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

Web services are a simple but effective method of connecting to network resources. To offer end-users data from their virtualized resources, all modern organizations provide web service interfaces. With the increased usage of multi-channel communication modes (mobile phones, tablets, and laptops), web services are excellent for guaranteeing seamless data flow across all platforms. While Web services address many difficulties, there are a few issues that consumers or requesters have when they locate a web service that satisfies their requirements. With the increased use of online services, there is a greater demand for an effective and almost flawless web service recommendation system. Cold start, sparsity, and overspecialization are some of the problems with recommender systems. To address this issue, in this paper, collaboration filtering and demographic filtering based on models are proposed as a new approach. Using this technique, recommendations can be generated based on the demographic characteristics of users, such as age, gender, occupation, etc. We propose using web services based on the results of both tests. This paper attempts to propose a solution to the cold start problem by using a hybrid filtering method.

School of Computer Engineering & Technology

Engineering and Technology

Recent Advances in Material, Manufacturing, and Machine Learning

Web service recommendation based on user's demographic attributes

With constant research in the last decade, Object Detection has become one of the rapidly evolving sub-fields of Deep Learning. As a result, the complexity and applications of Object Detection models have grown, necessitating larger datasets and multi-format labelled annotations for training and testing. To aid with the annotation work, a variety of tools based on various technologies, techniques, and features have been developed. However, some of the tools only cater to specific standards, requirements and problem domain. Researchers have observed that a drop in the quality of labelled annotations can have unintended consequences for model performance. This study provides an overview of various annotation tools with the goal of assisting researchers in determining the appropriate tool for their needs. The work will also serve as a reference for developers as they work on subsequent features to improve or create new tools. © 2022 IEEE.

World University root node

Proceedings - International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

A Review of Image Annotation Tools for Object Detection

The world has witnessed tremendous increase in the use of social media platforms in the last decade. Posting and updating on such platforms have become a way for people to express their opinions, experiences, feelings, views etc. Along with all these, they tend to post and share their personal Information. This Information can be used in advantageous way to understand their needs and requirements The approach, known as personality prediction, entails sepΑarating digital input into attributes and mapping it according to a personality model. In this paper, we are interested to predict the personality of such users. Researchers have shown based on the social behavior towards friends, number of friends, groups joined etc. can help in predicting the personality trait from Facebook and Twitter. The text posts for the user are converted to be suitable representation using GloVe algorithm. And then feeding it to Convolution Neural Net, a popular Deep Learning architecture on the text posts that was implemented for the traits present in the Big Five trait theory. This method helped to achieve comparable outputs with other recent models © 2022 School of Science, IHU. All rights reserved

Journal of Engineering Science and Technology Review

Convolution Neural Network Regression Model to Predict Personality Scores

One of the major platforms today to express your emotions, expressions, feelings, and views about something or about any product is social media. People put forth what they feel, therefore their individual posts can be judged as a reflection of their personality, but how to judge a personality of a person based on social media posts and that too of say large data, say thousands, and produce analysis on it. In our studies, we specialize in using deep learning to build a classifier that can classify individuals according to their Myers-Briggs Type Index (MBTI) solely using textual content samples from their social media posts as an input. There are two reasons for developing such a classifier. First, due to the fact social media is so ubiquitous, one of these classifiers might have lots of statistics to degree character, permitting extra human beings to realize their MBTI character type, possibly extra reliably and faster. Our second incentive is the opportunity that our classifier can be more correct than the cutting-edge assessments, as evidenced with the aid of using the low retest mistakes price for character assessments executed with the aid of using expert psychologists. It may be possible to use our classification method to verify the accuracy of these first assessments, to give individuals more confidence in their results. In some cases, a text classification may be more comprehensive than a single personality assessment. © 2022 Totem Publisher, Inc.

International Journal of Performability Engineering

Personality Prediction through Social Media Posts

One of the most significant and challenging areas of computer vision is object recognition and tracking, which is extensively utilised in many industries including health care monitoring, autonomous driving, anomaly detection, etc. The tracking of moving objects in videos is actively researched over the past two decades due to its practical applications in many fields such as event analysis, human-computer interaction, crowd analysis, video surveillance, behaviour analysis, etc. The effectiveness of object trackers and detectors has significantly increased with the rapid advancement of deep learning (DL) networks and GPU processing capability. New methods have been presented for object recognition and tracking in video as a result of extensive study in this field. This article addressed the several processes of object tracking in video sequences: object detection, object classification, and object tracking, in order to comprehensively comprehend the key advancements in the object detection and tracking pipeline. Additionally, we thoroughly examine the various approaches available for object recognition, categorization, and tracking. © 2022 IEEE.

Proceedings - International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022

DeepSORT: Real Time &amp; Multi-Object Detection and Tracking with YOLO and TensorFlow

Today’s world is witnessing great increase in the use of Social Media. People use them as a platform to share their feelings, emotions and experiences along with lot of personal information. All such information could be used in advantageous ways to help increase the Business and understand the user need. Personality prediction has gained lot of focus nowadays. It studies behavior of users and reflects the thinking, feelings etc. Traditional ways to take survey that was time consuming, for large number of users there is need of automatic prediction. Users are dynamic and can have their account on multiple platforms as they can have multi-context information. This survey gives an overview of different strategies used to predict the personality and behavior using the content available on social sites. Ability to predict user’s personality traits can help to build many customized services or products. Finally, the last section gives the Future trends and directions.

Fulltext

International Journal of Engineering Research and Technology

Personality prediction from social media text:An overview

Personality, a typical way of thinking, feeling, and behaviour. Personality embraces moods, attitudes and views and is expressed most obviously in relationships with others. It involves both intrinsic and acquired behavioural features that differentiate one individual from another and can be found in the relationships of people with the surroundings and with the social group. With the development of social networks, a broad variety of techn iques have been developed to identify user personalities based on their social activities and language usage practices. In terms of distinct machine learning algorithms, information sources and function sets, particular methods vary. Personality prediction has been an important research topic for describing user profiles and person not only in psychology but also in computer science. This paper presents a systematic survey of current work done of personality prediction from social networks. We also prepared a Comparison chart of existing techniques for personality prediction on the basis of relevant parameters. Based on this survey, we finally presented a few future research directions related to personality prediction.

International Journal of Recent Technology and Engineering (IJRTE)

Personality Prediction from Social Networks text using Machine Learning

Now a day, the mass events like political rallies, musical concerts, sports matches, school gathering and pilgrimage become an issue of the public security. So, Management of crowd at public places becomes a big issue. Crowd congestion prevents the smooth flow of the crowd and causes the unsafe condition; thereby causing many people lost their life. So, it is important to overcome such situation to provide the safety on time. It can be achieved by using the video monitoring device footage along with optical flow and standard deviation model to detect the crowd congestion and providing warning for managing the crowd. Higher the congestion level higher is the risk of life and occurrences of uncertainty. Based on the level of congestion and movement it is possible to predict the future flow of crowd in that direction. © 2016 IEEE.

International Conference on Automatic Control and Dynamic Optimization Techniques, ICACDOT 2016

Motion detection using optical flow and standard deviation

Businesses are increasingly using web services to expose and exchange data over the web. Last few years have viewed tremendous growth in web service as a technology for inter application logic sharing and data retrieval. As web services have been able to provide simple solutions of very generic business problems it has got much higher acceptance and presence over the internet. Today internet is flooded with web services which give rise to problem of selection of web service that will best fit the business use case. To work through the problem of overflow of web services over internet a very efficient web service selection mechanism is need of the hour. In this paper we proposed a novel approach for web service recommendation system. © 2016 ACM.

ACM International Conference Proceeding Series

A novel approach for web service recommendation

Now a day, the mass events like political rallies, musical concerts, sports matches, school gathering and pilgrimage become an issue of the public security. In the past the research is based on the detection of the human behaviour and for finding the hazardous location. Crowd is a collection or gathering of many people in the same area at the same time. In such situation the density of people is high and this may causes the disaster. The proposed system will help to overcome such situation. The system detects the congestion with the help of optical flow and the standard deviation and gives warning when the congestion is detected. © 2016 ACM.

A congestion detection system using optical flow and standard deviation in mass gathering

Smartphone applications are gaining popularity and with time many complex and computation/resource intensive applications are coming to market. These applications require more resources and consume lot of battery power, but the improvisation in battery capacity has been lagged far behind as compared to other components of mobile devices. On other hand, clouds are rich in resources and computational power. Therefore, by utilizing cloud resources for application execution we can reduce battery consumption and in turn increases battery charging intervals. This all is known as cyber foreign or computation offloading in Moble Cloud Computing. There are various factors affecting computation offloading like bandwidth. So, we had considered bandwidth as major factor in proposed offloading decision making algorithm. Our algorithm is based on divide and conquer strategy. This system focuses on time and energy saving. © 2015 ACM.

Time and energy saving through computation offloading with bandwidth consideration for mobile cloud computing

Due to heavy traffic the network monitoring is very difficult and cumbersome job, hence the probability of network attacks increases substantially. So there is the need of extraction anomalies. Anomaly extraction means to find flows associated with the anomalous events, in a large set of flows observed during an anomalous time interval. Anomaly extraction is very important for root-cause analysis, network forensics, attack mitigation and anomaly modeling. To identify the suspicious flows, we use meta-data provided by several histogram based detectors and then apply association rule with multidimensional mining concept to find and summarize anomalous flows. By taking rich traffic data from a backbone network, we show that our technique effectively finds the flows associated with the anomalous events. So by applying multidimensional mining rule to extract anomaly, we can reduce the work-hours needed for analyzing alarms and making anomaly systems more effective. © 2013 IEEE.