bluuebunny/crossref_metadata_2025 huggingface dataset:
Factor Influencing the Generosity Behaviour of Cash Waqf Donors in Malaysia: A Mediation Model using Structural Equation Modelling Approach
Authors: Mohd Burhan Yusof
Published: February 2025

This present study employs the structural equation modelling (SEM) approach to investigate the influence of philanthropic attitude (PA), subjective norms (SN), perceived behavioural control (PBC) and Islamic Humanitarianism (IH) on the generosity behaviour of cash Waqf donors (CWGB) in Malaysia. Additionally, this study also examined the role of generosity behavioural intention (GBI) as a mediator in the relationship between PA, SN, PBC and IH towards cash Waqf generosity behaviour. This resulted in a total of nine hypotheses for the study. The model was constructed by integrating the theory of planned behaviour (TPB), the theory of Islamic consumer behaviour (TICB) and the theory of welfare policy and support (TWPS). The survey was conducted randomly among 375 respondents in Technical and Vocational Education and Training (TVET) institutions in Malaysia, with 372 valid cases. The selection of academia was influenced by their diverse educational backgrounds, as prior behavioural studies have demonstrated the significance of education in various contexts. This research used a close-ended structured questionnaire to assess all the constructs involved in this study. The questionnaire was adapted from previous research using a 10-point interval rating scale to collect responses from the respondents. Pooled Confirmatory Factor Analysis (CFA) is employed to validate measurement model in this study. All constructs have achieved uni- dimensionality, validity and reliability requirement. The study’s findings indicate that 59% of the performance in cash Waqf generosity behaviour can be explained by the constructs PA, SN, PBC, IH, and GBI within the model. Remarkably, PA, IH and GBI demonstrate a positive and significant influence on the generosity behaviour of cash Waqf donors. While SN demonstrate a negative and significant influence on the generosity behaviour of cash Waqf donors. Nonetheless, the construct of PBC does not significantly influence the generosity of cash Waqf donors. Interestingly finding of mediation analysis revealed that GBI mediate the relationship between PA and SN (partial mediation), PBC and IH (full mediation) toward generosity behaviour among cash Waqf donors. This study presents a novel concept by defining the construct of generous behaviour to investigate the factors that affect this behaviour through SEM. This study focuses exclusively on generosity-related behaviours, particularly monetary donations in the form of cash Waqf. Future research may evaluate generosity behaviour by investigating two additional cross-cultural human behaviours: assisting strangers and dedicating time to volunteer for an organization.
An Extensive Survey of Deep Learning Models for Osteoporosis Detection
Authors: O. Venkata Siva
Published: February 2025

Osteoporosis is a condition characterized by low bone mass and structural deterioration of bone tissue and is increasingly becoming a crucial health concern, especially among the older population. The condition also affects the skeletal system in later life. Recognition of pre-disease states during early osteoporosis management plays a critical role in preventing osteoporosis development. The present study is a comprehensive investigation concerning the establishment and assessment of osteoporosis detection deep learning models developed with data obtained from individuals over fifty years. The investigation aimed to develop and compare predictive models engineered for the purpose to classify normal and osteoporosis users. To determine the performance of the newly developed deep learning models, this study compared the profound learning methods to prediction models. The data were collected from community-dwelling participants at a medical center in Taiwan who enrolled in a health check-up program. Various images were collected to extract the features. These modalities included in the prediction model were obtained from chest CT and CT radiomics, while the extracted features from dental radiographic images were also utilized to classify osteoporosis using deep convolutional neural network models. The results of this test prove that deep learning models could significantly increase the efficiency of osteoporosis determination. Such comparative analysis validates certain deep learning concepts, such as DenseNet121 in distinguishing normal and osteoporosis user sets with a high accuracy rate. Concerning these new machine learning concepts, new insights possible persons on a greater vein at risk may be classified for preventive measures.
An Efficient Automatic Fingerprint Authentication System Using a Template Protection Technique
Authors: D.Deepakraj
Published: February 2025

The disclosure of biometric template data is one of the possible weaknesses in a biometric system, posing major security and privacy risks. The majority of template protection methods on the market fall short of meeting all the necessary specifications for a workable biometric system, including high matching accuracy, security, privacy, and revocability. Research on automated fingerprint-based identification began in the early 1960s since fingerprints have been a vital tool for forensics and law enforcement for more than a century. Our proposal includes a system that uses extraction of minutiae approach to verify fingerprints and an automated system that takes attendance. Unimodal fingerprint biometric systems, which analyze distinctive sequences to protect authentication information, deal with issues including noisy data, non-universality, intra-class deviation, and susceptibility to spoof attacks. Multimodal fingerprint biometric systems overcome these issues by compensating for the shortcomings of different biometric sources. Our test findings demonstrate that, while maintaining template security, the suggested multi-biometric template protection strategy outperforms its unibiometric equivalents regarding verification outcomes.
Review on Liver Cirrhosis Detection using Machine Learning and Deep Learning Techniques
Authors: Gurmeet Kaur Saini
Published: February 2025

Liver illnesses account for further than 2.8% of all fatalities in India each year. However, it can be challenging to spot liver disease in the early stages when the symptoms are minor. The majority of the time, symptoms of liver illness don't appear unless a critical phase has been reached, making it difficult to recognize and diagnose. Therefore, a thorough literature survey is conducted that aims in identifying liver diseases among patients by employing different techniques. The paper starts by discussing briefly about liver and various diseases related to it. Moreover, we have also analysed the impact of covid-19 on liver. After analysing the literature survey, it has been analysed that majority of researchers are working majorly with two classes of Machine Learning (ML) and Deep Learning (DL). However, due to some limitations in these models, authors started to shift their attention towards Nature Inspired Optimization Algorithms. We have reviewed some of the latest works in these three categories and at the end of each category a comparison table and inferences are given. Moreover, it has also been observed that by using optimization algorithms along with ML algorithms, the accuracy of liver disease detection models is enhanced significantly.
PCA-SWF: A Principal Component Analysis-Based Stacked Model Approach for Weather Forecasting
Authors: Shimaila
Published: February 2025

Weather forecasting plays a crucial role across various sectors of society, enabling timely predictions of severe weather events such as hurricanes, tornadoes, storms, floods, and heatwaves. Accurate forecasts provide essential data for issuing public warnings, allowing individuals and authorities to take necessary precautions to safeguard lives and property. In agriculture, farmers rely on weather forecasts to optimize irrigation, planting, and harvesting schedules, ensuring efficient water resource management, maximizing crop yields, and minimizing damage caused by extreme weather. Additionally, weather predictions significantly impact transportation systems by providing insights into potential hazards such as ice, snow, poor visibility, wind speeds, and road conditions. This information helps railways, airlines, and shipping industries adjust schedules and ensure safe and efficient transport operations. In this study, we propose a weather prediction approach using a stacked model, which outperforms single classifiers such as Decision Trees, Support Vector Machines, K-Nearest Neighbors, and Multilayer Perceptrons. Principal Component Analysis (PCA) is employed for feature extraction to reduce dimensionality and improve prediction accuracy. The performance of the proposed model is evaluated using metrics such as accuracy, Matthews Correlation Coefficient (MCC), F1-score, and the Receiver Operating Characteristic (ROC) curve, along with the Area Under the Curve (AUC). The results demonstrate the effectiveness of the stacked model in achieving robust and reliable weather forecasting.
Strategic Growth Models for Engineering Colleges: A Management Approach
Authors: Reena Y. A
Published: February 2025

The background of this study evaluates certain domains of engineering education in India. This is because as per statastics report for the acadamic year 2023-24 provided by AICTE,India, it was seen that Karnataka has 512 engineering colleges, whereas there are a total of 5868 engineering colleges in India. The problem statement identified is low enrollment in engineering courses as it was seen that only 11.9% of students in India enroll for engineering courses. In addition, the problem statement was expanded to reflect the low employability rate of Indian engineers. The “Goal-setting Theory” was incorporated with the “Outcome-based Education” strategy of engineering colleges in Karnataka and the whole of India. Constructivism learning theory was also incorporated with upskilling courses for engineers. Secondary data was collected regarding the 6-year performance of 18 UG and 15 PG colleges across Karnataka from the NIRF e-portal. The Chart method growth function analysis provided the regression model with R2 values. It was seen that several colleges maintained a strong positive correlation with academic years. It was seen that the majority of colleges gained a “very weak” correlation with academic years regarding placements, which inaccuracy in their growth models.
AI-Driven Decision Support Systems for Green Management Cost Analysis: Optimizing Resource Allocation in Virtual Computer Systems
Authors: Inaam Ghadeer Almusawi
Published: February 2025

The increased need for computation requires proper resource management particularly in managing virtual computer systems. This research aims to examine the implementation of the AI-driven Decision Support System (DSS) in relation to resource management for the most effective and efficient resource utilisation for cost optimisation with emphasis on environmental impacts. Built with machine learning algorithms especially reinforcement learning, the DSS adapts resource allocation in real-time use thus enhancing resource utilisation and reducing energy usage. The empirical analysis of the study presents a marked improvement in the average CPU usage from forty-five percent to sixty-five percent and memory usage from fifty percent to seventy percent. Also, the level of effectiveness was reached 33 in terms of DSS importance among the employees. This means that it can lead to up to 3% decrease in energy use and up to 25% decrease in operational expenses. These improvements support the suggestion that the system can efficiently allocate resources based on dynamic needs in order to minimise waste. By focusing on its applicability to green IT practices, the established results point to a future associated with the constant optimization of AI-driven DSS. Due to the inherent flexibility and the ability to transfer the DSS to different virtual structures, it is highly qualified for usage by IT managers and decision-makers. This research helps further the literature on sustainable IT management and showcases how AI can help change the face of sustainable computing for various virtual computer systems for the better.
The Mediating Role of Trust on Social Media Review and Digital Product Purchasing Behavior among Libyan students
Authors: Mahmoud Alferjani Omar Etshaisha
Published: February 2025

Over the last few decades, social media has dramatically changed human communication and purchasing behaviour. The impact of social media reviews has tremendously captured scholars’ attention. Despite the substantial amount of research on social media reviews, there is a significant lacuna in predicting the effect of social media reviews on students’ purchasing behaviour especially related digital products. Moreover, many researchers explicitly mentioned that it is the right time to investigate the effect of online reviews on developing mutual trust. Therefore, this study aims to investigate the effect of social media reviews on both trust and students’ digital purchasing behaviour. Moreover, this study also investigates the mediating effect of trust on digital purchasing behaviour. In line with these goals, this study initially reviews several theories, including the Accessibility-Diagnosticity Model, Attribution Theory, Homophily, and Social Identity/Identification Theory. Moreover, this study also conducted an extensive systematic literature review in order to develop a conceptual framework. Based on these reviews, this study identified five important characteristics of social media reviews that significantly affect both trust and students’ purchasing behaviour. These attributes are review valence, relevance, content, credibility and volume. In order to test the hypotheses, this study developed a structured questionnaire by adapting items from several prior studies. This study targeted Libyan students as its respondents and distributed the questionnaire link through social media. Structural equation modelling (SEM) was applied to quantify the effect of all these variables. This study, indeed, is one of the few studies that quantifies the effect of social media review characteristics on trust, and purchasing decisions, especially for digital products. Additionally, this study will expand the current knowledge of the effect of social media effect on consumers’ buying decision.
Design and Implementation of a Blockchain based System for Access Control and Transformation History Tracking of Digital Images
Authors: Vijay P.  Bagdi
Published: February 2025

This paper presents the plan and execution of a blockchain-based framework for gets to control and change history following of computerized pictures. Within the period of computerized data, securing and checking the lifecycle of advanced resources is fundamental. Our proposed framework leverages the characteristic properties of blockchain innovation, such as immutability, transparency, and decentralization, to supply a vigorous arrangement for advanced picture administration. By joining blockchain with get to control components, we guarantee that as it were authorized clients can get to or adjust computerized pictures. Each get to or change occasion is recorded on the blockchain, making a comprehensive and tamper-proof history of the image's lifecycle. The framework design comprises shrewd contracts for characterizing get to arrangements and change rules, beside a decentralized capacity arrangement for productive picture dealing with. We execute a user-friendly interface for directors and clients to associated with the framework, guaranteeing ease of utilize without compromising security. Our test comes about illustrate the system's viability in keeping up information astuteness and giving a straightforward review path. This approach not as it were improves the security and traceability of computerized pictures but moreover clears the way for broader applications in regions requiring exacting information administration, such as computerized forensics, mental property administration, and secure information sharing.
Open-Source IDS/IPS Using Statistical and Deep Learning Based Anomaly Detection in SDN
Authors: Mohammed B. Al-Doori
Published: February 2025

This paper explores the implementation of open-source Intrusion Detection and Prevention Systems (IDS/IPS) using statistical and deep learning-based anomaly detection techniques in Software Defined Networking (SDN) environments. It aims to address the security challenges in cloud computing by proposing a hybrid approach that combines signature-based and anomaly-based detection methods. Our results demonstrate significant improvements in detection rates and reduced false positives compared to existing systems.
Enhancing Business Process Efficiency Through Digital Transformation
Authors: Vladyslav Ioanno
Published: February 2025

Introduction: The paper reveals the impact of digital transformation on the efficiency of business processes.
Objectives: The aim of the study is to reveal the impact of digital transformation on the efficiency of business processes. The object of the study is Ukrainian cultural heritage.
Methods: The study used the following research methods: description, comparison, analysis and synthesis, literature analysis, and generalisation.
Results: The study used the following research methods: description, comparison, analysis and synthesis, literature analysis, and generalisation. The article reveals the impact of digital transformation on the efficiency of business processes. It describes the essence of the concept of “digital transformation”. It characterises the main factors of influence on modern business processes and identifies trends in the business world. The main directions of digital transformation of the business environment are considered. The trends in the use of cloud technologies, digital services, data collection and analysis, and digital networks in companies' business processes are identified. The main advantages of using these technologies in the business environment are identified.
Conclusions: The costs of introducing digital technologies in Ukrainian companies' business processes are analysed. The directions for implementing digital transformations in Ukrainian companies' business processes in 2023 (in %) are considered. The trends of digital transformation in the business processes of domestic enterprises are identified.
Exploring the Opportunities and Obstacles of Implementing Artificial Intelligence in Accounting and Auditing
Authors: Lіudmyla Sakhno
Published: February 2025

Introduction: Artificial intelligence technologies are being introduced into the economic sector due to the possibilities of automating processes. However, conservative views on using the latest technologies characterise accounting and auditing. This is because there is a risk of errors and confidential data leakage. Nevertheless, artificial intelligence is gradually being introduced into financial accounting and auditing, and today it is indispensable for some processes.
Objectives: Our study aimed to determine the level of implementation of artificial intelligence technologies in accounting and auditing based on the survey results and identify the main factors influencing the level of artificial intelligence application in practice.
Methods: To achieve this goal, the following methods were used: analysis, synthesis, induction, systematisation, and a survey of 947 respondents, including business people, business consultants, accountants, and auditors.
Results: The survey results indicated a high level of use of artificial intelligence among business consultants and auditors, and the types of artificial intelligence were dominated by expert systems, machine learning, and customer support. At the same time, they identified a low level of use of complex systems, such as neural networks, blockchain technologies, and hybrid systems, against the background of low awareness among accountants and business people, accountants' fear of losing their jobs, and entrepreneurs' distrust of data protection and the potential for errors when using artificial intelligence.
Conclusions: Thus, it is advisable to train accountants to help them adapt their professional activities to the current requirements of society and to encourage auditors and business consultants to advise entrepreneurs.
Improving Educational Standards with Advanced Information Systems
Authors: Galyna Gordiichuk
Published: February 2025

Introduction: The study presents the potential of improving the quality of education with the help of modern information technologies.
Objectives: The article aims to analyse the potential of modern information systems to improve the quality of education.
Methods: The study uses complementary general scientific theoretical methods, including analysis, synthesis and abstraction.
Results: The study reveals the potential of innovative technologies and digitalisation in the context of innovative development of the educational space. The main components of innovative teaching technologies are highlighted. It is established that innovative directions for using digital technologies to form digital competence in the methodological and pedagogical work of an educational institution are concentrated in the formats of interactive learning, portal technologies, and immersive environments. It has been proved that diverse digital educational content, as a component of the information educational system, should be actively involved in the learning process to implement strategic directions for developing digital optimisation in the education system. It is substantiated that using information systems in the educational process increases students' motivation, stimulates the development of continuous self-education, diversifies learning and increases its effectiveness.
Conclusions: The relevance of the digital optimisation of the education system is due to the need to increase intellectual potential and improve the quality of education at various levels. Best educational practices have significant potential in implementing information systems tools, guaranteeing adaptability and sustainable development of the educational environment.
Innovative Digital Economy Strategies for Enhancing Financial Security in Logistics Optimisation
Authors: Oksana Desyatnyuk
Published: February 2025

Introduction: The digital economy is significantly changing logistics activities, providing new opportunities for optimising processes and improving the financial security of enterprises. The topic’s relevance is due to the need to adapt to rapid technological changes and the growing role of digital tools in the modern business environment.
Objectives: The research aims to study the impact of the digital economy on logistics processes and their financial security.
Methods: The research methodology is based on the analysis of scientific publications, statistical reports and comparative analysis, which allowed us to identify trends and opportunities for digitalising logistics processes.
Results: The findings showed that introducing technologies such as blockchain, the Internet of Things (IoT), artificial intelligence, and big data increases the efficiency of logistics processes by optimising routes and reducing delivery times and operating costs. Blockchain ensures transparency and reliability of transactions, which helps to reduce the risk of fraud and improve trust among supply chain participants. IoT and big data allow businesses to forecast demand better and manage inventory, reducing cost overruns and ensuring greater planning accuracy.
Conclusions: Thus, artificial intelligence helps to automate routine processes, increasing the accuracy and speed of tasks. The practical significance of the study is to identify promising areas of logistics digitalisation that contribute to enterprises’ financial stability and competitiveness.
Managing Cybersecurity Risks in the Era of Digital Transformation
Authors: Serhii Lysenko
Published: February 2025

Introduction: The issue of information security risk management in digital transformations is of particular relevance given the increasing number and scale of threats to cybersecurity, electronic services, information systems and telecommunications networks. In this context, it is essential to ensure the effective integration of modern analysis tools, the introduction of innovative technologies and the formation of an appropriate regulatory framework capable of adapting to the modern digital environment.
Objectives: The article aims to systematise the key aspects of information security risk management in digital transformations.
Methods: The study used several general scientific methods of knowledge, including case analysis, systematisation and generalisation, regulatory framework, statistical data analysis, and Paired Samples T-Test using JASP software to assess the relationships between key indicators of digital infrastructure and cybersecurity.
Results: The t-test results revealed a statistically significant impact of digital transformation on critical aspects of cybersecurity. In particular, the development of e-governance (t = 4.515, p = 0.02) and access to online services (t = 5.266, p = 0.013) significantly increase the effectiveness of digital services protection and the ability to manage cyber crises, while the improvement of telecommunications infrastructure (t = 3,314, p = 0.045) contributes to the resilience of information systems during crises, especially in the context of modern cyber warfare, which underscores the critical role of digitalisation in strengthening national cybersecurity.
Conclusions: Thus, the analysis has revealed the need for a comprehensive approach to information security based on resilience, innovation, and international cooperation as crucial elements in countering modern cyber threats, especially in the Ukrainian context, which is against the backdrop of ongoing cyber warfare.
A Comparative Review on Machine and Deep Learning Techniques for Thalassemia Identification and Classification
Authors: Gurbinder Kaur
Published: February 2025

Introduction: Thalassemia is widely recognized as a significant global health concern in the present-day society. Thalassemia and structural HB abnormalities are the most common monogenic disorders worldwide. If recognition is not conducted promptly, it might result in fatalities within certain categories. Therefore, it is necessary to create computational approaches or methods to identify thalassemia in its early phases. Machine Learning (ML) and Deep Learning (DL) has become the crucial method for identifying thalassemia through the use of either inherited or newly developed models.
Objectives: This article provides a thorough and organized analysis of the latest developments utilizing ML and DL along with some other ensemble models for detecting Thalassemia, a hereditary blood condition.
Methods: This study specifically examined recent research conducted within the past seven years through consideration of good published review, emphasizing notable technical progress in ML and DL based models.
Results: The evaluation of the work reviewed has also been done considering the classifiers used, evaluation metrics and the blood parameters in this paper.  
Conclusions: This study seeks to offer helpful insights into current research trends and forthcoming prospects in utilizing ML and DL for the identification and treatment of Thalassemia as well as related haematological sickness.
Development of an Econometric Model of the Relationship between Migration Dynamics and Demographic Factors in Azerbaijan
Authors: Vilayat Ismayilov
Published: February 2025

Purpose: The purpose of the article is to build an econometric model of the relationship between the dynamics of migrants and the factors of the demographic process.
Theoretical basis: Individual counteracted equations are created to account for the components of demographic change -births, deaths, and net domestic and international migration. The fertility and mortality equations are easier to model because of the stable changes from period to period depending on income levels and national demographic trends. The net migration equations are more difficult to model because economic conditions, particularly labor market conditions, affect this process over time.
Method: The dialectical method was used to identify common approaches for studying a complex population. Methods of analysis and synthesis, comparison, induction and deduction, etc. were also used for demographic analysis. When developing regression models between the number of migrants and the three identified groups of factors directly influencing it, an econometric regression model was applied.
 Results and discussion: A model of the relationship between the number of migrants and the system of demographic factors is built in a five-stage system. Independent variable demographic factors were combined in three groups. Based on the selected data, a correlation model of the relationship between the number of migrants and demographic factors was constructed and the adequacy of the model in the absence of autocorrelation was verified at the level of α=0.01 (1%), α=0.05 (5%).
Research results: The results of the study can be applied in regulating the flow of migrants and maintaining its pace at an optimal level.
Originality/value: The research contributes to the regulation of international migration, thanks to the theoretical and practical results obtained. The relevance and value of the study is confirmed by the influence of migration and demographic processes on the standard of living of the population.
Big Data Analytics and Machine Learning Framework for Optimizing Struvite Precipitation in Smart Wastewater Treatment Plants: A Decision Support System Approach
Authors: Mamta Thakur
Published: February 2025

The paper presents a novel, information-based approach using machine learning and big data analytics to support struvite precipitation in the context of wastewater treatment plants. The framework is based on three interdependent functional stacks: a data ingestion stack that accounts for process instrumentation, process parameters, and surrounding environment monitors that feed data into a common integrated representation of the system; an analytics stack that employs ML algorithms to establish nonlinear mappings between parameters and predict healthy operating condition rods; and finally, a decision support interface that translates the analytics insights into actionable operating guidance.
The data integration layer (at the bottom of the above) uses strong validation protocols to syncronize multiple streams of reliable data. The analytics layer not only grants predictive performance by creating machine learning models, the machine learning creates adaptive behavior across the system, enabling real-time functionality without compromising its predictive performance. These recommendations at the decision support interface level, on the other hand, are based on all this complexity, distilled into actionable insights, aligned with operational transparency and flexible control of the same.
The architecture then proceeds to discuss high-level implementation considerations including infrastructure, operator training needs and system security procedures. Its modular structure offers flexibility, enables the relevance with existing operational constructs while also creating a pathway to innovations that will ultimately be the ‘tomorrow’ of the industry. By improving knowledge of reactor-associated microbial communities and dynamic processes, this guide aims to help achievement of increased phosphorus recovery efficiencies and further development of smart waste water treatment technologies.
Cyber Security Management: Strategies for Protecting Corporate Assets
Authors: Pankaj Arvindrao Pethe
Published: February 2025

The speedy development of virtual technology has heightened the urgency for strong cybersecurity management to protect corporate assets from an more and more complicated array of cyber threats. This paper investigates the modern landscape of cybersecurity threats, evaluates the effectiveness of numerous management strategies, and gives actionable suggestions for enhancing company cybersecurity practices. Through a mixed-methods approach, such as a empirical facts collection thru surveys and interviews with cybersecurity professionals, and specific case study evaluation, this research offers a multifaceted view of powerful threat management. The take a look at highlights huge increases in cyber threats consisting of ransomware and phishing, rising trends like AI-pushed assaults and IoT vulnerabilities, and the persistent challenges posed by Advanced Persistent Threats (APTs). Findings display that while practices such as risk assessments and patch management are extensively adopted, corporations benefit most from a combination of defense-in-depth techniques, including firewalls, anti-malware, and user training. Case studies similarly illustrate the success of Zero Trust architectures and multi-factor authentication in mitigating protection incidents. Overall, this research underscores the importance of an adaptive and proactive cybersecurity posture, integrating both theoretical and sensible insights to strengthen corporate defenses towards evolving cyber threats.
Enhancing Operational Efficiency: A Study on Total Productive Maintenance for the Heidelberg Speed master 102V
Authors: Tota Pirdo Kasih
Published: February 2025

The aim of this intervention is centred on how Total Productive Maintenance (TPM – as commonly known in the literature) is applied on craftsmanship of the Heidelberg Speed master 102V offset printing machine with a view to improving the maintenance and consequently reducing the breakdown periods of the machine. The main aim is to assess how effective this management philosophy is in the reduction of degradation of equipment in the sector of printing media industry. The research methodology was quantitative, thus obtaining data from the maintenance records for the entire one-year period. Also, data for MTBF, MTTR, and machine availability (AVAIL) worked as key performance indicators. The result show that the use of TPM pursued almost all dimensions attributed with its usefulness in regard to not only the turnaround time of repair of machines but also the frequency of repairs. The results specifically support the assertion that there is a significant increase in production post the increase in the machine’s available time with the results of Regression Analysis MTBF=0.76, MTTR=0.68, OEE=0.72 and ANOVA producing F values MTBF=12.34, MTTR=15.67, OEE=9.45 which has p-value=<0.01 . Thus, it was concluded that TPM increases the maintenance initiatives in an organization and at the same time enhances the attainment of the organization’s industrial strategies. This is the rationale that supports how such a framework could be useful for organizations seeking to reduce the rate of maintenance and also enhance the levels of reliability of the equipment in the printing sector.
Perceptions of AI-Driven Dynamic Pricing Strategies and Their Financial Impact on Vietnamese Tourism Companies
Authors: Thi Thanh Nhan Nguyen
Published: February 2025

This study was conducted to answer the question of how artificial intelligence-based pricing systems affect customer satisfaction and the financial performance of Vietnamese travel agencies. The study was conducted to explore factors that enhance customer satisfaction and financial performance in travel agencies using artificial intelligence-based pricing strategies, with a specific focus on understanding the interaction between the effectiveness of perceived artificial intelligence systems, the extent to which artificial intelligence is applied, the trust of regulators, and perceptions of price fairness based on artificial intelligence. Using a linear structure model, we collected and analyzed survey data from 372 people to test five research hypotheses. The findings show that the perceived effectiveness of artificial intelligence systems, the level of application of artificial intelligence and the trust of managers significantly increase customer satisfaction, thereby positively impacting the financial performance of travel companies in Vietnam. However, the study did not find a statistically significant effect of perceptions of price fairness based on artificial intelligence on customer satisfaction. These results are important because they show that travel companies need to focus on the application of effective, adaptive and reliable artificial intelligence systems while addressing fairness concerns through transparency and effective communication with customers. The implications underscore how travel companies can improve the customer experience and achieve financial efficiencies by leveraging artificial intelligence technologies in their dynamic pricing strategies.
Development of a Hierarchical Fuzzy System-Based Tool for the Rapid and Precise Detection of ADHD
Authors: Santosh Kumar Mishra
Published: February 2025

Attention-deficit/hyperactivity disorder (ADHD) disorder is quite common in India. It is related to neuro development with common symptoms of impulsiveness, insensitivity, and hyperactivity. ADHD diagnosis is quite challenging because of its heterogenous symptoms and its occurrence with some disorders like autism as well. The paper suggests a fuzzy system capable of rapid and precise detection of ADHD. The suggested process is: i) Feeding of symptoms of the patient (emotional, hyperactivity, inattention) into Fuzzy Rule-based system; ii) Its triangular membership function; iii) Centroid defuzzification; iv) Considering option intersections, commodities, and output ADHD severity levels; and v) Doctor’s estimation of extremity and commodities. The fuzzy approach is based on modelling intense variations in substitution of the collected data. This system shows higher accuracy. It also performs better than the existing tools by detecting ADHD and commodities by i) Reducing manual operations; and ii) Working on a single platform. Another plus point of ADHD is its focus on precision diagnosis of neuro developmental disorders.
Hybrid Statistical-Textural and Intensity Feature-Based Approach for Accurate Discrimination of Retinal Diseases
Authors: D Kalyani
Published: February 2025

Retinal imaging features such as fundus lesions are crucial signs of several ocular diseases, such as diabetic retinopathy and age-related macular degeneration. The early and accurate detection of the aforementioned abnormalities is essential for correct diagnosis and therapeutic management. In this study, we propose a new hybrid statistical-textural and intensity feature-based method for the improvement of the discrimination between retinal diseases with machine learning classifiers. By incorporating statistical texture analysis to measure changes in pixel intensity, this method not only extracts features based on intensities of fundus images but also serves as the comparison tool for a complete evaluation of the retina. A robust feature set is developed by merging intensity-based features with several texture descriptors including GLCM, LBP etc. We extract these features and then use them to train and test the performance of a number of classifiers, such as Support Vector Machines (SVM) and Random Forests for more accurate detection. Experiments have been conducted on a public dataset of retinal images and with the support vector machine classifier, encouraging results compared to purely texture-based or intensity-based methods in terms of classification accuracy are obtained. The overall findings of this study underscore the promise of integrating both statistical-textural and intensity features for improved retinal disease detection process, which will serve as an important stepping stone for future investigations and production of automated screening tools in retinal. The method also holds potential to enable early intervention in the development of vision-related diseases and, ultimately, decrease their impact.
Adaptive Reinforcement Learning Framework for Real-Time Tool Wear Optimization and Predictive Maintenance
Authors: N V. Krishnamoorthy
Published: February 2025

Tool wear monitoring and predictive maintenance are critical in manufacturing, where traditional methods often struggle to adapt to changing conditions. This research presents an Adaptive Reinforcement Learning Framework for Real-Time Tool Wear Optimization and Predictive Maintenance (ARTOM). The framework integrates reinforcement learning with real-time sensor feedback to optimize machining parameters and maintenance schedules dynamically. Proximal Policy Optimization (PPO) is used to guide decision-making by balancing tool life, product quality, and operational costs. Multi-agent reinforcement learning divides tasks among agents to handle diverse machining scenarios, while sliding window techniques and dimensionality reduction ensure efficient data processing. The study has used the benchmark dataset, which include time-series sensor data and machining parameters. Metrics potential metrics have been used to evaluate prediction accuracy, while runtime and memory usage assess computational efficiency. Results has shown that ARTOM consistently achieves lower prediction errors and faster execution times than contemporary baseline models. These findings demonstrate ARTOM’s ability to adapt to different tool conditions and improve operational decision-making.
A Dynamic and Adaptive Framework for Efficient Configuration and Management in Mobile Ad Hoc Networks
Authors: K. Purnima
Published: February 2025

Mobile Ad Hoc Networks (MANETs) often face challenges such as frequent topology changes, limited resources, and security vulnerabilities, which affect their stability and performance. This study introduces DynaMANET, a framework designed to address these challenges by integrating routing, resource management, and address configuration. The framework employs a multi-layered approach: the Data Collection Layer gathers network metrics, the Learning and Decision-Making Layer uses Deep Q-Networks to adjust configurations, and the Context Awareness Layer applies Graph Neural Networks to detect network changes. The Feedback and Evaluation Layer utilizes Bayesian optimization to refine decisions, while the Execution and Control Layer ensures seamless implementation of configurations. Simulations were performed in a 100-node network environment modeled using the NS-3 simulator. Node mobility and varying traffic loads were considered to evaluate throughput, latency, packet delivery ratio, energy consumption, and intrusion detection rates. The framework demonstrated better throughput, reduced delays, and higher packet delivery ratios compared to E-OLSR and MARL models. Detection rates for security threats such as black hole and DDoS attacks were slightly higher. Scalability tests showed minimal throughput drop as the network size increased. In failure scenarios, the framework maintained stable communication and data delivery. These results suggest that DynaMANET addresses interconnected challenges in MANETs by combining adaptive decision-making with efficient resource management. The methods proposed provide a structured approach for improving MANET reliability under dynamic conditions.
Optimized Deep Feature Analysis for Enhanced Botnet Attack Prediction in IoT Networks
Authors: Sudarshan S. Sonawane
Published: February 2025

The Optimized Deep Feature Analysis (ODFA) framework targets botnet attacks in IoT networks by combining deep learning with machine learning techniques. It uses a hybrid CNN-LSTM model to extract spatial and temporal features from network traffic, applying Recursive Feature Elimination (RFE) with Cross-Validation (CV) for refining feature selection. A Random Forest classifier is then employed to classify different types of botnet attacks. Tests conducted on the UNSW-NB15 dataset demonstrate its ability to achieve high precision, sensitivity, specificity, and accuracy. Recurrent feature optimization allows the framework to adapt to changes in attack patterns, ensuring consistent detection performance. The reduction in false alarms and improved classification of attack types highlights its contribution to securing IoT networks.
Artificial Intelligence in Knowledge Management for Higher Education: Transformative Impact, Challenges, and Future Directions Post-COVID-19
Authors: Haftom Gebregziabher Hagos
Published: February 2025

The COVID-19 pandemic has accelerated the integration of Artificial Intelligence (AI) in Knowledge Management (KM) within higher education, particularly in the Federal Technical and Vocational Training (TVT) sector. This systematic review synthesizes current research on AI-based KM in higher education during the pandemic, aiming to elucidate its effectiveness, challenges, and future directions. Following PRISMA guidelines, we conducted a comprehensive search across major databases, including Web of Science, Scopus, and ERIC, identifying 69 relevant studies published between 2020 and 2023. Our analysis reveals that AI-based KM has significantly enhanced personalized learning experiences, improved health safety measures, and increased administrative efficiency in higher education. However, challenges persist, including technological barriers, ethical concerns, and the digital divide. The review highlights a notable research gap in empirical studies validating AI's effectiveness in real-world educational settings, particularly in developing countries. Furthermore, there is a critical need for comprehensive ethical frameworks governing AI use in education. Our findings underscore the transformative potential of AI-based KM in higher education, while emphasizing the necessity for context-specific implementations and rigorous evaluation methodologies. This review provides valuable insights for educators, policymakers, and researchers, guiding future developments in AI-based KM strategies for resilient and inclusive higher education systems in the post-pandemic era.
MO-DiPredict: Multi-Omics Data Integration Framework for Early Detection and Subtype Prediction of Blood Cancers
Authors: G. Chinna Pullaiah
Published: February 2025

The study introduces MO-DiPredict, a framework designed to combine multi-omics data for detecting blood cancers at early stages and identifying cancer subtypes. It focuses on integrating diverse data types, including genomic, transcriptomic, proteomic, clinical, and imaging data, addressing challenges like noise, inconsistencies, and varying data scales. The framework aligns features using Canonical Correlation Analysis and captures relationships between modalities through Graph Neural Networks. Machine learning methods such as XGBoost, CNNs, and Transformer networks process the data, with feature engineering refining input variables like tumor mutation scores, pathway activity, and radiomics features. Datasets from TCGA, GEO, CPTAC, SEER, and TCIA were used to evaluate the framework. Results show that MO-DiPredict performs better in metrics such as accuracy and recall when compared to MRGCN and MODILM, achieving an AUC-ROC of 0.93. Incremental improvements from feature engineering and multi-modal integration were confirmed through ablation studies. Clinical features contributed most to the predictions, followed by genomic and transcriptomic data. Scalability tests indicate consistent performance as dataset size increases. The study provides a method for integrating diverse biological and clinical data to improve cancer detection and classification. The findings demonstrate the framework's ability to handle complex datasets, making it a practical tool for exploring multi-omics in cancer research.