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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning features extraction Imbalance data Explainable AI Cybersecurity

A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

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Computers, Materials & Continua 2024年第1期78卷 1115-1133页

作者： Amira M.Idrees Abdul Lateef Marzouq Al-Solami Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt Alkamil College of Science and Arts University of JeddahJeddah21959Saudi Arabia

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual *** modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the *** research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice *** leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind *** modular design ensures flexibility,scalability,and ease of integration with existing assistive ***,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and ***-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification ***,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate ***,this research paves the way for the development of more advanced and robust systems for assisting visually impaired *** leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual *** work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

关键词： Social networks text analytics emoji prediction features extraction information retrieval

An Approach to Detect Structural Development Defects in Object-Oriented Programs

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Open Journal of Applied Sciences 2024年第2期14卷 494-510页

作者： Maxime Seraphin Gnagne Mouhamadou Dosso Mamadou Diarra Souleymane Oumtanaga Department of Mathematics and Computer Science University Félix HOUPHOU&#203T-BOIGNY Abidjan C&#244te d’Ivoire Research and Innovation Unit in Mathematics and Digital Sciences National Polytechnic Institute Félix HOUPHOU&#203T-BOIGNY Yamoussoukro C&#244te d’Ivoire

Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.

关键词： Object-Oriented Programming Structural Development Defect Detection Software Maintenance Pre-Trained Models features extraction Bagging Neural Network

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

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Computers, Materials & Continua 2022年第6期71卷 4871-4894页

作者： Yasmeen Al-Saeed Wael A.Gab-Allah Hassan Soliman Maysoon F.Abulkhair Wafaa M.Shalash Mohammed Elmogy Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Faculty of Computers&Artificial Intelligence South Valley UniversityHurghada84511Egypt Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBanha13511Egypt

One of the leading causes of mortality worldwide is liver *** earlier the detection of hepatic tumors,the lower the mortality *** paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign *** hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors *** proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines *** performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and *** proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.

关键词： Liver tumor hepatic tumors diagnosis CT scans analysis liver segmentation tumor segmentation features extraction tumors classification FGFCM CAD system

Smart Devices Based Multisensory Approach for Complex Human Activity Recognition

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Computers, Materials & Continua 2022年第2期70卷 3221-3234页

作者： Muhammad Atif Hanif Tallha Akram Aamir Shahzad Muhammad Attique Khan Usman Tariq Jung-In Choi Yunyoung Nam Zanib Zulfiqar Department of Electrical and Computer Engineering COMSATS University IslamabadWah Campus47040Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad Campus22040Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Electrical and Computer Engineering CUISahiwalPakistan

Sensors based Human Activity Recognition(HAR)have numerous applications in eHeath,sports,fitness assessments,ambient assisted living(AAL),human-computer interaction and many *** human physical activity can be monitored by using wearable sensors or external *** usage of external devices has disadvantages in terms of cost,hardware installation,storage,computational time and lighting conditions ***,most of the researchers used smart devices like smart phones,smart bands and watches which contain various sensors like accelerometer,gyroscope,GPS etc.,and adequate processing *** the task of recognition,human activities can be broadly categorized as basic and complex human *** of complex activities have received very less attention of researchers due to difficulty of problem by using either smart phones or smart *** reasons include lack of sensor-based labeled dataset having several complex human daily life *** of the researchers have worked on the smart phone’s inertial sensors to perform human activity recognition,whereas a few of them used both pocket and wrist *** this research,we have proposed a novel framework which is capable to recognize both basic and complex human activities using builtin-sensors of smart phone and smart *** have considered 25 physical activities,including 20 complex ones,using smart device’s built-in *** the best of our knowledge,the existing literature consider only up to 15 activities of daily life.

关键词： Complex human activities human daily life activities features extraction data fusion multi-sensory smartwatch smartphone

Realistic Smile Expression Recognition Approach Using Ensemble Classifier with Enhanced Bagging

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Computers, Materials & Continua 2022年第2期70卷 2453-2469页

作者： Oday A.Hassen Nur Azman Abu Zaheera Zainal Abidin Saad M.Darwish University Technical Malaysia Melaka Department of Information TechnologyMelaka76100Malaysia Institute of Graduate Studies and Research University of AlexandriaAlexandriaEl Shatby21526Egypt

A robust smile recognition system could be widely used for many real-world *** of a facial smile in an unconstrained setting is difficult due to the invertible and wide variety in face *** this paper,an adaptive model for smile expression classification is suggested that integrates a fast features extraction algorithm and cascade *** model takes advantage of the intrinsic association between face detection,smile,and other face features to alleviate the over-fitting issue on the limited training set and increase classification *** features are extracted taking into account to exclude any unnecessary coefficients in the feature vector;thereby enhancing the discriminatory capacity of the extracted features and reducing the computational ***,the main causes of error in learning are due to noise,bias,and *** helps to minimize these *** of multiple classifiers decrease variance,especially in the case of unstable classifiers,and may produce a more reliable classification than a single ***,a shortcoming of bagging as the best ensemble classifier is its random selection,where the classification performance relies on the chance to pick an appropriate subset of training *** suggested model employs a modified form of bagging while creating training sets to deal with this challenge(error-based bootstrapping).The experimental results for smile classification on the JAFFE,CK+,and CK+48 benchmark datasets show the feasibility of our proposed model.

关键词： Ensemble classifier smile expression detection features extraction

A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

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Computers, Materials & Continua 2022年第2期70卷 2261-2276页

作者： Farrukh Zia Isma Irum Nadia Nawaz Qadri Yunyoung Nam Kiran Khurshid Muhammad Ali Imran Ashraf Muhammad Attique Khan Department of ECE COMSATS University IslamabadWah Campus47040Pakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Electrical Engineering NUMLRawalpindiPakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan

Diabetes or Diabetes Mellitus(DM)is the upset that happens due to high glucose level within the *** the passage of time,this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy(DR)which can cause a major loss of *** symptoms typically originate within the retinal space square in the form of enlarged veins,liquid dribble,exudates,haemorrhages and small scale *** current therapeutic science,pictures are the key device for an exact finding of patients’***,an assessment of new medicinal symbolisms stays ***,Computer Vision(CV)with deep neural networks can train models with high *** thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction(DR).The proposed deep learning framework utilizes the strength of selected models(VGG and Inception V3)by fusing the extracated *** select the most discriminant features from a pool of features,an entropy concept is employed before the classification *** deep learning models are fit for measuring the highlights as veins,liquid dribble,exudates,haemorrhages and miniaturized scale aneurysms into various *** model will ascertain the loads,which give the seriousness level of the patient’s *** model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures.

关键词： Deep neural network diabetic retinopathy retina features extraction classification

A Healthcare System for COVID19 Classification Using Multi-Type Classical features Selection

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Computers, Materials & Continua 2023年第1期74卷 1393-1412页

作者： Muhammad Attique Khan Majed Alhaisoni Muhammad Nazir Abdullah Alqahtani Adel Binbusayyis Shtwai Alsubai Yunyoung Nam Byeong-Gwon Kang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University(PNU)P.O.Box 84428Riyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

The coronavirus(COVID19),also known as the novel coronavirus,first appeared in December 2019 in Wuhan,*** that,it quickly spread throughout the world and became a *** has significantly impacted our everyday lives,the national and international economies,and public ***,early diagnosis is critical for prompt treatment and reducing trauma in the healthcare *** radiologists primarily use chest X-rays,and computerized tomography(CT)scans to test for pneumonia *** used Chest CT scans to predict COVID19 pneumonia and healthy scans in this *** proposed a joint framework for prediction based on classical feature fusion and PSO-based *** begin by extracting standard features such as discrete wavelet transforms(DWT),discrete cosine transforms(DCT),and dominant rotated local binary patterns(DRLBP).In addition,we extracted Shanon Entropy and Kurtosis *** the following step,a Max-Covariance-based maximization approach for feature fusion is *** fused features are optimized in the preliminary phase using Particle Swarm Optimization(PSO)and the ELM fitness *** final prediction,PSO is used to obtain robust features,which are then implanted in a Support Vector Data Description(SVDD)*** experiment is carried out using available COVID19 Chest CT Scans and scans from healthy *** images are from the Radiopaedia *** the proposed scheme,the fusion and selection process accuracy is 88.6%and 93.1%,respectively.A detailed analysis is conducted,which supports the proposed system efficiency.

关键词： COVID19 features extraction information fusion optimization prediction

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

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Computers, Materials & Continua 2022年第12期73卷 4501-4518页

作者： Muhammad Ali Jamal Hussain Shah Muhammad Attique Khan Majed Alhaisoni Usman Tariq Tallha Akram Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Departmnt of Computer Science HITEC University TaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of EE COMSATS University IslamabadWah CampusPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Tumor detection has been an active research topic in recent years due to the high mortality *** vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI *** automated detection process is simpler and takes less time than manual *** addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for *** proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this *** tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)*** preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation ***,PSO-based segmentation is *** the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer *** a serial approach,features are extracted from both trained models and fused features for final *** classification,a variety of machine learning classifiers are *** dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of ***,the proposed method is compared to state-of-the-art existingmethods and outperforms them.

关键词： Magnetic resonance imaging(MRI) tumor segmentation deep learning features extraction classification