MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

World University root node

School of Mechanical Engineering

International Journal Of Advance Research, Ideas and Innovation In {\ldots}

Design , analysis and comparing for a different cylinder head of C . I . Engine

It is well known that a appropriately designed Exhaust manifold is important for the optimal performance of an IC engine. Though, the intake system is dominant on the cylinder filling process, the exhaust manifold has ability to influence the gas exchange process in various important aspects, like the piston work done during the exhaust stroke, the short-circuit of fresh charge from the intake into the exhaust. We know that if the back pressure of Exhaust gases is higher, it reduces efficiency of the engine. So aim should be to reduce the back pressure of the engine. Also amount of pressure drop is equal to back pressure. So ultimate goal is to reduce its pressure drop by changing its geometry, reducing friction in the manifold. In this paper only Geometry optimization is done. This paper presents 3-D simulation of Exhaust manifold of 4 cylinder, 4 Stroke, turbocharged diesel engine(4G11 Greaves Engine) of generator by using ANSYS-CFX code and results are discussed. A Steady state CFD simulations have been accomplished and experimental results are compared for validation. The CFD results gives an idea for changing its geometry.

International Journal of Current Engineering and Technology

Geometry optimization of exhaust manifold using CFD

International Research Journal of Engineering and Technology(IRJET)

Design &amp; Fabrication of Organic Fertilizer Manufacturing Machine

This paper describes a method to diagnose a subject with clinical depression. The approach taken utilizes EEG data from which specific channels are selected to obtain voltage fluctuations from Occipital and Parietal lobes of the brain. Studying these parts of the brain is highly informative about the subjects state of mind when he is awake and help us understand how he processes information, on these basis we can deduce if the subject is depressed or not. These channels are used to extract required alpha wave signals. Then Fast Fourier Transform is applied on the alpha wave and its fundamental frequency and corresponding amplitude of the alpha wave are obtained. The obtained information is used to determine if the test subject is depressed or not using a rule based classifier. The classifier classifies a subject as depressed if the fundamental frequency is below 8 Hz and normal if it is 8 Hz or above.

School of Computer Engineering & Technology

International Journal of Scientific Engineering and Research (IJSER)

An Advanced Design for Depression Analysisthrough EEG Signal

An evaluation and choice of learning algorithms is a current research area in data mining, artificial intelligence and pattern recognition, etc. Supervised learning is one of the tasks most frequently used in data mining. There are several learning algorithms available in machine learning field and new algorithms are being added in machine learning literature. There is a need for selecting the best suitable learning algorithm for a given data. With the information explosion of different learning algorithms and the changing data scenarios, there is a need of smart learning system. The paper shows one approach where past experiences learned are used to suggest the best suitable learner using 3 meta-features namely simple, statistical and information theoretic features. The system tests 38 UCI benchmark datasets from various domains using nine classifiers from various categories. It is observed that for 29 datasets, i.e., 76 % of datasets, both the predicted and actual accuracies directly match. The proposed approach is found to be correct for algorithm selection of these datasets. New proposed equation of finding classifier accuracy based on meta-features is determined and validated. The study compares various supervised learning algorithms by performing tenfold cross-validation paired t test. The work helps in a critical step in data mining for selecting the suitable data mining algorithm.

Evolving Systems

Evolving learners' behaviour in data mining

Today is a new era of computing technology i.e. Internet of Things (IoT). IOT can be termed as a “universal global neural network” in the cloud which connects various things. Internet is a revolutionary invention, it has always been transforming itself into some or other new kind of hardware and software which makes it irresistible for anyone. Nowadays the communication is in the form of human-human or human-device. But the future of internet i.e. IoT promises a machine-machine type of communication. The IoT is a system comprising various devices or systems which are intelligently connected which interacts and communicates with other machines, environments, objects and infrastructures. With the areas like business, transportation, medicine, energy, agriculture and others, the Internet of Things also finds a major implication in education. A university campus can be considered as the ideal place for the creation of a smart environment. The aim of the hereby paper is to describe a new concept called Smart University by providing a comprehensive overview of the IoT scenario and reviews its enabling technologies and the sensor networks.

School of Electronics & Communication Engineering

International Journal of Innovative Research in Computer and Communication Engineering

A Review on Smart Campus using IoT

Currently in India college ranking is the issue that is discussed a lot. In this paper, a common forum where students can get information about various engineering colleges and their ranking. There are many websites that give information about various engineering colleges but when it comes to students, for them it is important to know certain insights to parameters like faculty, campus life, hostel facility, placement etc. Students from small cities and villages have less exposure to various Colleges which are funded by state and central government for studies, Research and Innovation. If these students will have sufficient exposure then the young talent will be properly channelized as per their interest. In this we have developed a recommender system that understands information seeker need and accordingly generates recommendations for him/her through simple interface, which provides information to the students and advisors to improve the choice of courses. In our methodology we have opted for hybrid approach which combines advantages of Collaborative filtering algorithm and Content based filtering algorithm to improve our recommendations. The system consists of an interactive User Profiling [9] process in order to construct profile of the User. As the sample datasets that are used for experiments are large and also contain more number of feature sets, it is essential to understand dataset beforehand. Recommending items based on the users interest requires processing of these large datasets, thus if this information is properly profiled then the task of recommending would get a lot easier. Also when results are shown to the user, big challenge is how well data can be ranked so that user satisfaction is guaranteed. © 2017 IEEE.

Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2017

A user-friendly college recommending system using user-profiling and matrix factorization technique

Public Distribution System is a government-sponsored chain of shops entrusted with the work of distributing basic food and non-food commodities to the needy sections of the society at very cheap prices. It is also an important constituent of the strategy for poverty eradication and is intended to serve as a safety net for the poor people whose number is more than 33 Crores and are nutritionally at risk. The current Public Distribution System has several well documented problems such as lack of transparency, accountability, poor governance and poor service delivery mechanisms. A large number of poor and needy members of society are left out and a lot of bogus cards are also issued. This leads to increase in corruption. Our project proposes the improvised technique of implementing smart ration card system. It also depicts the automated version of the Public Distribution System. At the time of implementation, for identification purpose we are giving unique QR-code to each customer. This will help to keep track of their respective accounts. Customers will also receive SMS notification of their successful registration and stock allotted to them. Thus our system will help to create transparency in transactions and will also help to manage accountability. © 2017 IEEE.

A proposed architecture for automating public distribution system

Facial analysis in videos and images has been a relatively tough task for machine learning models. Recent use of deep learning approaches has demonstrated substantial improvement in results and reliability and can be used for problems such as face recognition, emotion recognition and emotion reaction prediction. In the case of emotion reaction, relevant information of emotions in individual frames often must be aggregated over a variable length sequence of frames and speech signal to produce an appreciable prediction. Emotion reaction prediction is a subset of sequence analysis task and heavily relies on dynamic temporal and spectral features. Convolution neural networks (CNNs) have been extensively used for emotion recognition problems and have produced reliable results. However, they lack the ability to extract time-series information from a sequence of inputs and cannot model an emotion transaction. Recurrent neural networks (RNNs) are being used profoundly due to their ability to yield impressive results on a variety of tasks in the field of sequence analysis. In this work, we propose a system for emotion recognition and reaction prediction in videos. The primary focus is experimental analysis of a hybrid CNN-RNN architecture for emotion transaction analysis that can recognize the emotion in a frame in a video and predict its appropriate reaction. © 2017 IEEE.

Proceedings - 2017 3rd IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2017

Emotion recognition and reaction prediction in videos

Music directly connects to soul and induces emotions in mind and brain. Hindustani Art music (HAM) has nine different aesthetic delights and in turn various moods and emotions associated with it. It is also associated with seasons and time of day. HAM has many applications in music therapy and meditation. So, an exclusive Recommender system for HAM will help listeners to change their mood and rejuvenate their psychological and physiological health conditions. A framework for recommendation of HAM by extracting various attributes, features and properties of music and using a hybrid filtering approach for recommendation is presented. The traditional Nava-Rasa model is suitable for HAM with which we can track the emotions associated with this music.