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.

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 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.

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.

Proposes computational models of human memory and learning using a brain-computer interfacing (BCI) approach

Human memory modeling is important from two perspectives. First, the precise fitting of the model to an individual''s short-term or working memory may help in predicting memory performance of the subject in future. Second, memory models provide a biological insight to the encoding and recall mechanisms undertaken by the neurons present in active brain lobes, participating in the memorization process. This book models human memory from a cognitive standpoint by utilizing brain activations acquired from the cortex by electroencephalographic (EEG) and functional near-infrared-spectroscopic (f-NIRs) means.

Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach begins with an overview of the early models of memory. The authors then propose a simplistic model of Working Memory (WM) built with fuzzy Hebbian learning. A second perspective of memory models is concerned with Short-Term Memory (STM)-modeling in the context of 2-dimensional object-shape reconstruction from visually examined memorized instances. A third model assesses the subjective motor learning skill in driving from erroneous motor actions. Other models introduce a novel strategy of designing a two-layered deep Long Short-Term Memory (LSTM) classifier network and also deal with cognitive load assessment in motor learning tasks associated with driving. The book ends with concluding remarks based on principles and experimental results acquired in previous chapters.

Cognitive Modeling of Human Memory and Learning A Non-invasive Brain-Computer Interfacing Approach will appeal to researchers in cognitive neuro-science and human/brain-computer interfaces. It is also beneficial to graduate students of computer science/electrical/electronic engineering.