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Smart Agriculture: Machine Learning Approach for Tea Leaf Disease Detection
Across the globe, plant infections from pathogens such as fungi, bacteria and viruses are the major issues in the agricultural sector. Agricultural productivity is one of the most important things on which the nations economy highly depends. The detection of diseases in plants plays a major role in the agricultural field. This study proposes a multi-stage network involving Convolutional neural network, Pattern identification and Classification using Siamese network. The main objective behind this study is to enhance the disease detection technique performance. The image data of Tea leaves chosen for this study will be gathered. The algorithms based on techniques of image processing would be designed. The proposed algorithm was tested on the following diseases namely Red rust, Blister blight, Twig dieback, Stem canker, Grey Blight, Brown Blight, Brown root rot disease and Red root rot disease in Tea leaves. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Conceptual Framework for AI Governance in Public Administration - A Smart Governance Perspective
With the public governance lagging behind the fast evolving of AI in their attempts to yield sufficient governance, corresponding principles are necessary to be in par with this dynamic advancement. As AI becomes more pervasive and integrated into various domains, there is a growing need for AI governance models that can ensure that the development and deployment of AI systems align with ethical, legal, and social standards. There are some answers that literature puts forward to the question onthe way the government and public administration has to react to the huge concerns related to AI and usage of policies to avoid the emerging challenges. In this survey, AI problems and the prior AI regulation techniques are analyzed. In this research study, a governance model for AI is proposed by combining all the facets and also implements a new procedure for governing AI. This study will help the decision makers to make smart government a reality by using AI governance framework. 2023 IEEE. -
Minimizing Energy Depletion Using Extended Lifespan: QoS Satisfied Multiple Learned Rate (ELQSSM-ML) for Increased Lifespan of Mobile Adhoc Networks (MANET)
Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the nodes vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the networks lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the nodes energy use for broadcasting the data from the origin to the target. Moreover, the nodes energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms. 2023 by the authors. -
Collaborative Model for Sustainable Energy Utilization in Cloud Infrastructure
As the infrastructures of cloud computing provides paramount services to worldwide users, persistent applications are congregated using large scale data centres at the customer sides. For such wide platforms, virtualization technique has been incorporated for multiplexing the essential sources available. Due to the extensive application variations in the workloads, it is significant to handle the resource allocation methodologies of the virtual machines (VM) for assuring the Quality of Service (QoS) of cloud. On concentrating this, the paper proposed a Decentralized Energy-Aware Collaborative Model (DEACM) for effectively managing the data centres in cloud infrastructures. Initially, the optimal model for system management and power management are declared. Then, functions of workload vectors and data collection about workloads has been carried out for optimal selection of virtual machines to migrate for balancing loads efficiently. This can be further applied for Target-based VM Migration Algorithm for determining the migrating target for VM. Moreover, the algorithm involved in energy utilization with managed QoS. The developed DEACM is evaluated using CloudSim platform and the results are discussed. The results exemplify that the DEACM can balance the workload across variety of machines optimally and provide reduced energy consumption to the complete system efficiently. 2025 IEEE. -
Integrating Hesitant Fuzzy Sets with Machine Learning for Enhanced Healthcare Predictive Analytics
This study examines how Hesitant Fuzzy Sets (HFS) and Machine Learning (ML) might improve healthcare predictive analytics. HFS, which accommodates uncertainty and hesitation in decision-making, is used to improve healthcare projections. Predictive analytics methods struggle with data ambiguity and imprecision, resulting in poor decision-making. Traditional ML algorithms may not be able to collect hesitant information, resulting in less accurate patient outcomes and treatment recommendations. The Integrating Hesitant Fuzzy Sets with ML (IHFS-ML) framework overcomes these issues by integrating HFS flexibility with advanced ML approaches. This connection allows the representation of ambiguous patient data for better healthcare analytics. Data pre-processing in the IHFS-ML framework improves healthcare analytics prediction. These methods transform uncertain fuzzy data into an ML-friendly format. Disease prediction, patient risk assessment, and therapeutic effectiveness analysis are recommended. The approach aims to improve healthcare decision-making and deliver new insights by merging hesitant and ambiguous information. IHFS-ML uses HFS to characterize imprecise and confusing patient data. These HFS are combined with powerful ML classifiers like Random Forest (RF) and Logistic Regression. The IHFS-ML system outperforms current prediction accuracy and reliability methods, suggesting it might transform healthcare analytics. HFS improves ML model interpretability, improving patient outcomes and healthcare decisions. Compared to other methods, the IHFS-ML model improves prediction analysis reliability by 99.7%, scalability by 97.6%, data pre-processing efficiency by 97.1%, interpretability by 98.9%, and accuracy by 97.8%. 2025, Research Expansion Alliance (REA). All rights reserved. -
A Hybrid FinTech Fraud Detection Model Integrating Multiscale Entropy and Transformer-GAT Techniques
Credit card fraud has become a major concern in the FinTech industry due to the rapid growth of digital payment platforms and the increasing sophistication of fraudulent activities. Accurate and timely detection of fraud is essential to minimize financial losses and maintain trust in FinTech services. This study presents a hybrid deep learning framework for credit card fraud detection using the 2023 Credit Card Fraud Detection Dataset. The proposed approach with data preprocessing, which includes handling missing values, removing duplicate entries, and encoding categorical features to ensure clean and structured input for modeling. Normalization is applied to scale features uniformly, preventing bias from varying magnitudes and improving model convergence. Multiscale Entropic (MSE) analysis is employed for feature extraction, capturing both short- and long-term temporal patterns within transaction sequences, enhancing the representation of complex transactional behaviors. The extracted features are then processed using a Transformer-GAT classifier, which combines the attention mechanism of Transformers with Graph Attention Networks (GAT) to learn complex inter-transaction dependencies and graph-based relationships. This hybrid architecture enables the model to capture both local and global patterns, improving fraud detection performance. On the training dataset, the model achieved outstanding results with 98.65% accuracy, 98.70% precision, 98.50% recall, and an F1-score of 98.60 %, demonstrating a strong balance between correctly identifying fraudulent transactions and minimizing false alarms. The approach offers significant advantages for FinTech applications, including robust handling of imbalanced data, effective detection of subtle fraud patterns, and strong generalization to unseen transactions. 2025 IEEE. -
An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks
Intelligent Transportation Systems (ITS) necessitate scalable, real-time, and adaptive traffic flow prediction models to enhance urban mobility and alleviate congestion. Conventional Graph Neural Network methodologies encounter difficulties in managing extensive road networks, long-range temporal relationships, and computing efficiency for real-time applications. An innovative deep learning framework named Meta Temporal Hyperbolic Quantum Graph Neural Networks that integrates hyperbolic embeddings, meta learning, quantum graph, Neural Ordinary Differential Equation (NODEs) to improve the ITS Performance. Across many cities, meta learning facilitates swift adaptation with minimum retraining whereas hyperbolic graph embeddings efficiently depict hierarchical route configurations The usage of Quantum Graph Neural Networks (QGNNs) enhances graph-based scheming, enabling real-time traffic flow to forecast for extensive networks. Also, NODEs summarize ongoing traffic progress, enhancing precision under dynamic sceneries. Datasets like Los-loop and SZ-taxi datasets are validated by experiments which highlights the impact of the proposed MTH-QGNN model, acquiringamean value RMSE of 4.5 and MAE of 3.5, ensuring minimal prediction error. MTH-QGNN model constantly sustained accuracy above 80% and R2 values exceeding 83%, representing robust predictive trustworthiness. MTH-QGNN effectively captures complex spatiotemporal traffic patterns with a variance score above threshold value. The Author(s) 2025. -
Graph Theory in Computer Science
This book is a vital resource for anyone looking to understand the essential role of graph theory as the unifying thread that connects and provides innovative solutions across a wide spectrum of modern computer science disciplines. Graph theory is a traditional mathematical discipline that has evolved as a basic tool for modeling and analyzing the complex relationships between different technological landscapes. Graph theory helps explain the semantic and syntactic relationships in natural language processing, a technology behind many businesses. Disciplinary and industry developments are seeing a major transition towards more interconnected and data-driven decision-making, and the application of graph theory will facilitate this transition. Disciplines such as parallel and distributive computing will gain insights into how graph theory can help with resource optimization and job scheduling, creating considerable change in the design and development of scalable systems. This book provides comprehensive coverage of how graph theory acts as the thread that connects different areas of computer science to create innovative solutions to modern technological problems. Using a multi-faceted approach, the book explores the fundamentals and role of graph theory in molding complex computational processes across a wide spectrum of computer science. 2026 Scrivener Publishing LLC. -
AI in Predictive HR Analytics for Talent Management
This paper presents the topic on how Machine Learning (ML) can be used to conduct Predictive HR Analytics to streamline Talent Management practices. The aim of the development of the project is mainly the application of Random Forest as a supervised learning model to forecast turnover of the employees, performance, and career-growth potential. With the historical employee data, such as performance reviews, tenure, and levels of engagement, Random Forest models would help determine the aspects that are significant factors to employee retention and performance. The model is incorporated with HR software solutions such as SAP SuccessFactors that help to gather information seamlessly to make predictions in real-time, and base decisions on data. It can be seen in the findings of this research that this approach to identifying the factors that influence the effort to retain employees based on the likelihood of them leaving was not only more accurate than other methods but much more effective in the retention efforts. Through predictive analytics, organizations are better placed to take the initiative of managing talent, minimizing turnover and streamline workforce productivity, which eventually lead to business success. This research demonstrates that such predictive models based on AI have a high potential to change HR practice. 2025 IEEE. -
Attachment to God: Narratives of Roman Catholic Priests
This narrative analysis was aimed at exploring the attachment to God narratives of 28 middle-aged Roman Catholic Religious priests rendering their service in various settings in South India. The study found that majority of the Roman Catholic priests had developed representations of a secure attachment to God. Twenty-six priests had developed representations of a secure attachment to God, and two priests of an insecure attachment to God. The Majority of the Roman Catholic priests had developed representations of a secure attachment to more than one spiritual attachment figures. Along with God, most priests had also developed representations of a secure attachment to the Virgin Mary. All the major themes related to attachment to God were found in the narratives of the Roman Catholic Priests. Author(s) 2020. -
Process of identity development and psychological functioning: A critical narrative review for the Indian context
Background: Identity is a crucial milestone achievement for adolescents to become contributing adult members in society. This narrative research focused on exploring the link between identity development and psychological functioning and understanding the process of Indian adolescents' and adults' identity development and psychological functioning. Often, the Indian identity researchers use the theories of identity development conceptualized by Erikson, James Marcia and Michael Berzonsky which have been primarily conceptualized to understand the process of individual's identity development in the western individualistic cultural context. These theorists based their theories on certain essential contextual conditions, for the individuals' identity development. This review article critically explored the availability and applicability of those contextual conditions for Indian adolescents' and adults' identity development. Methods: The articles for the review were mainly collected from the online databases such as PROQUEST Research Library, Taylor and Francis, the archives of the Indian Journal of Social Psychiatry, the archives of the Indian Journal of Psychiatry, EBSCO, and Google. A narrative review method was used to examine various elements of the process of identity development conceptualized by the mainstream identity development theorists Erikson, James Marcia, and Michael Berzonsky and their applicability to the process of Indian adolescents' and adults' identity development. Results: The review found that the processes of mainstream identity development theories have some serious limitations in their applicability to the Indian context. Conclusions: This article identified alternative identity development processes and interventions that could be used to enhance Indian adolescents' and adults' identity development. 2022 The Author(s). -
Analysis and Actions Planned for Programme Outcomes in Outcome Based Education for a Particular Course
In India many of the technical institutions are NBA (National Board of Accreditation) accredited and the accreditation is a way to maintain quality of education. The outcome-based education (OBE) plays an important role in technical education across the world. So, in this research we will show how we can implement the attainment process related to OBE for a particular course. In this paper we will discuss how the course outcome and mapping of course outcome with program outcome can be defined. Then we will discuss the process to calculate the attainment. Finally, the program gaps were identified for that course and actions were suggested. 2024 IEEE. -
A Methodology to Formulate Attainment Process of Outcome-based Education for Undergraduate Engineering Degree Programme
The Outcome-Based Education (OBE) has important role in accreditation of any engineering programme. The OBE involves attainment of programme mission, objectives and outcomes. The paper discusses a methodology to calculate attainment of programme educational objectives and programme outcomes. The results of particular batch 2020 were shown. The process would help in implementing OBE in any technical institution approved by AICTE, India. 2024 IEEE. -
Attainments of Mission Statements and Programme Educational Objectives in Outcome Based Education for a Degree Programme
The outcome-based education (OBE) assesses the outcomes of the students in the form of attainments. The attainments are of a programme which can be undergraduate, postgraduate or diploma. The OBE are assessed in the form of attainments of programme outcomes (POs), programme educational objectives (PEOs) and attainment of mission statements. The attainments are calculated on the basis of direct and indirect tools. The outcomes of theory and lab courses, projects, and placements are considered direct tools. Indirect tools include several surveys like alumni, curriculum, exit, etc. The attainments are finally calculated by combining direct and indirect tools. The attainments of different batches are compared and assessed. The entire attainment process is covered in this research article, and the attainment of a certain batch is shown by the attainments of PEOs and POs. 2025 IEEE. -
Harnessing Behavioural Insights for Autism Spectrum Disorder Prediction via Machine Learning
This paper exploration has been done for predicting autism spectrum disorder or ASD while using certain machine learning classifiers. The multisource dataset used, covers behavioral, genetic, neuroimaging and sensor data. The classifiers used are: Random Forest, AdaBoost, Extra Trees, Logistic Regression, K Neighbors, and Bernoulli Naive Bayes. The results achieved are: The Bernoulli Naive Bayes (92.10%), Random Forest (91.53%) and Logistic Regression (91.34%). AdaBoost and Extra Trees performed well with accuracies 90.34% and 90.20%, respectively. K Neighbors Classifier had the lowest accurate outcome with 87.65%. This study explores improving ASD diagnosis, highlighting the effectiveness of various models and emphasizing the need for further research to address challenges such as model interpretability and data quality. 2025 IEEE. -
A Systematic Approach for Enhancing the Curriculum Development based on the Gap Analysis to Meets the Standards of Accreditation
The article focuses on the identification of gaps in course outcomes and program outcomes in an outcome-based education. The identified gaps help in the redesigning of the curriculum as per the standard of accreditation, new education policy of India and industry-academia related gap. The gaps are identified on the targets and the attained value of course outcome of a particular course. The course outcomes are in turn related to program outcomes and on this basis, we could be able to identify the program outcomes which are not attained. This helps us in the formulation of new curriculum or redesigning of existing curriculum. In this research paper we will discuss how to calculate the attainment of any particular course, attainment of program outcomes, identification of gaps and the suggest the action plan to fill this gap through the revision of curriculum. 2026 IEEE. -
AI that Understands Us: LLM-Based Emotion and Stress Insights from Online Communication
The large language models (LLMs) show more accurate interpretation of human emotion and psychology from text which is available on the social media chats. This study analyses emotion recognition and stress detection capabilities of fine-tuned LLMs (GPT-2, FLAN-T5 and LLaMA-7B) using text data from social media and conversation logs. The models were tested for performance using the publicly available emotion language datasets, DailyDialog, EmotionX and GoEmotions. The models were evaluated for performance and efficiency using classification accuracy, macro-F1 score, and inference time. The results identify the performance spectrum of the models and the large models' enhanced ability to recognize detailed emotional states. These results offer real world applicability of LLM methodologies to stress detecting and decision-support automated systems in mental healthcare. 2026 IEEE. -
Optimized placement and sizing of solar photovoltaic distributed generation using jellyfish search algorithm for enhanced power system performance
The strategic integration of distributed generation (DG) units into distribution power networks (DPNs) is pivotal for augmenting system efficiency and stability. This study introduces an advanced metaheuristic optimization framework leveraging the Jellyfish Search Algorithm (JSA) for the optimal placement and sizing of solar photovoltaic (PV) DG units. The formulated multi-objective function incorporates real power loss (RPL) minimization, voltage deviation index (VDI) reduction, and voltage stability index (VSI) enhancement, employing a weighted sum approach (WSA) to ensure computational rigor. The efficacy of the proposed methodology is rigorously validated on the IEEE 33-bus radial DPN under single and multiple PV system deployment scenarios. For single PV system optimized inclusion, RPL of the DPN is cut down from 210.98kW to 102.89kW, total VDI is reduced from 1.8047 p.u to 0.5331 p.u, and minimum VSI is increased from 0.6671 to 0.7559. For two PV DG units inclusion, RPL is reduced to 82.99kW, total VDI is reduced to 0.6518 p.u with a least VSI improved to 0.8848. However, better result is obtained with three units of DG placement with RPL reduced to 69.59kW, total VDI decreased to 0.3293 p.u with a least VSI of the test system increased to 0.8916. Comparative analyses against state-of-the-art metaheuristic algorithms underscore the superior convergence efficiency and optimality of JSA in addressing nonlinearity and high-dimensionality constraints. Empirical results substantiate substantial RPL reduction, bus voltage enhancement, and system stability reinforcement, establishing JSA as an avant-garde paradigm in DG optimization. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2025. -
Simultaneous photovoltaic distributed generation and capacitor optimization for enhancing performance indices of radial power distribution system
This paper presents an effective metaheuristic framework using the Osprey Optimization Algorithm (OOA) for the simultaneous allocation of distributed generation (DG) units and capacitor banks (CB) in radial distribution systems (RDS). The method optimizes the locations and sizing for DG units and CB to minimize active power losses (APL), to reduce voltage deviation (VD), and to enhance voltage stability. The performance of the proposed approach is tested on IEEE 69-bus and 118-bus benchmark RDSs and the real-time Tala Egyptian RDS. The OOA achieved superior results compared to popular heuristic algorithms such as antlion optimizer (ALO), hunter-prey optimizer (HPO), and whale optimizer algorithm (WOA). Specifically, for three units of DG and single capacitor integration in the 69-bus system, OOA reduced the total APL by 75.1%, lowered the total voltage deviation (TVD) by 1.4835p.u., and improved the total voltage stability index (TVSI) by 3.0229. With optimal assimilation of three units of DG and capacitors each, APL reduction, TVD minimization, and TVSI improvement further extended to 79.9%, 1.5013p.u., and 2.2787, respectively. Furthermore, OOA validation on a variable-load 69-bus RDS and the real 37-bus Tala Egyptian RDS demonstrated consistent and superior performance, showcasing its robustness. Statistical analyses also confirm OOAs efficiency and ability to solve DG planning in the distribution networks. The Author(s) 2025.