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Two-stage revision in periprosthetic knee joint infections

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World Journal of Orthopedics 2023年第3期14卷 113-122页

作者： majd m alrayes mohamed Sukeik Department of Orthopedics Imam Abdulrahman bin Faisal UniversityKhobar 34423Saudi Arabia Department of Trauma&Orthopaedics Dr.Sulaiman Al-Habib Hospital–Al KhobarAl Khobar 34423Al KhobarSaudi Arabia

Periprosthetic joint infection(PJI)following total knee arthroplasty is one of the most catastrophic and costly complications that carries significant patient wellness as well as economic *** road to efficiently diagnosing and treating PJI is challenging,as there is still no gold standard method to reach the diagnosis as early as *** are also international controversies with respect to the best approach to manage PJI *** this review,we highlight recent advances in managing PJI following knee arthroplasty surgery and discuss in depth the two-stage revision method.

Isolated lateral leg compartment syndrome:A case report

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World Journal of Orthopedics 2023年第7期14卷 582-588页

作者： majd m alrayes mohammad Alqudah Walaa Bani Hamad mohamed Sukeik Department of Trauma and Orthopedics Dammam Medical ComplexDammam 32210Saudi Arabia Department of Trauma and Orthopaedics Dr.Sulaiman Al-Habib HospitalKhobar 34423Saudi Arabia Department of Radiology Dr.Sulaiman Al-Habib HospitalKhobar 34423Saudi Arabia

BACKGROUND Acute leg compartment syndrome is a well-known orthopedic emergency associated with potentially devastating consequences if not treated *** compartments are usually involved with a clear history of trauma and classic symptoms and ***,isolated lateral leg compartment syndrome is relatively rare and is often misdiagnosed due to the atypical presentation of no trauma and the lack of pathognomonic *** SUmmARY A 31-year-old male patient presented to our emergency room with excruciating left calf pain and inability to mobilize one-day after participating in a football match despite no clear history of preceding *** patient went to another hospital before presenting to us where he was diagnosed to have a soft tissue injury and was discharged home on simple *** clinical examination,the left leg showed a tense lateral compartment with severe *** pain was aggravated by dorsiflexion and ankle *** examination of the limb was *** suspected a compartment syndrome but as the presentation was atypical and an magnetic resonance imaging(mRI)was readily available in our institution,we immediately performed an mRI and this confirmed a large hematoma in the lateral compartment with a possible partial proximal peroneus longus muscle *** patient was taken immediately for an emergency open *** patient is now 18 mo postoperatively having recovered completely and engages fully in sports with no *** Atypical presentation due to the lack of pathognomonic signs makes the diagnosis of isolated lateral leg compartment syndrome *** on passive inversion and dorsiflexion and weak active eversion may be suggested as sensitive signs.

关键词： Isolated Lateral compartment Peroneal compartment Atraumatic compartment syndrome Case report

Optimal Bottleneck-Driven Deep Belief Network Enabled malware Classification on IoT-Cloud Environment

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Computers, materials & Continua 2023年第2期74卷 3101-3115页

作者： mohammed maray Hamed Alqahtani Saud S.Alotaibi Fatma S.alrayes Nuha Alshuqayran mrim m.Alnfiai Amal S.mehanna mesfer Al Duhayyim Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Digital media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection *** of the computer-based systems that effectively protect the data include Cyber-Physical Systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical *** software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the *** current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity malware Classification(OBDDBNCmC)*** presentedOBDDBN-CmCmodel intends to recognize and classify the malware that exists in IoT-based cloud *** attain this,Zscore data normalization is utilized to scale the data into a uniform *** addition,BDDBN model is also exploited for recognition and categorization of *** effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is *** scenario enhances the classification results and also shows the novelty of current *** experimental analysis was conducted upon OBDDBN-CmC model for validation and the results confirmed the enhanced performance ofOBDDBNCmC model over recent approaches.

关键词： malware detection security Internet of Things cloud computing machine learning parameter adjustment

Learning-Based Artificial Algae Algorithm with Optimal machine Learning Enabled malware Detection

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Computer Systems Science & Engineering 2023年第9期46卷 3103-3119页

作者： Khaled m.Alalayah Fatma S.alrayes mohamed K.Nour Khadija m.Alaidarous Ibrahim m.Alwayle Heba mohsen Ibrahim Abdulrab Ahmed mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with machine Learning(mL)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal machine Learning Enabled malware Detection(LBAAA-OmLmD)approach in Computer *** presented LBAAA-OmLmDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OmLmD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OmLmD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOmLmD model over recent algorithms.

关键词： Computer networks machine learning security malware detection feature selection ransomware

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

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Computer Systems Science & Engineering 2023年第9期46卷 3121-3139页

作者： Khaled m.Alalayah Fatma S.alrayes Jaber S.Alzahrani Khadija m.Alaidarous Ibrahim m.Alwayle Heba mohsen Ibrahim Abdulrab Ahmed mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** Industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional machine Learning(mL)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： Industrial IoT deep learning network security intrusion detection system attribute selection smart factory

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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Computer Systems Science & Engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali m.Al-Sharafi mahmoud Othman manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(mL)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning