Modern College of Business and Science

This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime shifts in the volatility dynamics of these prices. Using in-sample criteria, we find that regime-switching models have lower AIC (Akaike information criterion) than single-regime GARCH models. In addition, out-of-sample forecasts indicate that regime-switching GARCH models have superior Value-at-Risk (VaR) prediction ability relative to single-regime models, which is directly pertinent to risk management. These findings highlight the importance of incorporating regime shifts into volatility models for accurately assessing and mitigating risks associated with electricity price fluctuations in deregulated markets.

The first two decades of this century have witnessed a burst of legislation, court cases, and shareholder reform movements for multinational corporations. However, family firms usually deviate while practicing governance and sustainability-related business practices. This study aimed to shed light on why family firms adopt a deviant corporate governance attitude. By looking at the governance score of the family firms, we implement the Generalized Method of Moment (GMM) approach to find the relationship between the family firms’ corporate governance deviance index score. The findings demonstrate how deviance in corporate governance and deviance in family firms could influence sustainability performance. Our research supports the view that even in the presence of market- or country-based corporate governance laws and rules, the family firm usually develops governance practices that better balance its sustainability and profitability objectives. The study has implications for board members of family firms and theoretical implications for family business literature.

This study aims to develop an efficient deep‐learning‐based approach for the classification of signatures on educational certificates, focusing on enhancing verification processes to ensure the authenticity and integrity of academic credentials. Signature verification, as a critical biometric tool, plays a vital role in combating fraud and maintaining trust in academic environments. To address the challenges of distinguishing between genuine and forged signatures, we utilized a comprehensive dataset comprising authentic and fraudulent samples. The proposed methodology incorporates preprocessing techniques, such as bilateral filtering, to improve image quality, followed by feature extraction using advanced convolutional neural networks (CNNs). Our approach includes a comparative evaluation of multiple baseline architectures, including MobileNetV2, LeNet, and BiOpt‐SVF a CNN‐based model specifically designed for this task. Experimental results reveal that the proposed model achieves superior performance, with accuracy rates of 99.14% on the CEDAR dataset and 98.98% on the GPDS‐150 dataset, demonstrating its effectiveness and reliability in signature verification for academic applications.

Neurodegenerative conditions are defined by the progressive deterioration and death of nerve cells in the core neural system. Most neurodegenerative conditions are not curable. While there have been significant improvements and techniques used to treat these diseases early diagnosis continues to play a crucial role in the entire approach. Conditions are often diagnosed only once they start negatively impacting the daily life of those affected. Early detection and timely preventative treatment can help improve patient subjective well-being. This study examines the application of a non-invasive gait analysis technique for the detection of Parkinson’s disease. Publicly available data collected from patients suffering from Parkinson’s along with control groups is utilized and combined with long-short-term neural networks to construct models capable of detecting signs on Parkinson’s disorder. However, because of the significant reliance of models on appropriate parameters selection, metaheuristic algorithms are used to fine tune the selection process, and a modified variation of the strongly founded PSO algorithm was proposed. Several contemporary optimizers are compared based on their ability to optimize model performance. This suggested approach achieved the superior outcomes with an accuracy of 89.92%. The constructed models have been evaluated to determine feature importance using game theory based methods.

This study investigates the integration of employability skills in Oman's higher education institutions, pinpointing discrepancies between the recognized importance of these skills and their actual implementation. Despite widespread acknowledgment of their significance for bridging academic achievements with workplace requirements, there is a notable gap in how effectively these skills are taught and developed, as reported by students and employers. The research reveals that insufficient resources, inadequate training, and a misalignment between educational practices and market demands are significant barriers. Through surveys and interviews with students, faculty, and employers, this study proposes enhancing curricula integration, strengthening academia-industry collaboration, and improving skill assessment strategies. Addressing these issues is vital for improving graduate employability in Oman's evolving economy. Recommendations from this research aim to influence educational policies and practices, ensuring that the higher education system effectively meets and anticipates labor market demands.

Feature selection poses a challenge in high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There is not a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response,this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. Graphical abstract

This study investigates the transformative impact of blended learning on teachers’ pedagogical skills in the Sultanate of Oman, a region traditionally reliant on conventional teaching methodologies. Amidst the rapidly evolving global educational landscape, integrating traditional classroom instruction with digital learning resources presents a promising avenue for enhancing teacher effectiveness and student-centered learning. The research analyzes quantitative data from surveys administered to 664 teachers and 146 supervisors, complemented by qualitative. The findings reveal significant positive effects of blended learning on teaching skills, including increased pedagogical flexibility, enhanced engagement strategies, and improved digital literacy among educators. Nonetheless, the transition highlights challenges such as infrastructure limitations, the digital divide, and the necessity for comprehensive professional development programs. The study concludes with policy and practice recommendations advocating for systemic support in teacher training, technology infrastructure, and curriculum development to fully exploit the potential of blended learning in Oman's educational reform. This paper contributes to the broader discourse on educational innovation and teacher professional development in emerging economies, particularly in the COVID-19 pandemic's acceleration of digital education models.

This study ventures into the crossroads of Artificial Intelligence (AI) and economic development, employing advanced AI models for economic forecasting in Iraq, a quintessential rentier state. Harnessing OpenAI’s ChatGPT-4 and Google’s Gemini, it explores potential economic trajectories through rentier state theory and diversification strategies. The analysis reveals Iraq’s critical challenges due to its heavy dependence on oil revenues, socio-economic instability, and the sustainability of its bloated public sector. By generating various economic scenarios, the AI models highlight the urgent need for robust diversification strategies to mitigate oil dependency and public sector imbalances. The results underscore the socio-economic implications of economic stagnation and the potential benefits of strategic diversification. This study advocates integrating AI with traditional research methodologies to enhance economic forecasting and policy planning. It provides strategic insights into guiding Iraq toward a diversified and sustainable economic future, contributing to the broader discourse on economic development in resource-rich nations.

Academic interest in insurance and economic growth nexus has prospered in the last two decades. There needs to be more review‐based research in this area. We, therefore, reviewed the literature and presented future research directions helpful for the further development of the research field. This literature review seeks to enrich the discourse on insurance and economic growth through a comprehensive and detailed review of 126 articles covering 96 journals from 2004 to 2023. Using Theory, Context, Characteristics, and Methods (TCCM), a detailed analysis has been conducted on the prominent theories, research context, key variables, and the methodologies and analysis techniques employed in the literature over the past 19 years. Through content analysis, we present the findings across three knowledge dimensions related to insurance and economic growth: research focus, country focus, and insurance focus. Our research sheds light on under‐researched contexts, variables, and analytical techniques.

With the growing modern globe, breast cancer (BC) has become the foremost kind of cancer in women. The recognition of BC in the beginning stage is more significant; hence, the patients take the required treatment for extending their survival rate. Therefore, this work designs the hybrid SpinalNet-Fuzzy-Deep Kronecker Network (Spinal-Fuzzy-DKN) for BC detection. Magnetic resonance imaging (MRI) plays a crucial role in BC identification. In the preliminary stage of this process, the MRI is applied for image preprocessing. With the aid of a Gaussian filter, the noise level is diminished. The cancer area segmentation is significant for isolating the tumor. The Bayesian Fuzzy Clustering (BFC) model effectively segments the cancer part. Furthermore, the dimensions of the image are enhanced in the image augmenting stage. For extracting the effectual features, feature extraction is employed, in which the texture and the statistical features are extracted. In BC detection, the Spinal-Fuzzy-DKN is utilized. The accuracy, specificity, and sensitivity metrics are used to validate the Spinal-Fuzzy-DKN and yielded the optimal outcomes of 0.907, 0.914, and 0.924, respectively. The proposed method is effective for the early detection of BC, which enhances patient survival rates.

The pandemic crisis brings remarkable changes to the learning process since the conventional education system shifted to digital learning using various technological tools. However, the digital learning experience confronts key dilemmas that affect students’ psychological status. Therefore, this study aims to identify the influence of digital readiness and perceived stress on students’ socio-emotional perceptions among higher educational institutions students during the COVID-19 pandemic. Also, this research aims to define the mediated effect of perceived stress on digital readiness and students’ socio-emotional perception during the pandemic crisis. The proposed theoretical framework aims to deliver a conceptual understanding of the postulated relationships. This chapter used a non-probabilistic, purposive sampling strategy approach to collect quantitative data from higher educational institutions. Data from 300 students was collected and analyzed using Partial Least Squares (PLS-SEM). The results of this chapter revealed that there is a significant positive relationship between digital readiness, perceived stress, and socio-emotional perception. Thus, the outcomes of this research are expected to provide valuable contributions for decision-makers and practitioners in higher educational institutions, in which valuable insights will help regulate relevant digital learning authorities. Additionally, this chapter will provide timely and practical recommendations and suggestions.

Breast cancer with increased risk in women is identified with Breast Magnetic Resonance Imaging (Breast MRI) and this helps in evaluating treatment therapies. Breast MRI is time time-consuming process that involves the assessment of current imaging. This research work depends on the detection of breast cancer at the earlier stages. Among various cancers, breast cancer in women occurs in larger accounts for almost 30% of estimated cancer cases. In this research, many steps are followed for breast cancer detection like pre-processing, segmentation, augmentation, extraction of features, and cancer detection. Here, the median filter is utilized for pre-processing, as well as segmentation is followed after pre-processing, which is done by Psi-Net. Moreover, the process of augmentation like shearing, translation, and cropping are followed after segmentation. Also, the segmented image tends to process feature extraction, where features like shape features, Completed Local Binary Pattern (CLBP), Pyramid Histogram of Oriented Gradients (PHOG), and statistical features are extracted. Finally, breast cancer is detected using the DL model, SqueezeNet. Here, the newly devised Flamingo Search SailFish Optimizer (FSSFO) is used in training Psi-Net as well as SqueezeNet. Furthermore, FSSFO is the combination of both the Flamingo Search Algorithm (FSA) and SailFish Optimizer (SFO).

This study aims to create robust models for financial sustainability tailored to navigate pandemics. Key objectives include identifying strategies for businesses to enhance financial resilience during crises. Primary data from 123 Omani business owners, collected through a structured questionnaire, informed the study. Convenience sampling ensured accessibility and willingness to participate, with Cronbach's alpha ensuring data reliability. Utilizing Garrett Ranking and Factor Analysis, the research reveals a ten-point sustainability model, offering strategies for risk assessment, diversification, digital transformation, and more. Besides, new findings highlight the impact of emerging technologies, government policies, and community engagement on business sustainability. These insights emphasize proactive planning, adaptability, and continuous refinement of sustainability models to navigate uncertainties effectively.

Female entrepreneurs are autonomous individuals driven to pioneer novel ventures. They meet customer demands, generate job opportunities, and contribute to societal and economic progress by seizing opportunities, mobilizing resources, and embracing business risks. This study, conducted in India, aims to uncover the motivations behind women opting for entrepreneurship and their financial empowerment. Through convenience sampling, primary data was collected from 450 female entrepreneurs in India, and factor analysis was employed to scrutinize the gathered information. Results indicate that women entrepreneurs are primarily motivated by the desire for autonomy, the pride of initiating their own business, support from family members, the aspiration to secure a prosperous future for their children, the necessity to financially support their families, job security, and various other factors.

The study advocates a robust business sustainability model for pandemics using data from 123 Omani business owners. Objectives: identify crisis resilience strategies. Convenience sampling via a structured questionnaire ensures accessibility and participation. Cronbach's alpha ensures data reliability. Garrett Ranking and Factor Analysis reveal a ten-point sustainability model, emphasizing risk assessment, diversification, digital transformation, etc. New findings highlight the impact of emerging technologies, government policies, and community engagement. Insights stress proactive planning, adaptability, and continuous refinement of sustainability models for effective navigation of uncertainties.

The growing incorporation of digital technologies into education settings has transformed learning analytics to initiate personalized and adaptive learning. This paper presents the “Learner's Digital Twin," a framework for real-time learning analytics enabled by Fog Computing and Meta LLAMA (Large Language Model with Localized Adaptation) model to interpret multimodal data. The proposed new system aims to improve the learning process by providing students with instant, personalized alerts and feedback and insights for educators that can be acted upon. Fog Computing processes data at the network edge to reduce latency and deliver timely feedback, LLAMA can do context-aware text analysis of student’s processed data. This study would apply Linear Regression model for predictive analytics, and K-means clustering to cluster students based on learning patterns. Extensive empirical evaluation shows improved feedback relevance, system responsiveness and overall performance of the students. The findings demonstrate the ability of our Learner's Digital Twin framework to effectuate data-driven and adaptive learning environments enabling more efficient personalized learning with great fidelity as applied across a variety of educational settings.

The attainment of Graduate Attributes, GA-1: Creative and Solution oriented, GA-2: Effective Communicator, and GA-3: Critical Thinker, by the graduates of Modern College of Business and Science, Oman, was evaluated using the Direct Method. Student grades in various courses that embed these GAs were accessed by mining data from the examination archives. The average scores of these GAs were compared with the benchmarked global test values of TTCT, IELTS, and CCTST respectively. The analysis revealed that, while the attainment of GA-1 is satisfactory, GA-2 and GA-3 need focus. Gender differences were noticed wherein female graduates’ attainment of GAs was found to be higher than male graduates. The attainment of GAs was not uniform across various departments in the college. Based on VUCA Theory, Social Penetration Theory, and Theory of Inquiry, this study attains significance in the light of HEIs attempting to provide job-ready students to the industry globally. This study establishes the practical implications of GA assessment in higher education and enables us to identify the GAs to focus on.