Ch. Kalyani1, K. V. K. Saibhaskar2, K. Lavanya Lakshmi2, K. E. S. R. K. Raju2, Rehana Tabassum2, V. Sai Gowtham2, 1Assistant Professor, Computer Science and Engineering, ANITS, Visakhapatnam, Andhra Pradesh, India, 2Computer Science and Engineering, ANITS, Visakhapatnam, Andhra Pradesh, India
Sign language, which allows for effective expression through gestures and movements, is one of the most crucial forms of communication for those who are deaf or hard of hearing. However, tools for translating spoken or written language into Indian Sign Language (ISL) are limited. This paper introduces SignSynth, a novel system designed to convert audio, text, and video content into ISL videos. By leveraging speech-to-text conversion, natural language processing, and tokenization, the system processes meaningful words while omitting redundant terms. These words are mapped to a curated database of ISL gestures represented as videos, which are then merged into a seamless output using advanced video processing techniques. With over 63 million individuals in India experiencing hearing disabilities, SignSynth offers a scalable and cost-effective solution to bridge communication gaps. This system has significant potential to enhance accessibility and facilitate interaction between hearing and non-hearing individuals, paving the way for advancements in real-time sign language translation
Indian Sign Language, Speech-to-Text, Gesture Recognition, Neural Networks, Sign Language Translation.
Balasubramanian S1 and Natarajan M2, 1Research Scholar, Department of Computer and Information Science, Annamalai University, Tamil Nadu, India,2Assistant Professor/Programmer, Department of Computer and Information Science, Annamalai University, Tamil Nadu, India
Due to ever-changing market conditions, predicting crude oil prices is complex and challenging. Traditional models struggle to adapt, while advanced AI models like LSTMs can find hidden patterns but often lack transparency. This study compares Machine Learning (Random Forest) and Deep Learning (LSTM) to assess both accuracy and interpretability. It also explores Explainable AI (XAI) methods like SHAP and LIME to make AI predictions more understandable for policymakers. The results show that combining machine learning, deep learning, and XAI techniques improves both the accuracy and trustworthiness of crude oil price forecasts.
Explainable AI, Trade Forecasting, Random Forest, LSTM, SHAP, LIME, Machine Learning, Deep Learning, xAI, Crude Oil Trade.
Dr. Sasikumar P1, Dr. Suganya Karpagam S2, Dr. Pratima Roy3, Dr. Priti Sharma4 and Dr. Suneetha Y5, 1Department of Social Science, SRM College of Agricultural Sciences, SRM Institute of Science and Technology, Baburayanpettai, Chengalpattu, Tamil Nadu, India, 2Department of English, Emerald Heights College for Women, Fingerpost, Ooty, Tamil Nadu, India, 3Department of English, St. Josephs College for Women, Kurnool, Andhra Pradesh, India, 4FORE Academy of Management Education (FAME), Gurugram, Haryana, India, 5RGM College of Engineering and Technology, Nandyal, Andhra Pradesh, India
The application of Artificial Intelligence (AI) and Machine Learning (ML) to the meticulous examination and interpretation of English literature has the potential to revolutionise the processes of writing, understanding, and education. While still in its early phases, this multidisciplinary strategy is gaining momentum as the digital era advances, thereby transforming literary studies. Although research driven by extensive data provides unparalleled precision and understanding of literary works, the advent of new technologies such as generative writing tools is transforming creative methodologies. Using Natural Language Processing (NLP) tools, artificial intelligence and machine learning can find complex patterns in text data, which helps us gain deep insights into different subjects, language models, and cultural backgrounds in large literature collections. This article delves into significant enquiries surrounding authorship, originality, and creativity in works generated by artificial intelligence, while also examining the ethical implications of AI in the realm of literature. The analysis explores the potential of technology to democratise access to literature on a global scale, highlighting its promise to transcend linguistic and cultural barriers. This investigation highlights the dynamic interplay between computer technology and the humanities through case studies and experimental enquiries. It highlights the importance of maintaining a balance between the objective precision of artificial intelligence and the deeply humanistic qualities found in literature. This paper ultimately emphasises the importance of collaborative efforts across various disciplines to shape a future where literary studies maintain their historical commitments to creativity, critical analysis, and cultural enhancement in an increasingly digital world.
Artificial Intelligence, Machine learning, Digital, technology & Humanize.
Ayaan Bhoje, Shreya Shankar, SahilBiswas, Shreya Waingankar and Arfat Fakih, Department of Information Technology KJ Somaiya School of Engineering Mumbai, India
Software licensing and intellectual property (IP) protection are essential issues in the era of digital technology, where pirating, copyright infringement, and unauthorized distribution create major challenges. Conventional mechanisms of licensing depend on centralized powers, which may be susceptible to fraud, inefficiencies, and enforcement challenges. Blockchain technology, being decentralized, immutable, and transparent, provides a potential answer to these issues. This article discusses the use of blockchain technology in software licensing and IP protection, its ability to automate compliance, prevent tampering, and increase security using smart contracts and cryptographic authentication. Blockchain-based licensing models such as tokenization and decentralized digital rights management (DRM) are also discussed in terms of their ability to curb license violations and provide fair compensation to developers. The paper also examines legal and regulatory aspects, challenges in implementation, and future potential for blockchain-based software protection. Utilizing blockchains inherent capabilities, the software industry can make progress toward a safer, clearer, and more efficient system of managing intellectual property rights and licensing.
Blockchain, Software Licensing, Intellectual Property Protection, Smart Contracts, Digital Rights Management (DRM), Decentralized Licensing, Copyright Enforcement, Tokenization, License Compliance, Intellectual Property Rights (IPR).
Kundanika Pradhan and Muhammed Anish, Department of Computational Technologies, SRM University, Tamil Nadu, India
Guaranteeing consumer safety and regulatory compliance for the cosmetics and personal care market involves thoroughly examining product ingredients. Conventional methods proved inadequate in detecting hazardous materials accurately and with high efficiency. The OCR retrieves ingredient information from product labels and packaging and then analyzes it through ChemBERTa, a transformer model trained on chemical representations. A specially developed module retrieves the SMILES representation of each ingredient extracted via an API-based mechanism. ChemBERTa is compared with the usual machine learning classifiers such as Support Vector Machine (SVM), Random Forest, Decision Trees, Bagging, and XGBoost in this work. ChemBERTa is a better option than traditional classifiers, and it has better accuracy when predicting the carcinogenicity of chemical compounds. In addition, an easy-to-use interface has been created with Streamlit that combines ChemBERTa and Llama 3.2 to present an easy and informative experience for users.
oncogenic chemicals, deep learning, ChemBERTa, optical character recognition, classification model, chemical informatics, Streamlit, Llama 3.2.
Patel Kashish Harshadbhai, Darsh Patel, Krisha Bhalani, Vijayant Raj Raghav Singh, Prema Ramasamy, PES University, India
Understanding the factors that affect stunting and the role of contraception in shaping the health of women and infants is crucial for public health in India. By analysing data from the NFHS-4, NFHS-5, and HMIS, this study identifies key determinants using methods such as unsupervised discretization binning, recursive feature elimination, K-Means Clustering, and Association Rule Mining, along with expert validation. The goal was to find the factors that affect the most child stunting and the interplay of specific factors which, when focused, can significantly improve infant and maternal health in India. Our findings highlight the critical role of family planning and health care programs in reducing stunting and improving health outcomes for women and children. The insights gained can help inform the development of targeted public health policies and initiatives in India.
Child Stunting, Contraception Awareness, NFHS-4, NFHS-5, HMIS, Recursive Feature Elimination, K Means Clustering, Association Rule Mining.
Steve M.F. Hollands and John Edward Reagan III, Blackhills Quantum Computing and Marketing, Lentelaan 4, 3723 Kortessem, Belgium, Boredbrains Consortium, 9314 Brookmeade St Las Vegas
The Quantum Horizon series explored radical ideas at the intersection of quantum physics and cosmology. We consolidate these insights into a coherent framework unifying Möbius-Inspired Cyclical Transformation (MICT) theory with Primordial Black Hole (PBH) cosmology. MICT models reality as iterative information cycles, while PBHs are seen as early-universe information processors and potential dark matter candidates. We propose PBHs as active MICT agents—"quantum horizon" nodes bridging quantum processes and cosmological feedback. Recent advances—MIT’s quarton coupler for ultrafast qubit control and the discovery of hydrogen-6—support this synthesis. Both demonstrate strong coupling and emergent behavior under extreme conditions, aligning with MICT’s premise that feedback and iteration generate new physics. This MICT-PBH model addresses key puzzles including the black hole information paradox, dark matter origins, and quantum-gravity links. Extending the Quantum Horizon vision, we propose a new paradigm in which quantum information dynamics are intrinsically woven into cosmic evolution.
Quantization of Hamilton-Jacobi equations, Primordial Black Holes, Dark Matter, Bour-shaped Molecules, Hyperbolic Turbos