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Prognostic value of hematological parameters in older adult patients with acute coronary syndrome undergoing coronary intervention:a single centre prospective study

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老年心脏病学杂志:英文版 2023年第8期20卷 596-601页

作者： Umar Hafiz Khan Murtaza Rashid Pala Imran Hafeez Afshan Shabir Amrit Dhar hilal Ahmad Rather Geriatric Medicine Department of Internal MedicineSher-i-Kashmir Institute of Medical SciencesSrinagarIndia Department of Internal Medicine Sher-i-Kashmir Institute of Medical SciencesSrinagarIndia Department of Cardiology Sher-i-Kashmir Institute of Medical SciencesSrinagarIndia Department of Internal and Pulmonary Medicine Sher-i-Kashmir Institute of Medical SciencesSrinagarIndia

BACKGROUND Cardiovascular disease is a significant contributor to the disease burden in geriatric *** systemic inflammation is thought to be the cause of age-related changes in the bone marrow and a major risk factor for *** purpose of the study was to assess the accuracy of these hematological biomarkers in predicting 30-day mortality in older patients with acute coronary syndrome(ACS).METHODS This was a prospective observational study of 601 older adult patients(age>60 years)with ACS who underwent percutaneous coronary intervention over two years(2017-2019).The relationship between baseline hematological parameters and mortality was assessed during the 30-day *** regression analysis and receiver operating characteristic curve analysis were done to evaluate for diagnostic accuracy of various hematological *** The mean age of presentation was 77±17 *** mean neutrophil-lymphocyte ratio(NLR)value was 5.07±4.90 and the mean platelet-lymphocyte ratio(PLR)value was 108.65±*** univariate analysis,total leucocyte count[odds ratio(OR)=0.85,P=0.021],hematocrit(OR=0.91,P=0.018),NLR(OR=1.10,P=0.001)and PLR(OR=1.05,P=0.001)were associated with *** receiver operating characteristic curve analysis,NLR predicted mortality with 68.1%and PLR with 65.7%*** multivariate analysis,NLR(OR=1.096,95%CI:1.006-1.15,P=0.035)was an independent predictor of 30-day *** For the risk classification of all elderly ACS patients,we highly advise using NLR rather than the total white blood cell count.

关键词： patients coronary prospective

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Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

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Computers, Materials & Continua 2022年第11期73卷 3419-3432页

作者： Fadwa Alrowais Saud S.Alotaibi Sami Dhahbi Radwa Marzouk Abdullah Mohamed Anwer Mustafa hilal Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia University of Tunis EL Manar Higher Institute of ComputerResearch Team on Intelligent Systems in Imaging and Artificial Vision(Siiva)–Lab LimticAryanah2036Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Sign language recognition can be considered as an effective solution for disabled people to communicate with *** helps them in conveying the intended information using sign languages without any *** advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective *** optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification *** this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled *** aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled *** proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature *** addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO ***,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language *** proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several *** comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.

关键词： Sign language image processing computer vision disabled people deep learning parameter tuning

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Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

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Computers, Materials & Continua 2023年第2期74卷 2443-2459页

作者： Amani Abdulrahman Albraikan Nadhem NEMRI Mimouna Abdullah Alkhonaini Anwer Mustafa hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Melanoma remains a serious illness which is a common formof skin *** the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic *** recent advances in deep learning(DL)models find useful to examine the medical image and make proper *** this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is *** goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of *** ADL-MDC technique performs contrast enhancement and data augmentation at the initial ***,the k-means clustering technique is applied for the image segmentation *** addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction ***,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification *** exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several *** simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

关键词： Biomedical images dermoscopic images deep learning melanoma detection machine learning

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Resistive-type sensors based on few-layer MXene and SnO_(2)hollow spheres heterojunctions:Facile synthesis,ethanol sensing performances

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中国化学快报:英文版 2023年第8期34卷 502-507页

作者： Lihua Chu Hao Yan Wanfeng Xie Yuxin Shi Muhammad hilal Changxu Sun Ze Li Meicheng Li School of New Energy North China Electric Power UniversityBeijing 102206China College of Electronics and Information University-Industry Joint Center for Ocean Observation and Broadband CommunicationQingdao UniversityQingdao 266071China Department of Physics Dongguk UniversitySeoul 04620Republic of Korea

High-performance and low-cost gas sensors are highly desirable and involved in industrial production and environmental *** combination of highly conductive MXene and metal oxide materials is a promising strategy to further improve the sensing *** this study,the hollow SnO_(2)nanospheres and few-layer MXene are assembled rationally via facile electrostatic synthesis processes,then the SnO_(2)/Ti_(3)C_(2)T_(x)nanocomposites were *** with that based on either pure SnO_(2)nanoparticles or hollow nanospheres of SnO_(2),the SnO_(2)/Ti_(3)C_(2)T_(x)composite-based sensor exhibits much better sensing performances such as higher response(36.979),faster response time(5 s),and much improved selectivity as well as stability(15 days)to 100ppm C2H5OH at low working temperature(200°C).The improved sensing performances are mainly attributed to the large specific surface area and significantly increased oxygen vacancy concentration,which provides a large number of active sites for gas adsorption and surface catalytic *** addition,the heterostructure interfaces between SnO_(2)hollow spheres and MXene layers are beneficial to gas sensing behaviors due to the synergistic effect.

关键词： Heterostructure Few-layer MXene SnO_(2)hollow spheres Ethanol sensor Synergistic effect SnO_(2)/Ti_(3)C_(2)T_(x)composites

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Effective Video Summarization Approach Based on Visual Attention

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Computers, Materials & Continua 2022年第4期71卷 1427-1442页

作者： hilal Ahmad Habib Ullah Khan Sikandar Ali Syed Ijaz Ur Rahman Fazli Wahid Hizbullah Khattak Department of Computer Sciences Islamia College Peshawar KhyberPakhtunkhwaPakistan Department of Accounting and Information Systems College of Business and EconomicsQatar UniversityDoha2713Qatar Department of Information Technology The University of HaripurKhyber PakhtunkhwaPakistan Department of Information Technology Hazara University MansehraKhyber PakhtunkhwaPakistan

Video summarization is applied to reduce redundancy and developa concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes,the frames that stand out visually are extracted as key frames based on humanattention modeling theories. The schemes for modeling visual attention haveproven to be effective for video summaries. Nevertheless, the high cost ofcomputing in such techniques restricts their usability in everyday *** this context, we propose a method based on KFE (key frame extraction)technique, which is recommended based on an efficient and accurate visualattention model. The calculation effort is minimized by utilizing dynamicvisual highlighting based on the temporal gradient instead of the traditionaloptical flow techniques. In addition, an efficient technique using a discretecosine transformation is utilized for the static visual salience. The dynamic andstatic visual attention metrics are merged by means of a non-linear weightedfusion technique. Results of the system are compared with some existing stateof-the-art techniques for the betterment of accuracy. The experimental resultsof our proposed model indicate the efficiency and high standard in terms ofthe key frames extraction as output.

关键词： KFE video summarization visual saliency visual attention model

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Exploration of the Townsend regime by discharge light emission in a gas discharge device

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Chinese Physics B 2014年第1期23卷 289-296页

作者： hilal Yucel Kurt Gazi University Faculty of Sciences Department of Physics 06500 Ankara Turkey

The Townsend discharge mechanism has been explored in a planar microelectronic gas discharge device （MGDD） with different applied voltages U and interelectrode distance d under various pressures in air. The anode and the cathode of the MGDD are formed by a transparent SnO2 covered glass and a GaAs semiconductor, respectively. In the experiments, the discharge is found to be unstable just below the breakdown voltage Ub, whereas the discharge passes through a homo- geneous stable Townsend mode beyond the breakdown voltage. The measurements are made by an electrical circuit and a CCD camera by recording the currents and light emission （LE） intensities. The intensity profiles, which are converted from the 3D light emission images along the semiconductor diameter, have been analysed for different system parameters. Dif- ferent instantaneous conductivity ~t regimes are found below and beyond the Townsend region. These regimes govern the current and spatio-temporal LE stabilities in the plasma system. It has been proven that the stable LE region increases up to 550 Torr as a function of pressure for small d. If the active area of the semiconductor becomes larger and the interlectrode distance d becomes smaller, the stable LE region stays nearly constant with pressure.

关键词： Townsend discharge breakdown microelectronic gas discharge device semiconductor cathode

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Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

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Computers, Materials & Continua 2022年第10期73卷 327-342页

作者： Hanan Abdullah Mengash Radwa Marzouk Siwar Ben Haj Hassine Anwer Mustafa hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial intelligence(AI)techniques have received significant attention among research communities in the field of networking,image processing,natural language processing,robotics,*** the same time,a major problem in wireless sensor networks(WSN)is node localization,which aims to identify the exact position of the sensor nodes(SN)using the known position of several anchor *** comprises a massive number of SNs and records the position of the nodes,which becomes a tedious ***,the SNs might be subjected to node mobility and the position alters with ***,a precise node localization(NL)manner is required for determining the location of the *** this view,this paper presents a new quantum bird migration optimizer-based NL(QBMA-NL)technique for *** goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor *** QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating *** addition,an objective function is derived based on the received signal strength indicator(RSSI)and Euclidean distance from the known to unknown *** demonstrating the improved performance of the QBMA-NL technique,a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.

关键词： Artificial intelligence wireless communication wireless sensor networks metaheuristics quantum computing node localization

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Deep Learning Empowered Cybersecurity Spam Bot Detection for Online Social Networks

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Computers, Materials & Continua 2022年第3期70卷 6257-6270页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa hilal Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from *** this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be *** studies have developed different approaches for the detection of spam bots in *** those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be *** this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as *** proposed SBD-HDL technique focuses on the detection of spam bots that exist in *** technique has different stages of operations such as pre-processing,classification,and parameter ***,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification *** order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this *** experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.

关键词： Cybersecurity spam bot data classification social networks twitter deep learning

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Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

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Computers, Materials & Continua 2022年第9期72卷 4267-4281页

作者： Jaber S.Alzahrani Reem M.Alshehri Mohammad Alamgeer Anwer Mustafa hilal Abdelwahed Motwakel Ishfaq Yaseen Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making *** latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification *** the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality *** resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)*** FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier ***,the FSS-AICBD technique undergoes min-max normalization technique to prevent data *** addition,the information gain(IG)approach is applied for the optimal selection of feature ***,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO *** choice of IG and GSO approaches results in promising medical data classification *** experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare *** simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

关键词： Medical data classification feature selection deep learning healthcare sector artificial intelligence

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Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

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Computers, Materials & Continua 2022年第3期70卷 5871-5887页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd NAl-Wesabi Mohammed Alamgeer Anwer Mustafa hilal Manar Ahmed Hamza Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

An exponential growth in advanced technologies has resulted in the exploration of Ocean *** has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine *** Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental *** this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water *** models have been developed earlier for fish ***,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different *** this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN *** presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded ***-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish ***,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is *** model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate ***,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier *** performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under differe

关键词： Aquaculture background subtraction deep learning fish detection marine surveillance underwater sensor networks

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