors and also controlled remotely across a network. Application of IoT in the education domain is the new way of teaching and learning process and also renovates the experience of both students and educators. The IoT enabled campus becomes the smart campus. In smart campus, servers, peripherals, the infrastructure of the campus, students, faculties, and other staff are connected. The campus becomes a platform for innovation and in this way everything of the campus shares data through IoT. Therefore, the present paper gives its justification by discussing the influence and application of IoT in the field of teaching systems.

Keywords:Internet of Things(IoT), Smart Campus, Smart Learning, Smart Classroom, Smart Learning, Smart Building, Sensors

Abstract:Placement is the high-ranking initiative of any institute. During the placement process there are major three parts (a) Training (b) Placement administration (c) Suitable Placement. After this process the key point is does the students are satisfied with their placements? With respect to IT-Jobs, the job satisfaction changes frequently. But when an institute place some student at a company, it is very important for the institute to analyze that how many students are satisfied with their placements? Which Companies should get priority at on-campus, which companies should be invited for off-campus, which companies are there who deals with mass-students, which companies are there who deals with skills of students, what kind of training module do these companies are offering, what would be the lower and upper limit of the package offered by companies and how many companies should be invited for the placements in single session. These all parameters take place after the session of placement ends in the final year batch. These parameters can be common for wide streams but the result would be very-very different and very according to stream and market requirement. Thus it is required to perform this analysis after each session which can be conducted as post-placement analysis. This Paper represents the post-placement Analysis for Institute based in Gujarat (*not to mention name) for MSc-CA & IT program, which is performed on the dataset of “Placemet-2019-20” using Correlation in python between ‘students rating for training at companies and Package offered by the companies.

Keywords:Data Analysis, Placement Process, Placement Analysis, Correlation in python