Lidia Ghosh – författare

Artificial Intelligence and Animal Ecology: A Review explores the transformative synergy between AI and animal ecology, unveiling how cutting-edge technology is revolutionizing ecological research and conservation. This pioneering book bridges these dynamic fields, demonstrating how AI techniques—such as evolutionary algorithms and optimization methods—both draw inspiration from and advance the study of animal behavior, species interactions, and environmental adaptation. With a strong focus on innovation, it examines groundbreaking AI applications, from bio-inspired algorithms and adaptive learning to breakthroughs in animal communication and behavioral analysis. Readers will gain valuable insights into how AI deciphers complex ecological dynamics, including navigation, vocal communication, and interspecies relationships. The book also addresses ethical considerations, ensuring responsible AI integration in ecological research.More than just a review, this book is a call to action. It empowers researchers, conservationists, and ecologists to embrace AI-driven solutions, fostering interdisciplinary collaboration and expanding the frontiers of ecological knowledge. As AI continues to evolve, Artificial Intelligence and Animal Ecology: A Review provides a vital roadmap for addressing environmental challenges with innovation and a deeper appreciation of the natural world.

The rapid technological advancements in the healthcare industry over recent decades have been transformative. These innovations have not only enhanced our understanding of the morphology and physiology of various organs but have also significantly improved the early diagnosis and treatment of numerous diseases across different medical specialties. This progress has been largely driven by advancements in artificial intelligence (AI) and computer vision (CV). AI and CV enable the real-time collection, processing, interpretation, and analysis of vast amounts of static and dynamic medical data, revolutionizing disease characterization and patient selection. Early detection is crucial in treating life-threatening illnesses such as COVID-19, pneumonia, and cancer. Computer-based medical imaging techniques, including CT scans and X-rays, play a vital role in diagnosing these conditions. Similarly, biological signals like electroencephalography (EEG) and electrocardiography (ECG) help anticipate brain anomalies and heart diseases. Machine learning further enhances the accuracy of disease prediction, assisting clinicians in making precise diagnoses. By facilitating faster disease recognition, these technologies also enable wider access to healthcare, including remote and underserved areas. This book aims to develop machine learning algorithms that analyze diverse medical data and predict diseases based on their characteristics, ultimately advancing healthcare diagnostics and treatment strategies.

The use of intelligent technologies to enhance instruction and learning is introduced in pedagogy-based learning-teaching perspective. It covers digital library resources, AI-based tools, data analysis techniques, and NLP and NLU-powered smart assistants. Students will realize their improved efficacy through use of expandable AI systems improve educational efficiency, automate repetitive chores, and enable personalized learning. The course offers useful skills for implementing contemporary AI methods in educational institutions, classrooms, and online learning settings.This book provides concise summary of forthcoming Intelligent Tools and Techniques that are using AI-based Learning-Teaching systems to shape contemporary education. It describes how NLP and NLU applications enhance intelligent teaching assistants, showcases sophisticated library resources for promoting informal learning. The book delivers a succinct but thorough approach for implementing scalable, effective, intelligent solutions that improve learning environments across a variety of educational settings through focused insights into educational data analysis and frameworks for expandable AI.Teachers, researchers, and students who wish to apply intelligent technology in the classroom are the target audience for this book. It works well for developers making intelligent learning tools, librarians overseeing digital resources, and educators investigating AI-based approaches. The book provides clear instructions on using AI, data analysis, and intelligent systems to enhance teaching, learning, and educational resource management, which will be beneficial to academic institutions, policymakers, and EdTech experts.Key features:Contains applications of machine learning in performance analysis of students, which is helpful in designing rubrics for accreditation.Deals with comparative study about outcome-based education and conventional educational system through application of statistical techniques.Analyses role of emotional intelligence in measuring holistic performance of studentsEvaluates different pedagogical approaches like active, authenticate, flipped, blended learning using neural network approaches.Proposes different mathematical models for implementation of OBE for technical Institutions.

The technological advancements made in recent decades have not only helped us better comprehend the morphology and physiology of the organs of the human body, but they have also advanced the diagnosis and, therefore, the treatment of a number of diseases in a variety of medical specialties from very early stages. Artificial Intelligence (AI) and Computer Vision (CV) enable us to collect, process, interpret, and analyze a limitless quantity of static and dynamic medical data in real time, which improve the way each disease is characterized and the patients are chosen. Many potentially fatal illnesses, such as COVID-19, pneumonia, and cancer, can be cured if diagnosed in initial stages very early on. Computer-based medical imaging techniques, such as CT scan and X-rays are useful in detecting all of these illnesses. On the other hand, various brain anomalies and heart diseases can also be anticipated using biological signals, like electroencephalography (EEG), electrocardiogram (ECG) etc. The application of machine learning makes the predictions more accurate and help the clinician to detect appropriate one. This helps in faster recognition of disease as well as with the intervention of the technology, makes it feasible to spread to the remote places. The goal of the book is to create machine learning algorithms that aids in the analysis of diverse medical data and the prediction of diseases based on the characteristics of the data.