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Smart Energy management System Using machine Learning

Computers, materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas muhammad Adnan Khan Atifa Athar Taher m.Ghazal hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy management System(IEmS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(mL)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEmS is proposed for Smart Cities(SC)using the mL technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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Computers, materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.alqahtani Atif m.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of medical Things (IomT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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Development of a Novel Noise Reduction Algorithm for Smart Checkout RFID System in Retail Stores

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Computers, materials & Continua 2024年第7期80卷 1277-1304页

作者： Shazielya Shamsul mohammed A.H.Ali Salwa Hanim Abdul-Rashid Atef m.Ghaleb Fahad m.alqahtani Department of mechanical Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Centre for Sustainable and Smart manufacturing(CSSm) Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Industrial Engineering College of EngineeringAlfaisal UniversityRiyadh11533Saudi Arabia Department of Industrial Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

This paper presents a smart checkout system designed to mitigate the issues of noise and errors present in the existing barcode and RFID-based systems used at retail stores’checkout *** is achieved by integrating a novel AI algorithm,called Improved Laser Simulator Logic(ILSL)into the RFID *** enhanced RFID system was able to improve the accuracy of item identification,reduce noise interference,and streamline the overall checkout *** potential of the systemfor noise detection and elimination was initially investigated through a simulation study usingmATLAB and ILSL ***,it was deployed in a small-scale environment to validate its real-world *** show that RFID with the proposed new algorithm ILSL and AI basket is capable of accurately detecting the related itemswhile eliminating noise originating fromunrelated objects,achieving an accuracy rate of 88%.

关键词： Smart checkout system RFID technology improved laser simulator logic(ILSL) reduce noise checkout process

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mental health status among COVID-19 patients survivors of critical illness in Saudi Arabia:A 6-month follow-up questionnaire study

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World Journal of Clinical Cases 2024年第15期12卷 2560-2567页

作者： Abdullah m Alhammad Namareq F Aldardeer Abdulaziz alqahtani mohammad H Aljawadi Bushra Alnefaie Raghad Alonazi mansour Almuqbil Abdulrahman Alsaadon Rakan m alqahtani Rashid Alballaa Bader Alshehri mohammed I Alarifi Fahad D Alosaimi Department of Clinical Pharmacy College of PharmacyKing Saud UniversityRiyadh 12372Saudi Arabia Department of Pharmacy Services King Faisal Specialist Hospital and Research CentreJeddah 21499Saudi Arabia Corporate Department of Pharmacy Services King Saud University Medical CityRiyadh 12372Saudi Arabia College of Pharmacy King Saud UniversityRiyadh 12372Saudi Arabia Department of Critical Care medicine College of MedicineKing Saud UniversityRiyadh 12372Saudi Arabia Department of Psychiatry College of MedicineKing Saud UniversityRiyadh 12372Saudi Arabia

BACKGROUND Psychological assessment after intensive care unit(ICU)discharge is increasingly used to assess patients'cognitive and psychological ***,few studies have examined those who recovered from coronavirus disease 2019(COVID-19).There is a paucity of data from the middle East assessing the post-ICU discharge mental health status of patients who had *** To evaluate anxiety and depression among patients who had severe *** This is a prospective single-center follow-up questionnaire-based study of adults who were admitted to the ICU or under ICU consultation for>24 h for *** patients were contacted via *** patient’s anxiety and depression six months after ICU discharge were assessed using the Hospital Anxiety and Depression Scale(HADS).The primary outcome was the mean HADS *** secondary outcomes were risk factors of anxiety and/or *** Patients who were admitted to the ICU because of COVID-19 were screened(n=518).Of these,48 completed the *** mean age was 56.3±17.2 *** patients(62.5%)were *** main comorbidities were endocrine(n=24,50%)and cardiovascular(n=21,43.8%)*** mean overall HADS score for anxiety and depression at 6 months post-ICU discharge was 11.4(SD±8.5).A HADS score of>7 for anxiety and depression was detected in 15 patients(30%)and 18 patients(36%),*** from the multivariable ordered logistic regression demonstrated that vasopressor use was associated with the development of anxiety and depression[odds ratio(OR)39.06,95% confidence interval:1.309-1165.8;Pmonths after ICU discharge,30% of patients who had COVID-19 demonstrated a HADS score that confirmed anxiety and *** compare the psychological status of patients following an ICU admission(with vs without COVID-19),further studies are warranted.

关键词： COVID-19 Critically ill Anxiety Depression HADS Saudi Arabia

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Integrating Transformer and Bidirectional Long Short-Term memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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Computer modeling in Engineering & Sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar alqahtani Sangita Babu mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of Science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of Information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of Computer Science and Business Systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term memory(Bi-LSTm)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

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Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

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Computer modeling in Engineering & Sciences 2024年第6期139卷 3101-3123页

作者： mohammad mehedi Hassan Salman A.alqahtani mabrook S.AlRakhami Ahmed Zohier Elhendi Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable model-Agnostic Explanations(LImE)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LImE,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LImE not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

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A Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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Computers, materials & Continua 2024年第5期79卷 2945-2970页

作者： muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif mAlamri Salman A.alqahtani muhammad Fahad munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(mS)to mitigate the energy consumption problem through periodic network *** mobile sink(mS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(mS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(Rkm-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkm-ANN)for designing a fast,efficient,and most proficient mS path depending upon rendezvous points(RPs).The first algorithm optimizes the mS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using mS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(mS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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Congenital absence of the lateral meniscus:A case report

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World Journal of Orthopedics 2024年第5期15卷 477-482页

作者： Hamad Ali Alkhunayfir Abdulaziz Ali alqahtani Abdulrahman Jalwi Korkoman Department of Orthopedic Surgery Prince Muhammed bin Abdulaziz HospitalRiyadh 61422Saudi Arabia Department of Orthopedic Surgery Prince Sultan Military Medical CityRiyadh 12233Saudi Arabia Department of Orthopedic Surgery University of BishaBisha 67714Saudi Arabia

BACKGROUND Congenital absence of the menisci is a rare anatomical variation characterized by the absence or underdevelopment of one or both menisci in the knee *** menisci are crucial in load distribution,joint stability,and shock *** the clinical presentation,diagnosis,and management of this condition is important for optimal patient *** SUmmARY A 27-year-old male with a long-standing history of knee pain underwent diagnostic arthroscopy,revealing a congenital absence of the *** patient's clinical findings,imaging results,surgical procedures,and pertinent images are *** case presents a unique aspect with the congenital absence of the meniscus,contributing valuable insights to the literature on rare anatomical *** This case of congenital absence of the menisci highlights the diagnostic challenges posed by rare *** diagnostic arthroscopy played a crucial role in identifying the absence of the meniscus and providing an explanation for the patient's persistent knee *** case underscores the importance of individualized treatment approaches,including physical therapy,for optimal management of rare meniscal *** research is warranted to explore effective management strategies for the aforementioned cases and to expand our knowledge of these rare conditions.

关键词： Lateral meniscus Arthroscopy Knee joint menisci reformation magnetic resonance imaging Case report

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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Computer modeling in Engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa m.Amer Ashraf A.m.Khalaf Amr H.Hussein Salman A.alqahtani mostafa H.Dahshan Hossam m.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** m elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2m−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths m,m−1,andm,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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Using Flowable Composite as Gingival Retraction Cord in Prosthodontics Dentistry: Two Case Reports: A Dental Technique

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Open Journal of Stomatology 2024年第2期14卷 118-131页

作者： Fahad Hussain alqahtani Saudi Boards in Restorative Dentistry Center of Advanced Consultants in Healthcare Education and Training Riyadh Saudi Arabia

One of the most challenging factors affecting impression quality and good marginal fit is the gingival retraction method, which aims to separate the gingiva from the tooth to expose the finishing line of the prepared tooth. Bleeding and contamination from the crevicular fluid may compete for space with the impression material within the sulcus, leading to indistinct preparations and a lack of detail at the margin. In addition, such procedures might damage the PDL and cause permanent recession. Flowable composite cable, as opposed to traditional retraction cords, offers the proper marginal seal and visibility during the impression record because of its lower viscosity, improved adaptability, and elastic properties. The aim of this report is to present a brand-new, innovative method for controlling gingival hemorrhage in addition to the gingival retraction in restorative and prosthodontic treatment by utilizing a flowable composite during the final impression record procedure. In these case reports, the flowable composite cord technique is used instead of traditional gingival cords to modify and refine the gingival tissue before taking the traditional final impression or digital scan. Two cases were done for patients undergoing fixed prosthodontics and veneer treatment within a 2-year follow-up period. In addition, the flowable composite cord was used as temporary restoration before the final prosthetic cementation or under the provisional crowns. As a result, clean and healthy gingival tissues render cementation of the final prosthesis quicker and easier to perform. The patients in both cases claimed that their prosthodontic treatments, which involved the application of a flowable composite cord to temporarily retraction the gingiva, had produced good outcomes at the yearlong follow-up. In conclusion, the flowable composite cord technique is found to be a useful tool to improve the quality of impressions in fixed prosthodontics by providing excellent access for impre

关键词： Flowable Composite Gingival Retraction methods Impression material marginal Integrity Final Cementation

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