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

Industry professionals predict that the use of artificial intelligence and machine learning will significantly impact people's lives in the near future. They believe that it will allow individuals and organizations to perform more efficiently by improving their processes and making them more effective at their jobs. Machine learning could help improve employees' work-life balance by allowing them to perform more efficiently at their jobs. It could also increase the general quality of their lives. What would be the outcome of an experiment involving machine learning to find ways to improve the balance between work and life? The study aimed to analyze individuals' subjective feelings about work-life balance. Through a machine learning method, the researchers could identify the characteristics influencing the balance between work and life. They then used a forecasting model to analyze the data and determine how much women in the IT industry observe a work-life balance. The study was conducted on 150 female IT professionals. The researchers used a questionnaire to gather information about the participants. They then used various deep-learning models to analyze the data. The researchers analyzed the models' performance by calculating the mean absolute and square errors. They found that the models known as the Multilayer Perceptron and the Long Short-term Memory performed well in terms of their accuracy when forecasting future business conditions. Comparing the two models revealed that the Short-Term Long Memory performed better than the MLP in terms of accuracy. This finding could be beneficial for studies related to the behavior of organizations. © 2022 ACM.

World University root node

Engineering and Technology

ACM International Conference Proceeding Series

Deep Learning Model for Work-Life Balance Prediction for Working Women in IT Industry

This paper aims to present the survey of cloud computing security. A cloud computing security is defined so that it can help to clarify cloud security. Cloud computing has evolved to become an essential component of both current and future information technologies. This technology was created to be used in conjunction with the internet, and it includes features such as data storage, remote access, and so on. Various cryptographic encryption algorithms are compared in terms of their various key features. The primary goal is to highlight such attacks and provide recommendations for resolving data breaching issues using cryptographic algorithms such as Symmetric Key Cryptographic Algorithms (DES, 3DES, AES), Asymmetric Key Cryptographic Algorithms (RSA, DSA, Diffie-Hellman Algorithm), and Hashing Algorithms (MD5, SHA). So, in the paper cloud security challenges and the cryptographic algorithms used to prevent them are discussed. © 2022 IEEE.

Proceedings - 6th International Conference on Computing Methodologies and Communication, ICCMC 2022

Cloud Computing Security using Cryptographic Algorithms

In cloud computing, there are several security concerns. The security concern may increase due to one of the important techniques of virtualization. Cloud computing is also having several important characteristics which are used for its improvement and to provide several services to its end user. Some of the essential and important characteristics are on-demand self-service, broad network access, rapid elasticity, resource pooling, and measured services. By using these characteristics, cloud computing satisfies the needs of its end users, but the base to these characteristics is the virtualization technique. These observations depict the reason for several attacks and especially DoS and DDoS attacks or brute-force attacks. This article provides detailed information on these characteristics and also provides information about flaws and breaches. The reason is virtualization, which makes cloud computing insecure to use. Several techniques and algorithms are used to improve the cloud computing system, but flaws in their application have been discovered. These algorithms may degrade the quality of cloud computing system and may also makes cloud computing insecure to its end users. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Networks and Systems

A Survey Paper on Characteristics and Technique Used for Enhancement of Cloud Computıng and Their Security Issues

The global coronavirus pandemic and lockdown has had negative impacts on individuals’ mental health and well-being. The crisis has generated symptoms of depression in many, which may last even after the lockdown is over. To provide support to individuals in terms of counseling and psychiatric treatment, it is necessary to identify such depressive symptoms in a timely fashion. To address this problem, an artificial intelligence-based system is proposed to assess the changes, if any, in the mental health of an individual as a function of time, starting from the pre-lockdown period (in India from 20 April 2020). A Mental Health Analyzer has been implemented to automatically detect whether an individual is trending toward a state of depression based on his or her tweets over time. The deep learning models of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM have been implemented and compared for the emotion classification task, specifically to detect the emotions of sadness, fear, anger, and joy present in a person’s tweets. The system identifies the emotion of sadness present in tweets to detect depression. An ensemble maximizing model using CNN, LSTM, and Bidirectional LSTM is proposed to maximize the recall metric to improve the performance for the task of depression detection. The implemented system was tested using the dataset provided for the SemEval-2018 semantic evaluation tasks and achieves better results than previous models for the task of emotion classification and, further, can detect depression when tested on real Twitter data. © 2022 J. Adv. Inf. Technol.

School of Electronics & Communication Engineering

Journal of Advances in Information Technology

Mental Health Analyzer for Depression Detection Based on Textual Analysis

Corona viruses are a genus of viruses that infect vertebrate and birds, causing a variety of diseases. They induce a variety of respiratory problems in people. This study investigates COVID-19 infection rates and estimates the pandemic’s scope, recovery rate, and death rate. We used Support Vector Machine (SVM), Random Forest, Decision Tree, K-nearest neighbor, and other well-known machine learning and mathematical modeling approaches. For disease diagnosis, study used three unique disease data sets (Asthma, Diabetes, and AIDS) provided there in UCI machine learning repository. After getting positive results, we applied these algorithms on COVID-19 data set. We used several categorization algorithms, each with its own set of benefits. The study’s findings support the use of machine learning in disease early detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes on Data Engineering and Communications Technologies

Applying ML on COVID-19 Data to Understand Significant Patterns

Cyber-security is unavoidable in present days due to the security importance of each and every digital device part of a global network among millions of computing nodes. Industry 4.0 created much more impact on the automation of industrial micro functionalities to the manufacturing level, with more sensitive data to handle at every stage of organization, then security features of such platform are more important for a complete smooth working environment. This article will elaborate more on an understanding of Incremental learning with data analysis of huge Cyber-attack detection datasets which consist of Adware, Ransomware, SMS malware, scareware, Benign..etc. Incremental learning refers to a group of scalable various algorithms that as the ability to learn in terms of sequentially and can continuously update their models from huge infinite data streams. This working model helps to understand by adopting dynamic learning for varying incoming data inputs based on huge dataset inputs with scalable learning models of algorithms without discarding learned model outcomes. Number of incremental classification model were used where as ARFC gives very good results with higher accuracy, precision, recall, and f1-score values. The results revealed that the proposed model training generates a complete result by adopting variation in dynamic inputs which helps to improve classification higher accuracy by comparing with other training models. © 2022 IEEE.

Proceedings - 2022 2nd International Conference on Innovative Sustainable Computational Technologies, CISCT 2022

Streamed Incremental Learning for Cyber Attack Classification using Machine Learning

Personalization technologies can be a useful tool for improving user experience across a wide range of applications, but they also raise new privacy concerns. While the volume of personal data acquired by applications has continuously increased, user awareness of the ramifications has not followed the same trend. A Survey was conducted to better understand users' perceptions of the matter and their opinions in order to make better privacy-related decisions. The study focuses on topics such as user awareness of personalization and privacy, end user preferences for personalization and privacy, data misuse awareness, privacy awareness about continued usage of applications, and willingness to make better choices for enhanced privacy. The target demographic is between the ages of 19 to 22, sometimes known as "young adults."

13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

Analysing Data Privacy Concerns in young adults – apprehensions of engineering students

It’s been around two years from the outbreak of the coronavirus, thus labeled as Covid- 19, and there has been an explosion of literature being published by research scholars related to work done on Covid-19. Covid- 19 as a keyword has been mentioned in the titles of most of these papers. It was thought to analyse the number of papers and the titles of papers which include Covid- 19 in the title of the research papers. The various combinations of other words like, prefixes, suffixes, N-gram combinations with the keyword Covid- 19 in the titles of these papers were also analysed. The research publication repositories analysed were: IEEE Explore, ACM Digital Library, Semantic Scholar, Google Scholar, Cornel University etc. The domains of research publication title analysis were restricted to computer science / computer engineering related papers. As the term labeling the corona virus outbreak as Covid-19 was labeled in 2020, the timeline of title analysis was restricted from 2019 till December 2021.

Fulltext

Ubiquitous Intelligent Systems

Analysis of Research Paper Titles Containing Covid-19 keyword using Various Visualization Techniques

Human behavior is very complex and cannot be explained using traditional mathematical models. Intermediate forms, such as those obtained from personality data, can be used to predict behavioral aspects of a person, creating the hypothesis that arbitrating psychological models can be drawn directly from recordings of behavior. In recent years, smartphone addiction has increased to a great extent. Since the excessive use of smartphones has negatively affected our daily life, many applications to reduce dependence on smartphones have been developed around the world. Personal attributes or personality types can be extracted from data obtained directly from smart phones without the interaction of participants who may have social or health interventions. Many people who excessively use their smartphones have an uncontrollable urge to use the Internet. Internet addiction refers to uncontrolled use of the Internet which causes hindrance in our daily life. Due to its negative impact on the education and lives of people, it is necessary to detect tendencies of people toward addictive behavior and provide them with preventative support and treatment. Similarly, the development of social media has seen rapid growth in its usage. People often find themselves overusing utilities such as virtual communication, texting, and sharing information which have also caused various behavioral problems. This study provides a summary of the various methods and studies done on these behavioral problems and to analyze different techniques, and machine learning models are used to predict addictive personality types. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Psychoinformatics: The Behavioral Analytics

In the domain of image captioning, many pre-trained datasets are available. Using these datasets, models can be trained to automatically generate image descriptions regarding the contents of an image. Researchers usually do not spend much time in creating and training the new dataset before using it for a specific application, instead, they simply use existing pre-trained datasets. MS COCO, ImageNet, Flicker, and Pascal VOC, are well-known datasets that are widely used in the task of generating image captions. In most available image captioning datasets, image textual information, which can play a vital role in generating more precise image descriptions, is missing. This paper presents the process of creating a new dataset that consists of images along with text and captions. Images of the nearby vicinity of the campus of MIT World Peace University-MITWPU, India, were taken for the new dataset named MITWPU-1K. This dataset can be used in object detection and caption generation of images. The objective of this paper is to highlight the steps required for creating a new dataset. This necessitated a review of the existing dataset models prior to creating the new dataset. A sequential convolutional model for detecting objects on a new dataset is also presented. The process of creating a new image captioning dataset and the gained insights are described.