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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

computer Modeling in Engineering & sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

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computers, Materials & Continua 2023年第3期74卷 5527-5543页

作者： Hameedur Rahman Junaid Tariq M.Ali Masood Ahmad F.Subahi Osamah Ibrahim Khalaf Youseef Alotaibi Department of Creative Technologies Air UniversityE-9 Islamabad44230Pakistan Department of Computer Science National University of Modern LanguagesRawalpindiPakistan Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Al-Nahrain Nano-Renewable Energy Research Center Al-Nahrain UniversityBaghdad10072Iraq Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

Sentiment Analysis(SA)is often referred to as opinion *** is defined as the extraction,identification,or characterization of the sentiment from ***,the sentiment of a textual document is classified into binary classes i.e.,positive and ***,fine-grained classification provides a better insight into the *** downside is that fine-grained classification is more challenging as compared to *** the contrary,performance deteriorates significantly in the case of multi-class *** this study,pre-processing techniques and machine learning models for the multi-class classification of sentiments were *** augment the performance,a multi-layer classification model has been *** to similitude with social media text,the movie reviews dataset has been used for the *** machine learning models namely Decision Tree,Support Vector Machine,and Naive Bayes models have been implemented for the task of sentiment *** have compared the models of single-layer architecture with multi-tier *** results of Multi-tier model have slight improvement over the single-layer ***,multi-tier models have better recall which allow our proposed model to learn more *** have discussed certain shortcomings of the model that will help researchers to design multi-tier models with more contextual information.

关键词： Sentiment analysis machine learning multi-class classification svm decision tree naive bayes

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Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

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computers, Materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan M.Alanazi Abdullah M.Alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad Majed A.Alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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A Summary on Privacy and Security in Cloud Data using Various Approaches 4

A Summary on Privacy and Security in Cloud Data using Variou...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Jayaram, B. Sethukarasi, T. Sindhu, M. Jeyamohan, H. Rmk Engineering College Department of Computer Science and Business Systems 601206 India Rmk Engineering College Department of Computer Science and Engineering 601206 India Velammal Engineering College Department of Computer Science and Engineering Chennai600066 India

ISBN: (纸本)9798350300888

Nowadays online users are prone to lot of security related issues in protecting their data. In order to achieve this privacy preservation in cloud plays a major role. For this purpose various technologies related to cyber security was proposed according to application in which the security has to be implemented. Some of these techniques include machine learning, deep learning, federated learning natural language processing, block chain etc. This paper analyses various approaches proposed by researches. This paper is divided into 7 sections such as role of cyber security in privacy preservation, privacy preservation in cloud using cyber security, what is a hybrid model and how to build it to implement features of security, Malicious node deduction in wireless sensor network using cyber security, relation between cyber-security and block-chain, federated learning and differential privacy. © 2023 IEEE.

关键词： Cybersecurity

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3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

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computers, Materials & Continua 2023年第9期76卷 2861-2877页

作者： Kainat Nazir Tahir Mustafa Madni Uzair Iqbal Janjua Umer Javed Muhammad Attique Khan Usman Tariq Jae-Hyuk Cha Medical Imaging and Diagnostics Lab NCAIDepartment of Computer ScienceCOMSATS University IslamabadIslamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusWahPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Management Information Systems College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved *** a brain tumor usually begins with magnetic resonance imaging(MRI).The manual brain tumor diagnosis from the MRO images always requires an expert ***,this process is time-consuming and ***,a computerized technique is required for brain tumor detection in MRI *** the MRI,a novel mechanism of the three-dimensional(3D)Kronecker convolution feature pyramid(KCFP)is used to segment brain tumors,resolving the pixel loss and weak processing of multi-scale lesions.A single dilation rate was replaced with the 3D Kronecker convolution,while local feature learning was performed using the 3D Feature Selection(3DFSC).A 3D KCFP was added at the end of 3DFSC to resolve weak processing of multi-scale lesions,yielding efficient segmentation of brain tumors of different sizes.A 3D connected component analysis with a global threshold was used as a post-processing *** standard Multimodal Brain Tumor Segmentation 2020 dataset was used for model *** 3D KCFP model performed exceptionally well compared to other benchmark schemes with a dice similarity coefficient of 0.90,0.80,and 0.84 for the whole tumor,enhancing tumor,and tumor core,***,the proposed model was efficient in brain tumor segmentation,which may facilitate medical practitioners for an appropriate diagnosis for future treatment planning.

关键词： Brain tumor segmentation connect component analysis deep learning kronecker convolution magnetic resonance imaging

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Optimal Resource Allocation in Fog Computing for Healthcare Applications

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computers, Materials & Continua 2022年第6期71卷 6147-6163页

作者： Salman Khan Ibrar Ali Shah Nasser Tairan Habib Shah Muhammad Faisal Nadeem Department of Computer Software Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaK.S.A Informatics Complex H-8Islamabad44000Pakistan

In recent years,the significant growth in the Internet of Things(IoT)technology has brought a lot of attention to information and communication *** IoT paradigms like the Internet of Vehicle Things(IoVT)and the Internet of Health Things(IoHT)create massive volumes of data every day which consume a lot of bandwidth and ***,to process such large volumes of data,the existing cloud computing platforms offer limited resources due to their distance from IoT ***,cloudcomputing systems produce intolerable latency problems for latency-sensitive real-time ***,a newparadigm called fog computingmakes use of computing nodes in the form of mobile devices,which utilize and process the real-time IoT devices data in orders of *** paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the *** proposed algorithmic framework consists of the following components:task sequencing,dynamic resource allocation,and load *** consider electrocardiography(ECG)sensors for patient’s critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay,energy,network consumption and average *** proposed algorithm has been evaluated using the iFogSim tool,and results with the existing approach have been *** experimental results exhibit that the proposed technique achieves a 45%decrease in delay,37%reduction in energy consumption,and 25%decrease in network bandwidth consumption compared to the existing studies.

关键词： Fog computing internet of health things resource management

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Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

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computers, Materials & Continua 2023年第2期74卷 4483-4500页

作者： Hanan Abdullah Mengash Lubna A.Alharbi Saud S.Alotaibi Sarab AlMuhaideb Nadhem Nemri Mrim M.Alnfiai Radwa Marzouk Ahmed S.Salama Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTabuk UniversityTabuk71491Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Science and Arts at MuhayelKing Khalid UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In recent times,cities are getting smart and can be managed effectively through diverse architectures and *** cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic *** latest developments in digital data collection,AI techniques can be employed for clinical decision making *** the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the ***,wearables can be employed in healthcare systems that instigate the development of CVD detection and *** this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC *** proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data ***,the collected data is saved in cloud server for *** by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health *** accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)*** addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of *** experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

关键词： Smart cities e-healthcare decision support system cardiovascular disease deep learning feature selection

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BiLSTM for Resume Classification 22

BiLSTM for Resume Classification

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and informatics, SAMI 2024

作者： Jalili, Amirreza Tabrizchi, Hamed Razmara, Jafar Mosavi, Amir College of Science University of Tehran School of Mathematics Statistics and Computer Science Department of Computer Science Tehran Iran University of Tabriz Faculty of Mathematics Statistics and Computer Science Department of Computer Science Tabriz Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

ISBN: (纸本)9798350317206

In the era of digital recruitment and increasing volumes of job applications, the effective categorization and classification of resumes have become essential for streamlining the hiring process. The purpose of this paper is to present a Bidirectional LSTM architecture method designed to enhance the accuracy and efficiency of resume screening and classification. Leveraging the power of the presented bidirectional LSTM architecture allows the network to effectively capture complex information and context. To enhance the model's performance, we also incorporate word embedding, further enriching textual data representation. We evaluate the proposed method using a comprehensive dataset of resumes across various industries and job roles, demonstrating its superior performance in terms of classification accuracy and speed compared to traditional methods. Furthermore, we discuss the potential applications of this method in recruitment automation and offer insights into its scalability and adaptability in real-world scenarios, providing a valuable tool for human resource (HR) professionals and recruitment agencies seeking to optimize their hiring processes. © 2024 IEEE.

关键词： Data mining

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Analytically Determining the Robustness of Binarized Neural Networks 23

Analytically Determining the Robustness of Binarized Neural ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Alzahrani, Sahar M. Schewe, Sven Huang, Chao Huang, Xiaowei University of Liverpool Department of Computer Science Liverpool United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of Southampton Department of Computer Science Southampton United Kingdom

ISBN: (纸本)9798350374889

Binarized neural networks (BNNs) are a class of deep neural networks (DNNs) known for their minimal computational requirements during inference, making them ideal for low-performance environments. Despite their efficiency, BNNs are as vulnerable to safety and security issues as other types of DNNs, and hence analyzing their robustness is essential. In contrast to general DNNs, BNNs are relatively small and straightforward, making it feasible to assess their robustness analytically. We propose a method for rigorously analyzing BNN robustness based on 0-1 Integer Linear Programming (0-1 ILP), which utilizes the dual functionality of boolean variables within BNNs as both boolean (with values of 'true' and 'false') and real-valued variables constrained to binary values (0 and 1). By leveraging this duality, we formalize the robustness problem as a linear programming problem augmented with boolean reasoning. We minimize the distance to a differently classified image using either the L1-norm (binarized and non-binarized inputs) or the Lœ-norm (non-binarized inputs). We have implemented our approach using the Z3 solver and evaluated it on the MNIST and FashionMNIST datasets. Our results demonstrate the capability to construct optimal adversarial examples with minimal deviations from the ground truth images. We achieved a one-pixel difference between the original and perturbed images for all binarized images. We also identified minimal perturbations on grayscale images using the L1 and Lœ-norms. For the latter, we simplified the analysis to determine the existence of epsilon-close adversarial examples across a range of epsilon values. © 2024 IEEE.

关键词： Integer linear programming

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ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

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Neural Computing and Applications 2024年第24期36卷 15149-15164页

作者： Helaly, Hadeer A. Badawy, Mahmoud El-Gendy, Eman M. Haikal, Amira Y. Electrical Engineering Department Faculty of Engineering Damietta University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Computer Science and Informatics Taibah University Medina Saudi Arabia

The survival rate of lung cancer relies significantly on how far the disease has spread when it is detected, how it reacts to the treatment, the patient’s overall health, and other factors. Therefore, the earlier the lung cancer diagnosis, the higher the survival rate. For radiologists, recognizing malignant lung nodules from computed tomography (CT) scans is a challenging and time-consuming process. As a result, computer-aided diagnosis (CAD) systems have been suggested to alleviate these burdens. Deep-learning approaches have demonstrated remarkable results in recent years, surpassing traditional methods in different fields. Researchers are currently experimenting with several deep-learning strategies to increase the effectiveness of CAD systems in lung cancer detection with CT. This work proposes a deep-learning framework for detecting and diagnosing lung cancer. The proposed framework used recent deep-learning techniques in all its layers. The autoencoder technique structure is tuned and used in the preprocessing stage to denoise and reconstruct the medical lung cancer dataset. Besides, it depends on the transfer learning pre-trained models to make multi-classification among different lung cancer cases such as benign, adenocarcinoma, and squamous cell carcinoma. The proposed model provides high performance while recognizing and differentiating between two types of datasets, including biopsy and CT scans. The Cancer Imaging Archive and Kaggle datasets are utilized to train and test the proposed model. The empirical results show that the proposed framework performs well according to various performance metrics. According to accuracy, precision, recall, F1-score, and AUC metrics, it achieves 99.60, 99.61, 99.62, 99.70, and 99.75%, respectively. Also, it depicts 0.0028, 0.0026, and 0.0507 in mean absolute error, mean squared error, and root mean square error metrics. Furthermore, it helps physicians effectively diagnose lung cancer in its early stages and allows spe

关键词： Image classification

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