61 research outputs found
Efficient Short-Term Electricity Load Forecasting for Effective Energy Management
No full textShort-term electrical energy load forecasting is one of the most significant problems associated with energy management for smart grids, which aims to optimize the operational strategies of buildings. Electricity forecasting models are considered a key aspect of the provision of better electricity management and reductions in energy consumption. This motivates the researchers to develop efficient electricity load forecasting (ELF) models, based on historical nonlinear and high volatile data, which require appropriate forecasting strategies. Therefore, in this article, we present an innovative two-phase framework for short-term ELF. The first phase is dedicated to data cleansing, in which pre-processing strategies are applied to raw data. In the second phase, a deep residual Convolutional Neural Network (CNN) is designed to extract the important features from the refined data. To the best of our knowledge, this is the first work to introduce a deep CNN architecture for the extraction of spatial features from electricity data. The output of the residual CNN network is forwarded to a stacked Long Short-Term Memory (LSTM) network to learn the temporal information of the electricity data. The proposed model is then evaluated using the Individual-Household-Electric-Power-Consumption (IHEPC) and Pennsylvania–New Jersey–Maryland (PJM) datasets. The results reveal a significant reduction in the error rate over the IHEPC dataset in terms of Mean-Absolute-Error (MAE) (15.65%), Mean-Square-Error (MSE) (8.77%), and Root-Mean-Square-Error (RMSE) (14.85%) and over the PJM dataset our method reduced RMSE up to 3.4% as compared to baseline models i.e., linear regression, LSTM, and Gated Recurrent Unit (GRU). Furthermore, we performed several experiments with CNN, LSTM, and GRU models and evaluated it with additional Coefficient of Variation of the RMSE (CV-RMSE) metrics, which proves the effectiveness of our model for short-term load forecasting
Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems
No full text[EN] Rapid developments in deep learning (DL) and the Internet-of-Things (IoT) have enabled vision-based systems to efficiently detect fires at their early stage and avoid massive disasters. Implementing such IoT-driven fire detection systems can significantly reduce the corresponding ecological, social, and economic destruction; they can also provide smart monitoring for intelligent transportation systems (ITSs). However, deploying these systems requires lightweight and cost-effective convolutional neural networks (CNNs) for real-time processing on artificial intelligence (AI)-assisted edge devices. Therefore, in this paper, we propose an efficient and lightweight CNN architecture for early fire detection and segmentation, focusing on IoT-enabled ITS environments. We effectively utilize depth-wise separable convolution, point-wise group convolution, and a channel shuffling strategy with an optimal number of convolution kernels per layer, significantly reducing the model size and computation costs. Extensive experiments on our newly developed and other benchmark fire segmentation datasets reveal the effectiveness and robustness of our approach against state-of-the-art fire segmentation methods. Further, the proposed method maintains a balanced trade-off between the model efficiency and accuracy, making our system more suitable for IoT-driven fire disaster management in ITSs.Muhammad, K.; Ullah, H.; Khan, S.; Hijji, M.; Lloret, J. (2023). Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 24(11):13141-13150. https://doi.org/10.1109/TITS.2022.3203868S1314113150241
Cybersecurity Awareness and Training (CAT) Framework for Remote Working Employees
No full textCurrently, cybersecurity plays an essential role in computing and information technology due to its direct effect on organizations’ critical assets and information. Cybersecurity is applied using integrity, availability, and confidentiality to protect organizational assets and information from various malicious attacks and vulnerabilities. The COVID-19 pandemic has generated different cybersecurity issues and challenges for businesses as employees have become accustomed to working from home. Firms are speeding up their digital transformation, making cybersecurity the current main concern. For software and hardware systems protection, organizations tend to spend an excessive amount of money procuring intrusion detection systems, antivirus software, antispyware software, and encryption mechanisms. However, these solutions are not enough, and organizations continue to suffer security risks due to the escalating list of security vulnerabilities during the COVID-19 pandemic. There is a thriving need to provide a cybersecurity awareness and training framework for remote working employees. The main objective of this research is to propose a CAT framework for cybersecurity awareness and training that will help organizations to evaluate and measure their employees’ capability in the cybersecurity domain. The proposed CAT framework will assist different organizations in effectively and efficiently managing security-related issues and challenges to protect their assets and critical information. The developed CAT framework consists of three key levels and twenty-five core practices. Case studies are conducted to evaluate the usefulness of the CAT framework in cybersecurity-based organizational settings in a real-world environment. The case studies’ results showed that the proposed CAT framework can identify employees’ capability levels and help train them to effectively overcome the cybersecurity issues and challenges faced by the organizations
An Information Technology (IT) System–based Framework for Capabilities Readiness against Scalable Levels of Flash-Flood Risk in Saudi Arabia
No full textA Multivocal Literature Review on Growing Social Engineering Based Cyber-Attacks/Threats During the COVID-19 Pandemic: Challenges and Prospective Solutions
No full textThe novel coronavirus (COVID-19) pandemic has caused a considerable and long-lasting social and economic impact on the world. Along with other potential challenges across different domains, it has brought numerous cybersecurity challenges that must be tackled timely to protect victims and critical infrastructure. Social engineering-based cyber-attacks/threats are one of the major methods for creating turmoil, especially by targeting critical infrastructure, such as hospitals and healthcare services. Social engineering-based cyber-attacks are based on the use of psychological and systematic techniques to manipulate the target. The objective of this research study is to explore the state-of-the-art and state-of-the-practice social engineering-based techniques, attack methods, and platforms used for conducting such cybersecurity attacks and threats. We undertake a systematically directed Multivocal Literature Review (MLR) related to the recent upsurge in social engineering-based cyber-attacks/threats since the emergence of the COVID-19 pandemic. A total of 52 primary studies were selected from both formal and grey literature based on the established quality assessment criteria. As an outcome of this research study; we discovered that the major social engineering-based techniques used during the COVID-19 pandemic are phishing, scamming, spamming, smishing, and vishing, in combination with the most used socio-technical method: fake emails, websites, and mobile apps used as weapon platforms for conducting successful cyber-attacks. Three types of malicious software were frequently used for system and resource exploitation are; ransomware, trojans, and bots. We also emphasized the economic impact of cyber-attacks performed on different organizations and critical infrastructure in which hospitals and healthcare were on the top targeted infrastructures during the COVID-19 pandemic. Lastly, we identified the open challenges, general recommendations, and prospective solutions for future work from the researcher and practitioner communities by using the latest technology, such as artificial intelligence, blockchain, and big data analytics
A critical evaluation of the rational need for an IT management system for flash flood events in Jeddah, Saudi Arabia
No full textThe aim of this paper is to examine the value of creating an information technology (IT) system to assist Saudi Arabia in predicting and preparing the right training capabilities to manage scalable flood events. This paper sets forth the scope of emergency response training capabilities needed to manage low-, medium-, and high-intensity flooding events in Jeddah. With the use of primary data from local responders in Jeddah, the need for an emergency response training capabilities IT system that can be defined as able to map human resource training needs was assessed. This IT system could help decision makers calibrate the right response criteria in the event of scalable flash flooding across Jeddah.</p
Vision-based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
No full textScene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research directions for scene understanding pursued by related studies include person/vehicle detection and segmentation, their transition analysis, lane change, and turns detection, among many others. Unfortunately, these tasks seem insufficient to completely develop fully-autonomous vehicles i.e., achieving level-5 autonomy, travelling just like human-controlled cars. This latter statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. With this motivation, this survey defines, analyzes, and reviews the current achievements of the scene understanding research area that mostly rely on computationally complex deep learning models. Furthermore, it covers the generic scene understanding pipeline, investigates the performance reported by the state-of-the-art, informs about the time complexity analysis of avant garde modeling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The survey also includes a comprehensive discussion on the available datasets, and the challenges that, even if lately confronted by researchers, still remain open to date. Finally, our work outlines future research directions to welcome researchers and practitioners to this exciting domain
Strategic Management Model for Academic Libraries
No full textAbstractThis study utilized the qualitative approach including content analysis of literature and in-depth interviews in order to design Strategic Management Model for Academic Libraries. The literature reviewed indicates many models for strategic management, which were generally made to suit organizations from different sectors. These models were found lack important elements that academic libraries need to connect their strategies to the overall visions and goals of their parent institutions. The Model, which is presented in the present paper therefore, attempts to bridge this gab by providing a new route for articulating and implementing strategies for academic libraries through three main stages: pre-planning stage; planning stage; and post- planning stage. The first stage starts by providing the planning team or committee with skills and knowledge of strategic management. The second stage is achieved through the fulfilment of two components of the Model: strategy formulation; and strategy implementation. The last stage however, concerns with the evaluation process of the strategy to ensure that the quality of services provided, and the performance of all library units and employees are compatible with vision and objectives of the library, and aligned with the overall goals of the Mother Institution.Comments: This paper is a part of my PhD thesis titled “Strategic Management and planning practices at Academic Libraries in Oman”, which was submitted to the University of Sheffield, UK. The Value of Model presented in this paper would be seen in new issues that are involved in its’ structure such as pre planning stage and the alignment with the parent institution strategy, which are not included clearly in other Models. Moreover, all models that the Author could reach through the literature review of library management, either describes strategic planning steps, or strategy perspectives, and none has included the whole process and components of strategic management, which was the focus of this study. The Model also contains three components which have not been seen in other models
No Titels
No full textThis study investigates the relations between the Arab tribes that inhabited Palestine and Trans
Jordan during the periods of the Frankish (Crusader) occupation of the Levant, the Ayy bid State and the
Mamluk State. The study reveals the role of these tribes in these relations, on the one hand and the
displacement and mutual movement of the people of these two regions, despite their blood ties, on the other
hand. The importance of this study stems from the fact that its subject has not been thoroughly and clearly
discussed, to the best knowledge of the author of this paper.
Key words: Palestine Trans Jordan Crusaders Arab tribesAbstract: This study investigates the relations between the Arab tribes that inhabited Palestine and Trans
Jordan during the periods of the Frankish (Crusader) occupation of the Levant, the Ayy bid State and the
Mamluk State. The study reveals the role of these tribes in these relations, on the one hand and the
displacement and mutual movement of the people of these two regions, despite their blood ties, on the other
hand. The importance of this study stems from the fact that its subject has not been thoroughly and clearly
discussed, to the best knowledge of the author of this paper.
Key words: Palestine Trans Jordan Crusaders Arab tribesAl-Quds Universit
Relations Between the Arab Tribes in Palestine and Transjordan from the Frankish Occupation until the End of the Mamluk Era (583-922 AH / 1087-1516 CE)
No full textThis study investigates the relations between the Arab tribes that inhabited Palestine and TransJordan during the periods of the Frankish (Crusader) occupation of the Levant, the Ayybid State and theMamluk State. The study reveals the role of these tribes in these relations, on the one hand and thedisplacement and mutual movement of the people of these two regions, despite their blood ties, on the otherhand. The importance of this study stems from the fact that its subject has not been thoroughly and clearlydiscussed, to the best knowledge of the author of this paper
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