Computer Science and Engineering

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https://hdl.handle.net/10323/11886

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Browsing Computer Science and Engineering by Author "Edwards, William"

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    A Meta-Heuristic Algorithm Based on Modified Global Firefly Optimization: In Supply Chain Networks with Demand Uncertainty
    (2022-03-15) Altherwi, Abdulhadi; Zohdy, Mohamed; Malik, Ali; Edwards, William; Cho, Seong-Yeon; Alwerfalli, Daw
    Nowadays, many challenges affect global supply chain networks including disruptions, delays, and failures during shipment of products. These challenges also incur penalty costs due to customers’ unmet demands and failures in supply. In this dissertation, the model was developed as a multi-objective supply chain network under two risk factors including failure in supply and unmet demand based on three different scenarios. The objective of scenario I was to minimize the total expected transportation costs between stages for each supply chain and penalty costs associated with shortage of products. Supply chain with no failure in supply will communicate with supply chain with failure to deliver its product to the final customer. For scenario II, the objective was to maximize the profits of the supply chain that face extra inventory. This supply chain with surplus products will collaborate with supply chains with shortage of products to prevent any undesirable costs associated with extra inventory. The objective of scenario III was to develop a multi-objective function, which maximizes the profit and minimizes the total costs associated with production, holding, and penalties due to supplier failure of raw materials. Once a supply chain faces failure in supply of raw materials, other supply chains with no supply failure will collaborate to prevent any associated costs. This research investigates the applicability of the Modified Firefly Algorithm for a multi-stage supply chain network consisting of suppliers, manufacturers, storages, and markets under risks of failure. Commercial software cannot obtain the optimal results for these problems considered in this research. To achieve better findings, we applied a Modified Firefly Algorithm to solve the problem. Two case studies for a pipe and a steel manufacturing integrated supply chain demonstrated the efficiency of the model and the solutions obtained by the Firefly Algorithm. We used four optimization algorithms in ModeFRONTIER and MATLAB software to test the efficiency of the proposed algorithm. The results revealed that when compared with other four optimization algorithms, Firefly Algorithm can help achieve maximum profits and minimizing the total expected costs of supply chain networks.
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    Detecting and Classifying Malware in Electrical Power Grids Via Cyberdeception
    (2024-01-01) Omar, Tallal Mohamed; Zohdy, Mohamed A; Edwards, William; Caushaj, Eralda; Sutton, Sara
    Artificial intelligence (AI) has become an essential instrument for enterprises aiming to protect their digital assets within a progressively aggressive cyber landscape. As the dependence on digital technologies increases among companies and individuals, the risks associated with cyberattacks are also advancing in terms of complexity and magnitude. AI and the proliferation of technology has led to a significant concern over security, mostly due to the escalating prevalence of malware on industrial computers. This has resulted in potential physical harm to computer systems and the individuals involved. Malware is a collection of malicious programming code that aims to inflict harm against computer systems, programs, or online apps. These applications lack the ability to differentiate between legitimate system calls and those that are intended to cause harm. Therefore, it is imperative to ensure that computer systems and online applications are constructed in a manner that enables the identification and differentiation of malicious activities from legitimate application activities. The utilization of AI in the realm of cybersecurity is revolutionizing the domain of digital protection. There are various techniques that can be used to identify malicious activity, leveraging innovative concepts such as AI, machine learning, and deep learning. The present study presents a proposal for utilizing AI approaches to identify and mitigate malware activity in computer memory, with the aim of safeguarding against unauthorized access to and manipulation of physical data within the system. This research aims to combine the traditional K-means algorithm with other methods and functionalities to perform data aggregation tasks on a physical dataset. The primary objective is to identify anomalies in the dataset using clustering techniques. These anomalies will serve as triggers for creating a replica of the main process as a decoy thread. The decoy thread will be equipped with decoy sensors and actuators. The analysis will be conducted on the decoy thread rather than the main process, allowing for intrusive observation. The same host environment will be provided to memory-resident malware, enabling it to continue operating within the main operating system process. The analysis process involves utilizing a replicated instance of malware that resides within a deceptive thread.
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    Resilient Suppliers Selection System Using Machine Learning Algorithms and Risk Assessment Methodology
    (2022-01-01) Albadrani, Abdullah Meteb; Zohdy, Mohamed A.; Edwards, William; Ruegg, Erica
    Demand, supply, pricing, and lead time are all unpredictable in the manufacturingindustry, and the manufacturer must function in this environment. Because of the enormous amount of data available and the introduction of new technologies, such as the internet of things (IoT), machine learning (ML), and Blockchain, administrators and government officials are better able to deal with uncertainty by applying intelligent decision-making principles to their situations. All supply chains must make use of new technology and analyze previous data to forecast and improve the success of future operations. At the moment, we rely on the supply chain and its facilities for healthcare when we receive our vaccine, for our food when we go grocery shopping, and for transportation when we drive our cars. Supplier selection is exposed to the three most significant factors: quality, delivery, and performance history, which are all evaluated separately. The use of data analytic capabilities in the selection of robust supplier portfolios has not been thoroughly investigated. Manufacturers typically have three to four resilient suppliers for the same item, but occasionally one or two of them will fail, causing a ripple effect throughout the entire supply chain. This is a frequent problem that the supply chain must deal with on a daily basis. Supply chain resilience, on the other hand, ignores or is incompatible with the risk profiles of suppliers’ performance.