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Cybernet Model:A New Deep Learning Model for Cyber DDoS Attacks Detection and Recognition

computers, Materials & Continua 2024年第1期78卷 1275-1295页

作者： Azar Abid Salih Maiwan Bahjat Abdulrazaq Department of Information Technology Technical College of DuhokDuhok Polytechnic UniversityDuhokIraq Department of Computer Science Faculty of ScienceUniversity of ZakhoDuhokIraq

Cyberspace is extremely dynamic,with new attacks arising *** cybersecurity controls is vital for network *** Learning(DL)models find widespread use across various fields,with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and *** capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic *** study presents novel lightweight DL models,known as Cybernet models,for the detection and recognition of various cyber Distributed Denial of Service(DDoS)*** models were constructed to have a reasonable number of learnable parameters,i.e.,less than 225,000,hence the name“lightweight.”This not only helps reduce the number of computations required but also results in faster training and inference ***,these models were designed to extract features in parallel from 1D Convolutional Neural Networks(CNN)and Long Short-Term Memory(LSTM),which makes them unique compared to earlier existing architectures and results in better performance *** validate their robustness and effectiveness,they were tested on the CIC-DDoS2019 dataset,which is an imbalanced and large dataset that contains different types of DDoS *** results revealed that bothmodels yielded promising results,with 99.99% for the detectionmodel and 99.76% for the recognition model in terms of accuracy,precision,recall,and F1 ***,they outperformed the existing state-of-the-art models proposed for the same ***,the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.

关键词： Deep learning CNN LSTM Cybernet model DDoS recognition

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Lung Cancer Detection Using Modified AlexNet Architecture and Support Vector Machine

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computers, Materials & Continua 2023年第1期74卷 2039-2054页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Arfan Jaffar Muhammad Rashid Muhammad Amjad Iqbal Faculty of Computer Science and Information Technology The Superior UniversityLahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Faculty of IT University of Central PunjabLahore54100Pakistan

Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the *** is mostly caused by the instinctive growth of cells in the *** nodule detection has a significant role in detecting and screening lung cancer in Computed tomography(CT)scan *** detection plays an important role in the survival rate and treatment of lung cancer ***,pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung *** work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and Support vector machine(SVM)algorithm namely *** proposed model consists of seven convolutional layers,three pooling layers,and two fully connected layers used to extract *** vector machine classifier is applied for the binary classification of nodules into benign *** experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016(LUNA16).The proposed model has achieved 97.64%of accuracy,96.37%of sensitivity,and 99.08%of specificity.A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung *** experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

关键词： Lung cancer alexnet luna16 computed tomography support vector machine

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Modified CNN Model for Classifying Gender of Thermal Images Using Cloud Computing

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Informatica (Slovenia) 2023年第10期47卷 123-128页

作者： Jalil, Alyaa J. El-Seidy, Essam Daoud, Sameh S. Reda, Naglaa M. Department of Computer Science Faculty of Computer Science and Information Technology Basrah University Basrah Iraq Department of Mathematics Faculty of Science Ain Shams University Cairo Egypt

The utilization of thermal images has become widespread in various applications, particularly for thermal examination and night surveillance systems. Although many details associated with thermal imaging are virtually obscured or unclear, thermal imaging offers numerous benefits, including the ability to determine an individual's gender even in cases where human vision is impaired. Based on thermal faces, a convolutional neural network (CNN) has been developed to distinguish between people's genders. Since cloud computing offers a suitable setting and gained attraction, its concept has been adopted. Thermal databases were used for the experiments. The suggested model provides an overall accuracy of 99% and a gender categorization precision of 97.75%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Cloud computing

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Code switching: exploring perplexity and coherence metrics for optimizing topic models of historical documents

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 103-118页

作者： Yusof, Muhammad Abdullah Saee, Suhaila Faculty of Computer Science and Information Technology University Malaysia Sarawak Sarawak Malaysia

The Latent Dirichlet Allocation (LDA) model has two important hyperparameters that control the document-topic distribution known as alpha (α), and topic-word distribution known as beta (β). It is important to find the suitable values for both hyperparameters to achieve an accurate topic cluster. Using a single evaluation method to determine the optimal hyperparameters values is insufficient due to the size and complexity of the dataset. Thus, an experiment was conducted to study the relationship between the hyperparameters with perplexity, coherence scores and to establish a baseline for further topic modelling studies. It is the first study that focuses on multiple languages in Sarawak Gazette data for topic modelling. The study was conducted on LDA using Gensim package. The result shows that while perplexity scores were good indicator of the model’s ability to predict new or hidden data, the word cluster within topic does not always reflect the similarity or relationships between words which compromised topic interpretation. The lowest perplexity score was observed when α was set to 5 and β to 0.4. The coherence evaluation indicated the optimal number of topics for each set of hyperparameter values although the relationship with hidden words remains unclear. The coherence score is highest when the number of topics was 5 and 4. In conclusion, the perplexity scores are effective indicators of word prediction accuracy for each hyperparameter setting. While coherence captures the optimal number of topics needed to produce high-coherence word cluster within a topic. Combining both evaluation methods ensures optimal results, producing topics that are both accurate and interpretable. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Hyperparameter Latent Dirichlet Allocation perplexity topic coherence topic modelling

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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An Efficient Two-State Feature Attention-Based GRU for Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第15期49卷 95-112页

作者： Mohammed, Dhurgham Ali Patel, Kalyani A. Faculty of Computer Science Gujarat University Ahmedabad India Department of Computer Science Faculty of Education for Girl University of Kufa Iraq K.S. School of Business Management and Information Technology Gujarat University India

Text classification has become crucial for mechanically sorting documents into specific categories. The goal of classification is to assign a predefined group or class to an instance based on its characteristics. To attain precise text categorization, a feature selection scheme is employed to categorize significant features and eliminate irrelevant, undesirable, and noisy ones, thereby reducing the dimensionality of the feature space. Many advanced deep learning algorithms have been developed to handle text classification drawbacks. Recurrent neural networks (RNNs) are broadly employed in text classification tasks. In this paper, we referred to a novel Two-state GRU based on a Feature Attention strategy, known as Two-State Feature Attention GRU (TS-FA-GRU). The proposed framework identifies and categorizes word polarity through consecutive mechanisms and word-feature capture. Furthermore, the developed study incorporates a pre-feature attention TS-FA-GRU to capture essential features at an early stage, followed by a post-feature attention GRU that mimics the decoder’s function to refine the extracted features. To enhance computational performance, the reset gate in the ordinary GRU is replaced with an update gate, which helps to reduce redundancy and complexity. The effectiveness of the developed model was tested on five benchmark text datasets and compared with five well-established traditional text classification methods. The proposed TS-FA-GRU model demonstrated superior performance over several traditional approaches regarding convergence rate and accuracy. Experimental outcomes revealed that the TS-FA-GRU model achieved excellent text classification accuracies of 93.86%, 92.69%, 94.73%, 92.46%, and 88.23 on the 20NG, R21578, AG News, IMDB, and Amazon review dataset respectively. Moreover, the results indicated that the proposed model effectively minimized the loss function and captured long-term dependencies, leading to exceptional outcomes when compared to the

关键词： Recurrent neural networks

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Machine Learning Based Predictive Modelling for Fraud Detection in Digital Banking 21

Machine Learning Based Predictive Modelling for Fraud Detect...

引用

21st IEEE International Conference on computer Applications, ICCA 2024

作者： Thar, Kaung Wai Wai, Thinn Thinn University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9789998601673

Banking stands out as a pioneering industry, having amassed vast amounts of structured data over its history and being among the earliest adopters of data science technologies. The world is witnessing rapid growth in banking sector and hence payment transaction method is changing into the use of cashless transaction instead of the use of cash money. Digital banking gives customers a better service quality in the areas of prepaid card, credit card, cashless payment, money transfer, etc. However, the security of digital banking has received attention due to the fraudulent behavior of fraudsters as the absence of sufficient digital banking security has kept many customers away from the service till today. Although fraud is not a new issue, activities related with fraud always cost the global economy billions of dollars each year. Fraudulent activities involve inclusively significant financial risks which may threaten profitability, and the image of the whole of an economic sector. This study aims to use machine learning based predictive modelling to implement to improve fraud detection and prevention, inside of a public or private economic entity. In this paper, this problem is focused on the propose an autonomous and effective intelligent new ensemble method for fraud transaction detection. Firstly, Hidden Markov Model (HMM) is used for observing the hidden states of financial transaction, that is based on the idea of the probabilistic models designed for sequences of observations where the underlying process is assumed to be a Markov process with hidden states. And then, machine learning model called Gradient Boosting Classifier (GBC) is applied to classify the fraud. Finally, our hybrid method integrates GBC and HMM. Experiments are conducted to ensure the effectiveness of HMM and GBC. © 2024 IEEE.

关键词： Predictive analytics

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in information Communication technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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DLOps: Developing Deep Learning Aided Burmese Rice Price Forecasting 5

DLOps: Developing Deep Learning Aided Burmese Rice Price For...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： San, Khin Pyae Phyo Thein, Nwe Nwe Myint University of Information Technology Faculty of Computer Science Yangon Myanmar

ISBN: (纸本)9798331510046

In nowadays technology areas, a pivotal concern has emerged. It's was especially concerning architecting Deep Learning enabled Systems (DLS) and sustainability. Most of DLS were failed while release to production. That's because of uncertainties in data variations, evolutionary requirements, and model inconsistencies. The one suitable solution is Deep Learning Operations (DLOps). It enhances reproducibility and sustainability in building DLS. It still has challenges and are related to environmental impact, technical maintenance, and economic concerns. The self-adaptation helps to address and overcome these challenges. In this paper proposed a sustainable modularized DLOps pipeline thorough the conceptual MAPE-K Framework to solve technical maintenance and economic concerns. The proposed system addresses the uncertainties in data handling and model training then try to proof out efficient utilization from economic concerns point of view. Further, the proposed system is implemented for a Burmese Rice Price Prediction use case to display DLOps pipeline sustainability from technical maintenance point of view. © 2024 IEEE.

关键词： Sustainable development

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