This book, “Mastering Lecturette, GD & WAT: Essential Techniques for Persuasive Articulation,” is a comprehensive guide aimed at empowering aspirants preparing for the Services Selection Board (SSB) and similar high-stakes assessments. It offers structured techniques and strategies to master critical communication aspects, including Lecturettes, Group Discussions (GD), and the Word Association Test (WAT). The chapters are meticulously organized to address key areas such as impactful openings, motivational transitions, thought fillers, vocabulary enhancement, and effective conclusions. Additionally, the book provides insights into reframing negative words constructively and maintaining composure under pressure. With practical examples, contextual relevance, and actionable tips, this guide equips readers to demonstrate leadership, emotional resilience, and clarity of thought. Designed to inspire confidence and refine articulation skills, it is an indispensable resource for anyone aiming to excel in high-pressure evaluation scenarios.

Keywords: Lecturette, Group Discussion, Word Association Test, SSB preparation, public speaking, articulation skills, leadership qualities, thought fillers, transitions, impactful communication.

Book Citation: Magesh, S., & Martina, E. F. (2025). Mastering Lecturette GD and WAT : Essential Techniques for Persuasive Articulation (1st ed.). Jupiter Publications Consortium, Chennai, Tamil Nadu, India. ISBN: 978-93-86388-89-6, DOI: www.doi.org/10.47715/978-93-86388-89-6

The evolution of search engines from simple keyword-based systems to more sophisticated semantic-based models marks a significant advancement in the field of information retrieval. This monograph presents a comprehensive study on the development of a Semantic-Based Pattern Search Engine, designed to enhance the accuracy and relevance of search results by understanding the contextual and semantic relationships within data. The research encompasses the creation of various engines, including a concept search engine, topic search engine, affective term detection engine, and emotion prediction engine. Additionally, the application of these technologies in online opinion mining is explored, demonstrating their practical value. The findings of this research contribute to the broader understanding of semantic search and offer valuable insights for future advancements in the domain.

Keywords: Semantic-Based Pattern Search Engine, Concept Search Engine, Topic Search Engine, Affective Term Detection, Emotion Prediction, Online Opinion Mining, Information Retrieval, Semantic Search, Contextual Search

How to Cite:

Ajitha, P. (2024). SEMANTIC BASED PATTERN SEARCH ENGINE (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-47-6, DOI: www.doi.org/10.47715/978-93-86388-47-6

The integration of the Internet of Things (IoT) into healthcare has led to significant advancements in patient monitoring and care through Wireless Body Area Networks (WBANs). However, these innovations also present substantial challenges in ensuring the security and privacy of sensitive medical data. This research explores the various security threats and vulnerabilities associated with WBANs and IoT-based healthcare systems, proposing novel solutions to enhance data protection. The study introduces the ECG Hummingbird Algorithm, designed for secure data transmission in WBANs, and the Modified Feather Lightweight Block (MFLB) Cipher, which offers a lightweight encryption solution suitable for resource-constrained IoT devices. Experimental results demonstrate the effectiveness of these algorithms in mitigating security risks while maintaining system efficiency. This work contributes to the growing field of cybersecurity in healthcare,
offering practical approaches to safeguarding patient information in an increasingly interconnected world.

Keywords: Internet of Things, IoT, healthcare, Wireless Body Area Networks, WBAN, security, privacy, ECG Hummingbird Algorithm,
Modified Feather Lightweight Block Cipher, encryption, data protection.

How to Cite this Monograph:

Sivasangari, A. (2024). Security and Privacy in IoT based Healthcare (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-62-9, DOI: www.doi.org/10.47715/978-93-86388-62-9

The monograph “Optimizing Routing Protocols for Energy Conservation in UWSN” explores advanced strategies to address the critical issue of energy efficiency in Underwater Wireless Sensor Networks (UWSNs). These networks have significant potential in various applications, such as aquatic life management, pollution monitoring, and coastal surveillance, but are often limited by high energy consumption. This work proposes innovative routing protocols that aim to conserve energy, extend the operational lifetime of the network, and enhance its reliability. The research encompasses a detailed study of UWSN architecture, acoustic propagation, and specialized routing protocols. The proposed energy-efficient strategies are thoroughly analyzed and validated through extensive experimentation, providing practical solutions and valuable insights for researchers and practitioners. The findings of this monograph contribute to the advancement of sustainable and efficient UWSNs, offering a robust framework for future developments in this field.

Keywords: UWSN, energy efficiency, routing protocols, underwater communication, acoustic propagation, network lifetime, environmental monitoring, sustainable networks

How to cite this Monograph:

Gomathi, R. M. (2024). OPTIMIZING ROUTING PROTOCOLS FOR ENERGY CONSERVATION IN UWSN (1st ed.). Jupiter Publications Consortium. ISBN: 978-93-86388-77-3, DOI: www.doi.org/10.47715/978-93-86388-77-3

This book provides an in-depth exploration of computational data analytics within the broader context of data science. It covers the fundamental concepts, methodologies, and tools that define the field, while also delving into advanced statistical and machine learning techniques tailored for large datasets. The text is structured to facilitate understanding of both theoretical principles and practical applications, bridging the gap between data analysis and real-world challenges. Ethical considerations, privacy, and data governance are emphasized to ensure readers are aware of the responsibilities that come with handling data. Each chapter is enriched with case studies that illustrate the application of computational data analytics in various domains such as healthcare, finance, and environmental studies. The book concludes with a forward-looking discussion on the future of data analytics, highlighting emerging trends and technologies.

Keywords: computational data analytics, data science, machine learning, statistical methods, big data, ethical considerations, real-world applications

Keywords: cybersecurity, artificial intelligence, machine learning, threat intelligence, malware detection, network security, incident response, security analytics, compliance, application security, cloud security.

“AI and Healthcare: Monograph” presents an in-depth analysis of the integration of Artificial Intelligence (AI) into the healthcare sector. This book offers a comprehensive examination of AI’s role in revolutionizing various aspects of healthcare, from diagnostics and treatment to patient care and medical research. It begins by establishing a foundational understanding of AI and healthcare, including historical perspectives and future trends. The book then delves into the technical aspects of AI, such as machine learning and natural language processing, and their applications in healthcare. Special emphasis is given to personalized medicine, showcasing how AI contributes to advancements in genomic research and tailored treatments. Ethical, privacy, and regulatory considerations form a critical part of the discussion, highlighting the challenges and responsibilities of integrating AI in sensitive and impactful healthcare settings. The book also addresses the limitations and challenges of AI in healthcare, providing a balanced view of its potential and pitfalls. Finally, it looks ahead to the future of AI in healthcare, considering its role in upcoming health crises and its impact on healthcare providers, patients, and policy.

Keywords: AI, Healthcare, Machine Learning, Personalized Medicine, Ethical Considerations, Privacy, Future Trends, Diagnostics, Treatment, Medical Research

The convergence of deep learning and healthcare marks a pivotal moment in the evolution of both fields. “Deep Learning in Healthcare” is a comprehensive exploration of the profound impact of artificial intelligence (AI) and deep learning on healthcare. This monograph delves into fundamental deep learning concepts, practical tools and frameworks, and transformative applications across healthcare domains. From predictive models aiding in disease detection to precise medical image analysis, clinical text data processing, and the integration of wearable devices for remote monitoring, deep learning has revolutionized every facet of healthcare.
However, this transformation is accompanied by challenges related to ethics, privacy, and regulation. Ethical considerations, privacy concerns, and regulatory frameworks are integral to AI and healthcare. Real-world case studies within this monograph provide insights into successful implementations and lessons learned from failed projects, offering guidance, best practices, and recommendations.
In the concluding chapter, we contemplate the road ahead. The future promises boundless innovation, where AI and deep learning continue to enhance medical practice and global well-being. Collaboration, dedication, and a commitment to ethical principles will shape a future where deep learning elevates healthcare.

Keywords: Deep Learning, Healthcare, Artificial Intelligence, Predictive Models, Medical Imaging, Clinical Data, Wearable Devices, Ethical Considerations, Privacy, Regulation, Real-World Applications, Innovation, Collaboration.

This monograph provides a comprehensive exploration of deep learning, a transformative technology in the field of artificial intelligence. Beginning with an introduction to the background of machine learning and the rise of deep learning, the text delves into the historical evolution of artificial intelligence and neural networks. It offers a detailed examination of the foundations of deep learning, including neural network basics, architecture, activation functions, and essential techniques like backpropagation and gradient descent. The work then progresses to advanced neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms, including transformers. A significant focus is placed on practical aspects, like training deep models, regularization techniques, optimization strategies, and the utilization of transfer learning with pre-trained models. The monograph also explores the diverse applications of deep learning in fields such as image recognition, natural language processing, and autonomous systems, while addressing the challenges of overfitting, interpretability, and ethical considerations. Looking forward, it examines emerging trends like quantum neural networks, neural architecture search, and the intersection of neuroscience and deep learning. The text concludes with insightful case studies, reflecting on real-world implementations and milestones in deep learning. This work serves as a valuable resource for understanding the current state of deep learning, its practical applications, and future directions.

Keywords: Deep Learning, Machine Learning, Artificial Intelligence, Neural Networks, Backpropagation, Gradient Descent, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Training Models, Regularization, Optimization, Transfer Learning, Image Recognition, Natural Language Processing, Autonomous Systems, Overfitting, Interpretability, Ethical Considerations, Quantum Neural Networks, Neural Architecture Search, Neuroscience.