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Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Computers, materials & Continua 2023年第3期74卷 5417-5430页

作者： Khalid masood mahmoud m.Al-Sakhnini Waqas Nawaz Tauqeer Faiz Abdul Salam mohammad Hamza Kashif Department of Software Engineering Lahore Garrison UniversityLahore54000Pakistan School of Business Skyline University CollegeSharjahUAE Al-madinah International University–Faculty of Computer and Information Technology Kuala LumpurMalaysia College of Computer and Information Systems Islamic University of MadinahMadinahKingdom of Saudi Arabia School of IT Skyline University CollegeSharjahUAE Faculty of Engineering Computing and Science(FECS)-Swinburne University of TechnologySarawak Department of General Education Skyline University CollegeUniversity City of SharjahUAE Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan

Generally,conventional methods for anomaly detection rely on clustering,proximity,or *** themassive growth in surveillance videos,outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques *** research explores the structure of Graph neural networks(GNNs)that generalize deep learning frameworks to graph-structured *** node in the graph structure is labeled and anomalies,represented by unlabeled nodes,are predicted by performing random walks on the node-based graph *** to their strong learning abilities,GNNs gained popularity in various domains such as natural language processing,social network analytics and *** detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of *** Graph-based deep learning networks are designed to predict unknown objects and *** our case,they detect unusual objects in the form of malicious *** edges between nodes represent a relationship of nodes among each *** case of anomaly,such as the bike rider in Pedestrians data,the rider node has a negative value for the edge and it is identified as an *** encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible *** show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities,which shows a huge potential in automatically monitoring surveillance *** autonomous monitoring of CCTV,crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public *** suggested GNN model improves accuracy by 4%for the Pedestrian 2 dataset and 12%for the Pedestrian 1 dataset compared to a few state-of the-art techniq

关键词： Graph neural network deep learning anomaly detection auto encoders

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Foliar Application of Cytokinin modulates Gas Exchange Features,Water Relation and Biochemical Responses to Improve Growth Performance of maize under Drought Stress

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Phyton-International Journal of Experimental Botany 2022年第3期91卷 633-649页

作者： m.Rafiqul Islam m.Shahinur Islam Nurunnaher Akter mohammed mohi-Ud-Din mohammad Golam mostofa Department of Agronomy Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur1706Bangladesh Department of Crop Botany Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur1706Bangladesh Department of Biochemistry and molecular Biology Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur1706Bangladesh Institute of Genomics for Crop Abiotic Stress Tolerance Department of Plant and Soil ScienceTexas Tech UniversityLubbockTX 79409USA

Improvement of plant performance under drought stress is crucial to sustaining agricultural *** current study investigated the ameliorative effects of foliar-applied kinetin,an adenine-type cytokinin(CK),on growth and gas exchange parameters,water relations and biochemical attributes of maize plants under drought ***-day-old maize plants were subjected to drought by maintaining soil moisture content at 25%field capacity for 8 days followed by foliar application of kinetin at 0,75,150 and 225 mg L^(−1)(CK0,CK75,CK150 and CK225,respectively)to the plants for two-times at the 9-day *** revealed that drought stress markedly reduced stem diameter,dry weight,chlorophyll content,gas exchange parameters and water balance but increased proline,malondialdehyde and soluble sugar contents,electrolyte leakage and senescence in maize *** of exogenous CK remarkably improved maize performance by modulating growth,gas exchange-and water relation-related parameters in a dose-dependent manner under drought ***225 increased chlorophyll content(by 61.54%),relative water content(by 49.14%),net photosynthesis rate(by 39.94%)and transpiration rate(by 121.36%)and also delayed leaf senescence but decreased internal CO_(2)concentration(by 7.38%),water saturation deficit(by 40.40%)and water uptake capacity(by 42.49%)in both well-watered and droughtstressed ***,CK application considerably decreased electrolyte leakage,proline,malondialdehyde and soluble sugar levels in drought-stressed maize plants,as also supported by heatmap and cluster *** together,exogenous CK at proper concentration(225 mg L^(−1))successfully improved maize performance under drought conditions,thereby suggesting CK application as a useful approach to alleviate drought-induced adverse effects in maize plants,and perhaps in other important crop plants.

关键词： Cytokinin drought maize osmoprotection photosynthesis water-use-efficiency

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Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks

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Computers, materials & Continua 2022年第1期70卷 251-266页

作者： mohammad Riyaz Belgaum Fuead Ali Zainab Alansari Shahrulniza musa muhammad mansoor Alam m.S.mazliham malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia majan University College Muscat710Oman Institute of Business management KarachiPakistan malaysia France Institute Universiti Kuala LumpurKuala Lumpur43650Malaysia

Software-defined networking(SDN)plays a critical role in transforming networking from traditional to intelligent *** increasing demand for services from cloud users has increased the load on the *** efficient system must handle various loads and increasing needs representing the relationships and dependence of businesses on automated measurement systems and guarantee the quality of service(QoS).Themultiple paths from source to destination give a scope to select an optimal path by maintaining an equilibrium of load using some best ***,the requests need to be transferred to reliable network *** address SDN’s current and future challenges,there is a need to know how artificial intelligence(AI)optimization techniques can efficiently balance the *** study aims to explore two artificial intelligence optimization techniques,namely Ant Colony Optimization(ACO)and Particle Swarm Optimization(PSO),used for load balancing in ***,we identified that a modification to the existing optimization technique could improve the performance by using a reliable link and node to form the path to reach the target node and improve load ***,we propose a conceptual framework for SDN futurology by evaluating node and link reliability,which can balance the load efficiently and improve QoS in SDN.

关键词： Ant colony optimization load balancing particle swarm optimization quality of service reliability software-defined networking

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Intelligent Smart Grid Stability Predictive model for Cyber-Physical Energy Systems

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Computer Systems Science & Engineering 2023年第2期44卷 1219-1231页

作者： Ashit Kumar Dutta manal Al Faraj Yasser Albagory mohammad zeid m Alzamil Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical *** the same time,the machine learning(mL)modelsﬁnd useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using mL and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OmHA-mBLSTm)technique for smart grid stability predic-tion in *** proposed OmHA-mBLSTm technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OmHA-mBLSTm technique employs min-max normalization as a pre-proces-sing ***,the mBLSTm model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(mHA)is utilized for optimally modifying the hyperparameters involved in the mBLSTm *** ensure the enhanced outcomes of the OmHA-mBLSTm technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OmHA-mBLSTm technique over the recent models.

关键词： Stability prediction smart grid cyber physical energy systems deep learning data analytics moth swarm algorithm

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay m.Salih md Arafatur Rahman A.Taufiq Asyhari muhammad Kamran Naeem mohammad Patwary Ryan Alturki mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in mobile Cognitive Radio Networks(mCRNs).moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design mobile cognitive radio networks Routing protocol IIoT applications

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Diabetes and cognitive function:An evidence-based current perspective

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World Journal of Diabetes 2023年第2期14卷 92-109页

作者： meghna Julian Sebastian Shahanas KA Khan Joseph m Pappachan mohammad Sadiq Jeeyavudeen School of Life Sciences Keele UniversityStoke-on-Trent ST55BGUnited Kingdom Department of Endocrinology and metabolism Lancashire Teaching Hospitals NHS TrustPreston PR29HTUnited Kingdom Faculty of Science Manchester Metropolitan UniversityManchester M156BHUnited Kingdom Faculty of Biology Medicine and HealthThe University of ManchesterManchester M139PLUnited Kingdom Department of Endocrinology and metabolism University Hospitals of EdinburghEdinburgh EH164SAUnited Kingdom

Several epidemiological studies have clearly identified diabetes mellitus(Dm)as a major risk factor for cognitive dysfunction,and it is going to be a major public health issue in the coming years because of the alarming rise in diabetes prevalence across the *** and neural tissues predominantly depend on glucose as energy substrate and hence,any alterations in carbohydrate metabolism can directly impact on cerebral functional output including cognition,executive capacity,and *** affects neuronal function and mental capacity in several ways,some of which include hypoperfusion of the brain tissues from cerebrovascular disease,diabetes-related alterations of glucose transporters causing abnormalities in neuronal glucose uptake and metabolism,local hyper-and hypometabolism of brain areas from insulin resistance,and recurrent hypoglycemic episodes inherent to pharmacotherapy of diabetes resulting in neuronal *** decline can further worsen diabetes care as Dm is a disease largely self-managed by ***,it is crucial to understand the pathobiology of cognitive dysfunction in relation to Dm and its management for optimal long-term care plan for patients.A thorough appraisal of normal metabolic characteristics of the brain,how alterations in neural metabolism affects cognition,the diagnostic algorithm for patients with diabetes and dementia,and the management and prognosis of patients when they have this dangerous combination of illnesses is imperative in this *** evidence-based narrative with the backup of latest clinical trial reviews elaborates the current understanding on diabetes and cognitive function to empower physicians to manage their patients in day-to-day clinical practice.

关键词： Diabetes mellitus Dementia Cognitive function Antidiabetic medications Hyperglycemia Hypoglycemia

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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

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RELATION BETWEEN INTERFACIAL WAVE INSTABILITY AND TENSILE STRENGTH OF TWO-LAYER POLYmER STRUCTURE:AN EXPERImENTAL STUDY

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Chinese Journal of Polymer Science 2010年第6期28卷 887-894页

作者： mohammad m.Ranjbaran Department of mechanical Engineering Shahid Rajaee University

The existence of interfacial instability at the interface of multilayer polymeric flows is well *** article is designed mainly to provide guidelines for the development of experimental techniques for the improvement of two-layer polymer products in the polymer processing *** is done by performing a series of tensile tests on extrudate two-layer polymer melts in order to get insight into the relation between interfacial wave and mechanical properties of LLDPE/HDPE polymer *** variations of the mechanical properties have been related to the conformation of the interfacial wave so that a relationship between interface morphology corresponding to extrusion instabilities and mechanical characteristics of the interfacial strength for polymer melts extrudate is established.

关键词： Interfacial Instability Strength Wave number Extrusion

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ON THE KINETICS AND mECHANISm OF THE COPOLYmERIZATION OF ACRYLONITRILE WITH STYRENE IN THE PRESENCE OF COPPER CHLORIDE

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Chinese Journal of Polymer Science 2003年第1期21卷 57-64页

作者： mohammad m.Fares Department of Applied Chemistry Jordan University of Science & Technology P. O. Box 3030 Irbid 22110 Jordan

The copolymerization of styrene (St) and acrylonitrile (AN) complexed with CuCl_2 monomer by a free radicalmechanism was performed using benzoyl peroxide as an initiator at 65℃ under N_2 atmosphere for 150 min. The rate ofpolymerization (R_p) was found to increase linearly with the concentration (in mol/L) of CuCl_2, AN and St through scalingrelations. The activation energy of the copolymerization process in the presence and absence of CuCl_2 was found to be46.5 kJ/mol and 102 kJ/mol, respectively. The viscosity average molecular weigh of the copolymer and the k_p^2/k_t ratio weredctermired to further assess the accelerating effect of CuCl_2 on the copolymerization process. The copolymerization processin the presence of CuCl_2 has a radical complex mechanism.

关键词： Rate of polymerization (R_p) Scaling relations Viscosity average molecular weight Radical complex mechanism

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Imbalanced Classification in Diabetics Using Ensembled machine Learning

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Computers, materials & Continua 2022年第9期72卷 4397-4409页

作者： m.Sandeep Kumar mohammad Zubair Khan Sukumar Rajendran Ayman Noor A.Stephen Dass J.Prabhu School of Information Technology and Engineering Vellore Institute of TechnologyVelloreTamil Nadu632014India Department of Computer Science and Information Taibah UniversityMedinaSaudi Arabia College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia

Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood *** risk of diabetics can be lowered if the diabetic is found at the early *** recent days,several machine learning models were developed to predict the diabetic presence at an early *** this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PImA Indian to increase the prediction of *** proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of *** measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the *** results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector machines(SVm),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%*** SVm model is more efficient than other models which are 21.38%more than exiting machine learning-based works.

关键词： Diabetics classification imbalanced data split-vote instance duplication

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