Online tutoring is a substantial instructional approach in distance education, yet current literature lacks a structured framework that explains the contextual relationships between the factors of online tutoring. This research employs interpretive structural monitoring–Matrice d’Impacts Croisés Multiplication Appliquée à un Classement analysis to transform expert insights into an interpretive multi-level hierarchical structure out of the complex inherent relationships of factors in online tutoring. Results reveal that platform usability, flexibility, institutional support, and social reinforcement are the primary drivers of online tutoring in pre-university education. Prioritizing these key factors creates a cascading effect that promotes successful online tutoring implementation. These findings offer critical insights for stakeholders to improve tutor training, strengthen technological infrastructure, and design supportive policy guidelines. This research contributes to the limited pre-university literature and demonstrates the utility of interpretive structural monitoring–Matrice d’Impacts Croisés Multiplication Appliquée à un Classement in translating complex, qualitative expert judgments into actionable policy insights for successful outcomes.
The integration of artificial intelligence (AI) into creative industries is rapidly transforming how content is produced, distributed, and interpreted. This study uses a sociotechnical lens to investigate the adoption and diffusion of AI-generated content (AIGC) across media, gaming, and marketing sectors in the Asia-Pacific region. Grounded in sociotechnical systems and innovation diffusion theories, its research employs a quantitative methodology using secondary data sources to analyze patterns of technological integration, regulatory influence, and creative workflow evolution. Findings indicate that the uneven adoption of AI across sectors is shaped not only by a sector’s technological capabilities but also by its institutional readiness, national policy frameworks, and cultural attitudes toward automation. The study highlights the importance of human-centered design, ethical considerations, and platform dynamics in facilitating the sustainable and equitable implementation of AI tools.
Advanced manufacturing technologies have a positive impact on the efficiency of firms, facilitating product design and fabrication, improving connectivity among resources, and optimizing planning processes. Consequently, the adoption of these technologies has garnered increasing interest in the field. However, the literature has not fully explored the interrelationships among the enablers and barriers that influence adoption decisions. This study investigates these factors using interpretive structural modeling and cross-impact matrix multiplication applied to a classification analysis, complemented by bootstrap resampling to provide statistical validation of the results. The findings indicate that corporate structure as an enabler and economic barrier significantly drives adoption decisions. These results highlight the need for stakeholders to address these factors to ensure the successful implementation of advanced manufacturing technologies.
Digital transformation is widely recognized as a critical driver of organizational productivity; however, its effectiveness largely depends on the successful management of employee change. This study examines the relationships between digitalization, employee change management, and organizational productivity while reviewing the key theoretical perspectives that underpin these linkages. This study develops a theoretical framework that explains how digitalization and employee change management jointly influence organizational productivity. Digitalization is grounded in the resource-based view, and employee change management is theoretically anchored in the theory of planned behavior. Organizational productivity is framed using socio-technical theory to emphasize the principles of joint optimization between social and technical subsystems. The proposed framework demonstrates strong applicability across diverse organizational and national contexts and contributes to the literature by integrating strategic digital investments, employee engagements, and productivity outcomes.
This study investigates the impact of digital leadership on employees' innovative work behavior and knowledge sharing behavior, with a particular focus on the mediating role of psychological ownership. The independent variable, digital leadership, is examined through its supportive and innovative sub-dimensions, while psychological ownership is explored through emotional and work-based ownership. The dependent variables are innovative work behavior and knowledge sharing behavior. To test the research model, eight sub-models were developed and analyzed using PLS-SEM. Mediation was assessed through the Variance Accounted For (VAF) metric. Data were collected from 287 managers and employees working in IT departments within technology parks located in Ankara, Türkiye. The results reveal that both supportive and innovative forms of digital leadership have a positive and statistically significant influence on employees' innovative behavior and knowledge sharing. However, a portion of this effect operates indirectly through psychological ownership.
This work highlights an evaluation of blueprint reading competencies among university students, with particular attention to common and core competencies. Recognizing the ambiguity and imprecision arising from such an evaluation, Einstein aggregation operators on picture fuzzy sets were adopted to model the judgments of participants derived from a pool of mechanical technology students. Results reveal the students' competency level for each pre-identified task in blueprint reading. Although they display above-average performances, areas requiring enhancement in both competency types are identified. Pathways from these findings involve various strategies: conducting a separate in-depth study for a deeper understanding of the subject matter, incorporating particular emphasis on blueprint reading tasks, introducing competency-based exercises within relevant courses, and facilitating industry experts' collaboration. Comparative analysis with those of intuitionistic fuzzy sets and Dombi aggregation operators yields similar results.
Artificial intelligence (AI) and intellectual property (IP) share some key similarities, such as uncertainty in predictions, processing a massive amount of data, and machine learning. Yet, they also differ from each other. This paper provides background information on how these two domains have evolved over time. It also highlights how Saudi Arabia's IP system differs from those of other countries. Furthermore, this article explores the relationship between AI and IP and their application in copyright. This study is significant as it helps identify the challenges and opportunities that AI presents with respect to IP in terms of copyright. Finally, this article makes recommendations that will help protect both AI and IP.
This journal paper deals with data-Mining striving as emerging technique which plays the vital role in digging out the significant appropriate information from the vast stream of data collection. The present research focusses on the diagnosis of the brain tumours and the predictions of disease distinguishing the healthy individuals and the patients. To accomplish this predictions, machine learning algorithm Multinomial-Naive-Bayes algorithm in the classification technique to prediction of the results in relevance with the brain tumors disease. The proposed research consists of Collection of dataset, pre-processing technique, Feature-selection method, and organisation of the data in the normalised form, classification implementation and in the generation of the predicted results. These depicted results were subjected to the comparative analysis of the existing previous predictive models with the present proposed work which is superior to them.
In an increasingly globalized and knowledge-based economy, this study aimed to investigate the adoption of modern technologies for effective knowledge sharing and enhancing knowledge access in academic libraries. The study was underpinned by the organizational knowledge creation theory (OKCT) and knowledge sharing model. The findings reveal that although modern technologies, such as the internet of things (IoT) and blockchain technologies, have been seen as suitable knowledge sharing strategies by many institutions, the level of their adoption is still low in academic libraries in South Africa, especially in the area of knowledge management. Several recommendations are thus made, and among others are the improvement of technology infrastructure and the enactment of policies for promoting knowledge management and sharing.
This journal paper deals with Steganography technique which is a method for hiding secret communications inside a cover object during sender-receiver communication. From ancient times to the present, the security of secret information has been a key concern. It has long been an area of interest for researchers to create mechanisms for sending data without disclosing it to anybody other than the intended receiver. To facilitate the safe transport of data, researchers have periodically created a variety of approaches, including steganography. Using the synergies that may be obtained by combining cryptography with steganography. This paper's work seeks to improve an innovative approach for hiding a hidden message inside an image. This study developed a new encryption-based method for embedded image steganography, LSB with the RSA algorithm, to upsurge data security. These depicted results were subjected to the comparative analysis of the existing previous predictive models with the present proposed work which is superior to them.
This study presents a simulation of economic scenarios using Mind Genomics to evaluate the maturity and ability of students involved in the master's and bachelor's programs to make sound decisions. A third group of specialists in economics is added to the study to evaluate differences among these groups. A comprehensive investigation of the current literature is undertaken to comprehend how different studies have dealt with the issue of the post-COVID-19 recovery process. Literature findings are used to design the Mind Genomics experiment and enable comparisons of student responses with literature findings. The study shows that, in general, master students converge with specialists in economics. The study shows some discrepancies in the scenario-evaluation process between master's and bachelor's students. Finally, recommendations for program examination and potential improvements are suggested to further align the teaching process to market scenarios.
In the context of the “double reduction” policy, using big data to carry out precision teaching is an effective way to improve the quality of teaching in schools and reduce the burden on teachers and students and increase efficiency. The formal implementation of the “double reduction” policy has not only reduced students' academic burden, but also increased students' spare time. However, according to relevant data, the current level of anxiety among parents about their children's education is still high, and different parents have different levels of education anxiety. In order to study the impact of the “double reduction” policy on the educational anxiety of parents of primary and secondary school students in the context of big data, this article puts forward some suggestions related to eliminating the educational anxiety of primary school parents based on the existing problems and their causes, so as to create a good educational atmosphere.
Artificial intelligence (AI) and intellectual property (IP) share some key similarities, such as uncertainty in predictions, processing a massive amount of data, and machine learning. Yet, they also differ from each other. This paper provides background information on how these two domains have evolved over time. It also highlights how Saudi Arabia's IP system differs from those of other countries. Furthermore, this article explores the relationship between AI and IP and their application in copyright. This study is significant as it helps identify the challenges and opportunities that AI presents with respect to IP in terms of copyright. Finally, this article makes recommendations that will help protect both AI and IP.
The purpose of this paper is to explore the influence of education technology antecedents and perceptions of usefulness and ease of use of educational technology systems on attitudes towards computer-based teaching in Egyptian universities. The study is built upon on deductive quantitative approach where structured questionnaires were designed and distributed to the students in Egyptian universities. Results based on SEM analysis identify that the technology dimension partially affects the design dimension, as well there is a partially significant association between the technology dimension and attitude towards technology-based teaching. While the design dimension does not have a significant association with attitude towards technology-based teaching, attitude towards technology-based teaching does not have a significant relationship with student satisfaction. Furthermore, there is a partially significant association between course dimensions and student satisfaction.
In recent years, HRM has received a lot of traction. Human resource management is essential for all types of enterprises. The bulk of research indicates that HRM and organizational performance have a positive link. Each organization's main goal is to attain high levels of performance in its goals and objectives. In this paper, HRM and practices were investigated. What part do human resources play in achieving organizational objectives? This survey will also evaluate HRM practices by analyzing the 40 papers that have been submitted. In addition, this article offers a thorough examination of the chronological assessment based on each publication. In addition, the results of each research paper's analysis are displayed. A review of the literature was utilized to focus on and review the issue knowledge in this study. Finally, it expands on many research concerns that may be useful to researchers in doing more studies on HRM practices.
The difficulty in predicting early cancer is due to the lack of early illness indicators. Metaheuristic approaches are a family of algorithms that seek to find the optimal values for uncertain problems with several implications in optimization and classification problems. An automated system for recognizing illnesses can respond with accuracy, efficiency, and speed, helping medical professionals spot abnormalities and lowering death rates. This study proposes the Novel Hybrid GAO (Genetic Arithmetic Optimization algorithm based Feature Selection) (Genetic Arithmetic Optimization Algorithm-based feature selection) method as a way to choose the features for several machine learning algorithms to classify readily available data on COVID-19 and lung cancer. By choosing just important features, feature selection approaches might improve performance. The proposed approach employs a Genetic and Arithmetic Optimization to enhance the outcomes in an optimization approach.
This article presents an in-depth study of the legal landscape surrounding blockchain technology in the healthcare sector, with a special focus on case studies from European countries. Analyzing the existing legal framework and regulations, the research highlights the challenges and opportunities associated with the adoption of blockchain in healthcare. The most important research areas are data protection, security, consent, liability, and compliance. Through a comparative analysis of various European countries, the article illuminates the differences in legal approaches and points out possible areas of harmonization. The results clarify the legal aspects that must be addressed to ensure the integration of blockchain technology into healthcare systems, innovation while protecting patients' rights, and compliance with regulatory requirements.
Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.
The provision of information and communication technology (ICT) infrastructure is not eminent in many societies due to a lack of digital access and poverty. Therefore, there should be ICT provision in underserved communities to bridge digital literacy and contribute to human and indigenous knowledge development. This paper aims to investigate how ICT access can impact youth employability in underserved townships. The quantitative method was used for the data collection process, using a structured questionnaire to draft both close-ended and open-ended questions. These were drafted into Google forms and distributed on social media platforms. While the sustainable livelihood theory was used to guide the study. Results derived from this study depict the inherent environmental factors that hinder youth access to ICTs. This study recommends policymakers can implement measures to provide sufficient ICT development initiatives to support youths living in underserved communities.
Coronavirus sickness (COVID-19) recently adversely disrupted the medical care system and the entire economy. Doctors, researchers, and specialists are working on new-fangled methods to detect COVID-19 relatively efficiently, such as constructing computerized COVID-19 detection systems. Medical imaging, such as Computed Tomography (CT), has a lot of opportunity as a solution to RT-PCR approaches for quantitative assessment and disease monitoring. COVID-19 diagnosis based on CT images can provide speedy and accurate results. A quantitative criterion for diagnosis is provided by an automated segmentation method of infection areas in the lungs. As an outcome, automatic image segmentation is in high demand as a clinical decision aid tool. To detect COVID-19, Computed Tomography images might be employed instead of the time-consuming RT-PCR assay. In this research, a unique technique is provided for segmenting infection areas in the lungs using CT scan images from COVID-19 patients. “Ground Glass Opacity (GGO)” regions were detected using Novel Adaptive Histogram Binning Based Lesion Segmentation (NAHBLS) method. Many metrics were also employed to evaluate the proposed method, including “Sorensen–Dice similarity”, “Sensitivity”, “Specificity”, “Precision”, and “Accuracy” measures. Experiments have shown that the proposed method can effectively separate the lung infections with good accuracy. The results show that the proposed Novel Adaptive Histogram Binning Based Lesion Segmentation based on automatic approach is effective at segmenting the lesion region of the image and calculated the Infection Rate (IR) over the lung region in Computed Tomography scan.
