2771-1463ISSN (Online)
2771-1471ISSN (Print)
Rapid development has been seen in Artificial Intel license (AI), which has transformed the retail industry, including online shopping. Selecting the right size of shoes that varies with brands and design is one of the biggest challenges in the E-Commerce footwear industry. This research focuses on AI Powered virtual shoe fitting system using Lens Studio Software. In this, customers are able to try shoes virtually through augmented reality and customized 3D foot models. This innovation solves size issues and benefits online footwear retailers, resulting in greater customer satisfaction. The role of Lens Studio software includes the creation of customized shoes, 3D shoes models, lenses, and size accuracy with the foot tracking mechanism.
Read MoreDoi: https://doi.org/10.54216/JCHCI.090201
Vol. 9 Issue. 2 PP. 01-10, (2025)
Climate change, driven by human activities like burning fossil fuels, deforestation, and industrial agriculture, is one of the most urgent global challenges. The rise in greenhouse gases (GHGs), such as carbon dioxide (COâ‚‚), methane (CHâ‚„), and nitrous oxide (Nâ‚‚O), is contributing to global warming, sea level rise, and extreme weather events, with developing nations being particularly vulnerable. To address this, sustainability has become a key focus, involving the need to meet present demands without compromising the ability of future generations to meet theirs. Mitigation strategies include reducing emissions, transitioning to renewable energy sources like solar, wind, and hydropower, improving energy efficiency, and using reforestation to absorb carbon dioxide. Adaptation efforts, such as drought-resistant crops and resilient infrastructure, help communities cope with the impacts of climate change. The circular economy, which emphasizes resource efficiency, waste reduction, and recycling, further supports environmental sustainability. Governments, corporations, and individuals must also prioritize social justice, ensuring that underserved areas most affected by climate change receive the necessary support. Through collective action, we can work towards a sustainable future for all.
Read MoreDoi: https://doi.org/10.54216/JCHCI.090202
Vol. 9 Issue. 2 PP. 11-20, (2025)
The farm sector is challenged by various factors, such as climate volatility, ineffective resource management, and data security. In this paper, a new methodology is proposed where blockchain technology is combined with a chatbot platform to offer farmers real-time, secure, and accurate crop suggestions. Blockchain allows data integrity to be guaranteed, reducing risks from data tampering. The chatbot is an interactive platform, where farmers can enter soil parameters, location, and weather. The system processes these inputs and gives optimal crop recommendations based on past data and predictive analytics. The proposed solution is enhancing sustainable agriculture practices, boosting productivity, and ensuring long-term food security.
Read MoreDoi: https://doi.org/10.54216/JCHCI.090203
Vol. 9 Issue. 2 PP. 21-27, (2025)
Phishing attacks have emerged as a significant cybersecurity challenge, targeting individuals and organizations by tricking users into revealing sensitive information through deceptive websites. Traditional phishing detection methods, such as blacklists and heuristic-based approaches, struggle to keep pace with the rapid evolution of phishing techniques. Machine learning-based predictive models offer a promising solution by analyzing website attributes, URL structures, and behavioral patterns to distinguish between legitimate and phishing websites. This paper provides a comprehensive review of various machine learning techniques, including decision trees, support vector machines (SVM), random forests, deep learning models, and ensemble methods, employed in phishing website detection. It explores feature selection strategies, dataset characteristics, performance evaluation metrics, and real-world implementation challenges. Furthermore, the study discusses recent advancements such as adversarial resilience, natural language processing (NLP) integration, and real-time phishing detection frameworks. The review highlights existing research gaps and future directions to enhance phishing detection accuracy, scalability, and adaptability in evolving cybersecurity landscapes.
Read MoreDoi: https://doi.org/10.54216/JCHCI.090204
Vol. 9 Issue. 2 PP. 28-33, (2025)
Cricket is a physically demanding sport that exposes players to various acute and chronic injuries. Preventing these injuries is crucial for maintaining peak performance and prolonging careers. This project leverages artificial intelligence (AI) and machine learning (ML) to analyze key player data, including biomechanics, workload, fatigue, and mental stress, to assess and mitigate injury risks. Wearable sensors and tracking systems continuously monitor player movements, workload, and physiological parameters, providing real-time insights into their physical condition. By detecting patterns that indicate potential injury risks, the AI model enables early intervention through personalized training modifications and recovery programs. This proactive approach minimizes injuries, optimizes player fitness, and enhances performance. Ultimately, integrating AI-driven injury prevention strategies in cricket ensures better player management, increased longevity, and improved overall team efficiency.
Read MoreDoi: https://doi.org/10.54216/JCHCI.090205
Vol. 9 Issue. 2 PP. 34-43, (2025)
The rise of social media platforms has led to an increase in cyberbullying and hate speech, which can have severe consequences on individuals and communities. The detection of harmful content, especially in regional languages, remains a significant challenge due to the linguistic diversity, informal expressions, and limited datasets available for training machine learning models. This paper proposes a hybrid deep learning and natural language processing (NLP) model for the detection of cyberbullying and hate speech in regional languages. The model combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with advanced NLP techniques such as sentiment analysis and context-aware feature extraction. Preliminary experiments show that the proposed model achieves an accuracy of 86.7% for hate speech detection and 82.3% for cyberbullying detection in regional language datasets. Furthermore, the hybrid model outperforms traditional machine learning techniques by 15% in terms of precision and recall. This approach demonstrates the potential of combining deep learning and NLP to address the challenges of detecting harmful content in diverse linguistic environments.
Read MoreDoi: https://doi.org/10.54216/JCHCI.090206
Vol. 9 Issue. 2 PP. 44-53, (2025)