Volume 13 | Issue 2 |

 Abstract

With the rising concerns about unauthorized access and intrusions, there is a growing demand for robust security systems. This project aims to enhance security measures by leveraging advanced technologies to detect intruders promptly. The "Intruder Detection System for Modern web Architectures" is an innovative security solution designed to enhance the protection. The primary objective of this project is to develop a reliable and efficient system capable of detecting unauthorized access and promptly notifying relevant stakeholders through email and SMS alerts. The Intruder Detection and Automatic Email Alerting System is a sophisticated computer vision-based security solution designed to enhance the safety and security of residential and commercial spaces. The seamless integration of this automated alerting mechanism ensures that any intrusion is reported without delay, thereby minimizing response time and enhancing overall security. This innovative system eliminates the need for constant manual monitoring, providing a reliable, efficient, and scalable solution for intruder detection. In conclusion, the "Streamlit-based Intruder Detection and Email Alerting System" represents a sophisticated and accessible approach to enhancing security in various environments. By leveraging Streamlit capabilities for creating interactive web applications, the system provides an intuitive interface for users to monitor, configure, and receive timely alerts about potential security breaches, thereby contributing to a more responsive and user-centric intruder detection solution.

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 Keywords

Intruder Detection, Security System, Computer Vision, Email Alerts, Streamlit, Web Security, Real-time Monitoring, Unauthorized Access, Automated Alerting, Deep Learning.

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 Abstract

Most of the world trade are being transported by sea and Seaports are interface between land and sea. Seaports, hub of sea, rail & road transport to transfer cargo from one transport mode to other, are growth engine of the country. Many large cities in the world are developed around seaports because of the ease of international trade and transportation facilitated by maritime access. Multiple agencies are working together for smooth operation of the seaports. They use different types of hardware, software, database and communication technologies to ensure smooth, efficient, and secure port activities. Since these agencies operate with diverse technologies, a unified system/database is required to enable data sharing, interoperability, and data analytics. Big Data can play a crucial role in integrating and analyzing the vast amounts of information generated by these different agencies. Analysis of the data from different sources can be used to enhance efficiency of whole supply chain, safety and security of cargo and predictive decision-making.

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 Keywords

Big Data, Port Performance, Cargo, Loading and Unloading

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This paper examines the recurring themes of gender discrimination and the complex journey of female identity in Kavita Kane's novels. It also attempts to delve into how Kane's works subvert traditional gender roles, offer alternative perspectives on historical events, and ultimately contribute to a broader understanding of female identity and its evolution within the Indian context.

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 Keywords

Mythology, Female Identity,Gender , Patriarchy

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Sentiment analysis involves understanding the emotions that underpin individuals' thoughts, which are communicated in the digital landscape through reviews and tweets. These sentiments can be classified as positive, negative, or neutral. This document will explore various methods to conduct sentiment analysis of tweets related to US Airlines, employing different machine learning algorithms and evaluating their accuracies. Effective sentiment analysis is invaluable for business analysts, enabling them to address challenges related to identifying areas for profit and loss. By leveraging sentiment analysis, analysts can pinpoint weak points indicated by negative sentiments for improvement initiatives. Conversely, they can also recognize strengths highlighted by positive sentiments and strive to sustain these advantages consistently in the future.

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 Keywords

Sentiment Analysis, Social Media Mining Natural Language Processing (NLP)

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Electronic waste (e-waste) has emerged as one of the fastest-growing waste streams globally, presenting significant environmental and economic challenges. This review examines various aspects of e-waste generation, management, and recycling, with a focus on sustainability, legislative frameworks, and technological advancements. The study aims to analyse the determinants of e-waste production, particularly in sectors such as cryptocurrency mining, electrical and electronic equipment disposal, and industrial waste streams. Using a comprehensive literature review methodology, relevant academic and industry sources were selected based on their contribution to understanding e-waste generation patterns, environmental and health risks, metal recovery processes, and circular economy solutions. The findings indicate that improper e-waste management leads to hazardous material exposure, loss of valuable metals, and environmental contamination. Additionally, socioeconomic factors, such as income disparity and regulatory inefficiencies, influence e-waste collection and recycling rates. The study highlights advancements in hydrometallurgical recovery techniques, bioleaching, and policy-driven recycling initiatives that can mitigate the adverse impacts of e-waste. Implications of this review suggest the need for robust policies, improved waste management infrastructure, and increased adoption of circular economy models to enhance resource efficiency and environmental protection. The findings contribute to the ongoing discourse on sustainable e-waste management, offering insights for policymakers, researchers, and industry stakeholders

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 Keywords

Electrical and electronic equipment (EEE) ,Circular Economy (CE) Model

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 Abstract

In this paper, we study a class of almost vanishing H- curvature. As a consequence of this result, we prove that a general of Finsler metric called general (?,?) metrics and obtain an equation that characterizes these Finsler metrics (?,?)- metric has almost vanishing H- curvature if and only if it has almost vanishing ?-curvature

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 Keywords

Finsler space, General (?, ?)-metric, ?-curvature, H-curvature.

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This study, titled "A Study on Investors' Behavior Towards Stock Price Volatility in the Indian Stock Market," explores how investors react to fluctuations in stock prices in the rapidly evolving Indian market. The research examines the impact of socio-economic factors, macroeconomic indicators, and risk management practices on investor behavior. Utilizing a descriptive and quantitative research approach, data was collected from investors across various demographics through structured questionnaires. Key findings indicate that younger investors (25-34 years) and those with undergraduate education exhibit more proactive risk management strategies. Regression analysis suggests external influences significantly affect investor behavior, while risk perception has a weaker impact. ANOVA results confirm that age and education significantly influence risk management practices, but income levels do not. The study highlights the importance of financial education, portfolio diversification, and technology-driven investment tools to improve decision-making. The findings provide valuable insights for policymakers, financial educators, and investors aiming to navigate stock market volatility effectively.

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 Keywords

Investor behavior, stock price volatility, Indian stock market, socio-economic factors, macroeconomic indicators, risk management, regression analysis, ANOVA, financial education, portfolio diversification, technology-driven investment, decision-making, market fluctuations.

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Creative Commons Attribution 4.0 and The Open Definition

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INTRODUCTION: The ileo colic artery branch of the superior mesenteric artery, supplies blood to the terminal ileum and proximal colon. Anatomical variations in this artery are less common and can have significant implications for surgical and radiological procedures. CASE: An embalmed cadaver used for gross dissection during the gross anatomy dissection for undergraduate students at the Department of Sharir Rachana Alva's Ayurveda Medical College, Moodbidri. During the dissection of abdominal aorta, it was found that ileocolic artery is giving branch to right colic artery. CONCLUSION: In our case, ileocolic artery originated from superior mesenteric artery giving branch to right colic artery. Awareness and knowledge regarding the variations in Superior mesenteric artery and ileocolic artery are important for surgeons to avoid both intraoperative and postoperative complications during surgical and diagnostic procedures involving intestines. Keywords: Aberrant Ileocolic artery, Right colic artery, Superior mesenteric artery (SMA), Anatomical variation.

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Keywords: Aberrant Ileocolic artery, Right colic artery, Superior mesenteric artery (SMA), Anatomical variation.

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Creative Commons Attribution 4.0 and The Open Definition

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Sentiment analysis, a fundamental task in natural language processing (NLP), aims to determine the emotional tone conveyed in textual data, providing valuable insights into user opinions and preferences. With the rapid expansion of online platforms and the proliferation of customer feedback, businesses increasingly rely on automated sentiment classification to enhance decision-making and customer engagement.This study explores the potential of deep learning in sentiment analysis by employing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze sentiment in Amazon product reviews. The research leverages a large dataset of Amazon reviews, encompassing diverse product categories and varying sentiment intensities, to develop a robust classification model. The preprocessing pipeline includes text cleaning, tokenization, stopword removal, stemming, and vectorization to convert raw textual data into a structured format suitable for model training. Feature extraction is performed using techniques such as word embeddings (Word2Vec, GloVe, or FastText), ensuring that the deep learning models capture contextual relationships and semantic nuances within the text.The CNN model, known for its ability to detect local patterns and hierarchical features in text data, demonstrates a test accuracy of 90.66%, while the RNN model, capable of learning sequential dependencies and long-term relationships in textual sequences, achieves a test accuracy of 90.82%. These results underscore the effectiveness of deep learning in sentiment classification, with CNN excelling in computational efficiency and RNN offering superior sequential processing capabilities. The study also considers hybrid architectures, such as Long Short-Term Memory (LSTM) networks and Bidirectional Gated Recurrent Units (Bi-GRU), to enhance performance further.Beyond technical implementation, this research highlights the growing significance of automating large-scale sentiment analysis in the era of digital transformation. Businesses can harness AI-driven sentiment classification to analyze consumer feedback, improve product recommendations, predict market trends, and enhance customer satisfaction. The ability to extract meaningful insights from vast volumes of customer reviews provides a competitive advantage, enabling companies to make data-driven decisions, refine marketing strategies, and foster stronger customer relationships.This study contributes to the evolving landscape of AI-powered sentiment analysis, paving the way for scalable and real-time applications in e-commerce analytics, social media monitoring, and customer experience management. Future research directions include integrating attention mechanisms, transformer-based architectures like BERT or GPT, and domain-specific fine-tuning to further optimize sentiment classification models and improve interpretability.

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 Keywords

Sentiment Analysis, Amazon Reviews, Natural Language Processing (NLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Learning, AI-driven Analytics.

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One of the most alarming advancements is deepfake technology, a powerful instrument that obscures the distinction between reality and illusion. As we advance in technology, we obtain ever complex methods to generate, modify, and amalgamate content. Deepfake technology, propelled by artificial intelligence (AI) and machine learning (ML), facilitates the effortless substitution of faces, sounds, and entire scenes in videos, rendering it progressively challenging to differentiate between reality and fabrication. This technology initiates a new epoch of digital manipulation, eliciting significant worries regarding its effects on multiple aspects of society. This article offers a thorough examination of deepfake approaches and contemporary detection methods, thereby aiding the advancement of novel and more resilient strategies to address the growing complexity of deepfakes.

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 Keywords

deepfake, cyber abuse, Machine learning

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Creative Commons Attribution 4.0 and The Open Definition