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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Hasan, Md Jahid Ahmad, Wan Siti Halimatul Munirah Wan Ahmad Fauzi, Mohammad Faizal Abas, Fazly Salleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Center for Visual Computing Faculty of Computing and InformaticsFaculty of Engineering Cyberjaya Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350344349

In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (HandE) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied. © 2024 IEEE.

关键词： Image segmentation

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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Computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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Enabling data-driven anomaly detection by design in cyber-physical production systems

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Cybersecurity 2022年第3期5卷 34-51页

作者： Rui Pinto Gil Goncalves Jerker Delsing Eduardo Tovar Department of Informatics Engineering Faculty of EngineeringUniversity of PortoPortoPortugal EISLAB Lulea University of TechnologyLuleaSweden Research Centre in Real-Time and Embedded Computing Systems Polytechnic of Porto-School of EngineeringPortoPortugal

Designing and developing distributed cyber-physical production systems(CPPS)is a time-consuming,complex,and error-prone *** systems are typically heterogeneous,i.e.,they consist of multiple components implemented with different languages and development *** of the main problems nowadays in CPPS implementation is enabling security mechanisms by design while reducing the complexity and increasing the system’s *** the IEC 61499 standard is an excellent approach to tackle these challenges by enabling the design,deployment,and management of CPPS in a model-based engineering *** propose a method for CPPS design based on the IEC 61499 *** method allows designers to embed a bio-inspired anomaly-based host intrusion detection system(A-HIDS)in Edge *** A-HIDS is based on the incremental Dendritic Cell Algorithm(iDCA)and can analyze OPC UA network data exchanged between the Edge devices and detect attacks that target the CPPS’Edge *** study’s findings have practical implications on the industrial security community by making novel contributions to the intrusion detection problem in CPPS considering immune-inspired solutions,and cost-effective security by design system *** to the experimental data,the proposed solution can dramatically reduce design and code complexity while improving application maintainability and successfully detecting network attacks without negatively impacting the performance of the CPPS Edge devices.

关键词： Artificial immune systems Cyber-physical production systems IEC 61499 Function blocks Industrial Internet of Things Anomaly-based host intrusion detection system Model-based engineering

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Hybrid Deep Learning Architectures for Histological Image Segmentation

Hybrid Deep Learning Architectures for Histological Image Se...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Md Jahid Hasan Wan Siti Halimatul Munirah Wan Ahmad Mohammad Faizal Ahmad Fauzi Fazly Salleh Abas Faculty of Engineering Multimedia University Cyberjaya Malaysia Center for Visual Computing Faculty of Computing and Informatics Faculty of Engineering Multimedia University Cyberjaya Malaysia Faculty of Engineering and Technology Multimedia University Cyberjaya Malaysia

In histopathology image analysis, accurate segmentation of nuclei holds immense significance, particularly in the early detection and treatment of diseases like breast cancer. Nuclei segmentation is a fundamental but challenging task because of the intricate variations in nuclear shapes, sizes, densities, and overlapping instances. In this paper, we evaluate eight convolutional neural network (CNN) models, two of them existing models namely U-Net, SegNet, and six hybrid models by combining U-Net and SegNet modify decoder with ResNet, VGG and DenseNet (ResNet-UNet, ResNet-SegNet, VGG-UNet, VGG-SegNet, DenseNet-UNet, and DenseNet-SegNet. This experiment aims to identify the best deep-learning model for segmenting hematoxylin and eosin (H&E) stain images using a publicly available dataset called MoNuSeg. From the experimented work, we found that VGG-UNet outperforms other models with an F1 score of 0.8452 and IoU of 0.6929 respectively. This research will serve as a foundation for the future construction of more complex deep learning models with cascade or any combination of the models studied.

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John Hollerton: a life in industrial analytical spectroscopy, part 2

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Spectroscopy Europe 2023年第1期35卷 21-26页

作者： Davies, Antony N. Cashyap, Mohan Hollerton, John SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom MASS Informatics Harpenden United Kingdom Hollerton Scientific Software Consultancy St Albans United Kingdom

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Real-Time Segmentation of IHC Images From Microscope Using Deep Learning Architecture 2

Real-Time Segmentation of IHC Images From Microscope Using D...

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2nd IEEE National Biomedical engineering Conference, NBEC 2023

作者： Hasan, Md Jahid Siti Halimatul Munirah Wan Ahmad, Wan Ahmad Fauzi, Mohammad Faizal Tung Hiong Lee, Jenny Khor, See Yee Meng Looi, Lai Abas, Fazly Salleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Multimedia University Center for Visual Computing Faculty of Computing and Informatics Faculty of Engineering Cyberjaya Malaysia Sarawak General Hospital Department of Pathology Kuching Malaysia University Malaya Medical Center Department of Pathology Kuala Lumpur Malaysia Queen Elizabeth Hospital Department of Pathology Kota Kinabalu Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350338546

Segmentation of nuclei in digital histopathology image analysis plays a critical role in the early assessment of breast cancer and may enable patients to get appropriate treatment. In this paper, we create a real-time application that thoroughly examines the effectiveness of various deep learning models, including U-Net, SegNet, ResNet50-Unet, and ResNet50-SegNet, in the domain of real-time segmentation. For real-time implementation, we use an industrial machine vision camera mounted to the microscope, stream the image from the microscope glass slide and segment it using the model. This experiment aims to identify the best deep-learning model for real-time segmentation of nuclei for immunohistochemistry (IHC)-stained glass slides. The models are evaluated in offline mode using test images from estrogen receptor IHC stains, taken from whole-slide images. The effectiveness of the model for real-time work is based on its segmentation computational time. For offline evaluation, the highest F1-score and Jaccard index is achieved by ResNet50-SegNet (85.21%) and ResNet50-Unet (0.725) accordingly. These findings support the proof of concept that deep learning models can effectively segment nuclei in real-time from IHC-stained glass slides. This research serves as a foundation for the future construction of fast and efficient deep learning models for real-time histopathological analysis directly from the microscope. © 2023 IEEE.

关键词： Image segmentation

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Small Target Object Detection with Transformer (STO-DETR) Algorithm Based on Swin Transformer 4th

Small Target Object Detection with Transformer (STO-DETR) Al...

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4th International Conference on Advances in Computational Science and engineering, ICACSE 2023

作者： Xu, Fengchang Alfred, Rayner Chew, Jackel Vui Lung Du, Shifeng Lv, Ge Pailus, Rayner Henry Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu88400 Malaysia Department of Information Engineering Shandong Light Industry Vocational College 30 Misshan Road Zhoucun District Shandong Province Zibo City China Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan International Campus Labuan87000 Malaysia

ISBN: (纸本)9789819729760

With the development of deep learning, the method based on Convolutional Neural Network (CNN) has made a series of major breakthroughs in the detection field. However, due to the shortcomings of low image resolution, small object size, inconspicuous feature information, and lack of small target data, the detection of small objects is still a challenging task in the field of computer vision and a hot research topic worldwide. To improve the accuracy and speed of small object detection, a new algorithm model of Detection with Transformer (DETR) small target object detection based on Swin Transformer is proposed in this paper. This paper proposes a new algorithm for small target object detection based on Swin Transformer, called Swin Transformer’s DETR, which is different from DETR which utilizes the CNN as the backbone network to extract the feature information. It extracts the features of the input image by the Swin Transformer backbone and reduces its size, and then reshapes the output feature map as C × H W, and a 1 × 1 convolution reduces the dimension, and then input to Transformer Encoder. Self-Attention is used to make global analysis on the feature graph, and Transformer Decoder is used to realize one-to-one correspondence between the target prediction frame and token. The output results of each layer of the Decoder are also predicted by the last Feed Forward Networks (FFN) shared by parameters, and the loss is calculated, thus realizing deep supervision. Finally, several potential directions of this research field in the future are provided, and the experimental results of this detection algorithm are compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Robust Attacks Detection Model for Internet of Flying Things based on Generative Adversarial Network (GAN) and Adversarial Training

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IEEE Internet of Things Journal 2025年第13期12卷 23961-23974页

作者： Gaber, Tarek Ali, Tarek Nicho, Mathew Torky, Mohamed Engineering and Environment University of Salford School of Science Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Ismailia Egypt Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom Rabdan Academy Research and Innovation Centre Abu Dhabi United Arab Emirates University of Queensland School of Electrical Engineering and Computer Science Australia Egyptian Russian University-Badr City Faculty of Artificial Intelligence Cairo Egypt

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing Intrusion Detection Systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning attacks. This paper is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDS-IoFT). We introduce a novel IDS that leverages Generative Adversarial Networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four machine learning models, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR), on two datasets: ECU-IoFT and CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the Random Forest model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic d

关键词： Generative adversarial networks

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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Computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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Knowledge Base Processing Method Based on Text Classification Algorithm 5

Knowledge Base Processing Method Based on Text Classificatio...

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5th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2023

作者： Zhong, Baisheng Sainin, Mohd Shamrie Fun, Tan Soo Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia School of Engineering Guangzhou College of Technology and Businces Guangzhou China Research Centre Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350304152

The text classification algorithm's knowledge base processing method utilizes existing data from the knowledge base to guide the construction and training of the classification model. In practical application, the knowledge base processing method has been prove to have a good performance in text classification tasks. The utilization of knowledge base processing method in text classification has led to an average accuracy improvement of more than 17%. Furthermore, this method significantly reduces labeling costs by approximately 70% compared to traditional approaches. Recently, knowledge base processing methods have been widely used in supporting business applications, social media analysis and other fields. This paper proposes a knowledge base method to establish a feature model related to domain speciality and combine it with traditional text classification algorithm, so as to optimize the training and reasoning process of the classification model and improve the accuracy of classification effect. Lastly, we suggested strategies to overcome the shortcoming of the knowledge base method in improving the construction and training of the classification model. © 2023 IEEE.

关键词： Classification (of information)

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