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A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

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Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and Information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： Optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

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Neuroprotective effect of liraglutide and memantine in a rat model of Alzheimer’s disease

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Asian Pacific Journal of Tropical Biomedicine 2024年第5期14卷 207-214页

作者： Duaa M.Bakhshwin Foziya W.Alhalabi Mohamad Omar Barasheed Ahmed M.Bakhshwin Maha H.Jamal Mohammed A.Bazuhair Fatemah O.Kamel Rania M.Magadmi Zoheir A Damanhouri Samar M.Alsaggaf Department of Clinical Pharmacology Faculty of MedicineKing Abdulaziz UniversityJeddahSaudi Arabia Hera General Hospital MakkahSaudi Arabia Department of Pathology Faculty of MedicineKing Abdulaziz UniversityJeddahSaudi Arabia Department of Clinical Anatomy Faculty of MedicineKing Abdulaziz UniversityJeddahSaudi Arabia

Objective:To assess the effect of memantine combined with liraglutide on aluminum chloride(AlCl_(3))and D-galactose(D-GAL)-induced neurotoxicity in ***:Male Wistar rats were divided into 5 groups of 5 animals each:the positive control,the negative control,the memantine-treated group,the liraglutide-treated group,and the combination group treated with memantine and ***_(3)and D-GAL were used to induce *** tests,brain beta-amyloid protein,and oxidative stress biomarkers were ***:The Morris water maze test indicated an enhanced memory in the combination ***,the combination treatment of liraglutide and memantine resulted in a remarkable reduction in the beta-amyloid protein level in the brain *** inflammation and oxidative stress biomarkers were significantly reduced,and the levels of antioxidant parameters were ***:The combination of liraglutide and memantine exerts neuroprotective effects and enhances memory and cognitive functions in rats with Alzheimer’s disease.

关键词： Alzheimer’s disease Cognitive function Glucagonlike peptide-1 Liraglutide Memantine

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Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

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Computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： Saket Sarin Sunil K.Singh Sudhakar Kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier

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Agmatine ameliorates diabetes type 2-induced nephropathy in rats

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Asian Pacific Journal of Tropical Biomedicine 2024年第1期14卷 8-16页

作者： Fatemah O.Kamel Ohoud Shagroud Mai AAlim A.Sattar Ahmad Gamal S Abd El-Aziz Abdulhadi S.Burzangi Duaa Bakhshwin Maha Jamal Shahid Karim Department of Clinical Pharmacology Faculty of MedicineKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Anatomy Faculty of MedicineKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Objective:To assess the nephroprotective potential of agmatine in a rat model of streptozotocin-induced diabetic ***:A single dose of streptozotocin(40 mg/kg)coupled with a fructose diet induced diabetes in Wistar ***(40 and 80 mg/kg)was administered to rats for 12 *** body weight and fasting blood glucose were measured *** level,urine output,total protein,albumin,blood urea nitrogen,creatinine,and cystatin-C were also determined at the end of the ***,superoxide dismutase,glutathione,interleukin-1β,interleukin-6,and tumor necrosis factor-alpha were evaluated in kidney *** study was also performed using hematoxylin and eosin ***:Agmatine at both doses significantly increased final body weight,and lowered fasting blood glucose,urine output,insulin,total protein,albumin,blood urea nitrogen,creatinine,and cystatin-C levels compared with the diabetic group(P<0.05).Inflammatory markers and antioxidant effect were significantly improved in agmatine-treated ***,the histopathological changes in renal structure were ameliorated by agmatine ***:Agmatine alleviates diabetic nephropathy by improving renal functions and reducing inflammation and oxidative *** molecular mechanisms of its nephroprotective actions need to be investigated in future study.

关键词： Agmatine Type 2 diabetes Nephropathy Oxidative stress Nephroprotection

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul Latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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A comparative in vitro study on the effect of SGLT2 inhibitors on chemosensitivity to doxorubicin in MCF-7 breast cancer cells

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Oncology Research 2024年第5期32卷 817-830页

作者： SHAHID KARIM ALANOUD NAHER ALGHANMI MAHA JAMAL HUDA ALKREATHY ALAM JAMAL HIND A.ALKHATABI MOHAMMED BAZUHAIR AFTAB AHMAD Department of Clinical Pharmacology Faculty of MedicineKing Abdulaziz UniversityJeddahSaudi Arabia Department of Biochemistry Faculty of ScienceKing Abdulaziz UniversityJeddahSaudi Arabia Department of Biochemistry College of ScienceUniversity of JeddahJeddah21959Saudi Arabia Health Information Technology Department The Applied CollegeKing Abdulaziz UniversityJeddahSaudi Arabia Pharmacovigilance and Medication Safety Unit Center of Research Excellence for Drug Research and Pharmaceutical IndustriesKing Abdulaziz UniversityJeddahSaudi Arabia

Cancer frequently develops resistance to the majority of chemotherapy *** study aimed to examine the synergistic cytotoxic and antitumor effects of SGLT2 inhibitors,specifically Canagliflozin(CAN),Dapagliflozin(DAP),Empagliflozin(EMP),and Doxorubicin(DOX),using in vitro *** precise combination of CAN+DOX has been found to greatly enhance the cytotoxic effects of doxorubicin(DOX)in MCF-7 ***,it was shown that cancer cells exhibit an increased demand for glucose and ATP in order to support their ***,when these medications were combined with DOX,there was a considerable inhibition of glucose consumption,as well as reductions in intracellular ATP and lactate ***,this effect was found to be dependent on the dosages of the *** addition to effectively inhibiting the cell cycle,the combination of CAN+DOX induces substantial modifications in both cell cycle and apoptotic gene *** work represents the initial report on the beneficial impact of SGLT2 inhibitor medications,namely CAN,DAP,and EMP,on the responsiveness to the anticancer properties of *** underlying molecular mechanisms potentially involve the suppression of the function of SGLT2.

关键词： SGLT2 Cancer Cytotoxicity ATP Cell cycle

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Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

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Computer Systems Science & Engineering 2024年第3期48卷 627-643页

作者： Maha Farouk Sabir Mahmoud Ragab Adil O.Khadidos Khaled H.Alyoubi Alaa O.Khadidos Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia 3Department of MathematicsFaculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz UniversityJeddahSaudi Arabia

Big data and information and communication technologies can be important to the effectiveness of smart *** on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability ***-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make *** to air pollution occurs a main environmental problem in smart city *** effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution ***,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart *** projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city *** achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input *** air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL *** simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

关键词： Sustainability smart cities air pollution prediction ensemble learning coot optimization algorithm

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Geraniol alleviates cyclophosphamide-induced cardiotoxicity in mice

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Asian Pacific Journal of Tropical Biomedicine 2024年第10期14卷 427-435页

作者： Shahid Karim Rasheed A.Shaik Department of Clinical Pharmacology Faculty of MedicineKing Abdulaziz UniversityJeddahSaudi Arabia Department of Pharmacology and Toxicology Faculty of PharmacyKing Abdulaziz UniversityJeddahSaudi Arabia

Objective:To explore the effect of geraniol on cyclophosphamide-induced ***:Mice were divided into five groups:the control group,the cyclophosphamide group(200 mg/kg cyclophosphamide,*** day 7),the group treated with geraniol 100 and 200 mg/kg from day 1 to day 14,along with a single dose of cyclophosphamide on day 7,and the geraniol alone group(200 mg/kg geraniol from day 1 to day 14).At the end of the study,animals were sacrificed,and blood and heart were collected and analyzed for biochemical,histopathological,and immunohistochemical ***:Treatment with 200 mg/kg geraniol significantly reduced the levels of cardiac injury markers,malondialdehyde,and inflammatory and apoptotic markers,while increasing antioxidant activities in mice with cyclophosphamide-induced ***,it remarkably alleviated histopathological aberrations in cardiac ***:Geraniol attenuates cyclophosphamide-induced cardiotoxicity via antioxidant,anti-inflammatory,and antiapoptotic effects.

关键词： Geraniol Cardiotoxicity Natural product Cyclophosphamide Inflammation Apoptosis

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Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

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Computers, Materials & Continua 2023年第12期77卷 3921-3942页

作者： Abdulwahab Ali Almazroi Nasir Ayub College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddah21959Saudi Arabia Department of Creative Technologies Air University IslamabadIslamabad44000Pakistan

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution *** article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational *** overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational *** algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function *** outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path *** comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization *** findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient *** the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics *** presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

关键词： Hybrid optimization internet of things intelligent transport system optimal path finding smart logistics traffic congestion supply chain

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Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

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Computers, Materials & Continua 2023年第5期75卷 3729-3741页

作者： Abdulwahab Ali Almazroi Mohamed Meselhy Eltoukhy university of jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia

Smartphones have now become an integral part of our everyday *** authentication on smartphones is often accomplished by mechanisms(like face unlock,pattern,or pin password)that authenticate the user’s *** technologies are simple,inexpensive,and fast for repeated ***,these technologies are still subject to assaults like smudge assaults and shoulder ***’touch behavior while using their cell phones might be used to authenticate them,which would solve the *** performance of the authentication process may be influenced by the attributes chosen(from these behaviors).The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of *** began by concentrating on feature selection methods utilizing the grey wolf optimization *** random forest classifier is used to evaluate these *** testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public *** achieved a 97.89%accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly;processing power of the device and memory to validate ***,the findings revealed that our approach has a lower equal error rate(EER)of 0.5104,a false acceptance rate(FAR)of 1.00,and a false rejection rate(FRR)of 0.0209 compared to the methods discussed in the *** promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.

关键词： Smartphone authentication implicit authentication grey wolf random forest feature selection

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