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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Dwi Wahyu Prabowo Noor Akhmad Setiawan Johan Debayle Hanung Adi Nugroho Department of Electrical and Information Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia Department of Information System Faculty of Computer Science Universitas Darwan Ali Sampit Indonesia MINES Saint-Etienne CNRS UMR 5307 LGF Centre SPIN Saint-Etienne France

Emotion recognition using brain-computer interfaces (BCIs) is an emerging field with the ongoing challenge of developing robust and efficient classification methods. This study introduces an original approach, the multi-representation convolutional neural network (MrCNN), designed to exploit the complementary features of recurrence plots (RP) and spectrograms. The efficacy of this new method was empirically evaluated and compared with the standalone RP and spectrogram methods in emotion classification tasks using electroencephalogram (EEG) signals. Two widely used datasets, DEAP and SEED, were used as the experimental platforms for this study. Employing a multifaceted analytical approach involving descriptive statistics, data visualization, and hypothesis tests, this study revealed that MrCNN significantly outperforms the standalone RP and spectrogram methods in terms of key performance metrics. Specifically, MrCNN achieved an accuracy of 0.9663, sensitivity of 0.9848, specificity of 0.9825, kappa score of 0.9511, and F1 score of 0.9656 for the DEAP dataset. For the SEED dataset, the accuracy was 0.9695, with sensitivity, specificity, kappa scores, and F1 score of 0.9844, 0.9842, 0.9543, and 0.9695, respectively. These results were statistically validated, and a comparative analysis with existing literature substantiated the robustness and superiority of the MrCNN method. This study sets new performance benchmarks and offers a reliable and effective approach for emotion classification using EEG signals.

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Phase-Amplitude Coupling Between EEG Cortical Oscillations and Respiration: An Exploratory Study

Phase-Amplitude Coupling Between EEG Cortical Oscillations a...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： J. McLinden S. B. Borgheai C. Kumar N. Rahimi M. Shao K.M. Spencer Y. Shahriari Department of Electrical Computer & Biomedical Engineering University of Rhode Island Kingston RI USA Department of Neurology Emory University Atlanta GA USA Department of Computer and Information Science University of Massachusetts Dartmouth MA USA Department of Psychiatry VA Boston Healthcare System and Harvard Medical School Jamaica Plain Boston MA USA

Cortical oscillatory patterns are associated with a wide range of functions, including attention, motor functions, and memory. Selective modulation of different cortical oscillations is of interest in many brain-computer interface (BCI) and neurofeedback paradigms. Recent findings have suggested that respiration plays a role in modulating higher-frequency electrocortical activity. However, these previous works have mostly relied on invasive neuroimaging modalities, and the relationship between respiration and cortical oscillations recorded non-invasively though imaging techniques, including electroencephalography (EEG), remains underreported. In this study, we explore phase-amplitude coupling (PAC) between the phase of respiration signals and amplitude of EEG band power across several frequency bands. We recorded simultaneous EEG and respiration effort from nine healthy participants during an auditory task and applied a PAC algorithm to explore coupling between respiration phase and EEG band power amplitude. We observed significant PAC in at least three channels and one frequency band across all nine participants. Specifically, respiration-gamma PAC was observed in at least three channels in five of nine participants, while a similar pattern was observed in respiration-alpha PAC in four of nine participants. These findings reinforce previous observations in invasive studies of widely distributed respiration-cortical gamma PAC while suggesting that alpha band oscillations may also be modulated by respiration in some individuals. These results contribute to better understanding of the role of respiration in higher brain functions and could inform future neurofeedback paradigms that integrate respiration tracking.

关键词： Couplings Neuroimaging Pressure sensors Nose Picture archiving and communication systems Electroencephalography Task analysis

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Identification of Candidate Drugs and Molecular Pathways for Pulmonary Arterial Hypertension and Chronic Kidney Disease

Identification of Candidate Drugs and Molecular Pathways for...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Faruk Hosen Mst. Farjana Yasmin Md. Abul Basar Arifa Ferdoushi Trisha Khairul Alam Shadhin Md. Sarwar Mollah Muhammad Shahin Uddin Department of Computing and Information System Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh (GUB) Purbachal American City Kanchon Bangladesh Department of Software Engineering Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh

Pulmonary arterial hypertension (PAH) is biggest preventable cause of early deaths, and hence one of the global preferences of World Health Organization for determent. PAH also induces the vast majority of patients with chronic kidney disease (CKD). Both PAH and CKD are inherently connected, but the common molecular pathways and genes of these two disorders have not yet been demonstrated. In this study, we aimed to determine the similar molecular pathways and therapeutic hub proteins in PAH and CKD that may be used to anticipate the progression of the disease. We used Limma package to conduct differential analysis of gene transcripts of PAH and CKD obtained from the Gene Expression repository. The functional annotations of the genes were determined with the use of pathway analysis. Then proteinâprotein interaction (PPI) networks were constructed to determine the hub proteins, and a clustering technique was applied to determine the most significant PPI elements. We separately evaluated the PAH and CKD gene expression-based datasets and discovered 30 differentially expressed genes (DEGs) shared by both PAH and CKD. The pathways of KEGG revealed that the most prevalent DEGs are associated with the Malaria and African trypanosomiasis. Gene ontology (GO), TF and miRNA analysis and module investigation is treated the forthcoming work of this study. Finally, various drug compounds have been suggested based on the concordant DEGs.

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Identification of Molecular Biomarkers and Key Pathways among Idiopathic pulmonary fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD) and Lung cancer

Identification of Molecular Biomarkers and Key Pathways amon...

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International Conference on computer and Information Technology (ICCIT)

作者： Mst. Farjana Yasmin Md. Faruk Hosen Md. Abul Basar Khairul Alam Shadhin Arifa Ferdoushi Trisha Muhammad Shahin Uddin Department of Computing and Information System Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh Department of Computer Science and Engineering Green University of Bangladesh (GUB) Purbachal American City Kanchon Bangladesh Department of Software Engineering Daffodil International University (DIU) Ashulia Dhaka Bangladesh

Lung cancer (LC), idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the most fatal disorders in the globe, generating frequent human issues. Having IPF and COPD are the risk factors of the LC, but the molecular mechanisms that underlie among IPF, COPD, and LC are not yet elucidated. In this study, we looked for shared molecular indicators and pathways that might explain how individuals with IPF, COPD, and LC are related to one another. GSE24206, GSE76925 and GSE18842 microarray datasets are utilized for IPF, COPD and LC samples. The preprocessing of datasets has done using R language, and then concordant differentially expressed genes (DEGs) are discovered. After that the protein-protein interactions (PPIs) are built using the similar DEGs, and the hub genes are determined using topological analysis. ETS1, MSH2, SORD, RORA and NEDD9 are the PPI network’s top 5 hub genes. The pathways of KEGG demonstrated that the concordant DEGs are related to the colorectal cancer and pathways in cancer. Future work for this project will focus on miRNA, TF, and gene ontology (GO) analyses, as well as module analysis networks. Finally, based on the concordant DEGs, a number of potential medications have been suggested.

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An advanced deep neural network for fundus image analysis and enhancing diabetic retinopathy detection

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Healthcare Analytics 2024年 5卷 100303-100303页

作者： Mehedi Shamrat, F M Javed Shakil, Rashiduzzaman Sharmin Hoque ovy, Nazmul Akter, Bonna Ahmed, Md Zunayed Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Department of Computer System and Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Software Engineering Daffodil International University Dhaka 1216 Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka 1216 Bangladesh Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Biophotomatiχ Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia 4072 QLD Australia

Diabetic retinopathy (DR) involves retina damage due to diabetes, often leading to blindness. It is diagnosed via color fundus injections, but the manual analysis is cumbersome and error-prone. While computer vision techniques can predict DR stages, they are computationally intensive and struggle with complex data extraction. In this research, our prime objective was to automate the process of DR classification into its various stages using convolutional neural network (CNN) models. We employed the performance of fifteen pre-trained models with our novel proposed diabetic retinopathy network (DRNet13) model. We aimed to discern the most efficient model for accurate diabetic retinopathy (DR) staging based on fundus images from five DR classes. We preprocessed the image using a median filter for noise reduction and Gamma correction for image enhancement. We expanded our dataset from 3662 to 7500 images to create a more generalized training model through various augmentation techniques. We also evaluated multiple evaluation metrics, including accuracy, precision, F1-score, Sensitivity, Specificity, Area under the curve (AUC), Mean Squared Error (MSE), False Positive Rate (FPR), False Negative Rate (FNR), in addition to confusion matrices for an in-depth comparison of the performance of these models. Feature maps were employed to illuminate decision making areas in the DRNet13 model, which achieved a 97 % accuracy rate for DR detection, surpassing other CNN architectures in speed and efficiency. Despite a few misclassifications, the model's capability to identify critical features demonstrates its potential as an impactful diagnostic tool for timely and accurate identification of diabetic retinopathy. © 2024 The Authors

关键词： Classification Convolutional neural network Diabetic retinopathy network Diagnostic analytics Feature map

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Image Splicing Detection Based on Texture Features with Fractal Entropy

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computers, Materials & Continua 2021年第12期69卷 3903-3915页

作者： Razi J.Al-Azawi Nadia M.G.Al-Saidi Hamid A.Jalab Rabha W.Ibrahim Dumitru Baleanu Department of Laser and Optoelectronics Engineering University of TechnologyBaghdad10066Iraq Department of Applied Sciences University of TechnologyBaghdad10066Iraq Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia IEEE:94086547 Kuala Lumpur59200Malaysia Department of Mathematics Cankaya UniversityBalgat06530AnkaraTurkey Institute of Space Sciences R76900 Magurele-BucharestRomania Department of Medical Research China Medical UniversityTaichung40402Taiwan

Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more *** a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked ***,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered *** on detection of tampered images still carries great *** the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another *** proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and *** main advantage of FrEp is the ability to extract the texture information contained in the input ***,the“support vector machine”(SVM)classification is used to classify images into either spliced or *** analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection ***,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.

关键词： Fractal entropy image splicing texture features LBP SVM

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Advswap: Covert Adversarial Perturbation with High Frequency Info-Swapping for Autonomous Driving Perception

Advswap: Covert Adversarial Perturbation with High Frequency...

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International Conference on Intelligent Transportation

作者： Yuanhao Huang Qinfan Zhang Jiandong Xing Mengyue Cheng Haiyang Yu Yilong Ren Xiao Xiong School of Transportation Science and Engineering Beihang University Beijing P.R.China State Key Lab of Intelligent Transportation System Beijing P.R.China Zhongguancun Laboratory Beijing P.R.China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Perception module of Autonomous vehicles (AVs) are increasingly susceptible to be attacked, which exploit vulnerabilities in neural networks through adversarial inputs, thereby compromising the AI safety. Some researches focus on creating covert adversarial samples, but existing global noise techniques are detectable and difficult to deceive the human visual system. This paper introduces a novel adversarial attack method, AdvSwap, which creatively utilizes wavelet-based highfrequency information swapping to generate covert adversarial samples and fool the camera. AdvSwap employs invertible neural network for selective high-frequency information swapping, preserving both forward propagation and data integrity. The scheme effectively removes the original label data and incorporates the guidance image data, producing concealed and robust adversarial samples. Experimental evaluations and comparisons on the GTSRB and nuScenes datasets demonstrate that AdvSwap can make concealed attacks on common traffic targets. The generates adversarial samples are also difficult to perceive by humans and algorithms. Meanwhile, the method has strong attacking robustness and attacking transferability.

关键词： Wavelet transforms Training Neural networks Noise Visual systems Robustness Classification algorithms Data mining Information exchange Autonomous vehicles

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MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule

arXiv

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arXiv 2024年

作者： Cai, Guohui Zhang, Ruicheng He, Hongyang Zhang, Zeyu Ergu, Daji Cao, Yuanzhouhan Zhao, Jinman Hu, Binbin Liao, Zhibin Zhao, Yang Cai, Ying College of Computer Science and Artificial Intelligence Southwest Minzu University Chengdu China School of Intelligent System Engineering Sun Yat-sen University Guangzhou China Department of Computer Science University of Warwick United Kingdom Australian National University CanberraACT2601 Australia School of Computer Science and Technology Beijing Jiaotong University Beijing China University of Toronto Toronto Canada University of Adelaide Adelaide Australia La Trobe University Melbourne Australia

Pulmonary nodules are critical for early lung cancer diagnosis, but traditional CT imaging methods suffer from low detection rates and poor localization. Small nodule detection is challenging due to subtle differences in density and issues like occlusion. Existing methods such as FPN, with its fixed feature fusion and limited receptive field, struggle to effectively overcome these issues. To address these challenges, our paper proposed three key contributions: Firstly, we proposed MSDet, a multiscale attention and receptive field network for detecting tiny pulmonary nodules. Secondly, we proposed the extended receptive domain (ERD) strategy to capture richer contextual information and reduce false positives caused by nodule occlusion. We also proposed the position channel attention mechanism (PCAM) to optimize feature learning and reduce multiscale detection errors, and designed the tiny object detection block (TODB) to enhance the detection of tiny nodules. Experiments on the LUNA16 dataset show an 8.8% improvement in mAP over YOLOv8, achieving state-of-the-art performance. The code is available at https://***/CaiGuoHui123/MSDet. © 2024, CC BY-NC-SA.

关键词： Lung cancer

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Egcnet: Edge-Guided Context Real-Time Semantic Segmentation Network

SSRN

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SSRN 2023年

作者： Yu, Kaili Chen, Zhenxue Ye, Mengting Guo, Yixin Wu, Q.M. Jonathan School of Control Science and Engineering Shandong University Jinan250061 China School of Control Science and Engineering Shandong University Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada

Semantic segmentation is widely needed in many application areas. Driven by the development of deep learning, semantic segmentation methods have made significant progress in accuracy, robustness, and speed. Based on the short-term dense concatenate network, this paper proposes an edge-guided context real-time semantic segmentation network (EGCNet), which further improves the detail branch, extracts edge information from feature maps, and enhances the network’s ability to extract edge information through edge-assisted training. Because the top-level feature map contains minimal detailed information, the network only extracts the boundary of the low-level feature map such that the useful information is utilized as much as possible while reducing the computational load. In addition, we improve the attention-refining module by embedding position information, allowing it to capture the target structure more accurately. EGCNet achieves 71 FPS and 78.6\% mIoU on the Cityscapes dataset at a low computational cost, making it an efficient real-time semantic segmentation network. © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Deep Learning for Cyber Security: Deephoax Detection 2

Deep Learning for Cyber Security: Deephoax Detection

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Yakub, Sy. Yuliani Wowor, Rivo Juicer Agustriawan, David Irmawati Kristiyanti, Dinar Ajeng Winarno, P.M. Hassan, Rosilah Mohamad, Roshayu Razzaq, Randy Zahran Universitas Multimedia Nusantara Department of Informatics Tangerang Indonesia Information System Study Program Universitas Multimedia Nusantara Tangerang Indonesia Universiti Kebangsaan Malaysia Selangor Malaysia College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Uin Sultan Maulana Hasanuddin Department of Informatics Banten Indonesia

ISBN: (纸本)9798331506995

The increasing prevalence of deep hoaxes, such as fake news and phishing schemes, poses a significant threat to cybersecurity, undermining trust and spreading misinformation. In Indonesia, surveys indicate that more than 60% of people exposed to hoax news believe that it is true, emphasizing the urgent need for robust detection methods. Traditional cybersecurity approaches often struggle to keep pace with the growing scale and sophistication of these attacks. To address this challenge, this research investigates the use of deep learning techniques, specifically focusing on text-based hoax detection in the Indonesian language. The study fine-tunes IndoBERT, a pretrained deep learning model optimized for Indonesian text, to enhance the accuracy and scalability of hoax detection. The IndoBERT model was trained on a balanced dataset of 29,552 articles, comprising both hoax and real news content, collected from the Mafindo API and Kaggle's Indonesia News Dataset. The model was fine-tuned using supervised learning and evaluated using several key metrics, including accuracy, F1-score, precision, and recall. The results demonstrate that IndoBERT outperforms existing state-of-the-art approaches, achieving an accuracy of 98.51%, an F1-score of 98.44%, and a precision of 98.23% on the test set. These results highlight the effectiveness of IndoBERT for hoax detection, which offers a scalable solution to improve cybersecurity defenses against deceptive content. This research contributes to the integration of advanced deep learning models into cybersecurity systems, addressing the evolving landscape of cyber threats. © 2025 IEEE.

关键词： Cyber attacks

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