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

The duty of detecting pavement cracks is crucial for ensuring traffic safety. The process of manually detecting cracks takes a long time. To accelerate this process, an autonomous road fracture identification technology is necessary. However, because of the intensity inhomogeneity of cracks and the intricacy of the background, such as poor contrast with neighbouring pavements and probable shadows of similar intensity, it remains a difficult work. Recent breakthroughs in deep learning in computer vision have inspired us, so we will be using the most accurate method that involves deep learning to identify the gap in the pavement. In this research paper, we have compared various object detection techniques such as aYOLO v5 and Single Shot Detector(SSD) and come to a conclusion that SSD is most suitable for our purpose. It will also help for road safety purposes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

School of Computer Engineering & Technology

Engineering and Technology

Lecture Notes in Networks and Systems

Comparison Between YOLOv5 and SSD for Pavement Crack Detection

Multimedia Tools and Applications

Offline MODI script character recognition using deep learning techniques

From2018 to 2024, the global hair extension market is projected to grow by 8%, reaching more than 5 billion. Hair care was the second-largest segment in the global beauty industry in 2017, accounting for 18% of total sales, after skincare. A big driver of this market is the need to enhance one's physical appearance which most of the times are affected by any hair loss diseases like cancer, alopecia, pregnancy, thyroid, and so on. Endorsing wigs and hair extensions is becoming increasingly common on social media. Specialty shops, hair salons and beauty stores, and hair clinics account for a large portion of the sale of hair wigs and extensions.Our System provides a unique analytical view for solving problems of customers by helping them choose amongst various brands of hair extensions. It does this by using the video's content to perform sentiment analysis rather than just focusing on public comments on the videos. It also performs statistical analysis and provides an overview about the best channels and influencers. To give a better decision base to customers, the paper focuses on providing content extraction of various YouTube videos covering fashion influencers' reviews as well as common people's reviews and for the investors, insights of market trends to judge its potential. © 2021 IEEE.

Proceedings - 2021 1st IEEE International Conference on Artificial Intelligence and Machine Vision, AIMV 2021

YouTube Content Analysis for Hair Extension's Business

TEST Engineering and Management

Character Recognition using MODI script: Facts, Challenges and its future

Working stress is becoming very common. Handling working stress at the workplace is really going to be challenging. As a result, most of the time most of the time people start behaving in sarcastic ways through verbal communication, through different gestures, using emoticons, or writing reviews or comments that leads to increasing their anxiety sometimes promotes depression. It is difficult to identify sarcasm in written notes or communication. Feedback analysis is not a direct method since feedback or employer reviews are written in more formal language. This motivates the authors to work on the employee feedback system. The currently developed system helps to detect the sarcastic emotions by applying different methodologies on several types of statements. This will help corporations and other big organizations to identify reasons behind sarcastic behavior or increased anxiety. As a result, they can promote counseling programs, psychological treatment, or yoga-meditation camps.

International Journal of Synthetic Emotions (IJSE) (2019)

Sarcasm Detection for Workplace Stress Management

Technology is being used everywhere in our daily life to fulfill our requirements and aid our lives in every sphere including communication, traveling, entertainment, etc. But it has not been used up to its full potential in the field of health care. Real-time monitoring of patients and doctors is the primary concern of any healthcare facility and the conventional methods used in the hospitals are not being able to address these concerns efficiently. Our proposed system aims to provide a better service to the patients as compared to the conventional methods. It comprises of sensors for measuring temperature and pulse rate. A processor working in synchronization monitors the vitals of the patient and sends the captured data to the personal digital assistant (PDA) of head nurse and the doctors. In the PDA, an intelligent application gives statistical data of the vitals of patient in real-time manner. This data is used to analyze the health condition of patient over a period of time as well as monitor the patient in a real-time environment, remotely. We are also incorporating a coronary heart disease (CHD) predicting mechanism to determine whether a patient is susceptible to CHD. This mechanism is based on the concept of Traditional Chinese Medicine (TCM), which states that a large number of diseases (immaterial of the amount of severity they pose) can be diagnosed by sensing the pulse variations. This methodology is the foundation on which support vector machine (SVM) is predicting CHD in patients. © 2019, Springer Nature Singapore Pte Ltd.

Advances in Intelligent Systems and Computing

A proposed system for the prediction of coronary heart disease using raspberry pi 3

In the present epoch of technology medical field has become one of the favorite topics of researcher and cancer is among one of them. It is a topic of concern because actual treatment of this disease is not found till date. Patients having this disease can only be saved if and only if it is found in early stage (stage I and stage II). If it is detected in latter stage (stage III and stage IV) then chance of survival will be very less. Machine learning and data mining technique will help medical field to tackle with this problem. Cancer has various symptoms such as tumor, abnormal bleeding, more weight loss etc. It is not necessary that all kinds of tumors are cancerous. Tumors are basically of two types i.e. benign or malignant. To provide appropriate treatment to the patients, symptoms must be studied properly and an automatic prediction system is required which will classify the tumor into benign or malignant. In today's internet world, bulk of data is generated on social media or healthcare websites. From this huge amount of data, symptoms can be fetched by using data mining technique, which will be further useful for cancer detection or classification. This paper makes survey of such latest research study that makes use of online and offline data for cancer classification. © 2017 IEEE.

Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017

Survey on recent cancer classification systems for cancer diagnosis

Sarcasm is a form of language in which individual convey their message in an implicit way i.e. the opposite of what is implied. Sarcasm detection is the task of predicting sarcasm in text. This is the crucial step in sentiment analysis due to inherently ambiguous nature of sarcasm. With this ambiguity, sarcasm detection has always been a difficult task, even for humans. Therefore sarcasm detection has gained importance in many Natural Language Processing applications. In this paper, we describe approaches, issues, challenges and future scopes in sarcasm detection. © 2017 IEEE.

Literature survey of sarcasm detection

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

Sentiment analysis is the process of analyzing the text about a topic written in a natural language and classify them as positive or negative based on the human sentiments, opinions expressed in it. Due to the increasing growth in use of social media (e.g. reviews, forum, blogs, Twitter and postings in social network sites) on the Web, users now have many opportunities to express their opinions about a product or topic. Users express their opinion through the reviews. These reviews are used by the individuals and organizations for decision making purpose. It is impossible to read and extract user opinions from such huge number of reviews manually. To solve such problem an automated opinion mining approach is required. It is difficult for a user to read and understand all the reviews. Relevant and important information about these establishments should be fetched from reviews and presented to user in a summarized manner. Current approaches for opinion mining, attempts to detect the polarity of a sentence, paragraph or text regardless of the aspects mentioned in it. In this paper we proposed an aspect based approach for opinion mining which uses aggregate score of opinion words and aspect table together for opinion mining process. The main motive of the system is to develop an opinion mining application with improved accuracy by following an implicit approach. © 2016 IEEE.

International Conference on Computing, Analytics and Security Trends, CAST 2016

ISAR: Implicit sentiment analysis of user reviews

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.

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

A proposed architecture for automating public distribution system

Outsourcing of data is increasing with the rapid growth of internet. There is every possibility that data reaches illegal hands. As a result, there is increase in illegal copying of data, piracy, illegal redistribution, forgery and theft. Watermarking technology is a solution for these challenges. It addresses the ownership problem. It deters illegal copying and protects copyright of data. Watermarking technology mainly involves the process of watermark insertion and watermark extraction. Watermark insertion means embedding an imperceptible watermark in the relational database. In watermark extraction we extract the embedded watermark without the help of original database. In this paper we propose a new watermarking technique, which will watermark both textual and numerical data. Our proposed method also does watermark verification where, the watermark extracted from the database is compared with the original watermark that is known only to the owner of the database. This is accomplished through Levenshtein distance algorithm. © 2015 IEEE.

Proceedings - 2015 International Conference on Communication, Information and Computing Technology, ICCICT 2015

A robust technique for relational database watermarking and verification

Intrusion Detection System (IDS) is a software or hardware tool that repeatedly scans and monitors events that took place in a computer or a network. A set of rules are used by Signature based Network Intrusion Detection Systems (NIDS) to detect hostile traffic in network segments or packets, which are so important in detecting malicious and anomalous behavior over the network like known attacks that hackers look for new techniques to go unseen. Sometime, a single failure at any layer will cause the NIDS to miss that attack. To overcome this problem, a technique is used that will trigger a failure in that layer. Such technique is known as Evasive technique. An Evasion can be defined as any technique that modifies a visible attack into any other form in order to stay away from being detect. The proposed system is used for detecting attacks which are going on the network and also gives actual categorization of attacks. The proposed system has advantage of getting low false alarm rate and high detection rate. So that leads into decrease in complexity and overhead on the system. The paper presents the Evasion technique for customized apriori algorithm. The paper aims to make a new functional structure to evade NIDS. This framework can be used to audit NIDS. This framework shows that a proof of concept showing how to evade a self-built NIDS considering two publicly available datasets. © 2014 IEEE.