Production of Color Images From the Sketches Using Generative Adversarial Networks Saravanan Raju, P Vijayalakshmi, Kavin Francis Xavier, S. Soundararajan, B. Selvalakshmi, M. Rehena Sulthana, C. Christina Angelin, M. Shagar Banu Multidisciplinary Advancements in Human AI Augmentation, 2025 This chapter discusses the application of contingent adversarial networks (CANs) as among the better image-to-image translation task approaches. Traditional models are interested in input image maps to output images with a specific focus on applying attention to this process; CANs involve the application of a loss function as a direction guide toward carrying out the mapping. This universal loss function enables the model to learn and translate better and applies to various tasks that otherwise would need its loss function to be built. The method demonstrates the generality of CANs in performing tasks such as image generation from label maps, object reconstruction from terrain maps, and colorizing grayscale images. One of the strengths of this method is that the network is free from hand-designed feature mapping. This eliminates the need for domain knowledge in designing features and allows the model to produce highly good results. The network can produce acceptable results with little human intervention by eliminating the need for hand-crafted loss functions. This ability provides CANs with wide generalizability to image translation tasks with efficiency and flexibility at no performance cost and with a heavy tailoring burden.
Vision-Based Image Classification and Image Segmentation Algorithms for Plant Disease Diagnostics N. Ashokkumar, A. Manikandan, S. Hariprasath, P. Vijayalakshmi Computer Vision in Smart Agriculture and Crop Management, 2025 One of the main sources of income is agriculture. Many peasants in developing nations like India find work in agriculture. Many crops are grown in India's agriculture, which provides a living for about 70% of the country's people, according to a study. Indian farmers primarily employ manual planting due to outdated technologies. What crops will grow on a farmer's property is unknown. Exotic plant diseases that harm plants through their leaves have an impact on agricultural productivity and decrease in profitability. Moreover, it lowers the level and quality of agricultural output. For accelerated plant development and increased agricultural yields, leaves are crucial. Farmers and academics have had trouble detecting illnesses on plant leaves. Nowadays, farmers apply pesticides on plants that may have a negative impact on human health and economy. These plant diseases need to be found quickly, using a variety of ways. A variety of plant diseases are covered in this article, as well as cutting-edge methods for finding them.
Integrating Artificial Intelligence and Machine Learning for Enhanced Cyber Security in Industry 4.0: Designing a Smart Factory with IoT and CPS Kavin Francis Xavier, K. Subashini, P. Vijayalakshmi, B. Selvalakshmi, G. Sudhakar, K. Pradeepa Artificial Intelligence and Machine Learning for Industry 4 0, 2025 As a result of Industry 4.0, Fourth Industrial Revolution companies are now transforming into intelligent cyber-physical production systems (CPPSs) with complete supply-chain interconnectivity between people, products, and machines. Although streamlined, efficient, and rapid manufacturing are some of the many benefits of this digitalization. It also introduces several innovative methods of attack that endanger people's lives, manufacturing processes, and company services. In the framework of Industry 4.0, this article explores the detailed review of the integration of artificial intelligence (AI) with machine learning (ML) to strengthen cyber security precautions. The important objective is to construct a smart factory by integrating Internet of things (IoT) and cyber physical systems (CPS). Cyber risks, especially DDoS assaults, pose a major threat to the operational integrity of Industry 4.0 installations in the modern world. Intrusion detection systems (IDS) can successfully counteract such risks by utilizing ML and AI capabilities. The article addresses the process of merging the fundamental elements of an established industry to establish a smart production framework. An investigation into smart production using a drilling procedure is presented as an example of a simplified smart factory framework, and the investigation of the proposed model is discussed and confirmed through testing.
Enhanced Security Framework with Blockchain for Industry 4.0 Cyber-Physical Systems, Exploring IoT Integration Challenges and Applications P. Vijayalakshmi, B. Selvalakshmi, K. Subashini, G. Sudhakar, Kavin Francis Xavier, K. Pradeepa Artificial Intelligence and Machine Learning for Industry 4 0, 2025 The idea of “Industry 4.0” is revolutionary and has far-reaching effects on many different industries around the globe. Industry 4.0 uses new-generation, state-of-the-art engineering technologies and methodical adaptability to drive development as a technological accelerator. The Internet of Things (IoT), blockchain, Artificial Intelligence (AI), Augmented Reality (AR), printing in three dimensions, cloud computing and big data analytics are some of the key innovations that collaborate to realize the Industry 4.0 vision. A Cyber-Physical System (CPS) allows the exchange of information between physical things and a linked network of technologies and objects by combining computational and storage features with the physical objects. A novel distributed computing paradigm called blockchain offers a viable remedy for contemporary CPS applications. Strong options for CPS applications are offered by the natural combination of distributed memory and consensus techniques with cutting-edge security mechanisms. Blockchains in CPSs/IoT provide safe and preserved data for many industrial purposes and accomplish flexibility, procedure, and operational security, for instance, in activities related to production, logistics, medical care, and power.
Enhancing e-commerce data privacy in India's rapidly evolving cybersecurity landscape through AI-driven intrusion detection systems B. Selvalakshmi, G. Sudhakar, Anand Anbalagan, K. Subashini, P. Vijayalakshmi, F. Kavin Strategic Innovations of AI and ml for E Commerce Data Security, 2024 The rise of sophisticated cyber threats poses significant challenges to the safety and integrity of goods and services in today's interconnected digital environment. This paper introduces “Secure by Intelligence,” a paradigm that utilizes Artificial Intelligence (AI) to enhance security and mitigate risks. India's rapidly growing e-commerce sector is transforming business practices, with AI playing a crucial role in its technological advancements. As e-commerce applications increase in complexity, maintaining usability becomes more challenging. This paper proposes integrating AI with Intrusion Detection Systems (IDS) to ensure data safety in e-commerce. By employing advanced machine learning techniques like deep learning and anomaly detection, AI-driven IDS can strengthen the resilience of e-commerce systems against cyber threats. The study examines the architecture and advantages of these systems while addressing implementation challenges. Ultimately, integrating AI-driven IDS will enhance consumer trust, protect sensitive data, and ensure the reliability of e-commerce ecosystems.
Strategic integration of machine learning for fraud detection in e-commerce transactions P. Vijayalakshmi, K. Subashini, B. Selvalakshmi, G. Sudhakar, Anand Anbalagan, N. Bharathiraja, Gaganpreet Kaur Strategic Innovations of AI and ml for E Commerce Data Security, 2024 The rise in internet users has led to an increase in online payments, but this also comes with a surge in online fraud. To combat this, e-commerce firms must adopt device intelligence for fraud detection. Machine learning (ML) is crucial for analyzing large datasets to identify suspicious patterns. This study explores the effective application of ML in detecting fraudulent activities, focusing on various approaches, challenges, and recommendations. It starts with an overview of the prevalence and impact of e-commerce fraud, highlighting the need for robust detection systems. Key ML techniques, including supervised, unsupervised, and semi-supervised learning, are analyzed for their strengths and weaknesses. It emphasizes the importance of continuous monitoring and model adaptation to evolving fraud tactics, advocating for dynamic updates and feedback loops to enhance detection systems. By integrating ML algorithms effectively, e-commerce businesses can improve security, safeguard revenues, and build trust with consumers and partners.
Reviewing Facial Insight - A Smart Approach to Identity Unveiling Through Active Facial Patches and Multi-Task Cascaded Convolutional Networks Kavin F, Pradeepa K, Selvalakshmi B, Anitha P, Subashini K, Vijayalakshmi P 5th International Conference on Sustainable Communication Networks and Application Icscna 2024 Proceedings, 2024 This research work proposes an innovative method for enhancing facial recognition accuracy and efficiency by leveraging active facial patches and a Multi-Task Cascaded Convolutional Network (MTCNN). The review assesses the paper's contributions, methodology, experimental results, and potential impact on advancing facial recognition technology. It highlights the strengths of the proposed approach, including its clear problem statement, thorough methodology, compelling experimental results, and potential for real-world application. Additionally, the review offers recommendations for improvement, such as further exploration of potential biases and scalability considerations. Overall, Facial Insight presents a promising advancement in identity unveiling within facial recognition systems, contributing to the broader field of biometrics and computer vision.
WSN-ENHANCED AIR POLLUTION MONITORING AND MITIGATION FOR URBAN ENVIRONMENTAL SUSTAINABILITY Journal of Environmental Protection and Ecology, 2024
A Novel Hybridclustering Model Forwireless Sensor Networks P. Vijayalakshmi, M. Saravanan, M. Thillai Rani, M. Ashok, Ramya Palaniappan, V. Nagaraj Proceedings of the International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering Iceconf 2023, 2023
EASS: Encryption and authentication based security scheme to prevent power exhausting attacks in wireless sensor networks Ad Hoc and Sensor Wireless Networks, 2019
