Browse Items (16481 total)
Sort by:
-
Network Lifetime Enhancement by Elimination of Spatially and Temporally Correlated RFID Surveillance Data in WSNs
In wireless sensor networks (WSNs), radio frequency identification (RFID) plays an important role due to its data characteristics which are data simplicity, low cost, simple deployment, and less energy consumption. It consists of a series of tags and readers which collect a huge number of redundant data. It increases system overhead and decreases overall network lifetime. Existing solutions like Time-Distance Bloom Filter (TDBF) algorithm are inapplicable to the large-scale environment. Received Signal Strength (RSS) used in this algorithm is highly dependent on quality of tag and application environment. In this paper, we propose an approach for data redundancy minimization for RFID surveillance data which is a modified version of TDBF. The proposed algorithm is formulated by using the observed time and calculated distance of RFID tags. To overcome these problems, we design our approach to relevantly reduce the spatiotemporal data redundancy in the source level by adding the Received Signal Strength Indicator (RSSI) concept for energy-efficient RFID data communication in wireless sensor network scenario. We introduce in this paper the new improved idea of an existing algorithm which efficiently reduces the rate of data redundancy spatially and temporally. The implemented results overcome the limitations of existing algorithm for data redundancy reduction. Nevertheless, the performance evaluation shows the efficiency of proposed algorithm in terms of time and data accuracy. Furthermore, this algorithm supports multidimensional and large-scale environment suitable for sensor network nowadays. 2022 Lucy Dash et al. -
Network pharmacological evaluation for identifying novel drug-like molecules from ginger (Zingiber officinale Rosc.) against multiple disease targets, a computational biotechnology approach
Ginger (Zingiber officinale Rosc.) is a popular spice used globally in ethnic cuisines and witnessed its extensive use in traditional medicine. In this study, we identified 12 phytochemicals from the ginger rhizome extract (hexane) through GC/MS analysis. After evaluating drug-likeliness, these phytochemicals were docked with 16 target proteins in silico, and docking scores were compared with their respective control drugs. Furthermore, multivariate statistical analysis (principal component analysis-PCA) was performed, and three different chemical clusters were identified. Pharmacophore analysis further identified common functional descriptors in the compounds under study. Finally, we developed a unique three-level network taking phytochemicals, target proteins and associated diseases based on the optimum docking scores. Overall, Oleic acid, Palmitic acid and Shogaol showed the highest coverage to the target proteins (12, 10 and 9 targets, respectively) and Oleic Acid scored the highest (5956) in PatchDock when docked against Peroxisome proliferator-activated receptor gamma (PDB id 1KNU, UniProt id P37231). This work provided significant insight into developing the protocol for rapid identification of potential drug likeliness of the identified phytochemicals. Graphic abstract: [Figure not available: see fulltext.]. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature. -
Network Security Tools and Applications in Research Perspective
The modern world technology is civilized, globalized and modernized. The technological development of social networks and e-commerce applications produce larger data. This data communication is major task, because device to device communication need network terminal. This data transmission is not safe because of different types of tools and software available to destroy the existing network. In the field of network security during data transfer from one particular node to other node some security vulnerability is happened this is the one of the critical issue in this sector. The reason for this network security is different types of data attacks are happen in day to day life. It is easy to establish a new network but protecting the entire network is a big issue. This network security is generally two parameter first one is communication and second one is data automation. The network security field is directly or indirectly linked with the concept of data encryption. The development in this network security has taken us to a level that from signature again we came back to thumb print. For example maintain the data secure we use the lock system which is a finger print type. This technology helps us to protect the physical data theft, but logical data theft is still problem for data transmission. This article will brief about the network security it also presents the various network security types. Those types are wired and wireless network security. Apart from the network security the following topics is also discussed in this article. Those are network security protocols and simulation tools in network security. The research problems in network security are privacy and vulnerability of data. 2019 IEEE. -
Network selection system and method for wimax integrated network and wlan integrated network /
Patent Number: 201941032402, Applicant: Mr. Kathiresan J.
Disclosed is a network selection system and method for a Worldwide Interoperability for Microwave Access (WiMAX) network and a wireless LAN (WLAN) network. The method includes the step of tracking a Mobile or fixed devices (MoFD) for identifying a plurality of Quality of Service (QoS) requirements through a Handoff Decision Manager (HDM) module coupled with a plurality of Media Access Control (MAC) layers of the WiMAX network and the WLAN network. -
Networks Simulation: Research Based Implementation using Tools and Approaches
The advancements in computer networks and communication technology keep network-related research in high demand. Protocols are designed to improve the environment and it is mandatory to test their effectiveness before deploying them. Deploying an untested protocol in a full-fledged real environment is not desirable as there exists uncertainty about its success. Simulation software is one of the essential tools in network research areas. It gives a platform for testing and observing newly developed protocol's behavior with less cost and risk. Different kinds of network simulators are available., some are exclusive for wired or wireless., and some are for both. There are many simulators available hence selecting the most appropriate simulation tool among them is a difficult task. This paper focuses on giving a detailed review of popular simulation tools. 2022 IEEE. -
Neural correlates of auditory comprehension and integration of sanskrit verse: a functional MRI study
In this investigation, we delve into the neural underpinnings of auditory processing of Sanskrit verse comprehension, an area not previously explored by neuroscientific research. Our study examines a diverse group of 44 bilingual individuals, including both proficient and non-proficient Sanskrit speakers, to uncover the intricate neural patterns involved in processing verses of this ancient language. Employing an integrated neuroimaging approach that combines functional connectivity-multivariate pattern analysis (fc-MVPA), voxel-based univariate analysis, seed-based connectivity analysis, and the use of sparse fMRI techniques to minimize the interference of scanner noise, we highlight the brain's adaptability and ability to integrate multiple types of information. Our findings from fc-MVPA reveal distinct connectivity patterns in proficient Sanskrit speakers, particularly involving the bilateral inferior temporal, left middle temporal, bilateral orbitofrontal, and bilateral occipital pole. Voxel-based univariate analysis showed significant activation in the right middle frontal gyrus, bilateral caudate nuclei, bilateral middle occipital gyri, left lingual gyrus, bilateral inferior parietal lobules, and bilateral inferior frontal gyri. Seed-based connectivity analysis further emphasizes the interconnected nature of the neural networks involved in language processing, demonstrating how these regions collaborate to support complex linguistic tasks. This research reveals how the brain processes the complex syntactic and semantic elements of Sanskrit verse. Findings indicate that proficient speakers effectively navigate intricate syntactic structures and semantic associations, engaging multiple brain regions in coordination. By examining the cognitive mechanisms underlying Sanskrit verse comprehension, which shares rhythmic and structural features with music and poetry, this study highlights the neural connections between language, culture, and cognition. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Neural Dynamics of Heartfulness Meditation: EEG Alpha Modulation Across Experience Levels
Background: Electroencephalography (EEG) studies consistently associate alpha-band oscillations with relaxation, internalized attention, and sensory disengagement during meditation. However, limited evidence exists on how Heartfulness Meditation (HM), particularly its unique transmission phases, modulates alpha activity across different experience levels. Purpose: This study investigated experience-dependent modulation of EEG alpha-band power during multiple phases of HM, with a specific focus on transmission and post-meditation periods. Method: Thirty-three healthy adults were categorized as long-term meditators (LTMs; n = 12), short-term meditators (STMs; n = 11), and non-meditating controls (CGs; n = 10). High-density EEG (129 channels) was recorded across seven consecutive five-minute phases: baseline, meditation (M1, M2), transmission (T1, T2), and post-rest (P1, P2). EEG data were preprocessed using RANSAC-based bad-channel detection and independent component analysis. Alpha power (812 Hz) was computed using Welchs method and analyzed using linear mixed-effects models with false discovery rate correction. Results: A significant Group Phase Region interaction (pFDR < 0.05) indicated experience- and phase-dependent alpha modulation. Both LTMs and STMs exhibited higher alpha power than controls, particularly in frontal, parietal, and occipital regions during meditation and post-meditation phases. Effect sizes ranged from small to moderate (Cohens d = 0.340.70). Notably, STMs showed alpha enhancements comparable to LTMs during early meditation. Conclusion: HM induces region- and phase-specific increases in alpha-band EEG activity, reflecting enhanced internal attention and sensory disengagement. Even short-term practice produces measurable neural changes, underscoring the potential neuroplastic effects of HM. The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
Neural Network based Student Grade Prediction Model
Student final grade GPA is the collective efforts of their previous and ongoing efforts of each semester examination may predict accurately using the neural network which receives the input weight of each matrix element of variables to next neuron. The GPA prediction based on regular class performance and previous grades with background variables were found much significant. This research tries to explore the model comparison and evaluate student grade prediction using various neural network models. The single-layer half i.e., successful student model predicts 90 total accuracies than the single layer with five hidden layer neurons (88.5 percent). The multi-layer with two hidden layers (7,3) is 84 percent accuracy is less than one percent accuracy than multilayer with three hidden layers. Similarly, the multilayered with four hidden layered 25,12,7,3 model predicts the least accuracy (77 percent accuracy) for student grade. Similarly, the passed student prediction model has less accuracy than both students' 86 percent. 2022 IEEE. -
Neural network-assisted carbon nanotube electrochemical sensors for automated environmental risk assessment
The current research proposes an intelligent network system that continuously tracks the quality of water flow, with particular attention to pollutants frequently occurring in runoff water from agricultural practices. It deploys high-performance electrochemical sensors based on carbon nanotubes (CNT) combined with a small neural network that functions directly on the built-in microcontroller. It is deployed on floating buoys powered by solar energy, where it can detect some critical contaminants in the rural water bodies, including nitrates, phosphates, atrazine, cadmium, and lead. The sensors work by transmitting their electrical signals through the sensors to the neural network, which provides precise identification of the level of pollutants as one of three risk levels: safe (below detection levels), manageable (within regulatory levels), and hazardous (exceeding regulations). Regarding power performance, results can be delivered over a relatively small-time delay (1.2 milliseconds per reading) and with low memory usage (1.8 MB), making it ideal for remote and low-powered sensors. It is more accurate (93.6 %) than typical machine learning models. Should pollutants exceed the above-prescribed limits, an automated warning will be generated, and the information will be immediately uploaded to a cloud-based dashboard. The dashboard will be closely monitored via remote control, and trend analysis will be conducted. By eliminating the need for manual water sampling, the system offers a scalable and energy-saving method for autonomous environmental testing, particularly in inaccessible locations. In the future, the study will focus on the use of federated learning, a technique that retains data locally to protect privacy, enabling more intelligent and collaborative conclusions across sensor networks. This prepares the ground for more intelligent and secure environmental surveillance systems in the future. 2025 Elsevier B.V. -
Neuro-Divergence and COVID-19 Pandemic: A Reflexive Thematic Analysis on the Experiences of a Mother of an Autistic Child
The COVID-19 pandemic has altered how the experience for neuro-divergent individuals as well as their caregivers has been. A neuro-divergent person refers to a person with an autism spectrum disorder (ASD) or, more generally, to someone whose brain processes information in a way that is not typical of most individuals. In India, where gendered parenting is the norm and can significantly influence childrens development and functioning, it becomes imperative to examine the narratives of mothers of neuro-divergent children. The present case study has been undertaken to study the phenomenon of neuro-divergence in the context of the caregivers especially the mothers experience during the pandemic. The mother of an autistic child was telephonically interviewed to collect the experience of how she navigated through the complexities of the pandemic with her neuro-divergent child. The transcribed data were analysed by reflexive thematic analysis. The reflexive thematic analysis yielded six themes such as resilience in adversity, struggles with social isolation and educational support, adaptation and creativity, advocacy for change, hope and resilience, and newfound relationship. During the pandemic, the lack of physical, and social interaction, community support, and a sense of loneliness experienced during the challenging circumstances of the pandemic served as a bane to the overall experience. In the context of the findings, it is suggested that further research be conducted on the narratives of caregivers, systemic challenges serving as hindrances to the development of neuro-divergent individuals and the rise of caregiver burden due to gendered parenting among the neuro-divergent community. 2025 selection and editorial matter, Sonali Mukherjee and Swati Pathak. -
Neuro-fuzzy model optimization for laser sensor-based quality control for robotic welding of AISI 1030 steel
Robotic welding demonstrates considerable potential in the automation of metal joining processes, resulting in enhanced consistency. This study proposes a methodology for evaluating weld quality by utilizing a laser sensor in conjunction with a hybrid neuro-fuzzy model. The system, designed for AISI 1030 mild steel, utilizes a Design of Experimentation (DOE) methodology to collect empirical data and train the model. A MOTOMAN MA1440 robotic arm, integrated with an AccuFast-II laser sensor, was utilized to acquire real-time weld characteristics. The proposed model integrates fuzzy logic with artificial neural networks (ANNs) for predicting weld quality and is subsequently optimized using the Class Topper Optimization (CTO) algorithm. The model exhibited a high level of prediction accuracy, as indicated by R-squared values of 1.0, 0.99677, 0.99851, and 0.97561 for the training, testing, validation, and overall WQCI datasets, respectively. The process parameters obtained from the CTO analysis yielded a WQCI of 0.824, exceeding the highest experimental value of 0.808, which reflects a 1.98% enhancement in weld quality. The system demonstrated strong performance on both straight and curved weld paths, achieving a positional error of less than 0.29 mm, which falls within the acceptable weld gap range of 11.6 mm. This study emphasizes the practical implementation of a neuro-fuzzy prediction system integrated with an innovative metaheuristic for quality control in robotic arc welding. The integration improves weld consistency, minimizes defects, and increases production efficiency, representing a notable advancement in intelligent manufacturing. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2026. -
Neuro-Leadership: A New Paradigm in Leadership Thought
Leadership is not a static occurrence. It is a dynamic one that constantly evolves. Leadership is seen as a means to enhance ones personal, professional, and social lives. Organizations believe that leaders bring in unique assets to the organization; which contribute to the bottom line of the company. The conclusions drawn from research findings on leadership portray an image of a process that is far more sophisticated and complex than the condensed view, popularly accepted. This chapter will provide a comprehensive evaluation of different approaches to leadership and highlight the importance of a new paradigm. Ground breaking insights have started to surface regarding neurosciences and brain functioning that has significantly influenced leadership thought. The traditional approaches of leadership could not adapt to the world of unlimited information which needed continuous evolution; however, our brain can adapt and change leading to the emergence of neuro-leadership. The chapter will trace the journey of neuroleadership and its increasing relevance in the current scenario, especially in terms of employee and organization performance. 2024 by Nova Science Publishers, Inc. -
Neuro-Systemic applications in learning
Neuroscience research deals with the physiology, biochemistry, anatomy and molecular biology of neurons and neural circuits and especially their association with behavior and learning. Of late, neuroscience research is playing a pivotal role in industry, science writing, government program management, science advocacy, and education. In the process of learning as experiencing knowledge, the human brain plays a vital role as the central governing system to map the images of learning in the human brain which may be called educational neuroscience. It provides means to develop a common language and bridge the gulf between educators, psychologists and neuroscientists. The emerging field of educational neuroscience presents opportunities as well as challenges for education, especially when it comes to assess the learning disorders and learning intentions of the students. The most effective learning involves recruiting multiple regions of the brain for the learning task. These regions are associated with such functions as memory, the various senses, volitional control, and higher levels of cognitive functioning. By considering biological factors, research has advanced the understanding of specific learning difficulties, such as dyslexia and dyscalculia. Likewise, neuroscience is uncovering why certain types of learning are more rewarding than others. Of late, a lot of research has gone in the field of neural networks and deep learning. It is worthwhile to consider these research areas in investigating the interplay between the human brain and human formal/natural learning. This book is intended to bring together the recent advances in neuroscience research and their influence on the evolving learning systems with special emphasis on the evolution of a learner-centric framework in outcome based education by taking into cognizance the learning abilities and intentions of the learners. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Neuro-technology and counselling
[No abstract available] -
Neurobiological aspects of violent and criminal behaviour: Deficits in frontal lobe function and neurotransmitters
Many neurobiological abnormalities have been reported in patients with violent and criminal behaviour. Strong associations exist between aggressive/violent behaviour and brain dysfunction. Also, many studies support an association between frontal lobe dysfunction and increased aggressive or antisocial behaviour. The focal orbitofrontal brain injury is specifically associated with increased aggression. Deficits in frontal lobe executive functions may increase the likelihood of future aggression, but as of now, studies have reliably demonstrated a characteristic pattern of frontal network dysfunction predictive of violent crime. The evidence is strongest for an association between focal prefrontal damage and an impulsive subtype of aggressive behaviour. This paper covers dysfunctions in these regions contributing to severe aggressive and violent behaviour, as well as neurotransmitters implicated in the same. 2018 International Journal of Criminal Justice Sciences (IJCJS). -
Neurobiology of emotional regulation in cyberbullying victims
[No abstract available] -
Neurocognitive aspects of mathematical achievement in children
Neurocognitive factors, including information integration and executive functioning, contribute significantly to a child's early success in math achievement, even though the significance of home and school environments cannot be ignored. There are only a few studies that have systematically examined how information integration and executive function skills impact different aspects of learning math and math achievement. Using a comprehensive tool such as the brain-Based Intelligence Test (BBIT), a brain-based comprehensive approach to the understanding of cognition, for the assessment of information integration and executive function skills can have significant implications for mathematical education and remediation (brain plasticity). The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Neurocognitive modeling of emotional states using EEG and hidden markov models: A multidisciplinary approach
This interdisciplinary research cuts computational modeling and cognitive neuroscience approaches with the intention of studying dynamic emotional involvement with multimedia stimuli via HMM analysis of EEG data. In particular, the paper deals with advertisements that target excitement and love-type emotions, setting forth new paradigms for understanding the building and modulation of emotional experience across time in the human brain. EEG parameters such as amplitude, arousal, and frontal activation were studied as markers of neural reactions to emotionally arousing content. The neural markers are tracked over time to record the changes in emotional engagement. The HMMs use identifies hidden neural states and their probabilistic transitions, making the temporal description of neural dynamics during emotional processing rich and nuanced. The analytical approach provides identifiable neural patterns for excitement and love stimuli distinguished in terms of arousal, spectral amplitude, and hemispheric asymmetry in frontal activation. Due to these distinctions, we ascertain that the brain processes different affective tones distinctly, shedding light on the intricacies of emotion perception and its immediate brain counterpart. Using the results, a predictive HMM model is presented to model emotional changes when individuals are subjected to effective multimedia stimuli. The model serves as a bridge to further real-time developments in human-computer interaction, adaptive e-learning, immersive media conception, and affective UX (user experience) optimization. In other words, this enables the system to detect shifts in the user's emotions automatically and adapt content accordingly, representing truly affect-sensitive technologies. Amalgamating computational modeling with neurophysiological measurement, this study contributes to the birth of emotion-aware technology that can be dynamically responsive to the users' current affective state, thus harnessing engagement, personalization, and user satisfaction as opportunities. It builds on the interdisciplinary discourse between cognitive neuroscience, affective computing, and computational psychology to serve as a methodological guideline for future investigations into emotional dynamics and brain-computer interfaces (BCIs), as well as neuroadaptive technology. It makes a case for the relevance of temporal modeling in decoding emotional cognition and therefore advocates the continued employment of machine-learning approaches in brain activity and human affective behaviour studies. Copyright (c) 2025 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. -
Neurodiversity at the Workplace: The new paradigm of talent acquisition and retention
The importance of neurodiversity in the workplace has gained popularity in recent years. Companies can access a pool of distinctive skills and viewpoints that can stimulate innovation, creativity, and productivity by embracing neurodiversity in the workplace. This chapter examines the idea of neurodiversity in relation to hiring and retaining talent, emphasizing the advantages for both companies and workers. It covers methods for establishing welcoming environments at work that support neurodiverse workers and help them reach their full potential. It also looks at how corporate culture, HR regulations, and leadership all contribute to creating a welcoming workplace for individuals who are neurodiverse. Companies can promote diversity, equity, and inclusion at the workplace in addition to attracting and retaining neurodiverse employees (NDEs). A conceptual framework has been proposed to demonstrate the influence of various factors like awareness, perceived benefits, accommodation, organizational policy, stigma, and unconscious bias on retention of NDEs. 2024, IGI Global. -
Neurofeedback Therapy Meets Transformers: Rewiring Sleep Disorders Through AI-Driven EEG Modulation
Sleep disorders such as insomnia, sleep Apnea, and hypersomnia significantly impair neurophysiological functioning, yet conventional treatments like Cognitive Behavioral Therapy for Insomnia (CBT-I) remain resource-intensive and difficult to personalize. This study introduces a novel AI-powered neurofeedback simulation framework designed to detect dysregulated EEG frequency band activity across sleep stages and simulate targeted interventions. A Transformer-based model serves as the core component, offering a unique capability to model cross-epoch temporal dynamics and frequency-specific spectral patterns. Unlike traditional architectures that treat EEG epochs in isolation, the Transformer captures how EEG band activity evolves across the night, critical for identifying persistent dysregulation patterns and planning stage-specific interventions. Through its multi-head attention mechanism, the model can simultaneously monitor delta, theta, alpha, beta, and gamma fluctuations while preserving sleep architecture transitions using positional encoding. Dysregulated epochs are classified with 92% accuracy, and intervention simulations-such as beta suppression in N2 or delta enhancement in REM-led to measurable improvements: average WASO decreased by 23%, and Sleep Efficiency improved by 13%. This framework not only demonstrates the efficacy of Transformer-based temporal-spectral modelling in EEG but also lays the foundation for closed-loop, wearable-compatible, personalized neurofeedback systems for remote sleep therapy. 2026 A l A KA d V idh hi V