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

Music enthusiasts are growing exponentially and based on this, many songs are being introduced to the market and stored in signal music libraries. Due to this development emotion recognition model from music contents has received increasing attention in today’s world. Of these technologies, a novel Music Emotion Recognition (MER) system is introduced to meet the ever-increasing demand for easy and efficient access to music information. Even though this system was well-developed it lacks in maintaining accuracy of the system and finds difficulty in predicting multi-label emotion type. To address these shortcomings, in this research article, a novel MER system is developed by inter-linking the pre-processing, feature extraction and classification steps. Initially, pre-processing step is employed to convert larger audio files into smaller audio frames. Afterwards, music related temporal, spectral and energy features are extracted for those pre-processed frames which are subjected to the proposed gradient descent based Spiking Neural Network (SNN) classifier. While learning SNN, it is important to determine the optimal weight values for reducing the training error so that gradient descent optimization approach is adopted. To prove the effectiveness of proposed research, proposed model is compared with conventional classification algorithms. The proposed methodology was experimentally tested using various evaluation metrics and it achieves 94.55% accuracy. Hence the proposed methodology attains a good accuracy measure and outperforms well than other algorithms. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

School of Computer Engineering & Technology

Engineering and Technology

World University root node

Multimedia Tools and Applications

Multi-label emotion recognition from Indian classical music using gradient descent SNN model

Lecture Notes in Electrical Engineering

University Recommendation System for Higher Studies in USA

Recommendation system has become a requirement in today’s world. This recommendation system is spread over large sectors right from education, entertainment, health, business etc. The University Recommendation System is a system which recommend right university for the students based on their GRE, TOFEL score. Due to rapid increase in data and lack of learning in education filed, students often halt selecting wrong universities. Thus, there is a need of recommendation system which is trained to understand the user’s requirements and suggests them that suitable university which they need. Typically, recommendation mechanisms are used based on user’s historical data. It helps the user to discover information and settle on right choices where they do not have the required learning to judge a specific item. This paper explores the flow, types and uses of recommendation system

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International Journal of Recent Technology and Engineering (IJRTE)

The University Recommendation System for Higher Education

Probability of spare components adequacy is very crucial within the method of conventional operation of companies. This paper focuses on the military’s order forecasting of standby parts which has been paid much attention in military supply line which ultimately has the impact on nationwide guard finances. Formerly used time series analysis method needs more upgrading and precision so the machine learning ensemble techniques like Bagging and Boosting are proved to be useful for enhanced accuracy and suitability. Inventory dataset related to military like the most used aircraft, period during which aircraft saw the most runs, types of weapons used, etc. can be forecasted by applying ensemble learning. The principal advantage of using Ensemble techniques is that the model’s randomness is reduced and thus perks up the precision for stipulated forecasting.

Universal Review Journal

Demand Forecasting of Anti-Aircraft Missiles Using Ensemble Techniques.

Now-a-days demand forecasting is used in many countries for military applications such as spare parts of aircraft and for improving budget efficiency. In supply chain management demand forecasting is a major issue. Currently, time series technique is used to demand forecast but this technique resulted in lack of accuracy and improvement in accuracy is needed. So this paper focused on comparing the features which leads to improvement in the accuracy and propose a system for demand forecasting of spare parts of anti-aircraft missiles, which are based on machine learning and neural networks such that equipment's are properly utilized and alongwith that budget is also maintained. We have compared the existing features with the new features added and applied algorithms and looked upon at the accuracy. Experimental results proves that the new features added gave higher accuracy. Here, we also present an end-to-end boosting system called XGBoost and Multi-layer Perceptron. These new techniques were compared with traditional Machine Learning techniques. This experiment is conducted on the Vietnam War dataset. © 2019 IEEE.

Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019

Analysis on Machine Learning Algorithms and Neural Networks for Demand Forecasting of Anti-Aircraft Missile Spare Parts

Advance trend in research is emotion recognition. Emotion is nothing but a strong feeling deriving from one's circumstances, mood, or relationships with others. Research has been done on emotion recognition from face, images, speech, audio, video, text etc. using machine learning approach. Here showing various types of algorithm, tools and techniques are being used for recognition of emotion through various aspects. Machine learning algorithms are mostly used in the process of emotion recognition. Deep learning algorithm is an advanced machine learning approach used in research field of emotion recognition. In this paper we are discussing various deep learning algorithms used for emotion recognition from various aspects of human. © 2017 IEEE.

2017 International Conference on Energy, Communication, Data Analytics and Soft Computing, ICECDS 2017

A step towards advanced machine learning approach: Deep learning

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.

Proceedings - 2nd International Conference on Computing, Communication, Control and Automation, ICCUBEA 2016

An efficient framework for recommendation of hindustani art music

Medium of music has evolved specifically for the expression of emotions, and it is natural for us to organize music in terms of its emotional associations. Much of the difficulty in developing emotion-based features is the ambiguity of the ground-truth. Emotion recognition is an advanced trend in research field. Indian Classical music is an ancient tradition and also has very effective mathematical structure. In the field of emotion recognition, most of the work has been done on western music but very less on Indian Classical Music. In this paper, we are proposing a system which will help to recognize an emotion for Indian Classical Music. © 2017 IEEE.

Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, ICICCS 2017

A framework for emotion identification in music: Deep learning approach

Raga plays an important role in Indian classical music. Raga is made up from the swara or note. According to characteristics of raga, Indian classical music is further divided into two systems Hindustani / North Indian classical music, Carnatic / South India classical music. This paper introduces us with some basic terms in Indian classical music and terms associated with raga. Then we discussed different techniques for raga identification along with their system structure. Different challenges associated with raga identification. © 2014 IEEE.