International Journal of Internet Manufacturing and Services (31 papers in press)

Regular Issues

• E-Technology 2.0: The Role of Social Media in Shaping Modern Education  
  by Sushanta Kumar Mohanty, Chandrakant Mallick, Bijay Paikaray 
  Abstract: The use of social media in e-learning has become increasingly popular in recent years due to its potential to enhance the learning experience for students of higher education institutions. Social media has significant impacts on e-learning, with both positive and negative consequences. This paper discusses the benefits of incorporating social media into e-learning, including its ability to transform traditional teaching methods, as well as the challenges associated with integrating social media into the educational environment. Additionally, the paper explores the role of social media in e-learning for university students and the impacts it has on transforming the learning experience. However, it also highlights the negative effects of social media, such as the spread of misinformation, which can negatively impact the learning experience of today’s youth. The paper concludes with strategies for optimising social media in e-learning, addressing challenges, and leveraging opportunities.
  Keywords: e-learning; social media; Massive Open Online Courses; MOOC; e-technology.
  DOI: 10.1504/IJIMS.2025.10062402
   
  
• Enhancing Breast Cancer Risk Prediction through Comprehensive Ensemble Machine Learning Analysis: A Study on Clinical, Genetic, and Demographic Factors  
  by Chandrakant Mallick, Chitta Ranjan Behera, Subrat Kumar Parida, Bijay Kumar Paikaray 
  Abstract: Breast cancer is a major worldwide health problem, and early risk assessment plays a crucial role in improving patient outcomes. In this study, we employ supervised machine learning techniques to comprehensively analyse and predict breast cancer risk. Leveraging a diverse dataset comprising clinical, genetic, and demographic factors, explore the predictive power of machine learning algorithms. Our comprehensive analysis delves into feature selection, model evaluation, and performance optimisation. The proposed ensemble model has been validated on Wisconsin Breast Cancer Diagnostic medical dataset. The importance of this research in the context of improved patient care, screening programs, and risk assessment tools. It contributes to the ongoing effort to enhance breast cancer risk prediction through advanced data-driven methods, paving the way for more effective preventive strategies and early interventions. It show that effective data pre-processing performed to the raw data and feature selection the model resulted in an enhanced accuracy of 98.24%.
  Keywords: breast cancer; risk assessment; machine learning; predictive modelling; feature selection; early detection; personalised healthcare.
  DOI: 10.1504/IJIMS.2025.10064804
   
  
• Experimental Analysis of Mechanical Characteristics of fabricated Biodegradable Magnesium Hydroxyapatite MMCs for Biomedical Purposes  
  by Neeraj Kumar, R.A.J. Kumar Duhan, Bhaskar Chandra Kandpal, Varun Singhal 
  Abstract: In regards to materials, orthopaedic implants can be composed of magnesium alloys. They are absolutely biocompatible and have characteristics that comparable to the individuals of authentic bone. Their degradable ability indicates that they do not have to be removed out after the wound has healed completely. Magnesium, however, corrodes too quickly in the body's environment, so matrix composites can be a solution for controlling its corrosion rate and improving its mechanical qualities. In the present study, a stir casting technique was used for developing Mg HAP MMCs. As a reinforcing material, HAP powder has been utilised. After casting, composites were cut into required form samples for mechanical testing. In the final stage, tensile and compression tests were used. As a result of adding HAP powder, UTS, total tensile strain and compressive strength are significantly reduced. The elongation reduced, yield strength and young modulus shows distinct behaviours but compressive yield strength increased.
  Keywords: biodegradable magnesium hydroxyapatite metal matrix composites; stir casting technique; tensile test; compression test; Young’s modulus; percentage elongation.
  DOI: 10.1504/IJIMS.2025.10064809
   
  
• Lung Disease Detection Utilising HOG Features with SCA-ELM Model with FRFLICM Segmentation in Healthcare Systems  
  by Satyasis Mishra, Tizita Asfaw, Demissie Jobir, Bijay Kishor Shishir Sekhar Pattanaik, Bijay Paikaray 
  Abstract: Lung infections increase the mortality rate in the present day due to environmental pollution. The infection's size, shape, and position differ from a dissimilar patient's lung. It becomes problematic for a clinical physician to detect infected regions of the lungs from X-ray images. It was challenging for the medical practitioner to segment, detect, and extract infected lung areas from X-ray images. This research suggests a histogram oriented graph (HOG) feature-based hybrid modified sine cosine algorithm (SCA)-extreme learning machine model to classify infected and non-infected lung diseases. This technique was proposed by utilising modified sine cosine optimisation to increase the quality of images. The segmentation accuracy proposed improved the quality measures such as SSIM and PSNR were obtained as 0.9878 and 35.26, respectively. The proposed SCA-ELM classifier achieved 98.78%, sensitivity, 99.23% specificity, 99.26% accuracy, and 24.1128 seconds computational time with the proposed SCA-ELM models.
  Keywords: Histogram oriented graph; sine cosine algorithm; SCA; fuzzy local information C means; support vector machine; SVM; extreme learning machine; ELM.
  DOI: 10.1504/IJIMS.2025.10065637
   
  
• Benchmarking of SQL and NoSQL Performance on Database Management Solutions and their Respective Data Organisation Frameworks  
  by Mohammed Ali, Nail Adeeb Ali Abdu 
  Abstract: Relational databases have been the predominant data management system for many years due to their ability to provide structured data storage and transactional integrity. However, the emergence of big data and web-scale applications has led to the need for more flexible and scalable database solutions. NoSQL databases have become a popular alternative, offering high availability and horizontal scalability for large volumes of unstructured or semi-structured data. This paper explores the rationale behind the NoSQL movement, including the rise of big data and the limitations of relational databases in clustered environments. It outlines several NoSQL data models, such as key-value, document, column family, and graph databases, which sacrifice some data consistency for scalability and availability. Features such as flexible schemas, MapReduce views, and the Cloudant query language provide more appropriate tools for this semi-structured data. The different use cases, architectures, and trade-offs between these database paradigms are compared and show that NoSQL databases are suitable for large-scale, distributed systems requiring flexible schemas and high scalability.
  Keywords: NoSQL databases; document-oriented data model; inventory system; CAP theory; map-reduce technique; cloudant query language.
  DOI: 10.1504/IJIMS.2025.10065856
   
  
• Firefly Algorithm Based Optimal Scheduling Method for Intelligent Factory assembly line  
  by XiaoDong Zhao, QiLong Yu, Wenzhen Xiong 
  Abstract: In order to improve the production efficiency and resource utilization efficiency of the factory assembly line, an optimal scheduling method for the intelligent factory assembly line based on Firefly algorithm is proposed. Firstly, K-means clustering method is used to collect Big data of intelligent factory assembly line monitoring. Secondly, calculate the minimum delay for the start of workpiece processing and construct a scheduling plan with the goal of minimising the production time on the assembly line. Finally, the Firefly algorithm is used to calculate the optimal solution of the scheduling scheme, so as to complete the optimal scheduling of the intelligent factory assembly line. The experimental results show that the method proposed in this paper can improve the resource utilisation rate of the assembly line while improving its production efficiency. The production efficiency and resource utilisation rate of the method proposed in this paper both reach over 90%, verifying its strong optimisation scheduling performance.
  Keywords: Firefly algorithm; Intelligent factory; Assembly line; Optimise scheduling.
  DOI: 10.1504/IJIMS.2025.10065990
   
  
• Streamlining Colorectal Cancer Diagnosis: Leveraging MobileNet-V3 for Efficient Image Classification  
  by Artatrana Biswaprasanna Dash, Sachikanta Dash, Sasmita Padhy, Biswaranjan Mishra, Bijay Kumar Paikaray 
  Abstract: Colorectal cancer ranks second in cancer-related deaths, emphasising its impact on public health. Recent advancements in medical image analysis, particularly through deep-learning, have improved cancer diagnosis. This study focuses on utilising MobileNet-V3, a streamlined convolutional neural network, to classify colorectal malignancies using medical imaging data. Transfer learning-based MobileNet model is employed, trained on publicly available histopathological images from the Kather_image_tiles dataset. The model is fine-tuned to distinguish between malignant and benign tissues. Performance assessment involves thorough validation using a distinct set of images, evaluating key metrics, delivering a comprehensive analysis of its predictive proficiency. The performance of the suggested model is compared with existing deep learning and traditional classification methods. Results show the MobileNet-V3 approach achieves a test accuracy of 90%; performance metrics achieved F1-score of 76%, demonstrating its potential for accurate and efficient colorectal cancer classification. This application can be beneficial for medical practitioners for quick analysis of colorectal malignancy type at its initial stage that can save many lives in danger. The lightweight nature of MobileNet facilitates deployment on resource-constrained devices, paving the way for real-time clinical decision support systems.
  Keywords: colon malignancy; MobileNet-V3; cancer diagnosis; image classification.
  DOI: 10.1504/IJIMS.2025.10066081
   
  
• Smart E-Learning: An Intelligent Android App for Visually Impaired Learners  
  by Debabala Swain, Sony Snigdha Sahoo, Debabrata Swain, Bijay Kumar Paikaray 
  Abstract: The learning facilities should be communal for all kinds of learners. But, the visually impaired (VI) learners are facing a lot of hurdles to meet the basic learning provisions. Our education system seriously lacks in providing facilities that can enable them a smooth education. Thus, visual impairment creates barrier in the social, cognitive development and education of such individuals. Therefore, it is very vital, to arrange for some technical facilities to support them in learning. One of the better solutions is to provide an intelligent app in the smart phones, where, the users do not really need sight to operate such devices. Putting this in mind, an android based learning mobile application is developed for addressing the educational needs of a VI person. This paper proposes such an all-in-one solution application and discusses its features of self-enabled learning. The assessment system of the proposed app has also been tested and presented.
  Keywords: visually impaired learner; learning app; self-learning; voice assistant; text to speech; TTS; self-assessment system.
  DOI: 10.1504/IJIMS.2025.10066340
   
  
• Determinants of Smartwatch Continuance among Higher Education Students in UAE: an E-Technology Perspective  
  by Asraar Ahmed, Damodharan V. S., JYOTI RANJAN DAS, Bijay Paikaray 
  Abstract: According to Statista's (2023) report, the surge of smartwatch acceptance globally is mainly due to consumers becoming more health conscious. This research aims to identify the factors that affect continuance intention towards smartwatches. This study used the technology acceptance model (TAM) and the information system success model (IS-Success Model) as the theoretical background. The sample 412 was collected from smartwatch users living in UAE. The data was analysed using the structural equation modelling (SEM) technique with Smart-PLS 4 software. The results of this study show that perceived usefulness, perceived ease of use, social influence, hedonic motivation, perceived value, habit, trust, perceived risk, system quality, information quality, and external social influence are the major factors that influence continuance intention towards smartwatch students of higher education in UAE. The proposed model of this study had a strong explanatory power with R2 value 0.788 and the predictive relevance Q2 value 0.632.
  Keywords: Smartwatch; Technology Acceptance Model; Information System Success Model; Continuance Intention; UAE; Higher Education Institutions; Structural Equation Modelling;.
  DOI: 10.1504/IJIMS.2025.10066433
   
  
• Segmentation and Classification of Mammography Images using DenseNet and VGG19 Convolutional Neural Network  
  by Suvashisa Dash, Raj Kumar Pattanaik, Mohammed Siddique, Sasmita Kumari Nayak, Bijay Paikaray, Satyasis Mishra 
  Abstract: Breast cancer causes cancer death due to the abnormal growth in the thinning cell of breast. If the cancer not detected early, death rate will increase enormously. In recent years, machine learning and deep learning techniques have been used to classify images. In this study, an image classification from mammography can be achieved using DenseNet and VGG19 convolutional neural networks. We present the results of this study based on mammography images from digital database for screening mammography (DDSM). For the performance metric, we take into account precision, sensitivity, specificity, recall, and F1. DenseNet models achieve training, testing, and validation accuracy of 98.24%, 98.72%, and 97.85%, respectively, while VGG19 achieves 98.37%, 98.92%, and 97.94%. VGG19 performs better accuracy in comparison to DenseNet model. Moreover, the comparison results demonstrate the robustness of the proposed DenseNet and VGG19 models as fulfilling the sustainable development goal of good health and well-being.
  Keywords: convolution neural network; DenseNet; VGG19; deep learning; breast cancer; performance measure; benign and malignant.
  DOI: 10.1504/IJIMS.2025.10066435
   
  
• Blockchain Technology in the High-Tech Manufacturing Supply Chain: an Approach for Addressing Scalability and Support of Circularity  
  by Adrian Coronado, Ernesto Mastrocinque, Jung-Fa Tsai 
  Abstract: Industries characterised by complex and time-consuming testing and certification requirements and growing circularity/sustainability demands may benefit from blockchain technology. This work explores the feasibility of adopting blockchain in high-tech manufacturing supply chains by addressing two key issues: scalability through sharding using a comprehensive Byzantine fault-tolerant protocol and the support of circularity/sustainability policies using a case involving the supply chain of composite materials used in the manufacturing of components for the aerospace sector. Parts and components used in aerospace applications must be tested and certified, hence the use of sharding in blockchains may allow testing and certification processes to be streamlined. Furthermore, the blockchain can be used to store details about the energy employed in the manufacturing process of a component, the method for recycling and the energy associated to the recycling method. In the circular economy, knowing the value of energy used is of high importance in high-tech manufacturing.
  Keywords: manufacturing supply chains; blockchain technology; scalability; sharding; circularity.
  DOI: 10.1504/IJIMS.2026.10066840
   
  
• Leveraging Transfer Learning in Computer Vision for AI-Powered Orthopaedic Assistance: A Sustainable Approach for Healthcare 4.0  
  by Kavisha Shah, Kushal Panchal, Jayvardhansinh Chudasama, Ronak Bhoraniya, Chinmay Kulkarni, Debabrata Swain 
  Abstract: Bones are considered to be the most important support system of human body. Generally human bones are made up of protein, collagen, and minerals, especially calcium. Injury in human bone generally results as fracture. A bone fracture is an incident that leads to a crack or break in the bone structure, typically occurring due to accidents when sudden pressure is applied to any part of the bone. To detect bone fractures and provide appropriate treatment, traditional healthcare diagnostics utilise various imaging techniques such as X-rays. Orthopaedic specialists typically rely on their expertise and experience to accurately determine the presence of fractures. Diagnosis based on a doctor's expertise can sometimes result in inaccuracies. In this research work, a deep learning based system using transfer learning is developed for classifying bone fractures using X-ray images. For enhancing the performance, Adam optimiser is used in this work. To transform the image pixels, the raw X-ray images are pre-processed using BGR method. To prevent the model from overfitting issue, dropout regularisation method is applied in this case. The proposed VGG16 model has shown the highest validation accuracy of 96.62% during the model validation.
  Keywords: computer vision; transfer learning; dropout; Diagnosis 4.0; fracture detection; VGG16.
  DOI: 10.1504/IJIMS.2026.10066883
   
  
• Advancing Mining with E-Technology: Enhancing Safety, Productivity, and Environmental Sustainability  
  by Jitendra Pramanik, Singam Jayanthu, Tiruveeduula Gopikirishna, Jayanta Mondal, Bijay Paikaray, Abhaya Kumar Samal 
  Abstract: In order to sustain the supply of essential mineral resources, ensure energy and fuel availability, reduce human-induced accidents, and minimise environmental harm, the mining industry must continuously seek financial resources and innovative ideas from global sources. To achieve E-technology, new mining platforms must fully support Mining 4.0's cutting-edge competencies in social and economic growth, innovation, technology, and more. The mining sector is undergoing its fourth revolution as a result of the confluence of AI, ML, IoT, and automation. It is a reaction to technology shocks brought on by the rapid digital upgrading of the infrastructure and manufacturing sectors. This article focuses on the core and limits of mining development and aims to provide a broad overview of how the mining industry has changed as Industry technologies have spread. New research on automation, AI-enhanced processes, and their potential uses in mining and other industries are also discussed.
  Keywords: interest of things; IoT; smart sensors; automation; E-technology.
  DOI: 10.1504/IJIMS.2026.10066886
   
  
• A/B Split Test for Social Media Marketing Optimization: Comparing creative components using Facebook Ads Manager  
  by Rami Farhat, Qing Yang 
  Abstract: The rapid evolution of digital media and online marketing has significantly altered the interaction dynamics between businesses and customers. This study investigates the impact of social media marketing (SMM) on online purchase intentions, focusing on the effectiveness of different ad formats images versus videos on Facebook. Utilising an A/B testing approach, we analysed various creative components to determine their effectiveness in engagement campaigns. Data was collected from Facebook Ads Manager and through a focus group of 12 Lebanese digital media managers, each with over five years of experience in e-commerce. The findings indicate a strong preference for video content, particularly short videos over images and long videos, due to higher engagement rates and more effective message delivery. This study provides valuable insights into marketing campaigns that positively influenced brand perception, and cost per thousand impressions (CPM) was identified a reliable metric for evaluating ad effectiveness to increase online purchase intention.
  Keywords: digital media; a/b testing; marketing campaigns; cost per thousand impressions; ad formats.
  DOI: 10.1504/IJIMS.2026.10067299
   
  
• Accurate Detection of Hypothyroidism: a Hybrid Machine Learning Approach with Optimised Feature Selection and Hyper-Parameter Tuning  
  by Kanishk Munot, Tanvi Modi, Raju G, Debabala Swain, Bijay Paikaray, Debabrata Swain 
  Abstract: The thyroid gland is considered one of the vital organs in the human body. It secretes hormones crucial for maintaining metabolism. This disease creates several abnormalities in the human body. It has a myopic characteristic that has been unnoticeable for detection. For the detection of the thyroid, several complicated clinical tests are being performed. Sometimes, due to statistical human errors, it is not possible to detect the precise status of the disease. This work, a hybrid machine learning-based diagnostic screening system has been proposed for the accurate identification of thyroid disease. For boosting the performance of the system, less correlated features are removed by using the chi-square test. The hybrid machine learning-based classifier is formed by stacking different basic algorithms such as random forests, SVM, and KNN. The resultant system is optimised by performing hyper-parameter tuning using grid search CV. The highest accuracy obtained by the system is 99.79%.
  Keywords: thyroid; feature selection; chi-square; data modelling; machine learning; stacking classifier; grid search CV.
  DOI: 10.1504/IJIMS.2026.10067666
   
  
• Severity Prediction Analysis of Dengue Fever: Recent Methods of Virtual Screening Targeting Proteins for Novel Drug Discovery  
  by Purbasha Priyadarshini, Sunita Satapathy, Bibhudutta Mishra, Bijay Paikaray 
  Abstract: Dengue fever, also called break-bone fever, is caused by the Dengue Virus (DENV). Nonstructural-1 (NS1) is a protein replicating the dengue virus and pathogenesis. The NS1 protein is considered a primary therapeutic target for Dengue fever. The structure of the NS1 protein was studied through a protein data bank (PDB). Hence, in the present work, the binding site of the protein was identified by using the methodology of several software tools: BIOVIA Discovery studio, Virtual Screening (VS) through Auto Duck Vina, and ADMET (Chemical Absorption, Distribution, Metabolism, Excretion, &Toxicity). Most software tools are available to predict the highest binding affinity of NS1 with certain active ligands. ADMET analysis provides a systematic and efficient approach to identifying possible therapeutic leads of NS1 for further development. The current study proposes to design new drugs that lead against NS1 of all four serotypes of DENV to control the spread of dengue fever.
  Keywords: Aedes Aegypti; NS1; Virtual Screening; Autodockvina; BIOVIA Discovery Studio; DENV; ADMET.
  DOI: 10.1504/IJIMS.2026.10067698
   
  
• Critical Success Factors in Public Private Partnership in Construction Projects-Palestine  
  by Kawther Mousa, Zhang Zhenglian, Belal A.M. Abuhamra, Mohamed Abdelkhalek Omar Ahmed 
  Abstract: Public-private partnership projects have gained significant importance and are rapidly spreading, particularly in the construction sectors. Despite the growing adoption of PPPs, there remains a notable gap in the literature regarding the unique challenges and success factors specifically associated with implementing these partnerships in conflict zones, such as Palestine. This study was conducted to explore the factors that influence the success and failure of PPP projects in conflict-affected countries, specifically Palestine. Twenty critical success factors were identified from previous related studies and subsequently evaluated by experts using the Delphi method. The experts reached a consensus on 15 factors that significantly affect the success of PPP projects in Palestine. Among these, a favourable legal framework was considered the most influential factor. The researcher believes that this study will assist both the private and public sectors in better planning for PPP projects and enhancing the capacity of all parties to manage projects.
  Keywords: Public Private Partnership; Delphi Method; Critical Success Factors; Factor analysis-Palestine.
  DOI: 10.1504/IJIMS.2026.10068079
   
  
• How will Social Media Usage and Online Marketing Affect Consumers' Intent to Buy Eco-Labelled Food? Evidence from China  
  by Dimitrios Tatsis, Zhang Jian 
  Abstract: The paper investigates the variables that influence Chinese consumers' decision-making process when it comes to choosing eco-labelled food products. Data was gathered in China and analysed using Smart PLS version 3.3.9 with structural equation modelling. The results have shown that, among the several components of the theory of planned behaviour (TPB), only attitude and perceived behavioural control have an impact on consumers' eco-labelled food purchase behaviour. However, subjective norms do not have any influence on consumers’ eco-labelled food purchasing behaviour. Furthermore, research has revealed that the utilisation of social media platforms (SMP) and engagement with online marketing communication (OMC) positively and significantly impact the behaviour of consumers when it comes to purchasing eco-labelled food products. Increased dissemination of knowledge, personal experiences, viewpoints, and endorsements on eco-labelled foods through social media platforms can effectively stimulate consumer demand for such products.
  Keywords: theory of planned behaviour; TPB; green marketing; consumer behaviour; social media; green food; China.
  DOI: 10.1504/IJIMS.2025.10068367
   
  
• Enhancing Trust between PPP Partners: the Effect of Legally Binding Roles and Data Clarity  
  by Kawther Mousa, Zhang Zhenglian 
  Abstract: Agreements are crucial in managing the relationships between parties in Public-Private Partnerships (PPP). Nevertheless, the effect of agreements on trust levels between these parties is not well understood, especially within PPP frameworks. This research seeks to explore the ways in which various aspects of legally binding roles influence different types of trust, while also considering the moderating role of data clarity. Based on empirical data from Palestinian PPP professionals, the findings reveal that all three aspects of legally binding tasks positively impact both goodwill trust and competence trust, with legally binding adjustment having the most pronounced effect. Moreover, data clarity significantly strengthens the relationship between legally binding regulatory, adjustment, and trust. This research offers new insights into the interaction between agreement s and trust in PPP projects, suggesting that agreement design and data disclosure can effectively foster different forms of trust among PPP partners.
  Keywords: Data Clarity; Trust; Legally binding adjustment; legally binding coordination; legally binding regulatory; PPP projects.
  DOI: 10.1504/IJIMS.2026.10068452
   
  
• Advancements in Artificial Immune Systems: Unveiling Algorithms, Frameworks, and Real-World Applications  
  by B.J. Bejoy, Raju G, Bijeesh T. V, Debabrata Swain, Chinmay Kulkarni, Yugandhar Manchala 
  Abstract: The notions of biology and computers have been combined using bio-inspired computing processes to improve system design. The artificial immune system (AIS) is one such biologically inspired computational approach that uses the mechanisms and tools of the natural immune system to solve challenging computational issues. The ability of the human immune system to discern between self and non-self may be useful in developing adaptable frameworks AIS applies key immunological principles such as antigen representation, clonal selection, affinity maturation, and immune memory to develop robust anomaly detection models. By modelling the complex adaptive behaviour of the immune system, AIS can identify patterns within datasets and adjust to dynamic environments. This review provides an encompassing overview of the diverse landscape of AIS, encompassing algorithms, frameworks, and applications that have been developed and applied across various domains. Additionally, we will explore the relationships between AIS and related systems, such as cyber immune systems (CIS), and other practical applications that demonstrate the versatility and effectiveness of AIS in solving real-world problems.
  Keywords: Artificial Immune System; AIS Algorithms; Cyber Immune System; Immune Frameworks.
  DOI: 10.1504/IJIMS.2026.10068656
   
  
• Evaluating the Impact of Corporate Social Responsibility on Employee Frugal Innovation in Albanian SMEs  
  by Ernest Balili, Zhang Jian, Ahmed Hamdy 
  Abstract: Sustainability challenges have compelled SMEs, in emerging markets, to reassess their innovation strategies. Faced with resource and technological limitations, SMEs have increasingly adopted frugal innovation as a key approach to maintain competitiveness. Despite the increasing scholarly interest, the literature regarding the antecedents of frugal innovation remains limited. To fill this gap, this paper investigates the impact perceived CSR on employee frugal innovation alongside examining potential mediating roles of environmental-passion and prosocial-motivation and civility acting as a moderator. 412 employees from Albanian SMEs were surveyed while data was analyzed through PLS-SEM analysis technique. The results indicate that external CSR positively impacts frugal innovation, while internal CSR does not have impact. Prosocial-motivation and environmental-passion partially mediate these relationships. The moderating effect of civility is significant in the link among internal CSR and external CSR with environmental passion, however, civility does not moderate the association between both CSR dimensions with prosocial motivation.
  Keywords: Internal CSR; External CSR; civility; prosocial motivation; environmental passion; frugal innovation.
  DOI: 10.1504/IJIMS.2026.10068837
   
  
• Enhancing Cardiovascular Disease Prediction with AI: a Comparative Analysis of Deep Learning and Ensemble Models in Real-Time Health Monitoring  
  by Sumati Baral, Bijay Kumar Paikaray, Suneeta Satpathy, Rabi Satpathy 
  Abstract: According to WHO reports, approximately 12 million people die annually due to heart diseases Accurate prediction and continuous monitoring are challenging, especially in developing regions with limited medical resources which results in fatalities The deep learning models were optimized to capture complex structures and relationships within the data, while traditional classifiers handle varied distributions and feature interactions In this heart disease model, various Machine Learning and deep learning algorithms on a Kaggle dataset comprised of 918 records and 12 features are employed The algorithms used in the model include Logistic Regression, Support Vector Machine, Random Forest, AdaBoost, ExtraTree, GradientBoost, XGBoost, CatBoost, and Artificial Neural Networks The ensemble model utilizes a voting classifier to enhance overall performance and reliability Cross-fold validation is employed to ensure robust and unbiased evaluation Our model obtained a prediction accuracy of 9136%, and a precision rate of approximately 95%, significantly enhancing heart disease prediction capabilities.
  Keywords: Cardiovascular Disease; Deep Learning; Neural Networks; Ensemble Models; ADABoost; Real-Time Health.
  DOI: 10.1504/IJIMS.2026.10069238
   
  
• Optimising Self-Paced Learning Systems: Analysing Networked Infrastructure and AI-Driven Impact on Educational Quality  
  by Prajna Pani, Anita Patra, Bijay Kumar Paikaray, Satyasis Mishra 
  Abstract: This paper presents an insightful analysis of the relationship between effort and outcome in a self-paced training program for a placement environment in a state private university in Odisha, India. The study used a correlation research design and included 2000 students as subjects to determine the impact of self-paced learning on their pace of learning, motivation, and performance in reasoning and aptitude assessments. The study administered tests to evaluate student performance and found that students who experienced self-paced learning demonstrated improved learning pace and intrinsic motivation, significantly impacting their reasoning and verbal ability levels. The study provides evidence that self-paced learning can positively impact students' learning outcomes and motivation, thereby contributing to Sustainable Development Goal 4 (SDG 4). These adaptive learning strategies can be important in learning and refining educational justice.
  Keywords: Self-paced; learning; training; effort; outcome; SDG-4; assessment; AI-Driven.
  DOI: 10.1504/IJIMS.2026.10069717
   
  
• How Algorithmic Leadership Profiles Influence Employee Task Performance and Workplace Well-being: a Typological Approach  
  by Qiao Chang, Zhang Jian, Xiao Xiao, Shahan Bin Tariq 
  Abstract: Developing knowledge about algorithmic leadership effectiveness requires clarifying its behavioural manifestations. However, current research overly emphasises a single form algorithmic control while neglecting other manifestations. Drawing on leadership behaviour theory and research on algorithms impacts on employee task performance and workplace well-being, this study proposes two fundamental dimensions: algorithmic control and algorithmic norm. Using a typological approach, we construct a four-type model (high control-high norm, high control-low norm, low control-high norm, low control-low norm). A pilot study (N = 779) validates these dimensions, and the main study (N = 137) reveals that high-control, high-norm algorithmic leadership significantly enhances employees task performance and workplace well-being. Theoretically, this typology expands understanding beyond a human-oriented perspective; practically, it guides organisational implementation of algorithmic leadership.
  Keywords: Algorithmic Leadership; Typology; Task Performance; Workplace Well-being.
  DOI: 10.1504/IJIMS.2026.10070367
   
  
• A Pioneering and Robust Technique for Optimising Aerial Route Planning  
  by Dev Patel, Malhar Pandya, Kavisha Shah, Sanjay Patel, Rushin Bhatt, Debabrata Swain, Nirmal Keshari Swain, Ankush Raje 
  Abstract: In the realm of network optimization, the challenge of finding the best optimal path in a fully connected graph, where edges exceeding a certain length "x" are deemed unusable for travel, poses a complex and intriguing problem. The intricacies of this task extend beyond mere graph traversal and involve a delicate balance between various factors such as distance, efficiency, and connectivity. The goal is not only to identify a path that satisfies the imposed constraint but also to ensure that it represents the most optimal route possible. This optimization problem is inherently combinatorial, as it demands the exploration of numerous potential paths and their respective combinations to determine the one that minimizes the overall cost or maximizes a desired utility function.
  Keywords: Aerial route planning; Complexity; Optimal path finding; Network analysis; Route optimization.
  DOI: 10.1504/IJIMS.2026.10070708
   
  
• Trust and Price Sensitivity Matter: understanding Consumer Behaviour in Sustainable Online Shopping  
  by Yazeed Al-Hajj, Yanan Song, Al-Olfi Shaema 
  Abstract: This study examines the impact of perceived ease of use (PEU), perceived usefulness (PU), and green branding & eco-labeling (GB&EL) on consumer online buying behavior (COBB) within the Technology Acceptance Model (TAM) framework. Utilizing partial least squares structural equation modeling (PLS-SEM), survey data from 509 respondents was analyzed. Results indicate that GB&EL significantly influences COBB and trust, while PEU positively affects trust but does not directly impact COBB. Additionally, PU has a negative but significant effect on trust but does not directly impact COBB, and trust fully mediates the relationships between PEU, PU, and COBB, but partially mediates the relationships between GB&EL and COBB. Furthermore, price sensitivity moderates the trust-COBB relationship, underscoring the role of financial considerations in sustainable online shopping. These findings offer insights for marketers and policymakers, emphasizing the importance of trust-building strategies and pricing considerations to enhance sustainable e-commerce adoption.
  Keywords: TAM; green branding; eco-labeling; Trust; Price-sensitivity; Cunsumer online buying behavior.
  DOI: 10.1504/IJIMS.2026.10070738
   
  
• Eco-Labels and E-Commerce: exploring Consumer Purchasing Behaviour Through Digital Literacy, Credibility, and Emotions  
  by Shaema Ali Abdullah Al-Olfi, Yazeed Al-Hajj, Yanan Song 
  Abstract: This research investigates how eco-labels influence e-commerce consumer purchasing behavior, focusing on the mediating roles of digital literacy and information credibility, as well as the moderating effects of consumer guilt and self-efficacy. A total of 417 valid responses were collected from 500 distributed questionnaires through convenience sampling on Google Forms and Wenjuanxing. The survey was offered in Chinese, English, and Arabic to ensure inclusivity. Data analysis utilized Smart PLS 4.1.0 software for PLS-SEM. Findings reveal that eco-labels significantly affect purchasing behavior by enhancing digital literacy and information credibility, both acting as partial mediators. Consumer guilt emerged as a key moderator, while self-efficacy showed no significant moderating effect. This study fills a gap in the literature by examining the indirect effects of eco-labels through digital literacy and information credibility. It also emphasizes the importance of emotional factors, particularly consumer guilt, in developing effective eco-labeling strategies for sustainable purchasing.
  Keywords: Eco-labels; E-commerce ; Consumer purchasing behavior; Digital literacy; Information credibility; Consumer guilt; Self-efficacy.
  DOI: 10.1504/IJIMS.2026.10070741
   
  
• Analysing E-Learning Intentions through AI Chatbots: a TAM-TTF-Driven Case Study of ChatGPT in Networked Digital and Web Infrastructures  
  by Damodharan V. S., Asraar Ahmed, Glenn Muschert, Bijay Kumar Paikaray, A.K. Kranthi 
  Abstract: Recent data from Reuters (2024) highlights the remarkable success of artificial intelligence (AI)-based chat applications, with global revenue surpassing 1 billion and a monthly user base exceeding 100 million. This study explores and investigates the determinants influencing e-learning intention through AI chatbots, exemplified by 'ChatGPT.' Grounded in the Unified Theory of Acceptance and Usage of Technology 2 (UTAUT2), the researchers collected data through an online questionnaire targeting college students in urban areas of South India. People's choices about whether to use AI-based chatbots rely on several factors, including their perceived value, AI self-efficacy, trust, performance expectancy (PEEY), social influence (SLIE), personal innovativeness (PLIN), effort expectancy (EFEY), facilitating conditions (FGCN), and perceived risk (PRSK). A structural equation modeling with Smart PLS 4 software is to examine these factors. These findings deepen our understanding of the drivers behind AI chatbot adoption, providing valuable insights for their successful integration into various contexts.
  Keywords: E-learning; Artificial intelligence; Chatbots; UTAUT2; ChatGPT; Web Infrastructures;.
  DOI: 10.1504/IJIMS.2026.10070742
   
  
• End Trajectory Tracking Control of Flexible Space Manipulator based on Improved RBF Neural Network  
  by Bing Chen, Lei Wang, Junfeng Wu 
  Abstract: The end trajectory of a flexible space manipulator is susceptible to various interference factors during operation, resulting in significant deviations between the actual trajectory and the expected trajectory, which affects the accuracy of the model. A flexible space manipulator end trajectory tracking control method based on an improved RBF neural network is proposed. Analyse the problems with the end effector trajectory of flexible articulated robotic arms, improve the RBF neural network through weights and learning factors, construct a trajectory tracking control method, clarify the control step flow, adjust the joint information of the robotic arm in real time, and ensure accurate tracking of the end effector trajectory. The experimental results show that the improved method has an average relative position error of 3.01mm and a maximum error of 10.94mm in the motion space. The response speed is maintained at 10s, which can effectively control the end trajectory tracking effect. The main improvement is the weight and learning factor improvement of the RBF neural network, which enhances the tracking accuracy.
  Keywords: flexible space; Mechanical arm; end; Trajectory; Tracking control; Improve RBF neural network.
  DOI: 10.1504/IJIMS.2027.10071215
   
  
• Enhancing Healthcare Data Classification with Deep Edited Nearest Neighbours Ensemble Learning Systems  
  by Kavitha M, M. Kasthuri 
  Abstract: The prevalence of unbalanced datasets in medical diagnostics requires strong methods to improve prediction model accuracy and generalisability. The revolutionary Deep Edited closest Neighbours Ensemble (DENNE) framework addresses these difficulties by integrating deep learning and augmented closest neighbour approaches. This study tested DENNE against SMOTE and other conventional resampling methods. The approaches are tested using heart disease, missed abortion, and diabetes datasets. A hybrid edited closest neighbours approach refines input for an ensemble of classifiers after an autoencoder extracts features efficiently. This balances class disparities. The new strategy of DENNE combines complex feature transformation with improved noise reduction to increase prediction accuracy. DENNE significantly surpasses existing approaches in accuracy, precision, recall, and F1 scores across all datasets, according to comprehensive testing. These findings imply that DENNE effectively tackles medical dataset imbalance and sets a new threshold for medical diagnostic reliability, which has major significance for predictive healthcare analytics research.
  Keywords: Imbalanced Medical Data Classification; Deep Learning; Edited Nearest Neighbors (ENN); Ensemble Learning and Autoencoders; Medical Diagnostics; Treatment Planning.
  DOI: 10.1504/IJIMS.2027.10071460
   
  
• Enhancing Security Mechanisms in Cloud Computing Optimisation for Modern Data Protection Strategies  
  by K. Hazeena, C.Sathish Kumar, G. Prathap, S.Silvia Priscila, Edwin Shalom Soji, S. Suman Rajest 
  Abstract: This study explores the latest advancements in security mechanisms designed for cloud computing environments, addressing the urgent need for robust protection in an evolving technological landscape. By leveraging advanced encryption techniques, blockchain technology, and AI-driven anomaly detection, the study assesses the effectiveness of these solutions in protecting sensitive data from modern cyber threats. Data were collected from various cloud service providers, including public, private, and hybrid cloud models. The research utilized tools such as AWS CloudTrail for monitoring, IBM's QRadar for threat intelligence, and TensorFlow for developing AI algorithms to perform comprehensive security assessments. The findings reveal significant improvements in threat detection accuracy, faster response times, and enhanced data integrity. The paper concludes with recommendations for integrating these advanced security measures to strengthen cloud infrastructure against potential breaches. These insights aim to assist cloud service providers and users in adopting effective strategies to enhance their security posture in the face of evolving threats. By adopting these cutting-edge solutions, organisations can better safeguard their data, ensuring greater resilience and trust in their cloud computing environments.
  Keywords: Cloud Security; Encryption Techniques; Blockchain Technology; AI-driven Anomaly Detection; Cyber Threats; Cloud Service Providers; Threat Intelligence; Data Integrity.
  DOI: 10.1504/IJIMS.2027.10071761