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Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

computers, Materials & Continua 2023年第7期76卷 711-729页

作者： Muhammad Irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia College of Engineering Najran UniversityNajran61441Saudi Arabia

A brain tumor is a mass or growth of abnormal cells in the *** children and adults,brain tumor is considered one of the leading causes of *** are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)*** brain tumors as early as possible is essential,as this can improve the chances of successful treatment and *** this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain *** pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome *** dataset consists of 120 *** pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region *** have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient *** pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%*** contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

关键词： Brain tumor deep learning ensemble detection healthcare

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Decision Tree-Based IoT Botnet Attack Detection 2

Decision Tree-Based IoT Botnet Attack Detection

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia College Of Information Technology University Of Petra Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors. © 2024 IEEE.

关键词： Forecasting

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Answer Code Validation Program with Test Data Generation for Code Writing Problem in Java Programming Learning Assistant System

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engineering Letters 2024年第5期32卷 981-994页

作者： Wai, Khaing Hsu Funabiki, Nobuo Aung, Soe Thandar Lu, Xiqin Jing, Yanhui Kyaw, Htoo Htoo Sandi Kao, Wen-Chung Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

In order to support the learning of novice students in Java programming, the web-based Java Programming Learning Assistant System (JPLAS) has been developed. JPLAS offers several types of exercise problems to foster code reading and writing skills at different levels. In JPLAS, the code writing problem (CWP) asks a student to write a source code that will pass the test code given in the assignment where the correctness is verified by running them on JUnit. In this paper, to reduce the teacher’s workload during the marking process, we present the answer code validation program that verifies all the source codes from a large number of students for each assignment and reports the number of passing tests for each source code in the CSV file. Besides, to test a source code with various input data, we implement the test data generation algorithm that identifies the data type, generates new data, and replaces it for each test data in the test code. Furthermore, to verify the correctness of the implemented procedures in the source code, we introduce the intermediate state testing in the test code. For evaluations, we applied the proposal to source codes and test codes in a Java programming course in Okayama university, Japan, and confirmed the validity and effectiveness. © 2024, International Association of Engineers. All rights reserved.

关键词： Students

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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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Liver Ailment Prediction Using Random Forest Model

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computers, Materials & Continua 2023年第1期74卷 1049-1067页

作者： Fazal Muhammad Bilal Khan Rashid Naseem Abdullah A Asiri Hassan A Alshamrani Khalaf A Alshamrani Samar M Alqhtani Muhammad Irfan Khlood M Mehdar Hanan Talal Halawani Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan City University of Science and Information Technology PeshawarPakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the *** research has indicated that liver disease is more frequent in younger people than in older *** the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is *** several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early *** a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early *** dataset was picked from the University of California,Irvine ***’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and *** has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is *** widely acknowledged result of this work can be used as a starting point for subsequent *** a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.

关键词： Liver ailment random forest machine learning

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Towards an Improved Understanding of Features for More Interpretable Landscape Analysis

Towards an Improved Understanding of Features for More Inter...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Gallagher, Marcus Munoz, Mario University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia The University of Melbourne School of Computer and Information Systems ParkvilleVIC Australia

ISBN: (纸本)9798400704956

Landscape analysis has received increasing attention in the literature, with a major focus on producing feature data to be used in a machine learning pipeline for automatic algorithm selection and configuration. In contrast, the original purpose of landscape analysis (i.e. to develop theory and techniques to understand the structure of fitness landscapes) has been somewhat overshadowed. This paper aims to clarify the purpose of landscape features, emphasizing the need for clear definitions of distance or similarity between optimization problem instances. It argues that current methodologies' shortcomings can be better understood from this perspective and advocates for more interpretable features and distances to gain explainable insights into problem instances, algorithm-problem mappings, and improved benchmarking of black-box algorithms. © 2024 Copyright held by the owner/author(s).

关键词： Optimization algorithms

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