CompSci & AI Advances is a leading international journal focused on publishing high-quality research and developments in the fields of computer science and artificial intelligence. The journal provides a platform for academics, researchers, and professionals to explore and share innovative ideas, groundbreaking studies, and practical applications that drive the evolution of computing and AI technologies. CompSci & AI Advances has a wide-ranging scope that includes artificial intelligence (AI), computing systems, data science, human-computer interaction, robotics, automation, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing (NLP), Computer Vision, Autonomous Systems, Edge Computing, AI Ethics, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Big Data Analytics, Robotics, AI-Powered Automation, Data Mining, Generative AI, Artificial General Intelligence (AGI), Human-Computer Interaction (HCI), AI in Healthcare, Cognitive Computing, Explainable AI (XAI), AI-enhanced Software Development, Quantum Computing, Edge Computing, Cloud Computing, 5G Technology, High-Performance Computing (HPC), Parallel Computing, Blockchain Technology, Neuromorphic Computing, Cybersecurity, Internet of Things (IoT), Wearable Computing, Ubiquitous Computing, Green Computing, Solid-State Drives (SSD), Virtualization, Serverless Computing, Distributed Computing, Cryptography, Microprocessors, GPU Acceleration, Data Centers, Energy-Efficient Computing, Optical Computing, Embedded Systems, 3D Integration, Computer Architecture, Augmented Reality (AR), Virtual Reality (VR), cybersecurity, and so on. The journal is committed to advancing knowledge across all disciplines of computers and Artificial intelligence. We emphasize interdisciplinary approaches and contributions that address both theoretical foundations and real-world challenges, while also considering the ethical and societal implications of technological progress. The mission of CompSci & AI Advances is to foster a global exchange of ideas and to bridge the gap between academic research and industry practice, promoting responsible and impactful advancements in AI and computing for the betterment of society.
M. Senthil, G. Navaneetha Krishnan, P. Anitha, S. K. Heena, T. Jaya Sri
Summary: The proposed IoT-based smart home automation system offers an affordable and user-friendly platform for controlling household appliances. With Wi-Fi connectivity, voice commands, activity sensors, and a mobile app, the system supports seamless device integration and future scalability via an SDK. Security is reinforced using 256-bit AES encryption, while energy efficiency is achieved through features like automated device shutdown and real-time energy monitoring. These functionalities make the system a comprehensive solution for enhancing convenience, security, and sustainability in modern living spaces.
CompSci & AI Advances 1(4), 158-167 (2024)
https://doi.org/10.69626/cai.2024.0158Arthy P. S., Chandra Sekar P., Praveenkumar Babu
Summary: This research presents a novel multimodal interaction framework for enhancing Human-AI collaboration. Integrating sensory data fusion, context-aware processing, and adaptive learning, the framework facilitates dynamic and intuitive communication across diverse modalities. Experimental validation revealed significant improvements in task efficiency, accuracy, and user satisfaction compared to unimodal systems. Key contributions include reinforcement learning for decision-making, privacy-preserving mechanisms, and bias mitigation strategies. The study emphasizes its application in healthcare, education, and smart environments, establishing a foundation for natural, ethical, and scalable Human-AI systems.
CompSci & AI Advances 1(3), 112-122 (2024)
https://doi.org/10.69626/cai.2024.0112G. Peda Babu, L. Chandrasekhar, Joy Bea
Summary: This research applies Convolutional Neural Networks (CNNs) to detect and classify three tobacco leaf diseases with 95% accuracy using over 1000 annotated images from Andhra Pradesh farmers. The study highlights CNNs’ efficiency in addressing traditional diagnostic challenges, enabling early interventions to mitigate crop losses. It underscores the scalability, automation, and cost-effectiveness of deep learning in agriculture. The research advocates AI-driven precision farming to improve productivity and sustainability, with future directions including real-time implementations and mobile integration for broader farmer accessibility.
CompSci & AI Advances 1(3), 123-131 (2024)
https://doi.org/10.69626/cai.2024.0123Susani Antony Mrefu, Zubaer Ibna Mannan, Nur Alam MD
Summary: The study introduces an image-based method for detecting and segmenting plant leaf diseases through three stages: preprocessing, segmentation, and post-processing. Grayscale conversion simplifies image analysis, while thresholding isolates leaves from backgrounds for accurate disease detection. Experimental evaluation on 100 images achieved 66.66% accuracy, identifying 68% of diseased and 75% of healthy leaves. The method demonstrates potential for real-world agricultural applications, though limitations such as dataset diversity and omission of color data highlight opportunities for future improvements.
CompSci & AI Advances 1(3), 132-139 (2024)
https://doi.org/10.69626/cai.2024.0132Ashish Thapa, Sami Azam, Nur Alam MD, Zubaer Ibna Mannan
Summary: This research presents an automated breast cancer classification system that integrates SVM and CNN frameworks to classify histopathological images into benign and malignant categories. The SVM classifier outperforms CNN and traditional methods, achieving 94% accuracy on the IDC dataset. It excels in precision, recall, and F1-score, demonstrating its effectiveness in localizing cancerous tissues. The study highlights the system’s potential as a reliable computer-assisted diagnostic tool, emphasizing its role in enhancing diagnostic accuracy, reducing pathologist workload, and supporting personalized cancer care.
CompSci & AI Advances 1(3), 140-148 (2024)
https://doi.org/10.69626/cai.2024.0140S. Prabu, R. Uma Maheshwari, K. Kalpana, K. Mahendrakan
Summary: The Adaptive Learning Path Optimization Algorithm (ALPOA) dynamically customizes e-learning pathways using machine learning and rule-based techniques. Experimental findings show a 15% test score improvement, a 25% rise in engagement, and a 25% reduction in dropout rates. ALPOA’s scalability is demonstrated across K-12, higher education, and corporate training, offering a robust framework for personalized education. The study highlights its potential to optimize learning outcomes and foster learner satisfaction while addressing scalability and data security challenges in future developments.
CompSci & AI Advances 1(3), 149-157 (2024)
https://doi.org/10.69626/cai.2024.0149S. Anusooya , S. M. Kamali , Saravanan Kandaneri Ramamoorthy
Summary: This study introduces an AI-driven cybersecurity framework tailored for nuclear-powered data centers, addressing their unique security challenges. By leveraging machine learning techniques like anomaly detection and reinforcement learning, the framework enables real-time threat detection, automated incident response, and vulnerability analysis. Experimental results demonstrate improved detection accuracy, faster response times, and enhanced resilience against sophisticated cyber threats. The hybrid approach integrates AI with traditional security measures, offering robust, multi-layered protection for these critical infrastructures, ensuring their secure integration into the global data ecosystem.
CompSci & AI Advances 1(2), 64-73 (2024)
https://doi.org/10.69626/cai.2024.0064S. Prabu, P. Jeevitha, S. Ramya
Summary: The study investigates Connected and Autonomous Vehicles (CAVs), emphasizing their potential to enhance road safety and driving efficiency through advanced AI and deep learning technologies. It explores how CAVs use sensors and real-time decision-making to reduce collisions and improve traffic flow. While highlighting the benefits, the study also addresses challenges like cybersecurity threats and infrastructure readiness. By proposing adaptive AI-driven solutions, it provides a roadmap for integrating reliable and secure autonomous systems into modern transportation.
CompSci & AI Advances 1(2), 74-84 (2024)
https://doi.org/10.69626/cai.2024.0074Saravanan Kandaneri Ramamoorthy, Praveenkumar Babu, S.Sakena Benazer
Summary: The study explores the application of quantum optimization techniques in next-generation cloud computing, focusing on resource management challenges. It proposes innovative solutions using Quantum Annealing and Variational Quantum Eigen solvers to improve resource allocation, enhance scalability, and achieve energy efficiency. The research demonstrates reduced latency and optimized system performance, emphasizing the transformative potential of quantum computing. Future directions include developing hybrid quantum-classical models, addressing scalability, and integrating quantum optimization in edge computing and distributed systems for practical implementation.
CompSci & AI Advances 1(2), 85-94 (2024)
https://doi.org/10.69626/cai.2024.0085N. Gobi, M. Balakrishnan, S. R. Indurekaa, A. B. Arockia Christopher
Summary: The research explores the integration of quantum cryptography and blockchain to enhance cloud data security. It proposes a hybrid framework leveraging QKD for secure key exchange and blockchain for decentralized data integrity. Key innovations include a quantum-enhanced encryption mechanism, an optimized consensus algorithm, and strategies to counter quantum attacks. Performance assessments reveal improved resilience to breaches, robust key management, and scalability for cloud systems. The study provides a transformative security solution addressing both current and post-quantum cybersecurity challenges.
CompSci & AI Advances 1(2), 95-101 (2024)
https://doi.org/10.69626/cai.2024.0095Praveenkumar Babu, M. Thangamani, Chandra Sekar P.
Summary: This research proposes a federated fuzzy KNN approach for early diabetes detection, incorporating internal parameters (e.g., BMI, blood pressure) and external factors (e.g., climate, agriculture output). The fuzzy-based method models interdependencies between parameters, enhancing accuracy and patient alerts. Integrating privacy-preserving mechanisms ensures secure data handling in distributed systems. The model achieved 86% accuracy, surpassing traditional machine learning methods by 23%, highlighting its potential for healthcare applications and better outcomes in real-world diabetes management.
CompSci & AI Advances 1(2), 102-111 (2024)
https://doi.org/10.69626/cai.2024.0102Paulchamy B., V. Suresh Babu, A. Purushothaman, Uma Maheshwari, Anbu Karuppusamy, Mohammad Anas, Md. Tabrez Nafis
Summary: This study explores NeuralHealth, a pioneering approach to integrating deep learning into health information systems. By analyzing complex datasets from diverse sources using advanced neural network architectures, NeuralHealth enhances diagnostic accuracy, predicts risks, and personalizes treatments. Preliminary results reveal improvements in patient outcomes, reduced diagnostic errors, and streamlined care delivery. With scalable, privacy-focused frameworks, the study highlights the potential of deep learning to revolutionize healthcare, offering transformative solutions for data management and clinical decision-making.
CompSci & AI Advances 1(1), 03-15 (2024)
https://doi.org/10.69626/cai.2024.0003Saranya R., Prabu Thangavel, Ahmed Abdu Alattab
Summary: This paper introduces an Adaptive AI Framework designed to optimize healthcare through real-time data analysis and predictive diagnostics. The system integrates diverse datasets, including EHRs and wearable sensor data, to generate personalized care pathways and enable proactive interventions. Case studies in chronic disease management and predictive modeling demonstrate its efficacy in improving patient outcomes, reducing hospital readmissions, and enhancing care efficiency. By consolidating advanced AI technologies into a dynamic, learning-driven ecosystem, the framework represents a transformative leap in modern healthcare delivery.
CompSci & AI Advances 1(1), 16-26 (2024)
https://doi.org/10.69626/cai.2024.0016Jameer Basha A., S. Jeyabharathi, P. Jayachitra, Anbu Karuppusamy, A. S. Y. Bin-Habtoor, Raziullah Khan
Summary: This study introduces HealthCareAI, a Hybrid Fusion Learn-Enabled Software Product Line for healthcare optimization, integrating Gradient Boosting Machines (GBMs), Convolutional Neural Networks (CNNs), and Reinforcement Learning (RL). By processing structured healthcare data, the framework improves disease diagnosis, treatment planning, and patient risk stratification. GBMs achieve a 15% increase in prediction accuracy compared to traditional models, with a sensitivity of 92% and specificity of 89% for high-risk patient identification. HealthCareAI demonstrates the potential of hybrid machine learning approaches to enhance healthcare efficiency and patient outcomes.
CompSci & AI Advances 1(1), 27-37 (2024)
https://doi.org/10.69626/cai.2024.0027Hakkem B., Mahendrakan K, Prabu S., Reyazur Rashid Irshad
Summary: This study introduces an AI-driven adaptive clustering framework for Wireless Sensor Networks (WSNs) that enhances energy efficiency and extends network lifespan. By integrating machine learning with bio-inspired algorithms like Salp Swarm Optimization (SSO) and Genetic Algorithm (GA), the framework enables dynamic Cluster Head (CH) selection based on real-time network conditions. Compared to traditional protocols such as LEACH and HEED, the method reduces energy consumption by 30%, increases network lifetime by 25%, and improves data throughput by 20%, demonstrating its suitability for energy-critical applications like smart cities and healthcare.
CompSci & AI Advances 1(1), 38-50 (2024)
https://doi.org/10.69626/cai.2024.0038Uma Maheshwari R., Paulchamy B., Kalpana K., Ibrahim M. Alwayle, Raziullah Khan
Summary: This study introduces Intelligent HealthTech, an adaptive learning ecosystem designed to revolutionize healthcare by integrating AI-driven diagnostics, personalized treatment planning, and real-time monitoring. By leveraging machine learning, big data analytics, and continuous feedback loops, the system dynamically tailors care to individual patient needs. Experimental results highlight significant improvements in patient outcomes, resource utilization, and clinical decision-making efficiency. The modular, secure design ensures compatibility with existing infrastructures, making Intelligent HealthTech a scalable solution for modern, data-driven healthcare systems.
CompSci & AI Advances 1(1), 51-63 (2024)
https://doi.org/10.69626/cai.2024.0051Welcome to CompSci & AI Advances, a distinguished journal devoted to the rapid dissemination of high-quality research in the dynamic fields of computer science and artificial intelligence (AI). Our mission is to create a vibrant forum where researchers, scientists, and industry professionals can present their groundbreaking innovations and transformative discoveries.
At CompSci & AI Advances, we recognize the critical role of interdisciplinary collaboration in driving technological progress and addressing complex global challenges. Our journal serves as a nexus for cutting-edge research that bridges diverse domains, including engineering, data science, medicine, and social sciences, providing a valuable reference for the global research community.
By uniting the expertise of computer scientists, AI practitioners, engineers, ethicists, theorists, and technologists, CompSci & AI Advances fosters a collaborative and inclusive environment. With a focus on both fundamental theories and practical applications, the journal explores emerging technologies, innovative methodologies, and transformative applications that shape the future of AI and computational sciences.
We are committed to advancing knowledge in computer science and AI through the publication of original research articles, comprehensive reviews, and insightful case studies. Our goal is to support the global research community in unlocking the potential of AI and computational innovations, contributing to technological advancement and societal benefit.
We warmly invite researchers and scholars worldwide to contribute to CompSci & AI Advances, sharing their expertise and discoveries to propel the fields of computer science and AI forward.
Aims and Scope
CompSci & AI Advances is a multidisciplinary, peer-reviewed journal dedicated to fostering innovation, collaboration, and excellence in the fields of computer science and artificial intelligence (AI). The journal serves as an authoritative platform for disseminating state-of-the-art research, transformative applications, and comprehensive reviews that push the boundaries of knowledge and address real-world challenges. By bridging theoretical advancements and practical implementations, the journal seeks to inspire groundbreaking solutions and interdisciplinary approaches to the most pressing problems in technology and society.
Aim:
The journal aims to bridge the gap between theoretical advancements in computer science and practical applications of artificial intelligence, enabling transformative solutions for complex global challenges. It emphasizes interdisciplinary research and innovative methodologies that drive progress in both fundamental and applied aspects of computing and AI. The journal aims to:
The journal encompasses a comprehensive range of topics, encouraging submissions across diverse areas of computer science and AI, including but not limited to:
CompSci & AI Advances covers a comprehensive range of topics within the domains of Computer Science and Artificial Intelligence (AI), fostering innovation and exploration in both foundational and applied aspects of these disciplines. The journal welcomes contributions across the following areas and beyond:
CompSci & AI Advances encourages submissions that not only deepen understanding in these areas but also explore the interplay between them to solve complex global challenges and drive technological progress.
CompSci & AI Advances attracts a diverse and multidisciplinary readership, reflecting the broad scope and profound impact of computer science and artificial intelligence across various sectors. Our audience includes professionals, researchers, and enthusiasts from fields such as computer science, data science, engineering, information technology, robotics, mathematics, electronics, healthcare, environmental science, social sciences, and more.
Our readership spans academic institutions, research organizations, and industries, demonstrating the interdisciplinary and applied nature of AI and computational sciences. Whether from academia, government, or the private sector, our readers rely on CompSci & AI Advances to stay informed about groundbreaking research, innovative methodologies, and transformative applications. By offering cutting-edge insights and fostering collaboration across disciplines, CompSci & AI Advances serves as an indispensable resource for those shaping the future of technology and its applications.
Prof. Jie Yang
Chongqing Industry and Trade Polytechnic, Chongqing, China.
Email: jieyang@cqgmy.edu.cn
Prof. Nur Alam MD
Department of Artificial Intelligence
Kyungdong University Global, South Korea
Email: na@kduniv.ac.kr
Dr. Israel Pineda, Universidad San Francisco de Quito, Ecuador
Prof. Changjun Gu, Chongqing Post and Telecommunications University, China
Dr. Yali Nie, Mid Sweden University, Sweden
Prof. Qichao Wang, Xi’an International Studies University, China
Prof. S. M. Riazul Islam, University of Aberdeen, United Kingdom
Prof. Liu Xianxian, University of Macau, China
Dr. Jafar Ali Ibrahim Syed Masood, Vellore Institute of Technology, Vellore, Tamilnadu, India
Prof. Muhammad Mansoor Alam, Riphah International University, Islamabad, Pakistan
Dr. Tayara Hilal, Jeonbuk National University, South Korea
Prof. Tan Lijian, Chongqing Institute of Industry and Trade Vocational and Technical College, China
Prof. Wei Xianruo, Chongqing Industry and Trade Polytechnic, China
Dr. S. Prabu, Mahendra Institute of Technology, Namakkal, Tamil Nadu-637503. India
Prof. Li Qing, Chongqing Industry and Trade Polytechnic, China
Dr. Saurabh Singh, Woosong University, South Korea
Prof. Song Qun, Chongqing Technology and Business University, China
Dr. Zubaeer Ibna Mannan, Kyungdong University, South Korea
Dr. Karthikeyan Kaliyaperumal, Department of Information Technology, Ambo University, Ethiopia
Prof. Wang Han, Zhuhai People’s Hospital, China
Dr. Subash Thanappan, Kaaf University College, Ghana
Prof. Li Taifu, Chongqing University of Science and Technology, China
Prof. Rui Huang, Chongqing Electronic Science and Technology Vocational University, China
Dr. Ihsan Ullah, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, South Korea.
Prof. K. Lan, Quzhou College, China
Prof. Rigoberto Fonseca, Yachay Tech University, Ecuador
Prof. Tang Rui, Kunming University of Science and Technology, China
Prof. Md. Manzurul Hasan, American International University, Bangladesh
Prof. Huang Shigao, Xijing Hospital, China
Dr. S. Karthikeyan, K.S.Rangasamy College of Arts and Science, India
Prof. Guoying Zhang, Henan Agricultural University, China
Dr. N H Manzur-E-Maula, Texas Tech University, United States of America
Prof. Su Jiahao, Chongqing Qianyuan Longtai Technology Co., China
Dr. A. Jameer Basha, Hindusthan Institute of Technology, Coimbatore, India.
Prof. Eric Hitimana, UR – College of Science & Technology, Rwanda
Prof. Wei Zhoufei, Chongqing Chang’an New Energy Vehicle Technology Co., China
Dr. G, Selvakumar, Vinayank Missions Research Foundation (Deem to be University), India
Prof. Wen Zeng, Chongqing University, China
Prof. Chala Merga, Addis Ababa Institute of Technology, Ethiopia
Prof. Lin Zhang, Yangtze Normal University, China
Dr. Vetriveeran Rajamani, Vellore Institute of Technology, India
Dr. Jia Uddin, Woosong University, South Korea
Prof. Bo Qin, University of Electronic Science and Technology of China, China
Prof. Sunil Chinnadurai, SRM University, AP, India
Prof. Zenaida Castillo, Yachay Tech University, Ecuador
Prof. Evizal Abdul Kadir, Islamic University of Riau, Indonesia
Prof. Yongbin Gao, Shanghai University of Engineering Science, China
Welcome to the Instructions for Reviewers for CompSci & AI Advances. As a valued reviewer, your expertise and insights play a crucial role in maintaining the quality and integrity of the journal’s publications. Your thorough evaluation and constructive feedback are instrumental in shaping the direction of scientific discourse in the field of Computer Science and Artificial Intelligence (AI). Below are guidelines to assist you in conducting a comprehensive review of manuscripts submitted to CompSci & AI Advances.
Guide to Editors: CompSci & AI Advances
Welcome to the comprehensive Guide to Editors for CompSci & AI Advances. As an editor for our esteemed journal, your pivotal role revolves around ensuring the quality, integrity, and timely dissemination of groundbreaking research within the realm of Computer Science and Artificial Intelligence (AI). This detailed guide is designed to equip you with the necessary instructions and best practices to navigate the editorial process with proficiency and efficacy. Your dedication and commitment as an editor are invaluable to the success and reputation of CompSci & AI Advances.
Editorial Workflow:
Manuscript Handling:
Ethical Considerations:
Collaboration and Communication:
Continuous Improvement:
At CompSci & AI Advances, published by Ariston Publications, we uphold the highest ethical standards in scientific publishing to ensure the integrity, credibility, and trustworthiness of the research we disseminate. Our commitment to ethical practices extends across all stages of the publication process, from manuscript submission to post-publication dissemination. Our publication ethics policies are designed to guide authors, reviewers, editors, and all stakeholders involved in the publishing process. Adherence to these ethical principles is paramount to maintain transparency, fairness, and trust in scholarly communication.
1. Authorship and Author Responsibilities:Authors are expected to adhere to the following ethical principles:
Authorship Criteria:
Originality and Plagiarism:
Conflict of Interest:
Data Integrity:
2. Peer Review Process:
3. Editorial Responsibilities:
Editorial Integrity:
Conflict of Interest:
Editors are responsible for managing conflicts of interest transparently and impartially, ensuring that they do not compromise the integrity of the editorial process.
Transparency:
Editors should ensure transparency in the publication process by clearly communicating the editorial policies, peer review process, and any conflicts of interest.
4. Post-Publication Concerns:
Corrections and Retractions:
Ethical Concerns:
Any concerns about ethical issues, such as research misconduct or violations of publication ethics, will be thoroughly investigated by the journal and appropriate actions will be taken.
5. Compliance with Policies and Guidelines:
All stakeholders are expected to comply with the journal’s policies, guidelines, and ethical standards, as well as relevant regulatory requirements and best practices in scholarly publishing.
CompSci & AI Advances, while currently not indexed, is actively working towards being indexed in prominent databases and directories relevant to materials science and related fields. Our aim is to ensure that the valuable research published in CompSci & AI Advances reaches a wide audience of scholars, researchers, and practitioners in the field. We are in the process of applying for indexing in key databases and directories to enhance the visibility and discoverability of articles published in our journal. Stay tuned for updates as we progress in our efforts to expand the indexing coverage of CompSci & AI Advances, thereby increasing its impact and reach within the scientific community.
At present, there are no article processing charges (APCs) associated with publishing in CompSci & AI Advances. As an open-access journal, all articles are published free of cost to authors. The publisher covers the expenses incurred in the publication process, allowing authors to disseminate their research without any financial burden. There are no fees for submission, processing, or publication of articles in CompSci & AI Advances. This approach ensures equitable access to scientific knowledge and supports the dissemination of research findings across the global scientific community.
CompSci & AI Advances welcomes proposals for special issues that align with the journal’s aims, scope and objectives. Special issues provide an opportunity to delve into specific topics or emerging areas within Computer Science and Artificial Intelligence (AI) and related fields, offering a focused platform for in-depth exploration and discussion.
If you have a proposal for a special issue, please submit it to the editorial office for consideration. Your proposal should include a brief outline of the proposed topic, its significance and relevance to the field, potential contributors, and a proposed timeline for publication.
Once your proposal is received, it will undergo careful evaluation by the editorial team to assess its suitability for publication in CompSci & AI Advances. If approved, you will be invited to serve as a guest editor or co-editor for the special issue, working closely with the editorial team to oversee the review and publication process.
We look forward to receiving your proposals and collaborating with you to bring forth exciting and impactful special issues for our readership.
Please submit the special issue proposal at: info@aristonpubs.com
CompSci & AI Advances welcomes the opportunity to collaborate with organizers of conferences, symposiums, and workshops to publish special issues or proceedings featuring research articles presented at these events.
If you are organizing a conference or similar academic gathering and wish to publish selected research papers in CompSci & AI Advances, we encourage you to reach out to our editorial office with your proposal. Your proposal should include details such as the theme and scope of the conference, the number of anticipated submissions, and a proposed timeline for publication.
Upon receiving your proposal, our editorial team will review it carefully to assess its alignment with the journal’s scope and objectives. If approved, we will work closely with you to facilitate the submission and review process for the conference papers, ensuring timely publication in a dedicated special issue or proceeding.
By publishing conference-related research in CompSci & AI Advances, authors can benefit from the journal’s wide readership and open access model, maximizing the visibility and impact of their work within the Computer Science and Artificial Intelligence (AI) community. We look forward to the opportunity to collaborate with you on showcasing cutting-edge research from your conference in our journal.
M. Senthil, G. Navaneetha Krishnan, P. Anitha, S. K. Heena, T. Jaya Sri
Summary: The proposed IoT-based smart home automation system offers an affordable and user-friendly platform for controlling household appliances. With Wi-Fi connectivity, voice commands, activity sensors, and a mobile app, the system supports seamless device integration and future scalability via an SDK. Security is reinforced using 256-bit AES encryption, while energy efficiency is achieved through features like automated device shutdown and real-time energy monitoring. These functionalities make the system a comprehensive solution for enhancing convenience, security, and sustainability in modern living spaces.
CompSci & AI Advances 1(4), 158-167 (2024)
https://doi.org/10.69626/cai.2024.0158