The integration of edge computing for real-time data processing in autonomous systems has been identified as a promising solution to mitigate the performance bottlenecks and latency associated with traditional cloud-based models. Autonomous systems, including vehicles, drones, and robotics, rely heavily on quick data analysis to make timely decisions. However, cloud computing, with its inherent data transmission delays, hinders the responsiveness and efficiency of these systems. To address these challenges, edge computing is proposed as a means to process data locally, at the point of use, thus enabling faster decision-making processes and reducing data transfer overhead. This approach leverages distributed machine learning for decision-making and dynamic resource allocation to balance computational tasks between edge and cloud resources. Through extensive experimentation, it has been demonstrated that the edge computing paradigm can reduce latency by up to 65%, offering substantial improvements in both energy efficiency and data processing speed when compared to traditional cloud-based methods. Furthermore, the proposed system outperforms existing alternatives in terms of computational speed, reliability, and energy consumption. The introduction of an Edge Computing Decision Model (ECDM) and a Dynamic Resource Allocation Algorithm (DRAA) is shown to optimize system performance by balancing computational demands between local edge nodes and remote cloud servers. These innovations enable autonomous systems to function more effectively and efficiently, even in resource-constrained environments. This study highlights the importance of integrating edge computing into autonomous system architectures to meet the growing demand for low-latency, high-performance applications. The potential of edge computing to significantly enhance the reliability and operational capacity of autonomous systems has been established, paving the way for more reliable and scalable solutions in real-time environments.
Electroencephalography (EEG) provides a non-invasive approach for capturing brain dynamics and has become a cornerstone in clinical diagnostics, cognitive neuroscience, and neuroengineering. The inherent complexity, low signal-to-noise ratio, and variability of EEG signals have historically posed substantial challenges for interpretation. In recent years, artificial intelligence (AI), encompassing both classical machine learning (ML) and advanced deep learning (DL) methodologies, has transformed EEG analysis by enabling automatic feature extraction, robust classification, regression-based state estimation, and synthetic data generation. This survey synthesizes developments up to 2025, structured along three dimensions. The first dimension is task category, e.g., classification, regression, generation and augmentation, clustering and anomaly detection. The second dimension is the methodological framework, e.g., shallow learners, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Graph Neural Networks (GNNs), and hybrid approaches. The third dimension is application domain, e.g., neurological disease diagnosis, brain-computer interfaces (BCIs), affective computing, cognitive workload monitoring, and specialized tasks such as sleep staging and artifact removal. Publicly available EEG datasets and benchmarking initiatives that have catalyzed progress were reviewed in this study. The strengths and limitations of current AI models were critically evaluated, including constraints related to data scarcity, inter-subject variability, noise sensitivity, limited interpretability, and challenges of real-world deployment. Future research directions were highlighted, including federated learning (FL) and privacy-preserving learning, self-supervised pretraining of Transformer-based architectures, explainable artificial intelligence (XAI) tailored to neurophysiological signals, multimodal fusion with complementary biosignals, and the integration of lightweight on-device AI for continuous monitoring. By bridging historical foundations with cutting-edge innovations, this survey aims to provide a comprehensive reference for advancing the development of accurate, robust, and transparent AI-driven EEG systems.
Savings and Credit Cooperative Organizations (SACCOs) play a pivotal role in promoting financial inclusion, reducing poverty, and supporting social welfare especially in rural and underserved areas. However, 21% of DT-SACCOs do not operate with prudent financing decisions exposing themselves to financial stress and economic shocks. Even among the SACCOs that met compliance requirements, a drop in the capital adequacy ratio from 16.4% in year 2022 to 16.1% in year 2023 signaled alarming financial strain posing a threat to the existing SACCOs. Alarmingly, 35% of DT-SACCOs have ceased operations attributable to improper financing decisions with three delicensed in January 2025, raising significant concerns over their long-term financial health. Thus, the current study aimed to assess the moderating effect of SACCO size on the relationship between financing decision practices and the financial sustainability of Deposit-Taking Savings and Credit Cooperative Organizations (DT-SACCOs) in Kenya. Anchored on the pecking order theory, the research adopted a positivist paradigm and a cross-sectional survey design. A total of 176 finance managers representing 176 licensed DT-SACCOs constituted the study population. Data were collected by structured questionnaires with a 98% response rate as a sample of 122 respondents was selected by Yamane’s formula. Results from a binary logistic regression indicated that introducing the moderator led to a slight increase in the Nagelkerke R², while the inclusion of the interaction terms further strengthened the relationship between predictor variables and financial sustainability. The findings confirmed that SACCO size had a statistically significant moderating effect on this relationship. This study recommends integrating scenario-based stress testing into financing decisions to assess their long-term impact on different funding structures, so as to facilitate their confrontation of different economic conditions.
The rapid proliferation of mobile banking has transformed the delivery of financial services, necessitating a comprehensive understanding of service quality and its impact on customer satisfaction. In this study, bibliometric and content analyses were employed to examine the evolution of research on mobile banking service quality and customer satisfaction, with emphasis placed on research trends, influential contributors, thematic structures, and emerging gaps. Data retrieved from the Scopus database spanning 2003–2025 were analyzed using VOSviewer and Biblioshiny software to conduct co-word analysis, citation analysis, co-authorship mapping, and bibliographic coupling. Findings indicate a marked acceleration of research activity after 2015, with significant contributions originating from India, Indonesia, and Saudi Arabia, while University Tun Hussein Onn Malaysia emerged as one of the most active institutions. The International Journal of Bank Marketing was identified as the leading publication outlet, and scholars such as Lee and Chung were recognized as influential authors. Network analysis revealed that customer satisfaction, trust, security, service quality, and usability constitute the dominant themes in this research domain. Co-authorship analysis demonstrated robust collaborations among Saudi Arabia, China, the United Kingdom, and the United States, whereas bibliographic coupling confirmed that trust and service quality are central drivers of mobile banking adoption. The originality of this study lies in the provision of a structured synthesis of the intellectual landscape of mobile banking research and in the articulation of critical knowledge gaps. Limitations include reliance on Scopus-indexed studies and the exclusion of non-English publications, which may restrict global comprehensiveness. Future research should prioritize the integration of artificial intelligence in mobile banking, the role of mobile financial services in advancing financial inclusion, and the implications of evolving regulatory frameworks for customer trust and satisfaction. By consolidating existing evidence and highlighting strategic research directions, this study offers a foundation for advancing theoretical, methodological, and practical understanding of mobile banking services.
India annually produces about 62 million tons of municipal solid waste, comprising 50–60% of organic matter. Accelerating urbanization and population growth in this country have intensified the challenges confronted by managing food, agricultural, and biodegradable waste, as the waste if handled improperly, would lead to groundwater contamination, soil degradation, and methane emission from landfills. This review provided a comprehensive assessment of the organic waste management (OWM) landscape in India, ranging from conventional methods like composting and vermicomposting to advanced approaches such as anaerobic digestion and biogas generation. It also evaluated the influence of policy frameworks and community-led initiatives on promoting sustainable practices. The focus of this study on the emerging role of artificial intelligence (AI) in the OWM highlighted its potential for improving waste segregation, process optimization, and real-time monitoring. While the application of AI in waste management has demonstrated over 90% of segregation accuracy in the pilot and global studies, its adoption remains minimal in India. By systematically comparing national practices with global benchmarks, this review identified critical gaps in technology adoption, scalability, and integration between policy and infrastructure; to fill a noticeable void in the existing literature, AI-driven innovations were adopted to deal with the unique challenge of waste management in India. The findings underscored the need for targeted support, capacity building, and technological deployment to transform organic waste from an environmental liability into a renewable and value-generating resource. Practical recommendations were offered to align technology, governance, and community participation with sustainable and resource-efficient OWM.
Real-time traffic sign recognition (TSR) plays a crucial role in intelligent transportation systems (ITS) and autonomous driving technologies. It enhances road safety, ensures efficient traffic rule enforcement, and supports the seamless operation of both autonomous and driver-assist systems. This paper proposes a hybrid TSR model that integrates mathematical morphology, edge detection, and fuzzy logic to accurately identify and classify traffic signs across diverse environmental conditions. The preprocessing stage applies contrast enhancement and Gaussian filtering to improve the visibility of key features. Next, shape- and color-based segmentation using mathematical morphology extracts regions of interest that are likely to contain traffic signs. These regions are then analyzed using a fuzzy inference system (FIS) that evaluates features such as color intensity, geometric shape ratios, and edge sharpness. The fuzzy system handles the inherent ambiguity in visual patterns, enabling robust decision-making. The entire model is developed in MATLAB R2015a, ensuring both computational efficiency and real-time performance. The integration of classical mathematical techniques with fuzzy reasoning allows the system to maintain high accuracy and reliability across a wide variety of traffic scenes. The proposed approach demonstrates significant potential for practical deployment in ITS applications, including smart vehicles and automated road safety systems.
Operations managers and engineers in the automotive industry confront the key challenge in ensuring the reliability of the manufacturing process. To accurately classify failure modes, this study proposed a novel Multi-Criteria Decision-Making (MCDM) model integrated with Single-Valued Neutrosophic Sets (SVNSs) for operations management to prioritize actions in eliminating failure modes that had the greatest impact on the concerned reliability. The identification and evaluation of failure modes were grounded in the conventional Failure Mode and Effect Analysis (FMEA), while the relative importance of risk factors (RFs) was expressed through predefined linguistic terms modelled with the SVNSs. The assessment of these risk factors was formulated as a fuzzy group decision-making problem and the fuzzy weight vector was derived from the Order Weighted Averaging (OWA) operator. Failure rankings were conducted through a modified version of the Elimination and Choice Translating Reality (ELECTRE) method; being tested and validated with real-world data from an automotive company, the proposed FMEA-ELECTRE model could inspire stakeholders in various industries to explore this scientific contribution further.
The existing literature focused primarily on practical applications of the BIM in project management, sustainable development, and facility management (FM), while the theoretical foundations of the model remained largely underdeveloped. This article provides a systematic literature review on the basic mechanisms of the BIM, including information representation, data exchange mechanisms, decision support, and new network models integrating semantic, topological, and spatial aspects. Despite the widespread adoption of standards such as Industry Foundation Classes (IFC), Construction Operations Building Information Exchange (COBie), and BIM Collaboration Format (BCF), there is a lack of consistent ontologies integrating the function, structure, and behavior of objects. As data exchange mechanisms remain limited by interoperability issues, the impact of the BIM on decision-making processes has not been captured in universal theoretical models. The latest approaches, based on networked data representation, offer promising prospects but require further empirical validation. The results of the review imply the development of integrated ontological frameworks, formalization of information exchange processes, and creation of theoretical models to support decision-making.
For Authors
For Reviewers
For Editors
Open Access
