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

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.alqah.ani 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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Significance of including lid thickness and particle shape factor in numerical modeling for prediction of particle trap efficiency of invert trap

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Water Science and Engineering 2024年第2期17卷 166-176页

作者： salman Beg Deo Raj Kaushal Department of Civil Engineering Indian Institute of Technology DelhiNew Delhi 110016India

Sediment accumulation on the bed of open sewers and drains reduces hydraulic efficiency and can cause localized *** invert traps installed underneath the bed of open sewers and drains can eliminate sediment build-up by catching sediment *** three-dimensional(3D)computational studies have examined the particle trapping performance of invert traps of different shapes and depths under varied sediment and flow conditions,considering particles as *** two-dimensional and 3D numerical modeling,researchers assumed the lid geometry to be a thin line and a plane,*** this 3D numerical study,the particle trapping efficiency of a slotted irregular hexagonal invert trap fitted at the flume bottom was examined by incorporating the particle shape factor of non-spherical sewage solid particles and the thicknesses of upstream and downstream lids over the trap in the discrete phase model of the ANSYS Fluent 2020 R1 *** volume of fluid(VOF)and the realizable k-turbulence models were used to predict the velocity *** two-dimensional particle image velocimetry(PIV)was used to measure the velocity field inside the invert *** results showed that the thicknesses of upstream and downstream lids affected the velocity field and turbulent kinetic energy at all flow *** joint impact of the particle shape factor and lid thickness on the trap efficiency was *** both the lid thickness and particle shape factor were considered in the numerical modeling,trap efficiencies were underestimated,with relative errors of-8.66%to-0.65%in comparison to the experimental values of Mohsin and Kaushal(2017).They were also lower than the values predicted by Mohsin and Kaushal(2017),which showed an overall overestimation with errors of-2.3%to 17.4%.

关键词： Invert trap Lid thickness Particle image velocimetry Particle shape factor Turbulent kinetic energy Scanning electron microscope

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Current epidemiologic features and health dynamics of ESBL-producing Escherichia coli in China

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Biosafety and Health 2024年第1期6卷 40-49页

作者： Sayed salman Zeeshan Umar Yonghong Xiao Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases State Key Laboratory for Diagnosis and Treatment of Infectious Diseasesthe First Afiliated Hospital College of MedicineZhejiang UniversityHangzhou 310000China Marshall Laboratory of Biomedical Engineering School of MedicineShenzhen UniversityShenzhen 510000China

Extended‐spectrumβ‐lactamase(ESBL)‐producing Escherichia coli(***)are widespread in China,with occurrences documented in humans,animals,and the *** dissemination of ESBL‐producing *** is likely facilitated by the widespread use of antibiotics in human and animal agriculture,the presence of antibiotic‐resistant bacteria(ARBs)in animal feces,and close human‐animal ***,particularly those belonging to incompatibility(Inc)group,such as IncF,IncI,and IncH families,play a vital role in facilitating the horizontal gene transfer of ESBL genes across various sectors,from humans to animals and the *** 26 and IS 1 elements also significantly influences the mobilization and evolution of antibiotic‐resistance genes(ARGs),contributing to the spread of ESBL‐producing ***‐M-14,blaCTX-15,and blaCTX‐M-55 are prevalent in ESBL‐producing *** across the three domains and are often found in con-junction with other *** these challenges,it is imperative to take proactive measures to prevent the further spread of *** includes the judicious and responsible use of antibiotics and efforts to mini-mize contact with animal ***‐specific strategies should be developed to effectively educate and engage relevant personnel in tackling this multifaceted *** efforts are vital to combat the dissem-ination of ESBL‐producing *** and preserve public health.

关键词： Escherichia coli Extended‐spectrumβ‐lactamases CTX‐M

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Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： Syeda Shamaila Zareen Guangmin Sun Mahwish Kundi Syed Furqan Qadri salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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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.alqah.ani 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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Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

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Computers, Materials & Continua 2024年第8期80卷 3221-3242页

作者： Ayesha Khaliq salman Afsar Awan Fahad Ahmad Muhammad Azam Zia Muhammad Zafar Iqbal Department of Computer Science University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan School of Computing Faculty of TechnologyUniversity of PortsmouthSouthseaPortsmouthPO12UPUK Department of Mathematics and Statistics University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original *** approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent *** study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical *** approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text *** model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and *** results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document *** research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

关键词： Summarization graph attention network bidirectional encoder representations from transformers Latent Dirichlet Allocation term frequency-inverse document frequency

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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.alqah.ani 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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Understanding the Novel Approach of Nanoferroptosis for Cancer Therapy

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Nano-Micro Letters 2024年第9期16卷 501-556页

作者： Afsana Sheikh Prashant Kesharwani Waleed H.Almalki Salem salman Almujri Linxin Dai Zhe‑Sheng Chen Amirhossein Sahebkar Fei Gao Department of Pharmaceutics School of Pharmaceutical Education and ResearchJamia HamdardNew Delhi 110062India Department of Pharmacology and Toxicology Faculty of PharmacyUmm Al-Qura UniversityMakkahSaudi Arabia Department of Pharmacology College of PharmacyKing Khalid University61421 Asir‑AbhaSaudi Arabia State Key Laboratory of Southwestern Chinese Medicine Resources Pharmacy SchoolChengdu University of Traditional Chinese MedicineChengdu 611130People’s Republic of China Department of Pharmaceutical Sciences College of Pharmacy and Health SciencesSt.John’s UniversityNew York 11439USA Biotechnology Research Center Pharmaceutical Technology InstituteMashhad University of Medical SciencesMashhadIran Applied Biomedical Research Center Mashhad University of Medical SciencesMashhadIran

As a new form of regulated cell death,ferroptosis has unraveled the unsolicited theory of intrinsic apoptosis resistance by cancer *** molecular mechanism of ferroptosis depends on the induction of oxidative stress through excessive reactive oxygen species accumulation and glutathione depletion to damage the structural integrity of *** to their high loading and structural tunability,nanocarriers can escort the delivery of ferro-therapeutics to the desired site through enhanced permeation or retention effect or by active *** review shed light on the necessity of iron in cancer cell growth and the fascinating features of ferroptosis in regulating the cell cycle and ***,we discussed the effect of ferroptosis-mediated therapy using nanoplatforms and their chemical basis in overcoming the barriers to cancer therapy.

关键词： Ferroptosis Nanoparticles Ferrotherapy Cancer Genes Nanotechnology Toxicity Reactive oxygen species

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2315-2347页

作者： Lirong Yin Lei Wang Siyu Lu Ruiyang Wang Haitao Ren Ahmed AlSanad salman a.alqah.ani Zhengtong Yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization *** this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature *** module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating *** with dynamic convolution with scale information,the network can extract more differentiated feature *** introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature *** convolution and pixel-based gating mechanisms enhance the module’s ***,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB *** results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

关键词： Super-resolution feature extraction dynamic convolution attention mechanism gate control

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