bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Parkinson's Disease Detection from Brain MRI through Optimized Super Resolution Generative Network
Authors: Bali Devi
Published: February 2025

Introduction: Parkinson's disease (PD), a neurodegenerative disorder that results in motor symptoms like stiffness and tremors, is characterized by the death of dopaminergic neurons in the substantia nigra of the brain. Early and accurate diagnosis of PD is challenging due to overlapping symptoms with other neurological conditions and the limitations of traditional diagnostic methods.
Objectives: This study proposes a novel approach utilizing advanced deep learning techniques to enhance PD diagnosis.
Methods: A Super-Resolution Generative Adversarial Network (SRGAN) was developed, integrating a customized DeepResNet (ResNet-150 with skip connections), Generative Adversarial Networks (GANs), and Gaussian filtering to improve the quality of MRI and PET imaging data. Image preprocessing steps including contrast enhancement, noise reduction, gamma correction, and pixel value normalization were employed to ensure high-quality input for the model.
Results: The SRGAN demonstrated superior performance compared to existing convolutional neural network (CNN) models such as DenseNet, AlexNet, VGG 19, and ResNet, achieving an accuracy of 96.5%, an F1 score of 94.5%, and a balanced accuracy (BAAC) of 93.5%. Additionally, the Skip Connection Optimization (SCO) within the SRGAN outperformed other optimization techniques like Genetic Algorithms (GA), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Stochastic Gradient Descent (SGD), ADAM, and AdaMax.
Conclusions: The results highlight the effectiveness of advanced deep learning models in improving the diagnosis and potential treatment strategies for Parkinson's disease.
2D-Fibonacci Summation with Extorial Functions
Authors: T. Sathinathan
Published: February 2025

In this paper, we derive the second-order Fibonacci summation formula for the product of polynomial and extorial functions using the two-dimensional second-order Fibonacci nabla difference operator, its inverse, and the two-dimensional second-order Fibonacci numbers. The function obtained by substituting polynomial with factorial polynomial in the expansion of the exponential function is referred to as the extorial function. We also discuss the properties of the extorial function in this chapter. Additionally, we apply the extorial function to two-dimensional difference equations
Adaptive and Nonlinear Strategies based Switching Hybrid Model for Book Recommendation using User Activity and Data Availability
Authors: Satrughan Kumar
Published: February 2025

The diverse and constantly evolving preferences of the users require the library management system to always meet them. Therefore, in this digital era, Book Recommendation Systems online, in particular, have been the key to making it easy for the users by offering personalized book recommendations, thus raising user engagement as well as overall satisfaction. In this regard, we discuss major developments in recommendation methods, with emphasis on collaborative, content-based, and combined methods. In this paper, we introduce a novel hybrid recommendation model that dynamically switches between two well-established methods: K-Nearest Neighbors (KNN), and Neural CF (NCF). The main purpose of our approach is the hybridization of the strengths from two models. As observed, the KNN-based CF offers good interpretability with the relative ease, while the NCF is capable to retrieve the nonlinear interactions between users and items. Unlike other hybrid approaches which combine the outputs of multiple models, the proposed method follows a dynamic switching approach. This allows the system to select the best model based on specific user conditions to improve the recommendation process. In addition, we propose an enhanced version of this algorithm that makes use of expert users to optimize the precision of the recommendation system. The performance of the proposed models is measured in terms of metrics such as RMSE, MAE, precision, and recall, and it is observed that the proposed models outperform the baseline algorithms.
Some More Results on Neighborhood Connected Two Out Degree Equitable Domination Number of Graphs
Authors: M.S.Mahesh
Published: February 2025

For any graph G, the out degree of a vertex v with respect to D is denoted by and it is defined by). A sub set D of V(G) is said to be two out degree equitable dominating set if D is dominating set and for any two vertices  such that   The minimum number of vertices in two out degree equitable dominating set is called two out degree equitable domination number and it is denoted by Based on the above concept of out degree we introduce a new domination parameter called neighborhood connected two out degree equitable domination number and study this domination number for some standard graphs In this paper the proposed domination number verified for some new special graphs and semitotal point graph of some general graphs.
Systematic Investment Plan vs. Lumpsum Investment: A Comparative study across Time and Indexes
Authors: Pushkar Dilip Parulekar
Published: February 2025

Introduction: SIP is based on the logic of rupee cost averaging wherein regular periodic investments are made (generally monthly) as LI which means one time investment There is always a debate between active and passive investing. Even though some active investors might outperform passive investors, there will be balancing underperformers as well. Considering transaction cost and risk adjusted returns passive investors tend to outperform the active investors over the longer time horizon.
Objectives: This paper compares success of two popular methods of passive investing that could be used by retail investors for investing in Indian stock markets viz. Systematic Investment Plan (SIP) vs. Lumpsum Investment (LI). The paper calculates risk and return for buy and hold strategy for the period of 5 ,10 and 15 years in various indices.
Methods:The study was based on monthly data for seven indices over a period of 20 years from 1st October 2004 to 1st October 2024. There were four broad-based indices viz. Nifty 50, Nifty 100, Nifty 200 and Nifty 500. The other three were sectorial indices viz. Nifty AUTO, Nifty BANK and Nifty FMCG. They were evaluated on various time frames of 5 years, 10 years and 15 years. The evaluation parameters were Extended Internal Rate of return adjusted for investing time (XIRR) for SIP and Compounded Annual growth rate (CAGR) for LI. The two methods were evaluated based on Maximum, Minimum, Average, Standard deviation, Variance of XIRR and CAGR numbers. Comparative analysis was done using t-Test: Paired two Sample for means. The papers cover various market cycles and investment horizons commonly recommended for retail investors.
Results: Out of the 21 combinations of (3 timeframes * 7 indexes) SIP was better than LI in terms of risk related parameters in 19 combinations. However, in terms of returns there were many combinations wherein LI was better than SIP. Contrary to the popular belief, there was no conclusive evidence that SIP was better than LI particularly for large cap index like Nifty 50 and defensive index like Nifty FMCG across timeframes. For the timeframe of 5 years and 10 Years SIP was better as compared to LI for Nifty 200, Nifty 500, Nifty AUTO and Nifty BANK index. However, based on returns and absolute amount LI was better as compared to SIP investment.   
Conclusions: Passive investing in Index funds is highly recommended for retail investors because of transaction costs in Indian mutual funds. In the case of index funds, they can choose broad based funds or sectorial funds based on their risk appetite and time horizon and SIP or LI as a style of investment.
Binomial Labeling of Intersection Mirror Lagoon Step Graph and Intersection Opposite Lagoon Step Graph with Python Implementation
Authors: J. Vasanthi
Published: February 2025

Assume G = {V, E} be the graph with n vertices and e edges. The binomial labeling classification has been used for Intersection Mirror Lagoon Step Graph IM LnSm and Intersection Opposite Lagoon Step Graph IO Ln Sm in this paper. The concepts stated below in the paper have been implemented using Python coding.
Comparative Study of Machine Learning Models for Predicting Cardio Activities Based on Diverse Performance Metrics
Authors: M. Vijayakanth
Published: February 2025

The prediction of cardio activities is vital for enhancing the understanding of physical performance and facilitating effective health monitoring. This research investigates the implementation of diverse machine learning techniques, including Logistic Regression, Multilayer Perceptron, SMO, J48, Random Forest, and REP Tree, for forecasting cardio activity outcomes. The analysis is conducted using an extensive set of parameters, such as Date, Type, Distance (km), Duration, Average Pace, Average Speed (km/h), Calories Burned, and Climb (m). To assess the performance and reliability of these models, several metrics namely TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, ROC Area, and PRC Area are employed. The experimental findings provide a comprehensive understanding of the relative effectiveness of the models, offering practical insights for identifying optimal methodologies in the domain of cardio activity predictions.
Ternary Algebraic Structure Convey to New Type of Intuitionistic Q1 Anti Fuzzy Ideals of an Regular Ordered Ternary Semigroups
Authors: R. Balaji
Published: February 2025

We examine some of the properties of these ordered ternary semigroups, such as intuitionistic Q1 anti fuzzy left ideal ,Q1 anti fuzzy right ideal, Q1 anti fuzzy lateral ideal, Q1 anti fuzzy ideal and Q1 anti fuzzy bi-ideal. We introduce the idea of TSS. The Q1 anti anti fuzzy ideal is extended in a new way over ternary semigroups B.
Integrated Framework for Data Security in Cloud Computing Using Deep Learning Techniques
Authors: Gantela Prabhakar Simhadri Mallikarjuna Rao
Published: February 2025

Alongside the expansion of the digital economy, data centers have grown substantially in size and quantity. Data centers are becoming increasingly essential to the development of the economy and society. However, even a brief outage in a data center might have severely negative effects. Resolving this issue requires secure management of data centers' physical infrastructure. Defenses against different cyber threats are being developed for the Internet of Things (IoT) and Cyber Physical Systems (CPS). As malicious code becomes more prevalent, using cloud environments to find dangerous code might not be a viable approach in the future. Due to the growing inefficiency of traditional perimeter-based security models in today’s cloud-centric and remote work contexts, we employed integrated deep learning techniques for cloud data security in this article. According on risk profiles and real-time behavior, the suggested Zero-Trust security framework continuously evaluates and modifies the trust levels for users, devices, and applications. Using an integrated framework, we employed User and Entity Behavior Analytics (UEBA), Risk Scoring, and Adaptive Authentication approaches. This enabled us to reach an accuracy of 85–90% in all areas of data security when compared to the conventional methods.
Topological Study on Revised Fuzzy Metric Spaces and Their Generalization
Authors: Thangathamizh R,
Published: February 2025

Introduction
In this paper, we explore the concept of metric functions within a revised fuzzy metric space. The study focuses on understanding the relationships between these metric functions and the topological structures they generate. Specifically, we introduce the concept of a stratified function within this framework and investigate its implications.
Objectives
The main objectives of this paper are:

To define and analyze stratified functions in a revised fuzzy metric space.
To demonstrate that the topology generated by the family of stratified functions coincides with the topology generated by the revised fuzzy metric.
To derive the concrete form of the metric function under specific conditions.

Methods
We approach these objectives by first introducing the notion of a stratified function in a revised fuzzy metric space. Using this concept, we prove that the topology generated by the family of stratified functions is identical to the topology generated by the revised fuzzy metric. Additionally, we explore the conditions under which a specific form of the metric function can be determined.
Results
Our findings show that the topology generated by the stratified functions indeed coincides with the topology generated by the revised fuzzy metric. Moreover, under certain special conditions, we can obtain a concrete representation of the metric function.
Conclusion
This paper provides a deeper understanding of the structure of revised fuzzy metric spaces. The introduction of stratified functions serves as a key tool for analyzing the topology of these spaces, and our results offer a concrete form for the metric function under specific conditions, contributing to the broader study of fuzzy metric spaces.
Development of Probability Distribution Functions for Material Utilization in Road Construction Projects
Authors: Gaurav Gautam
Published: February 2025

Road construction is a major contributor to the country's development, and its development requires more attention than any other construction sector. The dynamic nature of these projects complicates planning and scheduling. There has been considerable research in various countries on supply chain management and its application in construction. However, very few studies have been conducted in the Indian context using real-time data for material usage as input for discrete event simulation modeling as a tool for supply chain management. The present research arrives at a probability distribution function for material usage for all layers of a Road construction project.
Quality Control in Interconnected Healthcare: Strategies for Success
Authors: Vinay Kumar Reddy Vangoor
Published: February 2025

Correctness, consistency, and dependability in data quality assurance define interoperable health systems. Here we evaluate the methods. Good patient care calls on data integrity and platform compatibility. When healthcare companies build integrated data exchange systems, which raise data discrepancies, repetitions, and mistakes, quality assurance becomes very vital. Data integration problems as well as technological and administrative approaches to improve healthcare data quality are covered in this paper.
Analytical Study of Systematic Predictive Model of Integral Machine Learning and Data Visualization Technologies for Modern Healthcare
Authors: Rajat Singhal
Published: February 2025

In this evolving world, machine learning and data visualization technologies are furnishing many domains worldwide including healthcare. Integrating these advance modern technologies are undoubtedly enhancing the accuracy and efficiency of predictivity analytics in modern healthcare marvelously. However, there still remains some limitations that need to be resolve for maximizing the potential of these technologies in modern healthcare. Objective of our review paper is to perform a analytical study by reviewing some recent studies regarding the utility of machine learning and data visualization in the field of healthcare and tries to address the possible research gaps that could assist researchers for carrying out further research in this area. This study also discusses the possible future trends that could probably bridge this existing research gap and can robust the use of machine learning algorithms and data visualization tools in predictivity and could create an user-friendly, scalable and trustable technological environment for individuals in healthcare sector.
The Role of Transformational Leadership on Innovative Work Behavior through Intrinsic Motivation and Job Crafting of Permanent Lecturers of PTS in Lamongan Regency
Authors: Rifky Ardhana Kisno Saputra
Published: February 2025

Introduction: This study explores the relationship between transformational leadership and innovative work behavior, both directly and through mediating variables, namely job crafting and intrinsic motivation.
Objectives: The primary objective is to analyze the direct and indirect effects of transformational leadership on innovative work behavior by examining the roles of job crafting and intrinsic motivation as mediators.
Methods: A quantitative descriptive approach was employed using Partial Least Square (PLS) equation modeling. Data collection methods included questionnaires and literature studies. The study population consisted of teachers from a vocational high school in Lamongan Regency, with a sample of 100 respondents selected using a probability sampling technique and simple random sampling method. Analysis was conducted using SmartPLS, which involved determining convergent validity, discriminant validity, composite reliability, and inner model evaluation.
Results: The findings indicate that transformational leadership does not have a significant direct effect on innovative work behavior. However, transformational leadership significantly influences innovative work behavior indirectly through the mediating variables of job crafting and intrinsic motivation.
Conclusions: The study concludes that while transformational leadership alone is insufficient to directly enhance innovative work behavior, it can positively impact such behavior through job crafting and intrinsic motivation. Future studies are encouraged to explore additional mediators and contexts to deepen understanding of this relationship.
Research on the Key Factors Influencing Female Consumers' Purchase Intention in Live - Streaming E - Commerce on the Douyin Platform
Authors: Kang Jinhui
Published: February 2025

The live streaming e-commerce industry has experienced explosive growth and become the dominant force in the digital market. The integration of live streaming videos and e-commerce has redefined the shopping experience and attracted a large number of female consumers. This study aims to explore the relationship between live streaming hosts, products, live streaming platforms, and female consumers' purchase intention in e-commerce live streaming, with a focus on the mediating role of consumer perceived value. In order to achieve these goals, we distributed online questionnaires to 420 Tiktok female users through the Credamo data platform, and collected detailed demographic data such as age, occupation, income, shopping preference, etc., so as to fully understand the sample situation. We use SPSS and AMOS software for data analysis. The research results show that the Anchor popularity, Effective interaction, Product value information, and reasonable price discounts significantly enhance consumers' perceived value and purchase intention; Brand awareness is equally important, reflecting consumers' emphasis on brand reputation and image. In contrast, the communication style, live atmosphere, and Platform popularity of the anchor have a smaller impact on perceived value, indicating that consumers are more concerned with substantive content rather than environmental factors. This study provides a reference for a deeper understanding of female consumers' live shopping behavior and provides guidance for companies to develop marketing strategies.
Exploring the Efficacy of Graph Neural Networks in Predicting Traffic Flow in India
Authors: Paras Patil
Published: February 2025

Accurate and real-time traffic forecasting is essen- tial for managing vehicle movement, reducing congestion, and generating optimal routes. However, existing traffic prediction systems rely on datasets specific to certain regions, making them unreliable for Indian traffic and road networks due to the lack of suitable training data. To address this issue, in this paper, we propose a Dataset for Indian Vehicular Traffic Analysis (DIVA) capturing the traffic features of the Indian Province. By training Graph Neural Networks on this dataset, we achieved comparable results to the state-of-the-art datasets (PEMSD7, PEMSD8), demonstrating the efficacy of the DIVA dataset in accurate flow prediction. Using this dataset, appreciable accuracy was achieved for traffic speed prediction using GNNs with STGCN achieving the lowest RMSE values. Moreover, we propose a novel route navigation approach that utilizes predicted future traffic speeds at nodes, represented as a future graph thus, provide a path that maximizes the traffic speed hence, minimizing the travel time.
Computational Approaches for Drug-Protein Interaction Analysis in Cancer: Machine Learning and Structural Bioinformatics Perspectives
Authors: Mary Margarat Valentine Neela, Subbarao Peram
Published: February 2025

Accurate drug-protein interaction is critical towards targeted cancer therapy and precision medicine. Conventional experimental methods for DPI identification are time-consuming and costly, necessitating computation approaches instead. Proposed in the study are: PCA-Cosine Similarity, an approach for DPI analysis, and CNN-Xception, a model for MRI-based brain tumor classification.The PCA-Cosine Similarity method uses PCA tools in performing dimension reduction of features while preserving predictive accuracy, rendering large-scale drug discovery more efficient. The CNN-Xception model blends CNN and Xception architecture via depthwise separable convolutions to offer improved tumor classification.
Experimental results show that PCA plus Cosine Similarity achieved 95.18% accuracy, outperforming raw similarity calculations very effectively and yet optimizing computational complexity. At the same time, the CNN-Xception model scored an impressive 100% across 656 test samples while differentiating between Glioma, Meningioma, Pituitary tumors, and non-tumor cases with great ease. From the comparative analysis, it is undoubted that deep-learning and similarity-based models work hand in hand, outperforming conventional methods in DPI prediction and brain tumor classification with a high degree of efficiency.Future enhancements will continue with molecular docking validation (AutoDock Vina) to tune DPI predictions, along with deep learning architecture integration to further such predictions. These developments will go a long way toward ushering in robust computational models for cancer drug discovery and precision oncology.
Decentralized Energy Trading Framework With Active Pricing Via Blockchain Smart Contracts
Authors: Garima Gurjar, Mangesh D.Nikose
Published: February 2025

With the use of IoT devices and blockchain technologies, consumers can purchase and sell energy straight from the grid instead of going via retailers. Here, existing research proposed a various model but it has several limitations such as scalability, less optimal, and efficiency. Therefore, this research proposed a novel Decentralized Energy Trading Framework via Block-chain Smart Contracts. Initially, we obtain a dataset, which consists of total incoming energy (from producers), total outgoing energy (to consumers), price for incoming energy, and price for outgoing energy. Then, this research proposed a Concatenate Hash approach, to generate private and public keys to enhance security and efficiency. Following that, this research proposed a Weighted Scoring Technique for energy trading here Deputized Proof of Stake (DPOS) is used to verify the integrity of blocks which increases scalability and efficiency by reducing the number of participants in the consensus process. Then, for energy trading the consumer could calculate a score for each prosumer using a weighted average considering both energy availability and price. Moreover, Profit generating is the primary factor used to encourage customers and buyers to take part in suggested P2P energy trading. Consequently, Income Generation based on an active pricing strategy was proposed by this research. As a result, our proposed approach has less latency, higher throughput, and security.
Literature Review on the Current State and Prospects of Human Resource Management and Supply Chain Management Education in Saudi Universities
Authors: Omar A. Baakeel
Published: February 2025

This review examines the current state of human resource management (HRM) and supply chain management (SCM) education in Saudi universities, focusing on curriculum development, faculty qualifications, industry collaboration, and technology integration. The findings from 26 articles suggest that while significant progress has been made in aligning university programs with industry needs, challenges persist, particularly in providing students with practical experience and ensuring faculty expertise. This review makes key recommendations, including enhancing partnerships between universities and industry to offer students more hands-on learning opportunities, developing faculty training programs to bridge the expertise gap, and incorporating digital tools and case-based learning into curricula to improve the relevance of the education provided. Future research should explore the impact of digital tools on HRM education and the integration of advanced supply chain technologies in university programs to ensure that the programs remain aligned with industry developments. Furthermore, a comparison of qualitative and quantitative research methods reveals that while qualitative approaches provide in-depth, context-rich insights into complex phenomena, the methods suffer from limited generalizability. Conversely, quantitative methods offer statistical relevance and broad applicability but may overlook nuanced, contextual issues. By addressing these challenges and incorporating these insights, Saudi universities can enhance their HRM and SCM programs, ensuring they equip graduates with the necessary skills to thrive in a rapidly evolving global business landscape.
The Role of Artificial Intelligence in Transforming Management Science Education: Implications for Curriculum Development and Teaching Methods
Authors: Inchara P
Published: February 2025

Artificial intelligence (AI) is transforming management science education through the improvement of curriculum design and optimization of pedagogies. This study explores the use of AI in improving the learning process through intelligent algorithms like natural language processing (NLP), reinforcement learning, neural networks, and decision trees. The study uses a student performance and course activity dataset to validate the impact of AI-based pedagogical methods. Experimental results suggest AI-based methods improve student engagement by 28%, knowledge recall by 35%, and accuracy of evaluation by 22% compared to traditional methods. Additionally, decision-making effectiveness in course structure design improved by 40%, demonstrating the potential of AI in improving curriculum design. A comparison of similar research works verifies the success of AI in digital health, sustainability research, and entrepreneurship research. Notwithstanding these benefits, ethical concerns and learning AI complexity remain major pitfalls in its universal adoption. The study concludes that the application of AI in management education has the potential to provide more adaptive, data-driven, and personalized education. Future studies must improve AI-based frameworks, address ethical concerns, and improve AI-based tests to further enhance the quality of education.
Hybrid Deep Learning Approach for Education Data Mining and Improvement of Education Practices
Authors: Shrishail Patil
Published: February 2025

The exponential growth of educational data necessitates innovative approaches to mining and utilizing this information to enhance educational practices. This study proposes a hybrid deep learning framework for educational data mining (EDM) that integrates various data sources, advanced feature engineering techniques, and state-of-the-art classification algorithms to improve learning outcomes and institutional decision-making processes. The research utilizes a diverse dataset comprising EDM applications, real-time educational data, and synthetic student data to develop robust models. Feature engineering is conducted using a hybrid approach that combines TF-IDF, N-gram, bigram relational models, autoencoders, and density-based techniques, aiming to maximize data representation and reduce dimensionality. The classification phase incorporates an array of traditional and deep learning methods, including Naïve Bayes (NB), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), AdaBoost, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and a novel hybrid RNN-SVM model. The proposed hybrid RNN-SVM classifier demonstrates superior accuracy and scalability by leveraging RNN's sequential learning capabilities and SVM's margin-based classification efficiency. Additionally, a recommendation module is designed to provide actionable insights, including class improvement strategies and industry-oriented suggestions, thus bridging the gap between academic performance and professional readiness. The hybrid deep learning framework not only enhances predictive accuracy but also facilitates informed decision-making for educators and policymakers. Experimental results validate the framework's efficacy in mining meaningful patterns from complex educational datasets and optimizing learning strategies. This research highlights the transformative potential of hybrid deep learning in advancing the field of EDM and fostering improved educational practices.
Integrating ICT with Artificial Intelligence for Transformative Education
Authors: Pushyanthkumar Reddy Mukkala
Published: February 2025

The integration of Information and Communication Technology (ICT) with Artificial Intelligence (AI) is transforming the educational landscape, fostering personalized learning, improving administrative efficiency, and enhancing student engagement. This paper explores the synergies between ICT and AI in education, highlighting their combined potential to create adaptive learning environments, automate routine tasks, and facilitate data-driven decision-making. AI-powered tools, such as intelligent tutoring systems, predictive analytics, and natural language processing, enhance the effectiveness of ICT-driven platforms by tailoring instruction to individual learning needs. Additionally, machine learning algorithms analyze student performance patterns to provide real-time feedback and targeted interventions, ensuring improved academic outcomes.
The study also examines the role of AI-driven ICT in bridging educational disparities by offering accessible and inclusive learning experiences. Virtual classrooms, chatbots, and AI-enhanced learning management systems enable students and educators to interact more effectively, overcoming geographical and economic barriers. However, the integration of these technologies presents challenges, including data privacy concerns, digital literacy gaps, and ethical implications related to AI decision-making. Addressing these issues requires robust policy frameworks, investments in teacher training, and the development of equitable AI models.
This paper synthesizes existing research on AI-integrated ICT applications in education, providing insights into their impact on pedagogy, curriculum development, and institutional efficiency. The findings underscore the need for a balanced approach that leverages AI's capabilities while ensuring ethical considerations and human-centered learning experiences. As AI and ICT continue to evolve, their seamless integration has the potential to revolutionize education, making it more responsive, efficient, and inclusive. Future research should focus on optimizing AI-driven ICT tools, assessing their long-term impact, and developing strategies to address emerging challenges in educational technology adoption.
Strategic Planning and Management of India’s Defence and Economic Presence in the Indian Ocean Region
Authors: Aman Nautiyal
Published: February 2025

India’s strategic management of naval and air power in the Indian Ocean region reflects a dynamic blend of deterrence, power projection, and regional cooperation. As maritime challenges intensify, India’s expanding carrier fleet, advanced submarines, and aerial surveillance assets enhance operational depth. Coupled with multilateral engagements and indigenous defence initiatives, this approach reinforces regional stability. Moreover, with the inclusion of two oceans and increasing strategic presence of Indo-Pacific, India hinges on a multi-dimensional approach integrating security, economic diplomacy and regional partnerships. This study delves into the evolution of India’s strategic planning and management in the Indian Ocean region reflecting a dynamic interplay of historical legacies, geopolitical imperatives, and evolving security and economic priorities. This study delves into the evolution of India’s strategic planning and management in the Indian Ocean region reflecting a dynamic interplay of historical legacies and evolving security and economic priorities. It explores the intersection of military strategy, management approach and economic diplomacy, highlighting India’s air and naval capabilities in securing its strategic interests. Furthermore, despite growing partnerships through Quad, Asean, and IORA, challenges persist, for instance, China’s strategic assertiveness, infrastructure gaps and the logistical constraints. However, leveraging indigenous defence production, multilateral engagements, and emerging technologies presents opportunities for India as well.
Digital Document Security System using Blockchain Technology
Authors: Muhammad Fachri, Haryono Soeparno
Published: February 2025

Blockchain represents a groundbreaking innovation poised to streamline various human tasks and yield positive impacts, particularly in the realms of managing and safeguarding digital records and data. With the rapid advancement of technologies dedicated to digital document management and security, these digital assets have become prime targets for cyberattacks, potentially resulting in undesirable consequences such as data tampering and forgery. This research delves into the development of a robust digital document security system leveraging Blockchain technology tailored to meet the specific requirements of institutions. Utilizing the Enterprise Blockchain Design Framework, the study aims to ascertain the suitability of Blockchain implementation in current business processes. Subsequently, various popular Blockchain platforms are evaluated based on factors such as functionality, speed, transaction costs, and security. The findings from this selection process inform the design of a Digital Document Security Framework utilizing Blockchain technology, thereby enhancing the integrity and security of digital records.
Sentiments Analysis Prediction of The Arabic Stock-Market News Based on Machine- and Deep-Learning Approaches
Authors: Eman Alasmari, Fahd Saleh Alotaibi
Published: February 2025

Stock market prediction of companies is a vital interest for financial analysts, investors, and other competitors. There is difficulty in predicting the future status of the companies' stocks. However, stock market behavior depends on the polarity prediction classification. Therefore, it is essential to use sentiment analysis to study attention indicators for stock market behavior in the news. Sentiment analysis (SA) can be used to extract public sentiments from stock news microblog platforms. Previous studies used machine learning (ML) algorithms to classify Arabic stock news into positive, negative, or neutral types. Recently, deep learning (DL) algorithms have provided good accuracy for Arabic SA. Motivated by such results; this study applies ML and DL techniques to classify sentiments of Arabic stock news. 30,098 articles were collected and preprocessed from the Saudi stock-market platform, Tadawul. For the sake of comparison, two ML and two DL techniques were performed for SA: Naive Bayes (NB), logistic regression, fast-text, and long short-term memory (LSTM). These algorithms were used to classify the sentiments of the collected data and help investors and stock analysts with decision-making. The results show that the DL techniques outperformed the ML algorithm. The experimental result of the LSTM model was 84%, which is the same as the reduced-features logistic regression model, but it has the lowest features over the same timeframe. Therefore, the LSTM model simultaneously has the best accuracy and the fewest features. On the other hand, the NB models achieve the worst performance. The Arabic SA models assist in decision-making based on the stocks news sentiments predicting the upcoming stock trends for the investors or analysts. These sentiment's models would limit the risks by supporting the decision-making analysts. Thus, this study would be a valuable resource to the stock market sector based on Arabic linguistic features.
Analysis of Machine Learning Security Blockchain Based IoT Enabled Web Platform Applications
Authors: S.Aruna Deepthi
Published: February 2025

Traditional criminal reporting methods include issues with witness hesitancy, security flaws, and inefficiencies. Even with the introduction of web platforms and mobile applications, there are still issues with data security, transparency, and user anonymity. These include of avoiding unwanted access, preserving confidentiality, and protecting data integrity. This research suggests using smart contracts and blockchain technology to overcome these problems. Improving the anonymity, efficiency, and security of crime reporting procedures is the main goal. In order to guarantee data integrity, transparency, and decentralization, the system makes use of blockchain's decentralized and immutable properties. The reporting process will be streamlined by the automation of tasks like complaint validation and proof verification through smart contracts. The suggested solution aims to transform crime reporting by tackling issues with confidentiality, data integrity, and access control. This will foster transparency, accountability, and confidence.
Robust Control and Synchronization of Fractional-Order Unified Chaotic Systems
Authors: Nawaf Alsowait
Published: February 2025

This work examines the understanding and regulation of chaotic systems via fractal system dynamics. The criteria for the asymptotic stability of the equilibrium point are examined. This signifies that the point is stable and demonstrates local attraction, which is essential for the system’s behavior over time. The independent uncertainty in the system, denoted by αj, may vary over time but is constrained by a constant δ > 0. This factor is crucial for comprehending the system’s responsiveness to varying environments. The system’s stability is further analyzed through the negative semi-definiteness of the fractional derivative of the Lyapunov function, indicating that the system’s origin stays stable given specific beginning circumstances. The study presents a thorough framework for analyzing and controlling chaotic systems using fractal system dynamics, emphasizing stability and the impact of uncertainty.
Intrusion Detection and Prevention: -Network Security in Cloud
Authors: Bhoopendra Singh
Published: February 2025

The growing adoption of cloud computing has introduced heightened vulnerabilities to cyber-attacks. Network Intrusion Detection Systems (NIDS) are pivotal in safeguarding cloud computing environments. However, the dynamic and distributed nature of cloud computing imposes challenges on traditional NIDS. This study examines the state of NIDS in cloud computing, their limitations, and potential improvements through machine learning and artificial intelligence. Ensuring NIDS can effectively detect both known and unknown threats is essential for cloud security, warranting ongoing research and innovation.
Development of a Deep Mask Region-Based Convolutional Neural Network with Improved Weighted Quantum Wolf Optimization for Sickle Cell Anaemia Detection and Classification
Authors: Arularasi Peter,
Published: February 2025

To effectively manage Sickle Cell Anaemia (SCA), a severe inherited blood condition, early detection and accurate categorization are essential. Timely and precise identification is hindered by the high error rates, limited scalability, and dependency on personal expertise associated with conventional diagnostic approaches. To address these challenges, this study proposes a novel Deep Mask Region-Based Convolutional Neural Network (DMRCNN) combined with Improved Weighted Quantum Wolf Optimization (IWQWO) for SCA identification and classification. By leveraging advanced feature extraction and segmentation methods, the DMRCNN enables precise localization and identification of abnormal cells in blood smear images. The IWQWO method optimizes the DMRCNN's hyper parameters enhancing the network's efficiency by achieving an optimal balance between convergence speed and prediction accuracy. The primary objectives of this study are to improve classification accuracy, reduce computational overhead, and minimize false positives and false negatives in SCA diagnostics. Experimental results demonstrate that the proposed system achieves 96.8% precision, 97.2% accuracy, 96.5% recall and 96.8% F1-score, surpassing existing approaches with superior metrics. These findings underscore the effectiveness of the proposed method in automating SCA detection and classification, paving the way for potential integration into clinical workflows for accurate and timely assessments.
Is Crop Diversification Impacting the Trade Basket of the Indian Economy?
Authors: Soni
Published: February 2025

Agriculture is important in the Indian economy, with more than half of the rural population. Agriculture is imperative for the Indian economy, with over half of the rural population relying on agriculture and interrelated activities as their foremost source of income. The introduction of New Agricultural Technology in the mid-1960s brought about significant changes in Indian agriculture, leading to further alterations in the following years. Since the 1990s, there has been a perceptible growth in diversification within the agriculture sector in India. Farmers have adopted crop diversification to reduce their vulnerability to risks and challenges in agriculture while also aiming to lift their farm income. The alteration towards more profitable and high-value crops in Indian agriculture reflects deviations in the country's consumption patterns. The study highlights the impact of crop diversification on trade, which is measured by using the share of the agriculture sector in India’s exports and imports. To check the impact of crop diversification on agriculture sector growth, a share of agriculture in GDP has been taken. The study's objective is to check the direction of the impact of crop diversification on the variables: agriculture's contributions to the GDP and India’s exports and imports. The Shannon index is used to identify the crop diversity in India. Share in GDP, import, and export are taken from various Government sites such as the Department of Commerce, Ministry of Statistics, and program implementation and agriculture statistics at a glance. The results are analyzed by using a multiple linear regression model. The results show a positive impact of crop diversification on GDP and Exports and a negative impact on imports..
Improving Intrusion Detection with Hybrid Deep Learning Models: A Study on CIC-IDS2017, UNSW-NB15, and KDD CUP 99
Authors: Vikrant Sharma
Published: February 2025

Intrusion Detection Systems (IDS) play a critical role in cybersecurity, protecting networks from evolving cyber threats. This study evaluates the effectiveness of deep learning models, including Capsule Networks (CapsNet), Bidirectional Long Short-Term Memory (BiLSTM), and a hybrid CapsNet + BiLSTM model across three benchmark datasets: CIC-IDS2017, UNSW-NB15, and KDD CUP 99. Experimental results show that the hybrid CapsNet + BiLSTM model outperforms individual architectures, achieving 99% accuracy on CIC-IDS2017, 97% on UNSW-NB15, and 98% on KDD CUP 99. The confusion matrices validate its robustness in detecting complex attack types, including DoS, DDoS, and botnets. These findings proposed that deep learning-based hybrid models can significantly enhance network security, improve anomaly detection, and strengthen real-time cyber threat mitigation strategies.
Empowering Patient-Centric Healthcare with AI-Driven Predictive Analytics in Blockchain
Authors: Krishna Prasad N Rao
Published: February 2025

This paper proposes a novel approach integrating Multilayer Perceptron (MLP) with Gated Recurrent Unit (GRU) for predictive analytics within blockchain-based health records systems. In contrast to conventional models, the suggested method combines the power of GRUs to identify temporal correlations in sequential data with the strengths of MLPs in extracting complex patterns from organized health data. More precise and dynamic predictions are made possible by this hybrid model, which is designed to comprehend complicated health trajectories. The proposed model combines the strengths of MLP in learning complex patterns from structured data with GRU's ability to capture temporal dependencies from sequential data. By integrating these components, the model achieves a comprehensive understanding of patients' health trajectories, enabling accurate predictions of future health outcomes. Key features of this approach include adaptive learning mechanisms to accommodate diverse patient populations and dynamic health conditions. Moreover, the model's architecture facilitates real-time analysis, allowing healthcare providers to proactively intervene and optimize treatment plans. The integration of MLP with GRU offers a scalable and versatile solution for extracting actionable insights from vast repositories of health data, ultimately improving patient outcomes and reducing healthcare costs. The proposed method is implemented in Python software and has an accuracy of about 93% which is higher than other existing methods like CNN-LSTM, CNN-GRU and MLP-LSTM.
Designing a 4-Layer Smart System Architecture for Scalable Emergency Response to Human-Elephant Conflicts
Authors: Agus Sukoco
Published: February 2025

Human-elephant conflicts have emerged as a significant challenge in regions where human populations encroach upon elephant habitats, leading to detrimental social and economic impacts on local communities and increased mortality rates among elephants. These conflicts are exacerbated by habitat fragmentation and land-use changes, necessitating effective management strategies to mitigate negative interactions. This study addresses the urgent need for an innovative approach to managing human-elephant conflicts through the development of a 4-Layer Smart System Architecture.
The proposed architecture integrates advanced technologies, including the Internet of Things (IoT), Big Data analytics, and Artificial Intelligence (AI), to enhance emergency response capabilities. The architecture consists of four interrelated layers: the Instrumentation and Control Layer for real-time monitoring of elephant movements, the Information System Layer for historical data storage and analysis, the Business Intelligence Layer for predictive analytics and strategic decision-making, and the Gamification Layer to engage local communities in conflict mitigation efforts.
By employing Design Science Research Methodology (DSRM), this study aims to create a comprehensive framework that not only improves the effectiveness of emergency responses but also fosters community participation and awareness. The expected outcomes include enhanced real-time monitoring of elephant behavior, improved predictive capabilities for potential conflicts, and increased collaboration among stakeholders, including local communities, conservation organizations, and government agencies. Ultimately, this research seeks to promote sustainable coexistence between humans and elephants, reducing the frequency and severity of conflicts while supporting conservation efforts. The findings of this study will provide valuable insights for policymakers and practitioners in wildlife management, contributing to the development of adaptive and effective strategies for mitigating human-elephant interactions in the smart system era.