Authors: Hari Om, Steven David

 

Abstract: Social media and review platforms have become key spaces for individuals to voice opinions, shaping trends across sectors like entertainment and business. This study introduces a comprehensive sentiment analysis framework that combines lexicon-based methods, machine learning models, and privacy-conscious data collection practices. Drawing on insights from three notable research works, the proposed approach effectively categorizes movie reviews and general online comments into positive, negative, or neutral sentiments. Emphasis is placed on thorough data preprocessing, accurate classification, and ethical data management, resulting in a practical and adaptable solution for sentiment analysis in real-world applications.

DOI: http://doi.org/

 

Published by: vikaspatanker

Authors: Professor Dr. Rachna Chavan, Ashish Singh

Abstract: Individuals with hearing and speech impairments rely on Indian Sign Language (ISL) for communication. Despite its importance, ISL lacks broad technological integration, limiting accessibility. This paper presents a vision-based recognition model built using Convolutional Neural Networks (CNNs) to classify static ISL gestures. The system undergoes preprocessing, augmentation, training, and real-time classification. A custom dataset was collected to ensure diversity in hand gestures and backgrounds. Our trained model achieved a classification accuracy of 96.4%, showing its capability to assist in inclusive communication tools.

Published by: vikaspatanker

Authors: Aarti Dengale, Dr. Nagsen Bansod, Dr. A. A. Khan, Dr. R. S. Deshpande

Abstract: Data leakage in cloud environments poses serious threats to data confidentiality, integrity, and availability. This paper proposes a robust system combining Role-Based Access Control (RBAC), watermarking, and anomaly detection to prevent and detect data leakage in real-time. Simulations demon- strateim proved performance in detecting unauthorized access attempts. We present the architecture, algorithms, requirements, and security mechanisms, along with a comprehensive literature survey and experimental results. Additionally, the paper discusses the integration of emerging concepts such as Zero Trust Architecture, Attribute-Based Access Control, and forensic auditing to enhance cloud data security.

 

 

Published by: vikaspatanker

Authors: Divyarth Rai, Tasneem Jahan

 

Abstract: Traditional image retrieval methods which use plain images suffer security risks in fields like medicine, military, space exploration, stocks and finance. Image classification using adversarial machine learning models are vital for enhancing security and detecting intrusion. This paper attempts to present a comparative study and highlights the potential of most promising models for efficient and effective retrieval with feature learning in image classification tasks. The best approaches can eventually strengthen its impact on the field for further implementation. The various machine learning models which could intercept adversarial attacks are classified with their results and advantages. Across social media websites and recommender systems, malicious advertisements are increasingly popular. The approaches discussed here are robust to classify the advertisement images as malicious or benign. This is a good strategy for ensuring smooth user experience and maintaining user security.

DOI: http://doi.org/10.61137/ijsret.vol.11.issue3.153

 

Published by: vikaspatanker

Authors: Assistant Professor Yamini Warke, Sairam Misal, Tushar Karwar, Nikita Wagh, Aishwarya Sawant

Abstract: Agriculture is vital to India's global economy and significantly contributes to GDP. As the human population grows, the nation's agricultural output is crucial in ensuring food security. Climate factors such as temperature, precipitation, soil quality, and fertilizers primarily influence a crop's yield. The variability of these elements adversely impacts productivity, posing a significant challenge for the agriculture industry to accurately estimate crop yields under fluctuating climatic circumstances. Recently, researchers have used machine learning algorithms to forecast crop yields before actual planting. This research study has introduced a machine learning technique, namely linear regression and multilayer perception, to forecast crop production based on characteristics such as state, district, area, seasons, NPK, pH values, rainfall, temperature, and area. To improve yield, this research study recommends a fertilizer tailored to soil conditions, including NPK levels, soil type, pH, humidity, and moisture. Fuzzy algorithms primarily guide the recommendation of fertilizers.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: P.Selvaraj, A Joshua Issac, Dr.S.Shanmuga, M.Bharathi

 

 

Abstract: The increasing advancement of generation of deepfake techniques – especially manipulations involving face-swapping has brought up major concerns related to integrity of online media, data privacy and societal trust. The computer generated videos, created using advanced models can easily replace an individual face with another often fool regular detection tools because changes in lighting, skin tone, facial expressions are so small and hard to notice. Although many AI-based methods have been developed to spot deep fake, most current models still struggle because they only look at single images , don't consider changes over time or require too much computing power. This research proposes a hybrid deepfake detection framework that leverages the strengths of Convolutional Neural Networks (CNNs) for robust spatial feature extraction and Vision Transformers (ViTs) for capturing temporal and contextual relationships across video frames. The CNN part looks for small changes and edits in the face, while the Vision Transformer looks at a series of frames to catch unusual expressions , movements and facial tone. Together, this combination aims to overcome the challenges posed by diverse and highly realistic face-swap techniques. The system is trained and tested on known datasets like FaceForensics++ and DFDC-Preview, providing a complete way to detect face-swap deep fake. By improving on current methods and looking at both the details in each frame and changes over time, this study helps create a stronger and more flexible deepfake detection system that can handle new and growing threats in visual content.

DOI: http://doi.org/

 

 

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Authors: Associate Professor Dr.K.Prem Kumar,, Dhareddy Rohith Reddy, Varkuti Nitish Reddy, Gummadi Manideep Reddy, Vadla Aakash

 

 

Abstract: Secure Haven – Enhancing Gated Community Management with IoT is an innovative initiative that utilizes advanced Internet of Things (IoT) technologies to improve the overall management and functionality of modern gated communities. By integrating a variety of IoT devices, including sensors and controllers, the system enables real- time monitoring and control of key community operations. This leads to greater efficiency, convenience, and safety for residents. These devices gather critical data related to energy consumption, environmental conditions, occupancy, and security. The collected data is processed using sophisticated algorithms and machine learning techniques to optimize community performance and decision-making. The project focuses on four main areas: energy management, environmental control, security and access control, and predictive maintenance. Energy efficiency is achieved through smart meters and intelligent lighting systems that help reduce energy waste and cut costs. The incorporation of renewable energy sources also supports sustainability. Environmental comfort is maintained using DHT11 sensors that monitor temperature and humidity, ensuring ideal indoor conditions. For security, RFID-based access systems are employed to allow secure and automated entry into the premises, while smart surveillance and real- time alert mechanisms provide an added layer of protection. Predictive maintenance is enabled by IoT-based monitoring systems, such as water level sensors, which facilitate continuous tracking of equipment performance and help.prevent failures through early detection.Overall, Secure Haven offers a smart, secure, and sustainable solution for managing gated communities by leveraging the power of IoT to create a more connected and responsive living environment.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Associate Professor Dr.K.Prem Kumar,, Dhareddy Rohith Reddy, Varkuti Nitish Reddy, Gummadi Manideep Reddy, Vadla Aakash

 

 

Abstract: Secure Haven – Enhancing Gated Community Management with IoT is an innovative initiative that utilizes advanced Internet of Things (IoT) technologies to improve the overall management and functionality of modern gated communities. By integrating a variety of IoT devices, including sensors and controllers, the system enables real- time monitoring and control of key community operations. This leads to greater efficiency, convenience, and safety for residents. These devices gather critical data related to energy consumption, environmental conditions, occupancy, and security. The collected data is processed using sophisticated algorithms and machine learning techniques to optimize community performance and decision-making. The project focuses on four main areas: energy management, environmental control, security and access control, and predictive maintenance. Energy efficiency is achieved through smart meters and intelligent lighting systems that help reduce energy waste and cut costs. The incorporation of renewable energy sources also supports sustainability. Environmental comfort is maintained using DHT11 sensors that monitor temperature and humidity, ensuring ideal indoor conditions. For security, RFID-based access systems are employed to allow secure and automated entry into the premises, while smart surveillance and real- time alert mechanisms provide an added layer of protection. Predictive maintenance is enabled by IoT-based monitoring systems, such as water level sensors, which facilitate continuous tracking of equipment performance and help.prevent failures through early detection.Overall, Secure Haven offers a smart, secure, and sustainable solution for managing gated communities by leveraging the power of IoT to create a more connected and responsive living environment

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Prof. Dr.Poornima Tyagi, Aditansh Rai, Khushi Gupta, Sohaib Ahmad, Anurag Paliwal

 

 

Abstract:The world of Internet is growing rapidly, many applications that previously created on the desktop start moving to the web. Many applications could be accessed anytime and anywhere easily using Internet. Developers need tools to create their applications, one of them named code editor. The purpose of this research is to design and develop a real-time code editor application using web socket technology to help users collaborate while working on the project. This application provides a feature where users can collaborate on a project in real-time. The authors using analysis methodology which conducting on a study of the current code editor applications, distributing questionnaires and conducting on literature study. Collabrix is a web application that provides workspace to writing, perform, display the results of the code through the terminal, and collaborate with other users in real- time. The application main features are providing workspace to make, execute and build the source code, real-time collaboration, chat, and build the terminal. This application supports C, C++, and python programming languages.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Anutosh Bajpai, Professor (Dr.) V.K. Paull

Abstract: Fire safety in auditoriums is a critical concern due to the high occupancy and unique spatial configurations that can hinder efficient evacuation during emergencies. This dissertation, titled Evacuation Strategies in Case of Fire in Auditoriums, aims to address the gap in understanding fire behavior and evacuation performance by integrating advanced simulation tools, PyroSim and Pathfinder, to analyze and improve evacuation strategies. A case study of an auditorium in New Delhi was conducted, focusing on four fire scenarios (circulation area, seating area, wall, and stage) under two material conditions: existing materials and code-compliant materials. The study revealed that existing materials, such as polyester curtains, untreated foam seating, and nylon carpets, significantly contribute to fire spread, smoke generation, and reduced visibility, thereby increasing evacuation times. Code-compliant materials, including fire-treated fabrics, non-combustible paneling, and fire-resistant carpets, demonstrated substantial improvements, reducing evacuation times by up to 30% and delaying smoke visibility loss by over 100 seconds in most scenarios. The stage area emerged as the most critical zone due to its highly flammable materials, requiring urgent intervention through material upgrades and enhanced suppression systems. The research highlights the importance of material selection, compliance with fire safety codes, and simulation-driven design improvements in mitigating fire risks. While limitations such as reliance on hypothetical data and the exclusion of behavioral factors were acknowledged, the findings provide actionable recommendations for policy reforms, material upgrades, and egress design enhancements. This study contributes to the evolving discourse on fire safety, emphasizing the need for a comprehensive, proactive approach to ensure occupant protection in public assembly spaces.

DOI: https://doi.org/10.5281/zenodo.15682340

Published by: vikaspatanker