Browse Items (14421 total)
Sort by:
-
Integrated Holistic Mental Healthcare
Integrated Holistic Mental Healthcare: Bridging Minds and Technology is a groundbreaking resource that unites traditional holistic practices with cutting-edge digital innovations to enhance mental health treatment. This comprehensive guide is tailored for mental health professionals seeking practical strategies to improve care efficacy and accessibility. The book begins with foundational concepts of holistic mental health and emphasizes the essential role of mindfulness and meditation as core components of effective care. It explores how technology can complement holistic practices, facilitating behavioral change and habit formation. Readers will discover how digital tools can expand access to mental healthcare through telehealth and virtual care models, while also addressing ethical considerations and stigma associated with mental health treatment. With insights into monitoring outcomes and case studies highlighting integrative approaches for specialized populations, this book equips readers to navigate the evolving mental health landscape. As it looks toward future trends, "Integrated Holistic Mental Healthcare" empowers individuals to enhance their well-being in a rapidly digitizing world, fostering a patient-centered care paradigm that honors both mind and technology. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Research methodologies and practical applications in psychoneuroimmunology
Research methodologies in psychoneuroimmunology (PNI) are diverse, incorporating a blend of experimental, clinical, and observational approaches to study the complex mechanisms underlying the brain-immune relationship. Techniques range from molecular and genetic analyses to neuroimaging, psychophysiological assessments, and behavioral interventions. The practical applications of PNI impact areas like stress management, mental health treatment, chronic disease prevention, and immune system functioning. By examining how psychological factors, such as stress and emotions, can affect immune responses and overall well-being, PNI offers valuable insights into personalized healthcare and the development of therapeutic strategies for holistic treatment. Research Methodologies and Practical Applications in Psychoneuroimmunology explores PNI, the interactions between behavior, the nervous system, the endocrine system, and the immune system. It examines theoretical frameworks, research methodologies, and practical applications within the field, offering insights into the mechanisms underlying health and disease. This book covers topics such as immunology, cognitive function, and neuroscience, and is a useful resource for psychologists, medical professionals, policymakers, healthcare workers, scientists, academicians, and researchers. 2025 by IGI Global Scientific Publishing. All rights reserved. -
NEUROSCIENTIFIC METHODS IN PRACTICE: Applications in Clinical Neuropsychology and Neuro-Forensic Psychology
This book presents an in-depth exploration of the convergence of neuroscience with clinical psychology, clinical neuropsychology, and forensic psychology, examining advanced methodologies, practical applications, and real-world case studies. K. Jayasankara Reddy provides a thorough examination of state-of-the-art neuroscientific methods and the revolutionary effects on both diagnosis and forensic inquiry. Reddy highlights the transformative impact of neuroimaging, neurophysiology, neuroelectrophysiology, and genetic analysis on our comprehension of brain function and behavior, using compelling case examples and empirical evidence. This book not only discusses methods but also critically examines ethical difficulties, merits, and challenges of the techniques, as well as the legal ramifications that may arise from the use of neuroscientific evidence in clinical and forensic settings. This book also highlights the need for a sophisticated comprehension of privacy issues, patient self-governance, and the use of neurobiological information within legal structures. Overall, it provides readers with the tools to negotiate complicated ethical landscapes while responsibly utilizing neuroscientific discoveries, advocating for a balanced approach that combines scientific rigor and ethical responsibility. This volume is an important resource for students, researchers, and practitioners of clinical neuropsychology, forensic psychology, and neuroscience. 2026 K. Jayasankara Reddy. -
The Science and Clinical Practice of Neuropsychoanalysis: Unlocking the Mind
In The Science and Clinical Practice of Neuropsychoanalysis, K. Jayasankara Reddy integrates contemporary findings from neuroscience with key psychoanalytic theories, providing readers with a comprehensive perspective on human behavior. Reddy explores essential themes in neuropsychoanalysis, including memory, emotion, trauma and self-identity. Throughout the book, he examines how these insights can help clinical practice, offering specific instances of how a neuropsychoanalytic approach can improve treatment methods for psychopathologies such as depression, anxiety and personality disorders. Reddy includes case studies and deep theoretical analysis to share profound insights into the unconscious mind. Employing a balanced methodology that blends the rigor of neuroscience and the profundity of psychoanalysis, The Science and Clinical Practice of Neuropsychoanalysis is a seminal work for comprehending the brain and the unconscious mind concurrently. This book is an essential resource for psychoanalysts and other mental health practitioners, as well as researchers interested in the intricate workings of the human psyche. 2026 K. Jayasankara Reddy. -
THE ROUTLEDGE INTERNATIONAL HANDBOOK OF NEUROCOGNITIVE REHABILITATION: Practices, Innovations, and Future Directions
The Routledge International Handbook of Neurocognitive Rehabilitation is a comprehensive resource highlighting the rapid advancements in neurocognitive science and their application in rehabilitation practices. Bringing together perspectives from top authorities in neuropsychology, neurology, neurosurgery, and neuropsychiatry, it explores cutting-edge techniques and resources including virtual reality (VR), augmented reality (AR), machine learning (ML), and artificial intelligence (AI) that are revolutionizing the area. By combining scientific rigor with accessibility, the book closes the gap between state-of-the-art technology and conventional rehabilitation techniques, giving readers academic and practical expertise. Sections focus on the foundations of neurocognitive rehabilitation, technology-driven innovations, clinical applications, and ethical, social, and practical considerations. Case studies and qualitative accounts are integrated into the chapters to illustrate the impact of neurocognitive rehabilitation techniques on patient outcomes, opening up new avenues for individualized and successful therapeutic approaches. It is an essential reference for students, researchers, and professionals to leverage emerging technologies for improving patient outcomes and advancing the discipline of neurocognitive rehabilitation. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
The Road Ahead: Charting Future Research Directions in Computational Intelligence
The synergy between neuroscience and computational intelligence fosters a dynamic exchange that significantly advances both fields. At its core, this convergence focuses on emulating the brains intricate mechanisms to inspire and enhance machine learning (ML) models. Neural networks (NNs), foundational to computational intelligence, are modeled after biological neurons and drive artificial neural networks (ANNs) that excel in tasks simulating human cognition. Neuromorphic computing furthers this concept by designing hardware and software with braininspired architectures, enabling energy-efficient AI systems. A notable breakthrough is brain-computer interfaces (BCIs), which translate neural signals into actionable commands, offering transformative solutions for individuals with paralysis. Additionally, cognitive computing leverages neuroscience insights to emulate higher-order mental processes, enabling the development of intelligent, context-aware systems. Conversely, ML algorithms, especially in pattern recognition, empower neuroscience by analyzing large-scale brain imaging data to uncover hidden patterns and correlations. This bidirectional interaction accelerates discoveries in neurology and psychology while deepening our understanding of brain function. Together, neuroscience and computational intelligence form a powerful alliance, shaping the future of intelligent technologies and brain science. 2026 by Apple Academic Press, Inc. -
Biowaste-derived hierarchical activated porous carbon with heteroatom-doping (N/S) for efficient symmetrical supercapacitors: A cow urine approach
The continuous accumulation of biowaste in the environment over extended periods can pose considerable ecological challenges. Hence, the conversion of natural biowaste into value-added products is essential. In this study, for the first time, carbon materials derived from cow urine, an animal waste, were explored as potential electrode materials for supercapacitors (SCs). Hierarchical, highly porous carbonaceous materials containing heteroatoms such as N and S were synthesized using a simple, template-free pyrolysis method, involving the direct carbonization of cow urine as a single precursor at 700 C (CCUR-700) and pre-KOH activation of the resulting cow urine deposit pyrolyzed at 700 C (A-CCUR-700) with a removal of inherent mineral salts. The resulting porous carbon materials were then employed as electrode materials for SC applications. The A-CCUR-700 electrode, with its abundant surface functionalities, high specific surface area (2651.7 m2/g), high porosity, good conductivity, and self-doped heteroatoms (N and S), demonstrated better charge storage performance compared to the CCUR-700 electrode. Notably, a two-electrode symmetric SC assembled using the A-CCUR-700 electrode demonstrated an excellent specific capacitance of 165 F/g at a current density of 0.5 A g?1. Furthermore, the A-CCUR-700 symmetric SC device achieved a high energy and power density of 22.9 Wh/kg and 5100 W/kg, respectively, with a capacitance retention of 95.3 % over 5000 cycles. Overall, the results of this study suggest that the synthesis of functionalized carbonaceous materials from cow urine may open up new possibilities for producing inexpensive electrode materials for electrochemical value-added applications. 2025 -
Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN
The Wireless Sensor Network (WSN) is a network of numerous devices that are interconnected via the internet and significantly impact the network. However, despite their significant applications WSNs face challenges related to network security energy levels and information transmission delays. To address these challenges, a method utilizing Multi-Objective Deep Reinforcement Learning (DRL) has been proposed. The proposed method aims to maximize energy utilization in the network by efficiently managing covered and uncovered cluster network routing. The performance of energy transmission is enhanced through the use of the Markov Decision Process model based on multi-objective DRL combined with training the network using Deep Q Network (DQN) to reduce network energy consumption. Training the network with multiple objectives may pose challenges requiring more samples and leading to higher sample complexity, which can be a limiting factor in real-world applications. Despite this, the proposed multi-objective DRL method demonstrates high performance compared to existing methods such as Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN). Specifically, multi-Objective DRL method yields superior results, achieving an energy consumption of 42J, Packet Delivery Ratio (PDR) of 90%, and an End-To-End Delay (ETED) of 45 S. These outcomes outperform existing methods in the context of WSNs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Fault-Tolerant Strategies and Federated Learning for Resilient Edge Computing: A Comprehensive Survey
Edge computing has emerged as a key technology for enabling real-time data processing in various applications such as Industrial Internet of Things (IIoT), smart cities, and autonomous systems. However, the distributed nature of edge computing makes it particularly vulnerable to system faults, such as hardware failures, network outages, and data corruption. To address these challenges, fault-tolerant strategies are essential for ensuring the reliability and resilience of edge systems. Furthermore, federated learning (FL) offers a decentralized approach to machine learning, which can enhance the resilience of edge computing environments by allowing edge devices to collaborate on training models without relying on a central server. This paper explores the integration of fault-tolerant strategies with federated learning to provide a comprehensive, resilient framework for edge computing. Various aspects are compared for fault-tolerant mechanisms with federated learning frameworks to analyze their effectiveness in enhancing system reliability and ensuring real-time performance in the face of failures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Optimizing Fraud Detection Systems in Credit Card Transactions Using Machine Learning Techniques
Rapid e-commerce services and emerging technologies have grown to use credit card usage as a widespread way of effecting payments, thereby increasing bank transaction volume. It is, therefore, equally increasing fraudulent activitiesthus showing the critical need for fraud detection methods development. Class-weighting hyperparameters are studied and applied to handle class imbalance between fraudulent and legitimate transaction classes. We mainly use Bayesian optimization for these hyperparameters tuning with consideration of unbalanced data problems. The key components of our method involve weight-tuning as a preprocessing step and using the extreme gradient boosting [XGBoost] algorithm to enhance further the light gradient boosting machine [LightGBM] based on an ensemble voting process. Moreover, we use deep learning for hyperparameter tuning with special consideration given to our introduced weight-tuning approach. Experiments on real-world datasets demonstrate the efficiency of our strategies. We follow recall-based metrics and the widely used ROC-AUC scores for the unbalanced datasets, which are more appropriate for measuring the model performance. All the algorithms are compared based on fivefold cross-validation, while the majority voting ensemble method is applied to evaluate the combined performance of the algorithms. The previous results prove that LightGBM and XGBoost perform best, with optimal performances obtained at ROC-AUC scores of 0.95, precision of 0.79, recall of 0.80, and an F1 score of 0.79. Further, deep learning with Bayesian Optimization achieves the ROC-AUC scores of 0.908, precision of 0.96, recall of 0.82, F1 score of 0.88, and Accuracy of 0.9996all of which were significant improvements over the previous approaches. This paper presents Bayesian-optimized LightGBM for fraud detection, where it improves accuracy and efficiently tunes hyperparameters. The main novelty here is integrating Bayesian Optimization into dynamically enhancing model performance for handling class imbalance and reducing false detections. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Alzheimer's Disease Detection using Deep Feature Extraction and Explainable Machine Learning
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by gradual cognitive decline, posing significant diagnostic challenges that necessitate automated detection systems to aid clinical decision-making. This study presents an explainable machine learning framework for binary dementia classification using deep feature extraction from magnetic resonance imaging. A pretrained ResNet50 convolutional neural network was employed to extract 2048-dimensional feature vectors from 86,437 MRI slices derived from the OASIS1 dataset, encompassing 347 subjects. The dataset was imbalanced, containing 67,222 Non-demented and 19,215 demented slices (combining very mild, mild, and moderate dementia). The aggregated features at the Subject-level were used to train three machine learning classifiers: Logistic Regression, Random Forest, and XGBoost. The XGBoost model achieved the highest accuracy of 77.14, with a precision of 0.84 and a recall of 0.87 for Nondemented cases, demonstrating strong discriminative capability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations highlighted the hippocampus and temporal lobes as key regions influencing predictions, aligning with established Alzheimer's pathology. The study demonstrates the potential of combining deep feature extraction with interpretable machine learning for automated dementia screening. 2026 IEEE. -
Enhancing the Recognition of Hand Written Telugu Characters: Natural Language Processing and Machine Learning Approach
Handwritten character recognition has wider application in many areas including heritage documents, education, document digitalization, language processing, and assisting the visually handicapped and other related areas. The paper tries to improve the accuracy and efficiency of recognizing handwritten letters of Telugu language scripts, a difficult task for computers. Telugu is most widely spoken language in southern part of India, it has rich cultural heritage. Using the Natural Language Toolkit (NLTK), this study investigates ways to enhance recognition accuracy by analyzing handwritten content and implementing methods such as feature extraction and classification. The purpose is to use NLTK's capabilities to develop handwritten character recognition. 2024 IEEE. -
Real-World Application of Machine Learning and Deep Learning
The world today is running on the latest computer technologies and one of those is machine learning. The real life example that most of us know is speech recognition. Google Assistant is the common example for this Speech recognition. This google assistant is not only limited till 'Ok Google', but it responds to all your questions in a smart way. It can manage all your calls or can book appointments. Imagine you fell down while de-boarding a bus. So, Next time you take care so that you don't fall that is something that your brain has interpreted from your past experience. This is what exactly deep learning is, it imitates human brain works. Deep learning is sub-branch of machine learning. It is able to build all new things based on its previous experiences. Many of us have heard about driverless cars and medical diagnosis. Recently google has developed a new technology where all your cardiovascular events can be predicted by eye scan so, that doctors can get a clear view of what is inside the body of a patient. These all are developed using machine learning. It has a capability to change the human world into a complete robotic world. Anyways, it also has its own disadvantages. This article discusses about those, Scope of machine learning, its Market potential, financial growth and Current applications of machine learning. 2019 IEEE. -
Preprocessing Big Data using Partitioning Method for Efficient Analysis
Big data collection is the process of gathering unprocessed and unstructured data from disparate sources. As data deluge, the large volume of data collected and integrated consist missing values, outliers, and redundant records. This makes the big dataset insignificant for processing and mining knowledge. Also, it unnecessarily consumes large amount of valuable storage for storing redundant data and meaningless data. The result obtained after applying mining techniques in this insignificant data lead to wrong inferences. This makes it inevitable to preprocess data in order to store and process big dataset effectively and draw correct inferences. When data is preprocessed before analytics the storage consumption is less and computation and communication complexity is reduced. The analytics result is of high quality and the needed time for processing is considerably reduced. Preprocessing data is inevitable for applying any analytics algorithm to obtain valuable pattern. The quality of knowledge mined from large volume of big data depends on the quality of input data used for processing. The major steps in big data preprocessing include data integration from disparate sources, missing value imputation, outlier detection and treatment, and handling redundant data. The process of integration includes steps such as extraction, transformation, and loading. The data extraction step gathers useful data used for analytics and the transformation process organize the collected data in structured format suitable for analytics. The role of load process is to store transformed data into secured storage so that data can be obtained and processed effectively in future. This work provides preprocessing techniques for big data that deals with missing values and outliers and results in obtaining quality data partitions. 2023 IEEE. -
The Effect of Viscous Dissipation on the DarcyBard Problem: Weakly Nonlinear Analysis and Strongly Nonlinear Computations
We consider the effect of viscous dissipation on the onset and nonlinear development of two-dimensional convection in a unit enclosure heated from below. First, we show that the linear theory is unchanged from that which arises when viscous dissipation is absent. Second, a weakly nonlinear analysis shows that convection becomes weaker with increasing values of Ge~, a modified Gebhart number. In addition, the rate of heat transfer at the lower and upper sufaces differ from one another. Third, nonlinear convection is found to lose both up/down and left/right symmetry as both Ge~ and Ra (the DarcyRayleigh number) increase. It is also found that once viscous dissipation increases in strength to unphysically large amounts, then the maximum temperature migrates from the lower boundary to the interior of the enclosure. The Author(s) 2025. -
Nanosheets of nickel, cobalt and manganese triple hydroxides/oxyhydroxides as efficient electrode materials for asymmetrical supercapacitors
Transition metals play a significant role in energy storage applications mainly as electrode materials in supercapacitors. In this work, triple hydroxide/oxyhydroxide nanosheets of a nickel, cobalt and manganese (NCM) composite were electrochemically deposited on carbon cloth (CC) and used as electrode materials in supercapacitors. In a three electrode system the composite delivered a specific capacitance of 707 F g -1 at a current density of 3 A g -1 which retained its stability even at a higher current density of 50 A g -1 . An asymmetric supercapacitor (ASC) was assembled and characterized using NCM as the positive electrode, activated carbon as the negative electrode and Whatman filter paper soaked in KOH as the separator. The device operated in a working potential window of 1.75 V and it delivered a power density of 13.12 kW kg -1 and an energy density of 23.7 W h kg -1 . 2019 The Royal Society of Chemistry. -
Investigation on thermal barrier effects of 8YPSZ coatings on Al-Si alloy and validation through simulation
In high temperature engineering field, protection of metal components operating at high temperatures has been a problem since the attempts to realize high efficiency aero engines in the 1940s. Researchers have been working on finding a solution for this issue and thermally insulating the surface of the base metal component with a suitable high temperature material, generally a ceramic, is one solution. The Thermal Barrier Coatings, popular worldwide as TBCs have found wide spread applications in aerospace and automobile industry after its successful application in aerospace engines in mid 1970s. In the field of aerospace, generally a super alloy will be the substrate and in automobile field this process is very much suited on aluminium casting alloys, which is the raw material for high speed diesel engine cylinder blocks and pistons. Although a good quantity of research work on TBCs have been completed in the field of aerospace, the published literature on such coatings on Aluminium castings alloys are limited. Present research aims to throw some light in this grey area by plasma spray coating Aluminium-Silicon (Al-Si) substrates with popular Yttria Partially Stabilized Zirconia as top coat and underlying nickel aluminide bond coat. Al-Si alloys are widely used in automobiles. Experiments were conducted to evaluate the temperature drop across a 250 mm thick TBC at different ceramic surface temperatures and then validating the experimental results by simulation in ANSYS. Experimental results and simulated results showed a close match, thereby validating the findings. 2019 Elsevier Ltd. All rights reserved. -
Residual stresses analysis on thermal barrier coatingsndt tool for condition assessment
Improvement in the engine efficiency follows reduction in fuel consumption which is possible by increasing the engine combustion temperature. Coating the piston of diesel engine with a high temperature-resistant material, known as thermal barrier coating, generally 68% Y2O3 stabilized ZrO2, is a popular method to reduce the temperature it experiences in service and to increase engine efficiency. Whether bare or coated component, fabrication and different thermal expansion coefficients of the ceramic coating and piston metal cause generation of residual stresses in them. These hidden residual stresses (tensile or compressive) play a significant role in governing the failure mechanism of the different sections of the components and their important role (also developed in service) is mostly neglected in engineering practices. Residual stresses analysis of components in service may throw light on the condition of the components without destroying them. In this work, portable X-ray residual stress analyzer was used to evaluate the condition of AlSi alloys piston flat plates that were coated with 250-m-thick 68% Y2O3 stabilized ZrO2 and subjected to thermal treatments. The analysis revealed (a) residual stress-free pattern for uncoated AlSi substrate, (b) compressive residual stress at the substrate (AlSi)coating (TBC) interface and (c) tensile residual stress at the substrate (AlSi)coating (TBC) interface of a thermal shocked coated substrate. The analysis method exhibited good possibility for using this as a tool for non-destructive testing for predicting the onset of failure at the coating substrate interface, without destroying the component in service. Springer Nature Singapore Pte Ltd 2020. -
Protection offered by thermal barrier coatings to Al-Si alloys at high temperatures - A microstructural investigation
Thermal barrier coatings, with ~50 mm thick Nickel-Aluminide bond coat and ~250 mm thick Yttria-Stabilized zirconia ceramic top coats were synthesized by Air Plasma Spray coating process on flat plates machined from Al-11Si alloy diesel engine pistons. Coating process parameters and qualifications that were followed were based on previous studies made on the same substrates. The ceramic coatings were subjected to various thermal treatments such as (a) thermal shock cycling tests and (b) continuous heating in a furnace. Uncoated Al-Si samples were simultaneously subjected to the same thermal treatments and used as reference to study the protection offered by the coatings to the base metal substrates. Thermal shock cycles tests involved subjecting the coated and uncoated Al-Si plates to oxy-acetylene flame to allow the ceramic surface to be maintained at 500 C for 1000 cycles (one cycle comprised of heating for 60 s, withdrawal from flame and forced cooling in ambient air for 60 s) and similar thermal shock cycles in an electric furnace. The specimen were also heated in a furnace at 300 C for 1000 continuous hours. Stresses induced during thermal shock cycles and oxidation of bond coat-ceramic coat interface during the exposure to heat are the main reasons for the coating's failure. Details of an investigation on the microstructural changes and oxidation behaviour of the substrate and the ability of the coatings to protect the metal substrates from oxidation are presented. Microstructural studies were carried out by employing a Scanning Electron Microscope attached with Energy Dispersive X-ray spectroscopy facility. The findings were compared on (a) uncoated Al-Si alloy and (b) thermal barrier coated Al-Si alloy with a goal to understand the capability of the coatings to protect the metal from the influences of thermal treatments, at temperatures lower than the melting point of the Al-Si alloy. 2019 Elsevier Ltd. All rights reserved. -
Thermal Barrier Coating Development on Automobile Piston Material (Al-Si alloy), Numerical Analysis and Validation
This work is focused on the thermal barrier coating (TBC) development on aluminium-silicon (Al-Si) alloy casting materials, widely used as automobile components (cylinder blocks, pistons etc.). TBCs enable enhanced combustion within the chambers of diesel engines resulting in improved performance and components life. Uniform coating thickness development on complex contours of automobile pistons is a challenging task worldwide which results in varying thermal barrier characteristics across the non-uniform thickness. In consistent (in thickness) coatings are most likely to lead to uneven thermal barrier effects across the TBC thicknesses which directly affect the performance and the lubrication system of the engine. This warrants the development of stable and consistently thick coatings for ideal performance of the Low Heat rejection (LHR) engine. The present research work involved building different thicknesses (100, 125 and 150?m) of commercial 6-8%Yttria stabilized zirconia (YSZ) TBCs on 50? to 75? thick nickel aluminide (NiAl) bond coat. The influence of thickness on thermal barrier characteristics via experimentation and numerical analysis has been studied. Flat plates machined from automobile pistons were used as substrates. The coatings were characterized for thermal barrier effects for hot ceramic surface face temperatures up to 550C (by using oxy-acetylene flame to heat up the TBC surface), structural phase analysis by X-ray Diffraction (XRD) and microstructure analysis in metallographic cross section by employing Scanning Electron Microscope (SEM). An analytical investigation also was carried out to determine the approximate temperature at each interface. A code was developed to calculate the temperature drops across the coated plate and the net heat available at the coated surface using MATLAB. This is important considering the effects, small changes in temperatures will bring on the creep life on the metal. 2019 Elsevier Ltd.