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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

Computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri arfat ahmad khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

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Computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla arfat ahmad khan Sultan Alharby Ahmed Alhussen Mohd Anul Haq Department of Computer Science&Applications Maharshi Dayanand UniversityRohtak124001India University Institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas khan ElSayed M.Tag El Din arfat ahmad khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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A Cost-Efficient Environment Monitoring Robotic Vehicle for Smart Industries

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Computers, Materials & Continua 2022年第4期71卷 473-487页

作者： arfat ahmad khan Chitapong Wechtaisong Faizan Ahmed khan Nadeem ahmad Suranaree University of Technology Nakhon Ratchasima30000Thailand University of Engineering and Technology LahorePakistan The University of Lahore LahorePakistan

Environmental monitoring is essential for accessing and avoiding the undesirable situations in industries along with ensuring the safety of ***,inspecting and monitoring of environmental parameters by humans lead to various health concerns,which in turn brings to the requirement of monitoring the environment by *** this paper,we have designed and implemented a cost-efficient robotic vehicle for the computation of various environmental parameters such as temperature,radiation,smoke,and pressure with the help of ***,the robotic vehicle is designed in such a way that it can be dually controlled by using the remote control along with the distant *** addition,contrary to the existing researches,the GSM modules are used to achieve the two-way long distance communication between the robotic vehicle and the distant *** the distant computer,the above-mentioned environmental parameters can be monitored along with controlling the robotic vehicle with the help of Graphical User Interface(GUI).In order to fulfill the given tasks,we have proposed two algorithms implemented at the robotic vehicle and the distant computer respectively in this *** final results validate the proposed algorithms where the above-mentioned environmental parameters can be monitored along with the smooth-running operation of the robotic vehicle.

关键词： Industry 4.0 sensors robotic vehicle distant communication environment monitoring

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Process Discovery and Refinement of an Enterprise Management System

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Computer Systems Science & Engineering 2023年第3期44卷 2019-2032页

作者： Faizan Ahmed khan Farooq ahmad arfat ahmad khan Chitapong Wechtaisong COMSATS University Islamabad Lahore54000Pakistan Sunaree University of Technology Nakhon Ratchasima30000Thailand

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more *** data can be extremely valuable for executing organizations because the data allows constant monitoring,analyzing,and improving the underlying processes,which leads to the reduction of cost and the improvement of the *** mining is a useful technique for analyzing enterprise systems by using an event log that contains *** research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and *** investigating and analyzing their order handling pro-cesses,this study aims at learning a model that gives insight inspection of the processes and performance ***,the animation is also performed for the better inspection,diagnostics,and compliance-related questions to specify the *** configuration of the system and the conformance checking for further enhancement is also addressed in this *** achieve the objectives,this research uses process mining techniques,*** discovery in the form of formal Petri nets models with the help of process maps,and process refinement through conformance checking and ***,the identified executed process is reconstructed by using the process discovery *** the reconstruction,we perform a deep analysis for the underlying process to ensure the process improvement and ***,some recommendations are made to improve the enterprise management system processes.

关键词： Process mining enterprise management system business process management process discovery conformance analysis process enhancement

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D^(2)PAM:Epileptic seizures prediction using adversarial deep dual patch attention mechanism

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 755-769页

作者： arfat ahmad khan Rakesh Kumar Madendran Usharani Thirunavukkarasu Muhammad Faheem Department of Computer Science College of ComputingKhon Kaen UniversityKhon KaenThailand Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennaiIndia Department of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(Deemed to be University)ChennaiIndia School of Technology and Innovations University of VaasaVaasaFinland

Epilepsy is considered as a serious brain disorder in which patients frequently experience *** seizures are defined as the unexpected electrical changes in brain neural activity,which leads to *** researches made an intense effort for predicting the epileptic seizures using brain signal ***,they faced difficulty in obtaining the patients'characteristics because the model's distribution turned to fake predictions,affecting the model's *** addition,the existing prediction models have severe issues,such as overfitting and false positive *** overcome these existing issues,we propose a deep learning approach known as Deep dual‐patch attention mechanism(D^(2)PAM)for classifying the pre‐ictal signals of people with Epilepsy based on the brain *** neural network is integrated with D^(2)PAM,and it lowers the effect of differences between patients to predict *** multi‐network design enhances the trained model's generalisability and stability ***,the proposed model for processing the brain signal is designed to transform the signals into data blocks,which is appropriate for pre‐ictal *** earlier warning of epilepsy with the proposed model obtains the auxiliary *** data of real patients for the experiments provides the improved accuracy by D2PAM approximation compared to the existing *** be more distinctive,the authors have analysed the performance of their work with five patients,and the accuracy comes out to be 95%,97%,99%,99%,and 99%***,the numerical results unveil that the proposed work outperforms the existing models.

关键词： artificial intelligence techniques classification learning(artificial intelligence)

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Automatic Leukaemia Segmentation Approach for Blood Cancer Classification Using Microscopic Images

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Computers, Materials & Continua 2022年第11期73卷 3629-3648页

作者： Anuj Sharma Deepak Prashar arfat ahmad khan Faizan Ahmed khan Settawit Poochaya School of Computer Applications Lovely Professional UniversityPhagwara144411India School of Computer Science&Engineering Lovely Professional UniversityPhagwara144411India Suranaree University of Technology Nakhon Ratchasima30000Thailand COMSATS University Lahore 54000Pakistan

Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells(WBC),and it is also called a blast blood *** the marrow of human bones,leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC,and if any cell gets blasted,then it may become a cause of ***,the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human ***,in terms of detection,image segmentation techniques play a vital role,and they turn out to be the important image processing steps for the extraction of feature patterns from the Acute Lymphoblastic Leukaemia(ALL)type of blood ***,the image segmentation technique focuses on the division of cells by segmenting a microscopic image into background and cancer blood cell nucleus,which is well-known as the Region Of Interest(ROI).As a result,in this article,we attempt to build a segmentation technique capable of solving blood cell nucleus segmentation issues using four distinct scenarios,including K-means,FCM(Fuzzy Cmeans),K-means with FFA(Firefly Algorithm),and FCM with ***,we determine the most effective method of blood cell nucleus segmentation,which we subsequently use for the Leukaemia classification ***,using the Convolution Neural Network(CNN)as a classifier,we developed a leukaemia cancer classification model from the microscopic *** proposed system’s classification accuracy is tested using the CNN to test the model on the ALL-IDB dataset and equate it to the current state of the *** terms of experimental analysis,we observed that the accuracy of the model is near to 99%,and it is far better than other existing models that are designed to segment and classify the types of leukaemia cancer in terms of ALL.

关键词： Leukaemia blood cell nucleus image segmentation HOG descriptor K-means FCM CNN microscopic images

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Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

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Computers, Materials & Continua 2023年第4期75卷 1331-1351页

作者： Anubha Ravneet Preet Singh Bedi arfat ahmad khan Mohd Anul Haq ahmad Alhussen Zamil S.Alzamil Department of Computer Applications I.K.G.Punjab Technical UniversityKapurthala144601PunjabIndia Research and Development I.K.G.Punjab Technical UniversityKapurthala144604PunjabIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Mobile adhoc networks have grown in prominence in recent years,and they are now utilized in a broader range of *** main challenges are related to routing techniques that are generally employed in *** Adhoc system management,on the other hand,requires further testing and improvements in terms of *** routing protocols,such as Adhoc On-Demand Distance Vector(AODV)and Dynamic Source Routing(DSR),employ the hop count to calculate the distance between two *** main aim of this research work is to determine the optimum method for sending packets while also extending life time of the *** is achieved by changing the residual energy of each network ***,in this paper,various algorithms for optimal routing based on parameters like energy,distance,mobility,and the pheromone value are ***,an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of *** simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest,which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.

关键词： Routing optimization reward penalty mobility energy throughout pheromone

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Intelligent Medical Diagnostic System for Hepatitis B

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Computers, Materials & Continua 2022年第12期73卷 6047-6068页

作者： Dalwinder Singh Deepak Prashar Jimmy Singla arfat ahmad khan Mohammed Al-Sarem Neesrin Ali Kurdi Lovely Professional University Punjab144402India College of Computing Khon Kaen UniversityKhon Kaen40000Thailand College of Computer Science and Engineering Taibah UniversityMedina41477Saudi Arabia Information System Department Sheba Region UniversityMarib14400Yemen

The hepatitis B virus is the most deadly virus,which significantly affects the human *** termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage;otherwise,it will become a severe problem and make a human liver suffer from the most dangerous diseases,such as liver *** this paper,two medical diagnostic systems are developed for the diagnosis of this life-threatening *** methodologies used to develop thesemodels are fuzzy logic and the neuro-fuzzy *** diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both *** classification accuracy of a multilayered fuzzy inference system is 94%.The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System(ANFIS)classifies the result corresponding to the given input is 95.55%.The comparison of both developed models on the basis of their performance parameters has been *** is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic ***,the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B *** other words,the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

关键词： Artificial intelligence fuzzy logic hepatitis B hybrid system medical diagnostic system neural network neuro-fuzzy technique

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Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

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Computers, Materials & Continua 2022年第9期72卷 5793-5806页

作者： Noreen Mushtaq arfat ahmad khan Faizan Ahmed khan Muhammad Junaid Ali Malik Muhammad Ali Shahid Chitapong Wechtaisong Peerapong Uthansakul COMSATS University Islamabad Islamabad Campus45550Pakistan Suranaree University of Technology Nakhon Ratchasima30000Thailand COMSATS University Islamabad Lahore Campus54000Pakistan Virtual University of Pakistan Islamabad Campus45550Pakistan COMSATS University Islamabad Vehari Campus61100Pakistan

Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain *** detection of brain tumor increases the survival rate and helps in reducing the fatality *** imaging modalities have been used for diagnosing by expert radiologists,and Medical Resonance Image(MRI)is considered a better option for detecting brain tumors as MRI is a non-invasive technique and provides better visualization of the brain *** of the challenging issues is to identify the tumorous region from the MRI scans *** segmentation is performed by medical experts,which is a time-consuming task and got chances of *** overcome this issue,automatic segmentation is performed for quick and accurate *** proposed approach is to capture inter-slice information and reduce the *** learning-based brain tumor segmentation techniques proved best among available segmentation ***,deep learning may miss some preliminary info while using MRI images during *** MRI volumes are volumetric,3D U-Net-based models are used but *** of multiple 2D U-Net predictions in axial,sagittal,and coronal views help to capture inter-slice *** approach may reduce the system ***,the Conditional Random Fields(CRF)reduce the predictions’false positives and improve the segmentation *** model is applied to Brain Tumor Segmentation(BraTS)2019 dataset,and cross-validation is performed to check the accuracy of *** proposed approach achieves Dice Similarity Score(DSC)of 0.77 on Enhancing Tumor(ET),0.90 on Whole Tumor(WT),and 0.84 on Tumor Core(TC)with reduced Hausdorff Distance(HD)of 3.05 on ET,5.12 on WT and 3.89 on TC.

关键词： Brain tumor deep learning detection conditional random field segmentation

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