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Innovative Fungal Disease Diagnosis System Using Convolutional Neural Network

Computers, Materials & Continua 2022年第12期73卷 4869-4883页

作者： Tahir Alyas Khalid Alissa abdul salam mohammad Shazia Asif Tauqeer Faiz Gulzar Ahmed Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of General Education Skyline University CollegeUniversity City of SharjahUAE Higher Colleges of Technology Abu DhabiUAE Department of Enterprise Computing Skyline University CollegeSharjahUAE Department of Computer Science University of South AsiaLahore54000Pakistan

Fungal disease affects more than a billion people worldwide,resulting in different types of fungus diseases facing life-threatening *** outer layer of your body is called the integumentary *** skin,hair,nails,and glands are all part of *** organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the *** It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating ***,light,damage,and illness are all protected by ***-caused infections are found in almost every part of the natural *** an invasive fungus takes over a body region and overwhelms the immune system,it causes fungal infections in *** primary goal of this study was to create a Convolutional Neural Network(CNN)-based technique for detecting and classifying various types of fungal *** are numerous fungal illnesses,but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis(IFDD)system of Candidiasis and Tinea *** paper aims to detect infected skin issues and provide treatment recommendations based on proposed system *** identify and categorize fungal infections,deep machine learning techniques are utilized.A CNN architecture was created,and it produced a promising outcome to improve the proposed system *** collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus *** CNN-Based can detect fungal diseases through medical images;earmarked IFDD system has a predictive performance of 99.6%accuracy.

关键词： Deep machine learning CNN ReLU skin disease fungal

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Identification of Anomaly Scenes in Videos Using Graph Neural Networks

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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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Acceleration of solar wind particles due to inertial Alfvén waves

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Communications in Theoretical Physics 2024年第6期76卷 137-141页

作者： Kiran Batool Imran A Khan M Shamir abdul Kabir S Ayaz Theoretical Physics Department National Centre for PhysicsIslamabad 44000Pakistan Department of Space Sciences Institute of Space TechnologyIslamabad 44000Pakistan Space and Astrophysics Research Lab(SARL) National Center of GIS and Space Applications(NCGSA)Islamabad 44000Pakistan salam Chair Department of PhysicsGC University LahoreKatchery RoadLahorePunjab 54000Pakistan Department of Space Science and CSPAR University of Alabama in HuntsvilleHuntsvilleAL 35899United States of America

Gaining an understanding of the effects and dynamics of the solar wind is crucial for the study of space weather,Earth's magnetosphere,spacecraft protection,the dynamics of the Solar System,and various other *** show that Alfvén waves effectively transfer energy to resonant *** study demonstrates how inertial Alfvén waves deliver their energy to resonant plasma particles in different solar environments under certain *** analysis shows that inertial Alfvén waves experience more rapid damping with increasing parallel wavenumber,ambient magnetic field strength,and particle number density,coupled with a decrease in *** rate of energy transfer to resonant particles intensifies with higher temperatures and reduced parallel wavenumber and particle number *** with higher initial velocities actively participate in Landau damping,especially in regions with a stronger ambient magnetic field.

关键词： solar wind inertial Alfven waves resonant particles

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Swarm-Based Extreme Learning Machine Models for Global Optimization

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Computers, Materials & Continua 2022年第3期70卷 6339-6363页

作者： Mustafa abdul salam Ahmad Taher Azar Rana Hussien Artificial Intelligence Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt Scientific Computing Department Faculty of Computers and Artificial IntelligenceBenha UniversityBenha13518Egypt

Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ***,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space *** ELM,the hidden layer typically necessitates a huge number of ***,there is no certainty that the arrangement of weights and biases within the hidden layer is *** solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization *** paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression *** proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear *** the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of *** best weights and preferences were calculated by these algorithms for the hidden *** results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression *** in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.

关键词： Extreme learning machine salp swarm optimization algorithm grasshopper optimization algorithm grey wolf optimization algorithm moth flame optimization algorithm bio-inspired optimization classification model and whale optimization algorithm

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Hepatic and renal effects of oral stingless bee honey in a streptozotocin-induced diabetic rat model

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World Journal of Experimental Medicine 2024年第1期14卷 113-123页

作者： Suriati Mohd Nasir Anis Farihan Ismail Tuan Salwani Tuan Ismail Wan Faiziah Wan abdul Rahman Wan Amir Nizam Wan Ahmad Tengku Ahmad Damitri Al-Astani Tengku Din Kuttulebbai Nainamohammed salam Sirajudeen Department of Chemical Pathology School of Medical SciencesUniversiti Sains MalaysiaKota Bharu 16150KelantanMalaysia Endocrinology Laboratory Department of Chemical PathologySchool of Medical SciencesUniversiti Sains MalaysiaKota Bharu 16150KelantanMalaysia Department of Pathology School of Medical SciencesUniversiti Sains MalaysiaKota Bharu 16150KelantanMalaysia Biomedicine Program School of Health SciencesHealth CampusUniversiti Sains MalaysiaKota Bharu 16150KelantanMalaysia Department of Basic Medical Sciences Kuliyyah of MedicineInternational Islamic University MalaysiaBandar Indera MahkotaKuantan 25200PahangMalaysia

BACKGROUND Diabetes is known damage the liver and kidney,leading to hepatic dysfunction and kidney *** is believed to help in lowering the blood glucose levels of diabetic patients and reducing diabetic ***,the effect of stingless bee honey(SBH)administration in relieving liver and kidney damage in diabetes has not been *** To investigate the effect of SBH administration on the kidney and liver of streptozotocin-induced(STZ;55 mg/kg)diabetic Sprague Dawley *** The rats were grouped as follows(n=6 per group):non-diabetic(ND),untreated diabetic(UNT),metformin-treated(MET),and SBH+metformin-treated(SBME)*** successful diabetic induction,ND and UNT rats were given normal saline,whereas the treatment groups received SBH(2.0 g/kg and/or metformin(250 mg/kg)for 12 *** biochemical parameters and histological changes using hematoxylin and eosin(H&E)and periodic acid–Schiff(PAS)staining were *** On H&E and PAS staining,the ND group showed normal architecture and cellularity of Bowman’s capsule and tubules,whereas the UNT and MET groups had an increased glomerular cellularity and thickened basement *** SBH-treated group showed a decrease in hydropic changes and mild cellularity of the glomerulus vs the ND group based on H&E staining,but the two were similar on PAS ***,the SBME-treated group had an increase in cellularity of the glomerulus on H&E staining,but it was comparable to the SBH and ND groups on PAS *** diabetic rats had tubular hydropic tubules,which were smaller than other *** fatty vacuole formation and dilated blood sinusoids in liver tissue were seen in the SBH ***,the UNT group had high glucose levels,which subsequently increased MDA levels,ultimately leading to liver *** treatment reduced this damage,as evidenced by having the lowest fasting glucose,serum alanine transaminase,aspartate transaminase,and alkaline phosphatase l

关键词： Diabetes Streptozotocin Stingless bee honey Hematoxylin and eosin Periodic acid-Schiff Liver Kidney

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Insights on the molecular mechanism of neuroprotection exerted by edible bird’s nest and its bioactive constituents

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Food Science and Human Wellness 2023年第4期12卷 1008-1019页

作者： Weiyi Chu Chia Wei Phan Seng Joe Lim abdul salam Babji Department of Pharmaceutical Life Sciences Faculty of PharmacyUniversiti MalayaKuala Lumpur 50603Malaysia Clinical Investigation Centre University Malaya Medical CentreLembah Pantai Kuala Lumpur 59100Malaysia Mushroom Research Centre Universiti MalayaKuala Lumpur 50603Malaysia Department of Food Sciences Faculty of Science and TechnologyUniversiti Kebangsaan MalaysiaUKM BangiSelangor 43600Malaysia Innovation Centre for Confectionery Technology(MANIS) Faculty of Science and TechnologyUniversiti Kebangsaan MalaysiaUKM BangiSelangor 43600Malaysia

Neurodegenerative diseases are often associated with the accumulation of oxidative stress and *** bird’s nest(EBN)is a glycoprotein(sialylated mucin glycopeptides)found to be beneficial against neurodegenerative ***,anti-inflammatory,and anti-apoptotic properties of EBN in preserving neuronal cells were widely researched using in vitro and in vivo *** effects of EBN are often linked to its great number of antioxidants and anti-inflammatory *** compounds in EBN,especially sialic acid,add value to neurotrophic potential of EBN and contribute to neuronal repair and *** studies reporting the neuroprotective effects of EBN,their molecular mechanisms,and neuroactive composition were gathered in this review to provide better insights on the neuroprotective effects of EBN.

关键词： Antioxidant Composition Edible bird nest Neurodegenerative disease Neuroprotection Sialylated mucin glycopeptide

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Measuring powder flowability with a modified Warren Spring cohesion tester

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Particuology 2011年第2期9卷 148-154页

作者： salam A.Mohammed Ezzat C.abdullah Derek Geldart abdul Aziz A.Raman Department of Chemical Engineering Faculty of EngineeringUniversity of Malaya Powder Research Ltd. Harrogate& University of Leeds

The measurement of powder flowability is a major concern for most industrial processes that deal with the handling of bulk solids as raw materials, intermediates, or products. The development of devices that measure the flowability of non-aerated powders has not progressed as rapidly as might have been hoped since most research activities have been based on various types of shear testers intended to aid the design of hoppers. A new flowability indicator named as weighted cohesion （WS） is established using newly improved version of direct cohesion texture. A cornerstone of the proposed technique is that the procedure is automated, using a digital Warren Spring tester called Warren Spring-University of Malaya cohesion tester （WSUMCT）, thus making results operator-insensitive. Besides being a practical tool to diagnose the cohesion of experimental powders, the ratio between measured cohesion （using WSUMCT） and aerated density （using Hosokawa PT-S） provides us with a powerful technique to research fundamental particle internal cohesion forces directly and use these data to indicate the flowability. In this work, a series of fine （9.4μm） and coarse （60 μm） porous silica gel particle mixtures, and mixtures of fine （28μm） and coarse （72 μm） glass ballotini as well, were used as test powders. The results from these tests agree well with relative flowability determined on our newly driven indicator using WSUMCT. The validation of aerated weighted cohesion （WSA） as a flowability indicator was authenticated by comparing the conducted parameter with established measured Hausner ratio （HR） and angle of repose （AoR）.

关键词： Flowability Cohesion strength Hausner ratio Particle sizeAngle of reposeCohesive powder

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Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation Problem

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Computers, Materials & Continua 2022年第12期73卷 5245-5268页

作者： Nashwa Nageh Ahmed Elshamy Abdel Wahab Said Hassan Mostafa Sami Mustafa abdul salam Faculty of Science Suez Canal UniversityIsmailiaEgypt Faculty of Computers and Artificial Intelligence Helwan UniversityHelwanEgypt Faculty of Computers and Artificial Intelligence Benha UniversityBenha 13518Egypt Faculty of Computer Studies Arab Open UniversityCairoEgypt

Team Formation(TF)is considered one of the most significant problems in computer science and *** is defined as forming the best team of experts in a social network to complete a task with least *** real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF *** has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation *** paper proposes two improved swarm-based algorithms for solving team formation *** first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)*** also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima *** second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search *** HBO’s optimization process,a logistic chaotic map is *** performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and ***,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.

关键词： Team formation problem optimization problem genetic algorithm heap-based optimizer simulated annealing hybridization method chaotic local search

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Rough Sets Hybridization with Mayfly Optimization for Dimensionality Reduction

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Computers, Materials & Continua 2022年第10期73卷 1087-1108页

作者： Ahmad Taher Azar Mustafa Samy Elgendy Mustafa abdul salam Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer Studies Arab Open UniversityCairoEgypt Faculty of Information Technology and Computer Science Nile UniversityShikh ZaidEgypt

Big data is a vast amount of structured and unstructured data that must be dealt with on a regular *** reduction is the process of converting a huge set of data into data with tiny dimensions so that equal information may be expressed *** tactics are frequently utilized to improve classification or regression challenges while dealing with machine learning *** achieve dimensionality reduction for huge data sets,this paper offers a hybrid particle swarm optimization-rough set PSO-RS and Mayfly algorithm-rough set MA-RS.A novel hybrid strategy based on the Mayfly algorithm(MA)and the rough set(RS)is proposed in *** performance of the novel hybrid algorithm MA-RS is evaluated by solving six different data sets from the *** simulation results and comparison with common reduction methods demonstrate the proposed MARS algorithm’s capacity to handle a wide range of data ***,the rough set approach,as well as the hybrid optimization techniques PSO-RS and MARS,were applied to deal with the massive data ***-hybrid RS’s method beats other classic dimensionality reduction techniques,according to the experimental results and statistical testing studies.

关键词： Dimensionality reduction metaheuristics optimization algorithm mayfly particle swarm optimizer feature selection

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Moisture and Temperature Response of Structural and Lithology based Thermophysical and Energy Saving Traits of Limestone using Experimental and Least-Square Fitting Methods

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Journal of Thermal Science 2021年第2期30卷 551-561页

作者： Farah Anjum Muhammad Yasin Naz abdul Ghaffar Shazia Shukrullah Nasser M AbdEl-salam Khalid Aref Ibrahim Department of Physics University of AgricultureFaisalabad 38040Pakistan Arriyadh Community College King Saud UniversityArriyadh 11437Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh 11451Saudi Arabia Department of Physics College of Arts and SciencesUniversity of JhangJhang 35200Pakistan

Thermophysical analysis of the crustal rocks is important for heat transfer determination and insulation purposes to save energy in *** the presented work,thermophysical properties of four limestones were investigated in dry and moist state under ambient conditions by using a transient plane source method.A thermal constant analyzer was used to raise the sample temperature and to measure the thermal properties in the temperature range of 300 K to 433 *** conductivity and diffusivity were measured by developing a linear relationship between temperature response of TPS (transient plane source) sensor and dimensionless time function through least-square fitting *** experimental observations and predicted thermal conductivity of samples have shown that in-situ observations are important to determine the thermal properties *** effect of temperature on thermal properties of limestone was defined by considering the chemical composition of the samples and associated heat transfer *** thermal conductivity and diffusivity of limestone decreased while specific heat capacity increased with a rise in *** overall findings indicate that the spinoff of this research is useful in studying the reliance of thermophysical properties of rocks on their structures and lithology.

关键词： limestone thermophysical properties transient plane source(TPS) thermal conductivity

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