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Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

World Journal of Gastroenterology 2022年第46期28卷 6551-6563页

作者： Surjeet Dalal Edeh Michael Onyema Amit Malik Department of CSE Amity UniversityGurugram 122413HaryanaIndia Department of Mathematics and Computer Science Coal City UniversityEnugu 400102Nigeria Department of CSE SRM UniversityDelhi-NCRSonipat 131001HaryanaIndia

BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal *** global community has recently witnessed an increase in the mortality rate due to liver *** could be attributed to many factors,among which are human habits,awareness issues,poor healthcare,and late *** curb the growing threats from liver disease,early detection is critical to help reduce the risks and improve treatment *** technologies such as machine learning,as shown in this study,could be deployed to assist in enhancing its prediction and *** To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection,diagnosis,and reduction of risks and mortality associated with the *** The dataset used in this study consisted of 416 people with liver problems and 167 with no such *** data were collected from the state of Andhra Pradesh,India,through https://***/datasets/uciml/*** population was divided into two sets depending on the disease state of the *** binary information was recorded in the attribute"is_patient".RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36%and 73.24%,respectively,which was much better than the conventional *** proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis(scarring)and to enhance the survival of *** study showed the potential of machine learning in health care,especially as it concerns disease prediction and *** This study contributed to the knowledge of machine learning application to health and to the efforts to

关键词： Liver infection Machine learning Chi-square automated interaction detection Classification and regression trees Decision tree XGBoost hyperparameter tuning

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hyperparameter tuning Based Machine Learning Classifier for Breast Cancer Prediction

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Journal of Computer and Communications 2023年第4期11卷 149-165页

作者： Mohammed Mijanur Rahman Asikur Rahman Swarnali Akter Sumiea Akter Pinky Department of CSE Southeast University Dhaka Bangladesh

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer’s favourable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model’s overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, F1 score and finally the AUC & ROC curve. In this study of hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

关键词： Machine Learning Breast Cancer Prediction Grid Search hyperparameter tuning

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

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Computers, Materials & Continua 2023年第7期76卷 347-366页

作者： Niharika Gupta Baijnath Kaushik Mohammad Khalid Imam Rahmani Saima Anwar Lashari Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatraJ&KIndia College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer *** prediction of diabetes is crucial to taking precautionary steps to avoid or delay its *** this study,we proposed a Deep Dense Layer Neural Network(DDLNN)for diabetes prediction using a dataset with 768 instances and nine *** also applied a combination of classical machine learning(ML)algorithms and ensemble learning algorithms for the effective prediction of the *** classical ML algorithms used were Support Vector Machine(SVM),Logistic Regression(LR),Decision Tree(DT),K-Nearest Neighbor(KNN),and Naïve Bayes(NB).We also constructed ensemble models such as bagging(Random Forest)and boosting like AdaBoost and Extreme Gradient Boosting(XGBoost)to evaluate the performance of prediction *** proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the *** combined ML models used majority voting to select the best outcomes among the *** efficacy of the proposed and other models was evaluated for effective diabetes *** investigation concluded that the proposed model,after hyperparameter tuning,outperformed other learning models with an accuracy of 84.42%,a precision of 85.12%,a recall rate of 65.40%,and a specificity of 94.11%.

关键词： Diabetes prediction hyperparameter tuning k-fold validation machine learning neural network

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Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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Computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： Mohammed Maray Haya Mesfer Alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different *** phenomenon has increased the number of Internet of Things(IoT)devices and their *** IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks *** scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic *** Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT *** Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this *** current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT *** presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature ***,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT ***,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the *** process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s *** proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

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Deep Learning Enabled Computer Aided Diagnosis Model for Lung Cancer using Biomedical CT Images

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Computers, Materials & Continua 2022年第10期73卷 1437-1448页

作者： Mohammad Alamgeer Hanan Abdullah Mengash Radwa Marzouk Mohamed K Nour Anwer Mustafa Hilal Abdelwahed Motwakel Abu Sarwar Zamani Mohammed Rizwanullah Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Early detection of lung cancer can help for improving the survival rate of the *** imaging tools such as computed tomography(CT)image was utilized to the proper identification and positioning of lung *** recently developed deep learning(DL)models can be employed for the effectual identification and classification of *** article introduces novel deep learning enabled CAD technique for lung cancer using biomedical CT image,named DLCADLC-BCT *** proposed DLCADLC-BCT technique intends for detecting and classifying lung cancer using CT *** proposed DLCADLC-BCT technique initially uses gray level co-occurrence matrix(GLCM)model for feature ***,long short term memory(LSTM)model was applied for classifying the existence of lung cancer in the CT ***,moth swarm optimization(MSO)algorithm is employed to optimally choose the hyperparameters of the LSTM model such as learning rate,batch size,and epoch *** demonstrating the improved classifier results of the DLCADLC-BCT approach,a set of simulations were executed on benchmark dataset and the outcomes exhibited the supremacy of the DLCADLC-BCT technique over the recent approaches.

关键词： Biomedical images lung cancer deep learning machine learning metaheuristics hyperparameter tuning

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Intelligent Deep Learning Enabled Wild Forest Fire Detection System

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Computer Systems Science & Engineering 2023年第2期44卷 1485-1498页

作者： Ahmed S.Almasoud Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

The latest advancements in computer vision and deep learning(DL)techniques pave the way to design novel tools for the detection and monitoring of forestﬁ*** this view,this paper presents an intelligent wild forestﬁre detec-tion and alarming system using deep learning(IWFFDA-DL)*** pro-posed IWFFDA-DL technique aims to identify forestﬁres at earlier stages through integrated *** proposed IWFFDA-DL system includes an Inte-grated sensor system(ISS)combining an array of sensors that acts as the major input source that helps to forecast theﬁ***,the attention based convolution neural network with bidirectional long short term memory(ACNN-BLSTM)model is applied to examine and identify the existence of *** hyperpara-meter tuning of the ACNN-BLSTM model,the bacterial foraging optimization(BFO)algorithm is employed and thereby enhances the detection ***,when theﬁre is detected,the Global System for Mobiles(GSM)modem transmits messages to the authorities to take required *** extensive set of simulations were performed and the results are investigated interms of several *** obtained results highlight the betterment of the IWFFDA-DL techni-que interms of various measures.

关键词： Forestﬁre deep learning intelligent models metaheuristics integrated sensor system hyperparameter tuning

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Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

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Computers, Materials & Continua 2023年第2期74卷 2725-2738页

作者： Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemMecca24382Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data *** same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall *** this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)*** proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental ***,data pre-processing and correlation matrix(CM)based feature selection processes are carried *** addition,deep belief network(DBN)model is applied for the effective prediction of rainfall ***,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO ***,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN *** demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct *** simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

关键词： Data mining rainfall prediction deep learning correlation matrix hyperparameter tuning metaheuristics

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Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

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Computers, Materials & Continua 2023年第6期75卷 5433-5449页

作者： Haya Alaskar Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Automatic deception recognition has received considerable atten-tion from the machine learning community due to recent research on its vast application to social media,interviews,law enforcement,and the *** analysis-based techniques for automated deception detection have received increasing *** study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection(SAPFF-DLADD)model for analyzing facial ***-tially,the input video is separated into a set of video ***,the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of *** long short-term memory(LSTM)model is exploited for deception detection and *** the final stage,the SAPFF technique is applied to optimally alter the hyperparameter values of the LSTM model,showing the novelty of the *** experimental validation of the SAPFF-DLADD model is tested using the Miami University Deception Detection Database(MU3D),a database comprised of two classes,namely,truth and *** extensive comparative analysis reported a better performance of the SAPFF-DLADD model compared to recent approaches,with a higher accuracy of 99%.

关键词： Deception detection facial cues deep learning computer vision hyperparameter tuning

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Attention-Based Deep Learning Model for Early Detection of Parkinson’s Disease

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Computers, Materials & Continua 2022年第6期71卷 5183-5200页

作者： Mohd Sadiq Mohd Tauheed Khan Sarfaraz Masood Department of Computer Engineering Jamia Millia IslamiaNew Delhi-110025India Department of Math and Computer Science Augustana CollegeRock IslandIllinois-61201USA

Parkinson’s disease(PD),classified under the category of a neurological syndrome,affects the brain of a person which leads to the motor and non-motor *** motor symptoms,one of the major disabling symptom is Freezing of Gait(FoG)that affects the daily standard of living of PD *** treatments target to improve the symptoms of *** of PD at the early stages is an arduous task due to being indistinguishable from a healthy *** work proposed a novel attention-basedmodel for the detection of FoG events and PD,andmeasuring the intensity of PD on the United Parkinson’s Disease Rating *** separate datasets,that is,UCF Daphnet dataset for detection of Freezing of Gait Events and PhysioNet Gait in PD Dataset were used for training and validating on their respective *** results show a definite rise in the various performance metrics when compared to landmark models on these problems using these *** results strongly suggest that the proposed state of the art attention-based deep learning model provide a consistent as well as an efficient solution to the selected *** valueswere obtained for various performance metrics like accuracy of 98.74%for detection FoG,98.72%for detection of PD and 98.05%for measuring the intensity of PD on *** model was also analyzed for robustness against noisy samples,where also model exhibited consistent *** results strongly suggest that the proposed model provides a better classification method for selected problem.

关键词： Parkinson’s disease freezing of gait the attention mechanism hyperparameter tuning attentive-FoGPDNet

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