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

Offensive Internet chats, particularly the child-exploiting type, tend to follow a documented psychological behavioral pattern. Researchers have identified some important stages in this pattern. The psychological stages broadly include befriending, information exchange, grooming, and approach. Similarities among the posts of a chat play an important role in differentiating as well as in identifying these stages. In this article a novel similarity measure is constructed which gives high Inter-post-similarity among the chat-posts within a particular behavioral stage and low inter-post-similarity across different behavioral stages. A psychological stage corpus-based dictionary is constructed from mining the terms associated with each stage. The dictionary works as a background knowledge-base to support the similarity measure. To find the inter-post similarity a modified sentence similarity measure is used. The proposed measure gives improved recognition of inter-stage and intra-stage similarity among the chat posts compared with other types of similarity measures. The pairwise inter-post similarity is used for clustering chat-posts into the psychological stages. Results of experiments demonstrate that the new clustering method gives better results than some current clustering methods. © 2014 ASIS&T.

World University root node

Engineering and Technology

Journal of the Association for Information Science and Technology

Constructing an inter-post similarity measure to differentiate the psychological stages in offensive chats

The chemical name extraction has a great importance in the biomedical field. Named Entity Recognition is the subtask of information extraction that is used to identify named entities in the given data. There are various dictionary-based, rule-based and machine learning approaches available for Named Entity Recognition. Rule based techniques include hand written rules. In this paper an extensive survey of machine learning models such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Conditional Random Fields (CRFs) etc. that are used to develop NER systems is carried out.

International Journal of Computer Science and Information Technologies

A Survey on Machine Learning Techniques toExtract Chemical Names from Text Documents

Aspect-oriented modeling (AOM) has been developed to modularize crosscutting concerns appropriately in UML models. In software engineering, aspects are concerns that cut across multiple modules. In requirements modeling, we analyze interactions and potential inconsistencies. We use UML to model requirements in a use case driven approach. During requirements specification a structural model of the problem domain is captured with a class diagram. Use cases refined by activities are the join points to compose crosscutting concerns. Graph transformation systems provide analysis support for detecting potential conflicts and dependencies between rule-based transformations.

Springer Book :New trends in Networking,Computing, E-Learning,System Sciences,and Engineering

Requirement and Interaction Analysis Using Aspect-Oriented Modeling

Normal moles are mostly small brown in color that are spots or growths on the skin that is by birth or emerge in the first few decades of life in almost everyone. Skin cancer most often appears as moles. Seldom people are aware about the skin cancer. If detected at early stages then it can be cured. We propose an improved design of Gabon filter which is combined with Vedic algorithm (Urdhva Triyagbhyam) to give faster convolution result. The exertion has proved the efficiency of Urdhva Triyagbhyam which is the Vedic method of multiplication that enables parallel generation of intermediary products that eliminates unwanted multiplication steps with zeros and scaled to higher bit. In this paper we employ the filter design appropriate for detecting the early stages of skin cancer using textural properties of skin. © 2015 IEEE.

Proceedings - 1st International Conference on Computing, Communication, Control and Automation, ICCUBEA 2015

Efficient gabor filter using vedic mathematic for high speed convolution in skin cancer detection

Depression is one of the most common mental health disorders with strong adverse effects on personal and social functioning which can hamper the lives of individuals. The absence of any objective diagnostic aid for depression leads to a range of biases in the diagnosis and ongoing monitoring. This study throws light upon the contribution of gestures and facial points for depression analysis. This paper discusses a novel cumulative video analysis proposed by us based on action units and fictional points for analysis of facial moment. Experimental results are carried out using real world clinical data and interactive sessions with neuro experts. This smart framework developed by us is useful for detection of depression disorders through gesture recognition. The diagnosis is done and appropriate action is taken according to scale of the depression in the patient and the severity of it. © 2015 IEEE.

2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015

Cumulative video analysis based smart framework for detection of depression disorders

There is a long list of words that describe depression; sadness, unhappiness, sorrow, dejection, low spirit, despondency, woe, gloom, pessimism, desolation, despair, hopelessness, moodiness, and a host of others. This study throws light upon the contribution EEG signal for depression analysis. In this paper, classification of depressed patients from normal subjects are identified by using EEG signal. Experimental results are carried out with the help of 13 depressed patients and 12 normal subjects. This paper tries to classify person's mental state either normal or depressed with the help of EEG signal using signal processing technique FFT and machine learning technique SVM. These noninvasive signal techniques are useful for detection of depression disorders through EEG signals. The proposed work is compared with the other methods. The diagnosis is done and appropriate remedies are taken according to scale of the depression in the patient. This work shows that linear analysis of EEG can be an efficient method for identifying depressed patients from normal subjects. It is recommended that this analysis may be a supporting aid for psychiatrists to identify severity level of depressed patients. © 2015 IEEE.

IntelliSys 2015 - Proceedings of 2015 SAI Intelligent Systems Conference

Non invasive EEG signal processing framework for real time depression analysis

Facial expression analysis plays pivotal role for all the applications which are based on emotion recognition. Some of the significant applications are driver alert system, animation, pain monitoring for patients and clinical practices. Emotion recognition is carried out in diverse ways and facial expressions based method is one of the most prominent in non verbal category of emotion recognition. The paper presents detection of all the six universal emotions based on statistical moments i.e. Zernike moments. The features extracted by Zernike moments are further classified through Na{\"{i}}ve Bayesian classifier. Rotation Invariance is one of the important properties of Zernike moments which is also experimented. The simulation based experimentation provides average detection accuracy as 81.66% and recognition time less than 2 seconds for frontal face images. The average precision with respect to positives is 81.85 and average sensitivity is obtained as 80.60%. Robustness of system is verified against rotation of images till 360 degrees with step size as 45 degrees. The detection accuracy varies with reference to emotion under consideration but the average accuracy and detection time remains at par with frontal face images.