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

School of Computer Engineering & Technology

Substantial accumulations of computerized photos are consistently made. Various collections of digital photos like in the area of technology, private organization, etc. are structured because of the result of digitizing existing accumulations of simple pictures, charts, outlines, canvases, and archives. For the most part, the main strategy for investigating these accumulations was by perusing or indexing of index words, Digital photo databases begin the best approach to searching based on content. In this paper, we have shown a strategy that has no past information about the picture inside the database, yet retrieval is carried out considering the content data of the pictures prone to be called as content based on image retrieval. Here we are attempting to enhance the image retrieval framework for more exactness and efficiency by utilizing Radial base Function neural system. This arrangement with multilayer feed forward system recognition. By utilizing these procedures we can get into know the exact relevant image gave by the client according to the query. The Scale Invariant Feature Transform (SIFT) is a standout amongst the most nearby feature detector and descriptors which is utilized as a part of the vast majority of the vision programming. In this paper in regards to CBIR framework we can use SIFT algorithm to concentrate the nearby feature of the pictures. The retrieval method has been implemented on two CBIR systems, one using multiple features and the other using only RBG feature. The results obtained are positive and we have obtained higher precision (81.93%) using multiple features than that of using only RGB (41.18%). Experiment demonstrates that the proposed system get better results of retrieval system.

Engineering and Technology

World University root node

International Journal of CiiT

“SIFT based Efficient Content based Image Retrieval System using Neural Network

The world wide web including social sites, blogs, forums etc. generate a huge amount of data in the form of views, emotions, opinions and arguments regarding different products, social activities, brands and politics. This has prompted increasing interests in analytical domain. Thus, all these comments have a great influence on readers, vendors politicians. In political domain, political parties and other experts make use of facebook, twitter and blogs to communicate with the people and to check voice of public. Hence, an appropriate model which gives correct analysis and results about peoples' sentiments related to political parties and political diplomats is built. Data is captured from social media sites and pre-processing is carried out. Word2vec model is used for word embedding process. For final evaluation neural network is used. After detailed study and comparison efficient neural network model is chosen and positive/negative sentiments are identified. Experimental evaluation signifies the effectiveness of the proposed model. This system is also helpful for understanding people's response to particular political decision which will help in better decision making during elections and political campaigns. © 2018 IEEE.

1st International Conference on Data Science and Analytics, PuneCon 2018 - Proceedings

Political Sentiment Assessment through Social Media

Wireless Communication has established itself as an efficient and reliable medium for data transfer. But with the number of end users increasing exponentially and the availability of radio spectrum diminishing at a fast pace there is a need of an alternate approach for data transfer. The optical wireless communication proves to be an efficient alternate solution to solve the radio frequency spectrum crisis and also to overcome the constraints of the Wireless-Fidelity (Wi-Fi) technology. The LEDs used in our daily lives for the purpose of providing light when integrated with Li-Fi technology can make an efficient communication network for data transfer. This paper proposes cost-effective and working application of the Li-Fi technology to overcome the constraints of the existing data transmission technologies and to provide an alternate data transmission medium which is built on the existing infrastructure. © 2018 IEEE.

Proceedings - 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018

A New Approach to Wireless Data Transmission Using Visible Light

“Surya Namaskar” is the key for Good health! Today’s social life can be made easier and healthier using the mantra of “YOGA”. Nadi Parikshan is a diagnostic technique which is based on the ancient Ayurvedic principles of Wrist Pulse analysis. Nadi describes the mental and physical health of a person in great depth. This information can be used by practitioners to prevent, detect as well as treat any ailment. Surya Namaskar is a Yoga exercise which has multiple health benefits and a direct impact on Pulse. Prakruti of a person is a metaphysical characteristic and a combination of the three doshas in Ayurveda viz. Vatta, Pitta, and Kapha which remains constant for the lifetime. Experimentation was carried out to analyze the effect of Surya Namaskar exercise on the Pulse of different Prakruti. The Pulse was recorded for a group of young students aged between 19 and 23 years with different Prakruti before Surya Namaskar and after Surya Namaskar for a period of 4 days. This paper analyzes the effect of Surya Namaskar on human Pulse and proposes a framework to predict Pulse after Surya Namaskar. The changes that Surya Namaskar causes in the Pulse are studied and used to predict Pulse after performing Surya Namaskar. This analysis helps understand how Surya Namaskar benefits the health of a person. Performing Surya Namaskar in our daily routine would improve the health of the society as a whole making the subjects energetic and active. © 2018, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

Analysis and prediction of the effect of Surya Namaskar on pulse of different prakruti using machine learning

Sarcasm is a sophisticated form of sentiment expression where speaker express their opinions opposite of what they mean. Sarcasm detection and Emotion detection from social net-working sites has been a great field of study. With the growth of e-services such as e-commerce, e-tourism and e-business, the companies are very keen on exploiting emotion and sarcasm analysis for their marketing strategies in order to evaluate the public attitudes towards their brand. Thus efficient emotion and sarcasm modeling system can be a good solution to the above problem. This work aims at developing a system that groups posts based on emotions, sentiment and find sarcastic posts, if present. The proposed system is to develop a prototype that help to come to an inference about the emotions of the posts namely anger, surprise, happy, fear, sorrow, trust, anticipation and disgust with three sentic levels in each. This helps in better understanding of the posts when compared to the approaches which senses the polarity of the posts and gives just their sentiments i.e., positive, negative or neutral. The posts handling these emotions might be sarcastic too. The Sentiment emotion identification module identifies the sentiment or emotion of the post by evaluating score of each word in the comment which is used by different sarcasm detection methods to detect sarcasm. The emotion identification module uses the lexical databases WordNet, SentiWordNet to find the right sentiment scores for the words with respect to each emotion. It also uses Sarcasm detection algorithms like Emoticon sarcasm detection, Hybrid sarcasm detection, Hashtag Processing, Interjection Word Start (IWT). © 2018 IEEE.

Proceedings of the International Conference on Inventive Research in Computing Applications, ICIRCA 2018

Sarcasm Detection of Online Comments Using Emotion Detection

Handwriting analysis is the scientific method or way of determining or understanding or predicting the personality or behavior of a writer. Graphology or graph analysis is the scientific name of handwriting analysis. Handwriting often called as brain writing or mind writing, since it is a system of studying the frozen graphic structures which have been generated in the brain and placed on paper in a printed or cursive style. Many things can be revealed from handwriting such as anger, morality, fears, past experience, hidden talents, mental problems. Handwriting is different from person to person. People are facing various psychological problems. Teenagers also face so many mental problems. Criminals can be detected by using handwriting analysis. Counselor and mentor can also use this tool for giving advice to clients. Proposed work contains three main steps: image preprocessing, identification of handwriting features, and mapping of identified features with personality traits. Image pre-processing is the technique in which the handwriting sample is translated into a format which can be easily and efficiently processed in further steps. These steps involve noise removal, grayscale, thresholding, and image morphology. In feature identification, there is an extraction of handwriting features. Three features of handwriting are extracted that are left margin, right margin, and word spacing. Lastly, extracted features are mapped with personality using the rule-based technique. The personality of a writer with respect to three handwriting features is displayed. The proposed system can predict 90% accurate personality of the person. © Springer Nature Singapore Pte Ltd. 2018.

A model for determining personality by analyzing off-line handwriting

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

Testing a product has become one of the most important tasks for any organization (Be it small scale or large scale). Without testing the product, it is not delivered to the customer. Testing is an ongoing activity from the beginning of a product’s development. A performance testing suite shall be developed using Apache JMeter for the purpose of testing a product. To perform performance testing on client- and server-type softwares, a 100% pure Java application named Apache JMeter is used. Apache JMeter is not a browser, it works at protocol level. Static and dynamic resources performance testing can be done using JMeter. A high level performance testing suite will be developed in capturing aspects of performance at UI and System level. Developing the testing suite helps in saving the time and cost of the organization. The discussion follows and describes benefits of performance testing and the performance testing suite. © 2018, Springer Nature Singapore Pte Ltd.

Development of Performance Testing Suite Using Apache JMeter

arXiv preprint arXiv:1809.08651

Detecting hate speech and offensive language on twitter using machine learning: An n-gram and tfidf based approach

Recently facial based age estimation has become increasingly important because of many potential real time applications. Age estimation is predicting someone's age by analyzing his/her biometric trait such as bone density, dental structure or face. Amongst these face is important trait so facial based age estimation has become more popular due to its vast real time applications. Age estimation is defined as to label the face image automatically with the exact age or age group. Estimating age from images has been one of the most challenging problems within the field of facial analysis due to uncontrollable nature of the aging process, high variance of observations within the same age range, lighting, facial expressions, pose, occlusion, blur, camouflage due to beards, moustache, glasses, makeup and the difficulty to gather complete and sufficient training data. This paper presents analysis of earlier techniques proposed by researchers for facial based age estimation. Different feature extraction and estimator learning methods used in this domain are also discussed. © 2017 IEEE.