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Deep Reinforcement Learning for Dynamic Resource Allocation in Intelligent Communication Networks
As intelligent communication implementations like 5G and IoT-enabled infrastructure meet technological advancement, provisioned network resource allocation dynamically and efficiently will be deemed crucial to accommodate diverse service demands and guarantee an optimized part of the network on demand. Resource management strategies based on traditional approaches often lack a sufficient response to the dynamic posture of network states and complex, heterogeneous environments. To address these challenges, deep reinforcement learning (DRL) has emerged as a powerful methodology wherein deep neural networks are employed to enable intelligent and adaptive decision-making based on dynamic network conditions. In this paper, we study the potential of DRL for dynamic resource management in intelligent communication networks. We build a DRL-driven agent that enables optimal allocation policy learning by interacting with high-dimensional, stochastic network environments with variable traffic loads, user mobility, and heterogeneous quality-of-service (QoS) requirements. Realistic simulation scenarios show that the proposed DRL framework outperforms conventional allocation heuristics in terms of throughput, latency, and fairness among users. We elaborate on the ramifications of explorationexploitation tradeoffs, convergence stability, and compute efficiency in the context of scale deployments. This way, our results prove that DRL is a potential candidate for dynamic resource allocation in future intelligent communication networks due to its better adaptability and performance. 2025 IEEE. -
ENHANCING FOREST ECOSYSTEM RESILIENCE TO CLIMATE CHANGE WITH VANET AND INTEGRATED NATURAL RESOURCES MODELLING
Forest ecosystems are immediately threatened by rising global temperatures and changing climatic patterns. Periodic assessments also contribute to a reduction in the frequency of monitor-ing, which could cause environmental changes to go unnoticed. This work develops a novel real-time monitoring and early warning system to meet this difficulty. By integrating Vehicular Ad Hoc Networks (VANET) with sophisticated natural resources modelling, the proposed method aims to revolutionise the way forest ecosystems are managed. This study strives to design and implement a comprehensive system that harnesses the power of VANET to collect real-time data from sensors deployed on vehicles, and integrates advanced modelling to predict, assess, and mitigate risks to forest ecosystems. The proposed method involves deploying a network of vehicles equipped with environmental sensors within VANET. These sensors continuously collect data on crucial environmental parameters, such as temperature, humidity, air quality, and spatial information. The data are transmitted through a secure VANET communication protocol to a centralised processing unit, where it is integrated with climate models and ecosystem dynamics models. Resilience metrics and thresholds are defined to trigger a tiered early warning system. Preliminary testing of the system demonstrates promising accuracy and responsiveness. The integrated approach allows for dynamic risk assessment, enabling the identification of potential threats such as extreme weather events, invasive species, or disease outbreaks. Early warnings prompt adaptive management strategies, showcasing the systems potential to significantly enhance forest ecosystem resilience. This research presents a pioneering solution to the escalating challenges faced by forest ecosystems in the time of climate change. The real-time monitoring, early warning system, amalgamating VANET and integrated modelling, stand as a robust tool for forest managers, policymakers, and communities to proactively address environmental changes. The findings underscore the systems potential to transform forest management practices, marking a critical step toward sustainable and resilient ecosystems. 2024, Scibulcom Ltd. All rights reserved. -
Applications of neuroscience in education practices: A research review in cognitive neuroscience
The human brain is the most complex and mysterious organ in the body responsible for learning. Applications of neuroscience and genetics need to be comprehended to modulate teaching and learning practices in education. Considering the scope for application of advanced sciences in education practices, this book chapter simplifies and reviews ten critical research findings relevant for students and teachers for classroom applications and for modulating learning patterns for different age groups. The concept is also relevant for parents and the academic fraternity at large. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Effect of Chelating Agent Concentration on the Pseudocapacitive Performance of V2O5 Flakes Prepared by the Hydrothermal Process for Supercapacitor Applications
Vanadium pentoxide (V?O?) flakes were synthesized via a hydrothermal method by varying the amount of lemon juice (5mL for sample-1 and 10mL for sample-2) as a natural chelating agent. Structural and morphological analyses were performed using X-ray diffraction (XRD) and scanning electron microscopy (SEM), confirming crystalline V?O? with flake-like morphologies influenced by chelating agent concentration. Electrochemical performance was evaluated using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS) in a 3M KOH electrolyte. Sample-2 exhibited a Significantly higher Specific capacitance of 1536 F?g?1 (CV at 1mVs?1) and 212.37 F?g?1 (GCD at 1 A?g?1) compared to sample-1, demonstrating that increasing lemon juice concentration enhances the capacitive behavior of V?O? flakes by improving ion diffusion and electroactive surface area. The Author(s) under exclusive licence to Sociedade Brasileira de Fica 2025. -
A Metal-Free KOtBu-Mediated Protocol towards the Synthesis of Quinolines, Indenoquinolines and Acridines
An expeditious strategy has been developed for the synthesis of diverse quinolines, indenoquinolines and acridines using KOtBu-mediated reaction conditions. The designed process utilizes 2-aminoaryl carbaldehydes/2-aminoaryl ketones and methyl/methylene group containing ketones as readily available feedstock. The chemical transformation was affected at room temperature within a short duration of time to obtain diverse N-heterocycles yields up to 92 %. The established process also exhibits considerable functional group tolerance with an operational simplicity. 2024 Wiley-VCH GmbH. -
Liquid Crystal as a Potential Biosensing Material
Liquid crystal (LC) biosensors are based on the mechanism of ordering transformations of LC molecules. Because of the anisotropic nature of LC molecules, LC has an extraordinary response to external stimuli and has highly responsive optical properties. The elastic force between LC molecules helps them for their orderly arrangement, which changes in response to a multitude of external stimuli such as temperature, adding biomolecules, and applying electric and magnetic fields. LC biosensors can be classified into LC-solid, LC-aqueous, and LC-droplet interface biosensors based on the LC-interaction surfaces. LC biosensors can detect target molecules such as nucleic acid, proteins, amino acids, and glucose. Aptamer-based LC biosensors have also been developed because of their high sensitivity. The advantage of LC in making a label-free biosensor lies in the fact that it requires minimal instrumentation, which heavily reduces the expense. There, it is one of the most promising types of biosensing techniques that are being developed. 2025 Scrivener Publishing LLC. -
Adverse Childhood Experiences, Psychological Well-wBeing, and Grit: A Comparative Study between LGBTQIA+ and Cis-Heterogeneous Sample of India
Adverse childhood experiences (ACEs) is a major concern that has been related to serious health consequences. Moreover, lesbian, gay, bisexual, transgender, intersex, asexual, and queer (LGBTQIA+) individuals are more likely to experience ACEs than cis-heterosexual individuals, especially in India. However, research in India has been scarce. This study compared these variables between Indian LGBTQIA+ individuals (n = 102) and cis-heterosexual individuals (n = 118) aged between 18 and 25. The findings of this comparative study reveal significant differences between LGBTQIA+ and cis-heterogeneous groups in terms of ACEs and grit levels. Notable differences were also discovered in three domains of psychological well-being: environmental mastery, positive interpersonal relationships, and self-acceptance. However, the vulnerability of LGBTQIA+ individuals in India reveals itself in descriptive statistics that report they are susceptible to negative outcomes in mental health. This study further emphasizes the importance of implementing focused interventions and support to increase psychological well-being and grit in the LGBTQIA+ community. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
Sentimental analysis on voice using AWS comprehend
Sentimental analysis plays an important role in these days because many start-ups have started with user-driven content [1]. Sentiment analysis is an important research area in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect-oriented product analysis, sentiment analysis and text classification etc [2]. This process will improve the business by analyse the emotions of the conversation. In this project author going to perform sentimental analysis using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract the content of the document. By using this service can extract the unstructured data like images, voice etc. Thus, will identify the emotions of the conversation and give the output whether the conversation is Positive, Negative, Neutral, or Mixed. To perform this author going to use some services from Aws like s3 which is used for the data store, Transcribe which is used for converting the audio to text, Aws Glue is used to generate the metadata from the comprehend file, Aws Comprehend is used to generate the sentiment file from the audio, Lambda is used to trigger from the data store s3, Aws Athena is used to convert text into structured data and finally there is quick sight where he can visualize the data from the given file. 2020 IEEE. -
High-Gain Sequentially Rotated LHCP Metasurface Antenna Array for Uplink Ka-Band CubeSat Applications
This paper presents a compact circularly polarized 2 2 microstrip antenna array with a metasurface superstrate designed for Ka-band uplink CubeSat communication applications. The proposed antenna array operates at 28 GHz and consists of four-square patch elements arranged in a sequential rotation, connected through a sequential-phase feed network to achieve stable circular polarization. To enhance gain and axial ratio performance, a 6 6 rotated plus-shaped metasurface layer composed of periodic unit cells is placed above the antenna array.The antenna structure, with overall dimensions of 22 20 6.04 mm3, is designed and simulated using ANSYS HFSS. Simulation results demonstrate an impedance bandwidth of 8.12% (26.74-29.00 GHz) for |S11| < -10 dB and a 3-dB axial ratio bandwidth of 1.52% (27.42-27.84 GHz). The antenna array achieves broadside LHCP achieves a maximum gain of 14.8 dBi at 27.5 GHz with a half-power beamwidth of 9 along the Phi = 90 plane. The inclusion of the metasurface layer results in a gain improvement of approximately 5.2 dBi and with a peak gain of 15.5 dBi and a total efficiency greater than 95%. 2025 IEEE. -
Cloud based ERP Model using Optimized Load Balancer
Enterprise Resource Planning (ERP) and Cloud computing are turning out to be increasingly more significant in the field of Information Technology (IT) furthermore, Communication. These are two distinct segments of current data frameworks, and there are a few inside and out examinations about Enterprise Resource Planning on cloud computing framework. ERP frameworks are related with a few issues, for example, shared synchronization of multi-composed assets, constrained customization, massive overhauling cost, arrangement mix, industry usefulness, reinforcement support and innovation refreshes. These issues render ERP frameworks execution excruciating, complex and time-devouring and create the need for a huge change in ERP structure to upgrade ERP frameworks foundation and usefulness. Cloud Computing (CC) stages can defeat ERP frameworks inconsistencies with financially savvy, redid and profoundly accessible figuring assets. The objective of this examination is to blend ERP and CC benefits to lessen the factor of consumption cost and execution delays through a proposed system. For this reason, investigate the unmistakable issues in current ERP frameworks through a complete correlation between ERP when moving to CC condition. Also, a conventional structure is proposed for 'Cloud-based ERP frameworks'. 2020 IEEE. -
An Integrated Approach to Green Cloud Solutions for Energy-Efficient Sustainable IT and Carbon Footprint Reduction
Cloud computing has become a very important part in everyday life, but this has also made a lot of carbon footprint because of the energy consumption in the data centers. The pandemic had affected these emissions, and they quickly came back, which has shown the requirement for sustainable solutions which will help in fighting the increase in carbon footprint. For these problems, the green computing technology will give probable solutions by promoting the technology that would be responsible enough to decrease these effects of harming environment. It will have techniques like smarter system designs, operations that are energy efficient, and smart techniques for optimization. This study explores how the above set principles can reduce the overall digital carbon footprint and help to create economically viable businesses. This approach provides a forward path for technology progress and profitability aligning with the environment sustainability which is a necessary component for business longevity. 2025 IEEE. -
Workplace bullying in the service sector
Context: Bullying is a problem that people, the world over, grapple with. It is manifest in different forms among different sections of people. Despite its prevalence, workplace bullying has not received much attention in scholarly literature in India. It is also not widely acknowledged as a threat to individual and organizational well-being. The purpose of this study is to add to the existing body of literature on the topic and to draw attention to the gravity of the issue. Aims: The primary objectives are to identify if there exist variations in its incidence on the basis of gender and years of experience, to identify the source of negative behavior, and the type of bullying that is most prevalent. Settings and Design: The study is a type of cross-sectional, descriptive study. Subjects and Methods: Data have been collected from a sample of 84 respondents using the Work Harassment Scale. All respondents are white-collar employees of the service sector in the cities of India. The data were analyzed using IBM SPSS v25. Results: The results find that there is no difference in the incidence of bullying on the basis of either gender or years of experience. Moreover, the source of negative behavior is generally one's superiors, and the most prevalent type is 'verbal aggression.' Conclusions: The study concludes with suggestions of steps to be implemented at the national and organizational level, to combat the problem. 2022 Authors. All rights reserved. -
The development and validation of the digital intelligence scale for students
Digital intelligence is increasingly recognized as a vital skill in navigating both academic and personal digital environments, yet existing tools often use multidimensional or adult-oriented frameworks. This study aims to develop and validate the Digital Intelligence Scale for Students (DISS), a unidimensional self-report instrument designed to assess the general digital intelligence of school, college, and university students. The study was conducted in two phases. In the first phase, data was collected from 786 students in India to examine the factor structure of the model. The analysis supported a unidimensional model, indicating that all items measured a single underlying construct. In the second phase, data was collected from 611 students in India to confirm the unidimensional model. Results supported a robust unidimensional structure, with excellent internal consistency (? = 0.954). The DISS was found to be significantly correlated with Internet Skills Scale and Digital Literacy Scale, providing evidence for convergent validity. Divergent validity was assessed using State-Trait Anxiety Inventory and Big Five Personality Inventory. This scale provides a practical framework for evaluating digital readiness in educational settings and guiding interventions. Subsequent studies could validate its relevance across cultural contexts and examine developmental trajectories in digital intelligence. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
A review of artificial intelligence enhanced cognitive behavioural therapy using the BECK AI BOT for mental health interventions
The integration of artificial intelligence (AI) and cognitive behaviour therapy (CBT) is a revolutionary solution to the global mental health issue, characterized by increasing need and decreased access to treatment. This research investigates the potential of AI-fortified cognitive behavioural therapy technologies, including chatbots, virtual reality, and adaptive learning modules, to enhance the efficacy, accessibility, and individualization of treatment for anxiety, depression, and PTSD. The study evaluates the scalability, ethical issues, and therapeutic efficacy of the therapies by combining peer-reviewed and experimental data. The suggested methodology combines AI-driven conversational therapy with predictive modelling to deliver individualized, real-time mental health treatment. In this study, a conceptual chatbot prototype, designated BECK-AI BOT, was developed to illustrate the applications interface and functionality, enhancing accessibility for both patients and therapists in the future. This study does not present new clinical trial data. All reported symptom-reduction and engagement findings are drawn from previously published studies of existing AI-driven CBT systems (e.g., Woebot, Wysa, Eleos, Limbic). The present work offers a narrative synthesis of current evidence and introduces a conceptual architecture and prototype (BECK-AI BOT), without evaluating it clinically. Notwithstanding these difficulties, problems persist, including a lack of long-term efficacy statistics, cultural sensitivity issues, and moral reservations about over-reliance on AI during emergencies. The argument comes in the form of AI possibly improving, not replacing, human therapists, emphasizing hybrid systems for fair treatment. Future research needs to advance emotional intelligence within AI, which combines AI-driven conversational therapy and predictive modelling to deliver real-time, personalized mental health services. The Author(s) 2026. -
Intensity of hospital waste generation and disposal in the selected hospitals in Kerala, India: an analysis based on hospital ownership
Management of hospital wastes has been considered as an integral part of hospital hygiene and infection control, which in turn depends on the intensity of waste generation and disposal. This study analyses the ownership-wise intensities of hospital waste generated, treated and disposed in the selected hospitals in the state of Kerala, India. These intensities are examined using secondary data collected from four districts of Kerala for the period from 2010 to 2014. The intensity of hospital waste generation is measured on the basis of per bed per kilogram per day and also per patient per kilogram per day basis. The study shows that private hospitals are producing significantly higher amount of waste than government and co-operative hospitals. However, private hospitals are found to be more efficient compared to government hospitals in treating and disposing the hospital waste. It is also found that the co-operative hospitals are well-organized in treating and disposing the liquid waste compared with other hospitals in Kerala. 2023, The Author(s), under exclusive licence to Springer Nature Japan KK, part of Springer Nature. -
CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network
Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patients information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Perceptive VM Allocation in Cloud Data Centers for Effective Resource Management
Virtual Machine allocation in cloud computing centers has become an important research area. Efficient VM allocation can reduce power consumption and average response time which can benefit both the end users as well as the cloud vendors. This work presents a perceptive priority aware VM allocation policy named P-PAVA algorithm, which takes into account the priority of an application along with its compute, memory and bandwidth requirement. The algorithm performs allocation of the applications based on the priority it gets using a machine learning based prediction model. Furthermore, to reduce the overhead of the allocation algorithm, parallelization is employed before assigning various workloads. To achieve this, the algorithm employs the First fit technique as a baseline for the requests allocation with a criteria as low priority. When compared to the state of the art algorithm for VM allocation for priority aware applications, P-PAVA performs better on several criteria such as average response time, execution time and power consumption. 2021 IEEE. -
Hybrid feature optimization and radial basis function networks for cardiovascular disease prediction
The study addresses the critical challenge of accurately predicting cardiovascular disease (CVD), a leading cause of mortality worldwide, where early diagnosis is crucial for effective intervention. Traditional models often struggle with high-dimensional data, imbalanced classes, and nonlinear feature interactions, limiting prediction reliability. Motivated by these gaps, this research proposes a hybrid methodology integrating Harris Hawks Search (HHS) for feature optimization with Radial Basis Function Networks (RBFN) to enhance CVD risk assessment. The HHS algorithm efficiently selects key predictive features such as chest pain type and number of vessels, reducing dimensionality while preserving vital information. Trained on optimized features, the RBFN classifier achieved superior performance with 92.1% accuracy, high sensitivity, and specificity, surpassing conventional models like Logistic Regression (81.2%) and Random Forest (86.7%). Ablation studies confirm each component's contribution, with significant gains validated statistically (p < 0.05). The hybrid model also offers computational efficiency with training times around 31.7 s. Future work aims to validate this approach on diverse, larger datasets and integrate it into real-time clinical decision support systems, advancing personalized, interpretable, and efficient cardiovascular healthcare tools. 2026 Elsevier Ltd -
Effectiveness of gamification in facilitating microlearning for gen Z
This chapter offers a thorough examination of the uses, advantages, and difficulties of gamification in higher education. In contrast to game-based learning, gamification uses specific game features to improve the learning experience. This chapter investigates the use of gamification to engage and inspire Generation Z (Gen Z) pupils with the goal of enhancing their academic performance. It underlines the necessity for game development that increases motivation and engagement in educational settings and highlights the measurement of student progress based on completed activities. Effective instructional approaches are crucial in a time where there is a constant stream of information and people have short attention spans. A promising approach to overcoming these difficulties in both online and offline education utilizing ICT technologies is offered as gamified microlearning, which combines microlearning and games. 2024, IGI Global. -
Deep learning architectures for multimodal fusion
The advancement in technology during the recent years has provided deep learning technology as an emerging and powerful paradigm which can be used for processing and understanding complex data across various domains. Multimodal fusion is integrating the information that is collected from various sources or modalities, which requires a comprehensive understanding of data like autonomous driving, medical diagnosis, etc. In this chapter, we will explore the various advanced deep learning architectures that have been specially designed based on the multimodal fusion. The various challenges that are being faced in multimodal data, which include heterogeneity, noise, reliability of data, etc. Various deep learning architectures that are built to address the various challenges, like convolutional neural networks, recurrent neural networks, are reviewed, and the suitability of the fusion strategies is highlighted. The various techniques that are used for combining the information from disparate modalities, like early fusion, late fusion, and hybrid approaches, are also discussed with their pros and cons. Various real-time applications in the field of healthcare, multimedia, robotics, etc., are demonstrated based on the impact of the architectures. Finally, the potential of deep learning architecture based on the revolutionary multimodal fusion will be discussed. 2026 Elsevier Inc. All rights reserved.