Volume 12 | Issue 11 |

 Abstract

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Stress, Coping strategies, Female football players

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The rise of digital technologies has revolutionized the way f the individuals to express themselves and interact with the world , leading to to the emergence of digital footprints- traces left by the users through through their online activities on social media, websites, and various online platforms. These footprints, formed through online activities, provide valuable insights into personality traits.Digital footprints, comprising online behaviors, social media activity, and other internet usage patterns, provide rich data sources for understanding personality traits. By analyzing these footprints, researchers can gain insights into personality traits,giving the way for applications in marketing, mental health, and education, while also raising important ethical considerations. This review explores the ways for personality prediction using digital footprints or the digital presence of an individual, examines key findings and applications. We have combined the concept of psychology and data science for predictions , focusing on the potential benefits and challenges in this new emerging field.

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 Keywords

personality prediction, Big five inventory, Myers-Briggs Type Indicator

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Ethics, morals, values, are the guiding force of our behaviour; Ethics which is plays central role stability and continuity of any society. This article deals with the philosophical background of strong ethical values of Buddhism; how it was destroyed by the arrival of Islam and the governing system in Central Asia. The transmission of Buddhism into central Asia is evolving as archaeological interpretation of the history of the central Asia. The spread of Buddhism in Central Asia by the Kushan (Kusana) dynasty through the silk trade route which was also became an channel for the transmission of art, literature, architecture, technology, culture, religion, philosophy, etc to different countries in Asia. Religion has played a major role in the history of Central Asia and religion is one of the important factors of social development. However, the conversion to Islam on revive of the Arabs and the demolition of Buddhist stupas and destruction of manuscripts, persecution of Buddhists which shown that how the Buddhism was declined by the Arabs. The Silk Route which is also known as the 'Ancient International Trade Road' that spread from China to Italy existed from second century BC to fifteenth century AD. This silk route was known for trade, many items such as silk, paper, raw materials and other goods was exported through India to China and Central Asia. This route also became a channel for the transmission of art, culture, religion, literature, architecture, philosophy, technology, etc to different countries in central Asia and beyond. Buddhism which was developed in India is one of the most important religions that transmitted through the Silk Road to central Asia.

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Central Asia, Religion, Buddhism, Silk Road, Islamization

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Deep Learning (DL) has become a crucial tool in predicting heart disease, offering significant advancements in early detection and personalized healthcare. These models can then predict the likelihood of heart disease in new patients based on similar input data. The existing system does not predict the specific type of heart disease, such as coronary artery disease, heart failure, or arrhythmia and it does not provide personalized treatment recommendations based on patient-specific characteristics and medical history. The system can improve patient care by enabling healthcare professionals to make informed decisions and provide personalized treatment plans. This system can detect heart disease at an early stage, enabling timely interventions and improving patient outcomes. The existing system does not predict the specific type of heart disease, such as coronary artery disease, heart failure, or arrhythmia and it does not provide personalized treatment recommendations based on patient-specific characteristics and medical history. CNNs can analyze medical images and data with high accuracy, enabling early detection of heart disease. Early diagnosis allows for timely intervention, reducing the risk of severe complications and improving overall public health. By accurately identifying high-risk patients, healthcare resources can be allocated more effectively, ensuring that those who need it most receive timely care.

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Heart Disease Prediction, Deep Learning, Convolutional Neural Networks (CNNs), Healthcare Resources, Heart Failure

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The rapid expansion of Android devices in the consumer and business markets highlights how important it is to have strong security standards in place to guard against changing online threats. Inherent weaknesses still exist despite improvements in security protocols, providing opportunities for malevolent actors to take advantage of. The strategic integration of an ATTiny85 microcontroller via the USB interface to take advantage of potential weaknesses is the main emphasis of this project, which explores the complex world of Android device security. Additionally, by highlighting potential flaws and associated exploitation methods, this initiative adds to the continuing discussion about Android device security. It seeks to clarify the complexities of contemporary security threats and promote a proactive strategy for protecting Android devices against malevolent intrusions through empirical research and testing.

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Project Scope and Limitations, Technical Restrictions, Problem with Compatibility, Legal and Moral Aspects to consider, Risk to Security, Objective of the Proposed Model, Attity 85 USB, Injecting Brute Force Code, Plug Android to Device, Attempt to unlock device, Algorithm Code, System Requirements, Future scope and Limitations, Conclusion

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The segmentation of fingerprints is a crucial step in automating the identification process. It is necessary to separate the ridge and valley structure of the fingerprint from the background, which often contains irrelevant data that can interfere with accurate feature extraction. In the proposed approach, fingerprint segmentation is treated as a classification problem, where the input image is classified into either the foreground or background class. To achieve this, an unsupervised learning algorithm called Stacked Sparse Autoencoder is employed to learn deep features that effectively distinguish the background region from the foreground. These deep features are then inputted into the SVM classifier. The results of the experiments demonstrate that the proposed method achieves state-of-the-art performance across various applications.

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Autoencoder, fingerprint segmentation, morphology

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This study presents a hybrid cyberbullying detection model that integrates advanced machine learning algorithms--Linear Support Vector Machines (LSVM) and Recurrent Neural Networks (RNNs)--to address the growing issue of cyberbullying on social media platforms like Twitter and Facebook. As cyber threats become increasingly sophisticated, traditional security measures often fall short, making the application of machine learning crucial for real-time detection and mitigation. Utilizing a dataset of text expressions, the system achieves an impressive 85% classification accuracy with LSVM and an 81% feature extraction accuracy with RNN, outperforming both deep learning and traditional machine learning techniques. The research employs Object-Oriented Analysis and Design Methodology (OOADM) to create a modular and efficient software solution that processes real-time messages for cyberbullying characteristics. The results highlight the superior capabilities of RNN and LSVM in analyzing complex patterns, essential for effective cyberbullying detection. By combining technical advancements with human engagement, this study emphasizes the need for a multifaceted approach to combat cyberbullying. The proposed framework not only enhances cybersecurity measures but also contributes valuable insights into the intersection of machine learning and online safety. Ultimately, this research offers a proactive defense strategy, demonstrating the potential of cutting-edge AI methods to address emerging cyber threats effectively.

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Cyberbullying Detection, Online Bulling, Machine Learning, Bulling, Text Tracking

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This study aims to assess depression, anxiety, and stress levels among college students using the DASS-21 (Depression, Anxiety, and Stress Scale-21) instrument. Given the growing mental health concerns within the college demographic, especially with the unique pressures of academic, social, and financial demands, the need for reliable tools to evaluate these mental health conditions is paramount. The DASS-21 scale, a concise and validated measure, is widely used for assessing the severity of these psychological states. Through a cross-sectional survey, students' responses to the DASS-21 items will provide insights into prevalent mental health issues and help identify key risk factors. Findings from this study aim to inform university counseling services and mental health support initiatives, contributing to strategies for early intervention and support systems tailored to student needs.

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Depression, Anxiety and Stress Scale-21, Postgraduate students of Jain University.

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The desire for happiness is universal. Not all are able to actively pursue it and if they do, they fall short since the concept of Happiness is vastly misunderstood. Happiness is assured when there is purpose and meaning to life. Finding our life purpose is however challenging. In this article, I attempt to revive one of the ancient tools for revealing our life purpose, Vedic Astrology, describe who the contenders are, and the role played by the Atmakaraka.

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Happiness, Life Purpose, dharma, Archetypes, Vedic Astrology, Atmakaraka

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Media trials have become increasingly prevalent in India, with media entities often assuming quasi-judicial roles in high-profile cases. This paper analyses the implications of media trials on the judicial process and the fundamental rights of the accused in India. Through examining key case studies and judicial responses, it investigates how media trials shape public opinion and affect fair trial rights, often leading to sensationalism, bias, and infringement of privacy. While media trials may aim to promote accountability, this paper argues for a regulatory framework to balance media freedom with safeguarding judicial integrity and the rights of individuals involved in legal proceedings. The paper offers insights into the need for responsible journalism that upholds democratic values without compromising due process and fairness.

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Media Trials, Judicial Process, Fundamental Rights, Fair Trial, Privacy, India

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Creative Commons Attribution 4.0 and The Open Definition