Category Archives: Uncategorized

Authors: Professor Pradnya Patange, Atharv Pate, Harsh lonari, Mayuresh Kshirsagar, Manish Patil

Abstract: The rapid advancement of deepfake technology has introduced significant challenges to digital media authenticity, enabling the creation of highly convincing synthetic images and videos that are difficult to distinguish from genuine content. This study proposes an advanced deepfake detection framework based on the Temporal Vision-Language Transformer (TVLT), a cutting-edge multimodal deep learning architecture that jointly learns from visual, temporal, and semantic representations. Unlike traditional convolutional or recurrent models that focus solely on spatial or temporal domains, the proposed TVLT-based system integrates cross-modal attention to capture complex correlations among video frames, motion patterns, and audio-text alignment cues. The model efficiently identifies inconsistencies in facial movement, speech synchronization, lighting, and micro-expressions—features that deepfake generation methods struggle to replicate authentically

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Authors: A.K. Makwana, Prutha Priteshkumar Shah

Abstract: This research paper aims to understand consumer preferences toward ready-to-eat (RTE) and ready-to-cook (RTC) food products. With the growing pace of urbanization, changes in lifestyle, and time constraints, there has been an increasing inclination toward convenience foods. A survey was conducted among consumers to analyze their frequency of purchase, influencing factors, spending patterns, and preferences. The results indicate that taste, convenience, and price play significant roles in consumer decision-making, with the majority preferring vegetarian options and spending less than ₹500 monthly on such products. The study concludes with insights and recommendations for manufacturers to better meet consumer needs.

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Authors: Siddhi Pramod Lande, Samruddhi Ravindra Alhat

Abstract: Recognizing emotions is very important for connecting human emotions with artificial intelligence. This study introduces Emotion Sense, a sophisticated real-time facial emotion recognition system utilizing deep learning and explainable AI (XAI). The suggested system uses a better MobileNetV3 architecture along with Coordinate Attention (CA) and Grad-CAM visualization to get high accuracy and make the results easy to understand. The model recognizes seven fundamental human emotions: happiness, sadness, anger, surprise, fear, disgust, and neutrality. The FER-2013 data set. Emotion Sense solves two big problems that traditional CNN-based models have by combining real-time performance with explainability. This makes it both accurate and clear. The experimental results show that it is 90.2% accurate and runs smoothly at 25 frames per second on CPU devices. This shows that it is useful for real-world applications like healthcare, education, and human-computer interaction. This research is unique because it uses a hybrid design that balances speed, accuracy, and interpretability while staying strong in different real-world situations

DOI: http://doi.org/10.5281/zenodo.17491066

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Authors: Abhendra Pratap Singh, Riya Rani, Arpit Dwivedi, Nandini Sharma, Aakriti Sharma

Abstract: Cloud computing has historically been vital to the rapid advancement of multi-robot systems and autonomous cars for data processing, model training, and decision-making. However, the increasing demand for scalability, data privacy, and real-time responsiveness has exposed significant limitations of centralized cloud systems, such as excessive latency, bandwidth dependence, and security flaws. Federated Learning (FL) and Edge Computing (EC), which work together to provide decentralized and privacy-preserving intelligence, have become the focus of research in an effort to overcome these limitations. This paper addresses autonomous vehicles and multi-robots operated as edge nodes that train machine learning models locally on their own data, sharing just updates or model parameters with a central server instead of sending unprocessed information. This decentralized method greatly lowers communication costs, improves data secrecy, and facilitates real-time decision-making—all of which are crucial for operations that depend on safety. This paper’s contribution is to thoroughly analyze the issues including non-IID data dissemination, constrained computational and energy resources, and possible security risks, notwithstanding their benefits. In order to improve scalability, trust, and dependability, future research will combine block chain technology, 6G connectivity, and digital twin simulation. All things considered, the shift from cloud-centric computing to federated edge intelligence represents a critical advancement in the development of intelligent, safe, and effective autonomy in robotic ecosystems and next-generation automobiles

DOI: http://doi.org/10.5281/zenodo.17490951

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Authors: Abhendra Pratap Singh, Riya Rani, Devanshi Sharma, Nandini Sharma, Prince Kumar Sharma, Vanshika Dua

Abstract: Exchange of information, social interaction, and behavior are all impacted by autism spectrum disorder (ASD), which is a complicated neurodevelopmental disorder. Traditional therapies have demonstrated benefits in increasing quality of life, but they are still resource intensive and have limitations due to high costs and an inadequate number of specialists. With recent technical advancements, robotics has emerged as a promising addition to traditional treatment options. Socially Assistive Robots (SARs) are being investigated as therapies for assisting children with autism by increasing engagement, boosting social skills, also providing constant and compatible interaction. This study looks at the growing amount of research on the use of robotics in autism therapy, with an importance of robot design, human robot interaction, and medical applications. SARs can promote focus, emotional expression, and social communication. However, obstacles remain in terms of robot design, ethical considerations, and the necessity for standardized methods of assessing effectiveness in the real world. This paper's contribution is to thoroughly analyze existing methodologies and present a framework for designing robotic therapies based on individual needs. By synthesizing information related to education, healthcare, and robotics, this review identifies critical areas for further study and outlines future options for developing effective, accessible, and ethically feasible robotic therapy for children with ASD.

DOI: http://doi.org/10.5281/zenodo.17490817

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Authors: Ms. Vaibhavi P. Gawande, Dr. R. S. Durge

Abstract: Decentralized crowdfunding platforms represent a paradigm shift in fundraising, harnessing blockchain technology to disrupt traditional models dominated by intermediaries. By leveraging smart contracts on networks like Ethereum, these platforms facilitate direct interactions between creators and backers globally, ensuring transparency, security, and reduced transaction costs. Core features include immutable transaction records, automated fund releases governed by smart contracts, and enhanced trust through decentralized validation. Key benefits encompass increased accessibility for global participants, lower fees compared to conventional platforms, and improved security against fraud. Technologically, these platforms integrate Web3 interfaces, cryptocurrency wallets like MetaMask, and programming languages such as Solidity for smart contract development.

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Authors: Vaishnavi F. Thakare, Prof .D. G. Ingale, Dr. A. P. Jadhav, Prof. S. V. Raut, Prof .S. V. Athawale, Dr.D.S.Kalyankar, Prof. R. N. Solanke

Abstract: The evolution of wireless communication has reached a critical juncture with the emergence of the sixth generation (6G) technology, envisioned to revolutionize global connectivity beyond the capabilities of 5G. 6G aims to deliver ultra-high data rates up to terabits per second, near-zero latency, enhanced reliability, and seamless integration of terrestrial and non-terrestrial networks. It leverages advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Terahertz (THz) frequency bands, Blockchain, and Quantum Communication to enable applications like holographic communication, digital twins, autonomous systems, and immersive extended reality (XR). This paper explores the fundamental principles, key enabling technologies, potential applications, and challenges of 6G, offering insight into its role in shaping the future of intelligent and sustainable communication systems.

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Authors: Abhijit Ranjan, Chandraveer Singh

Abstract: In the recent years, the demand for intelligent medication recommender systems has increased tremendously with the evolution of digital health technology. This research targets the development of a symptom-based medication recommender system from a structured and diversified healthcare database. The database is descriptive in nature with information regarding patient symptoms, associated medicines, dietary advice, exercise plans, precautions, and doctor specialties. Early steps of this project include extensive data exploration and preparation from several CSV files to create a clean and solid base for model training. For building the central recommendation engine, traditional machine learning algorithms such as Decision Tree, Random Forest, Naive Bayes, and Logistic Regression were utilized, which try to predict symptoms and suggest the most suitable medicines out of a pre-defined list. Among the models used, the Decision Tree classifier had the best performance, followed by Random Forest, Naive Bayes, and Logistic Regression. The system is smart enough for users to put in symptoms and get suggested medicines for the same, providing useful help for non-emergency medical conditions and employment in resource-constrained environments. Future developments will involve incorporating patient medical history, dosage calculation, drug interaction screening, and implementing the system through a mobile platform to enhance accessibility and real-time use.

DOI: http://doi.org/10.5281/zenodo.17482512

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Authors: Dr Anitha S, Mohammed Muzammil S, Prince P, Sridhar L

Abstract: This proposed work designs an Automatic Waste Segregation System that focuses on separating metallic waste using a magnetic belt mechanism. This IoT based system uses a conveyor belt that carries mixed waste materials through a detection unit containing an electromagnet. When the magnet is activated, it attracts and separates the metallic components from the remaining waste. The metallic waste is then released into a separate compartment once the magnet is deactivated. A sensor is used to measure how much of the metal compartment is filled, and the fill level is displayed as a percentage on a digital screen. The entire operation is controlled by a microcontroller to ensure smooth and accurate functioning. This system helps reduce manual labor, increases sorting efficiency, and supports effective recycling management. It offers a simple, low-cost, and eco-friendly device to waste segregation that can be applied in both domestic and industrial settings

DOI: https://doi.org/10.5281/zenodo.17482056

 

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Authors: Kanak Patil

Abstract: This paper provides an overview of cybersecurity and digital forensics, two related fields that are critical for digital security. It explains the basic idea that cybersecurity is about preventing attacks (like a shield), while digital forensics is about investigating them after they happen (like a sword). The paper will look at how both fields developed over time, the main areas within cybersecurity, and the standard frameworks used, like the one from NIST. It will also cover the step-by-step process of a digital forensics investigation, including the importance of keeping a "chain of custody" for evidence. Using real-world examples like the Stuxnet worm, the Equifax data breach, and the WannaCry ransomware attack, this paper shows how these concepts are used in practice. It also discusses the legal and ethical challenges, such as data privacy laws like GDPR and CCPA. Finally, the paper looks at future challenges, including the shortage of skilled professionals, new ways hackers are hiding their tracks, the role of Artificial Intelligence, and the threat of quantum computing to modern encryption. The main point is that to be effective, cybersecurity and digital forensics must work together, with the results of investigations helping to build stronger defenses for the future.

 

 

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