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Remote Sensing Image Change detection Based on Multi-Level Diversity Feature Fusion

IEEE ACCESS 2024年 12卷 81495-81505页

作者： Xie, Honggang Ma, Wanjie Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Peoples R China

Urban topographic changes have a crucial impact on the environment, which serves as the foundation for human survival. However, due to the different spectral response characteristics of various remote sensing satellite sensors and the presence of aging issues, the captured remote sensing images exhibit color imbalance under different seasons and lighting conditions. Existing change detection methods fail to establish the correlation between changed and unchanged information in color-imbalanced urban topographic remote sensing images, thereby reducing the utilization of feature extraction information. Additionally, using fixed thresholds for region determination may inaccurately and incompletely generate change areas. Therefore, this paper proposes a remote sensing image change detection method based on multi-level and multi-diversity feature fusion. Firstly, a ResNet-18 network is employed to extract dual-temporal features. Secondly, to enhance the discrimination between change and unchanged regions during feature extraction and improve the utilization of feature extraction information, a Diversity Feature Fusion Module (DFFM) is designed to reduce false alarms and omissions in the detection results. Furthermore, to effectively address the problem of boundary misjudgment in change areas caused by fixed thresholds, an Adaptive Threshold Module is devised to adaptively learn the texture features of change and unchanged regions, enabling the generation of more accurate thresholds for boundary determination, thereby improving the robustness of the algorithm model and alleviating false alarms. Finally, experimental tests demonstrate that our method achieves excellent performance in two public change detection datasets.

关键词： Feature extraction Remote sensing Adaptive systems Data mining Transformers Spatial resolution Robustness detection algorithms Threshold current Change detection remote sensing diversity feature adaptive threshold

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Enhancing Cyberattack detection Using Dimensionality Reduction With Hybrid Deep Learning on Internet of Things Environment

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IEEE ACCESS 2024年 12卷 84752-84762页

作者： Duraibi, Salahaldeen Mujawib Alashjaee, Abdullah Jazan Univ Coll Engn & Comp Sci Dept Elect & Elect Engn Jazan 45142 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 73213 Saudi Arabia

Cybersecurity in the Internet of Things (IoT) ecosystem is vital to protect sensitive data, stop unauthorized access, and mitigate the risk of disruptive cyberattacks. Cyberattack recognition utilizing an intrusion detection system (IDS) is a major imperative given the increase in the number of connected devices. Advanced cybersecurity methods deploy machine learning (ML) approaches, anomaly detection, and behavioral analysis to analyze IoT network traffic for irregular patterns indicative of potential cyberattacks. The combination of feature selection (FS) and deep learning (DL) approaches in cyberattack recognition suggests a proactive and sophisticated manner to bolster cybersecurity. Leveraging DL structures like neural networks (NNs) assists the automatic extraction and analysis of intricate patterns in the difficult IoT data landscape. This paper develops an Improved Mayfly Optimization Algorithm with a Hybrid Deep Learning based Intrusion detection (IMFOHDL-ID) approach in IoT environments. The designed IMFOHDL-ID approach's main goal is to classify intrusions and accomplish security in the IoT environment. The IMFOHDL-ID technique initially follows data normalization as a preprocessing stage. In addition, the IMFOHDL-ID technique makes use of the IMFO-based feature selection (FS) method to elect feature subsets. For IDs, the IMFOHDL-ID technique applies the Long Short Term Memory based Deep Stacked Sequence-to-Sequence Autoencoder (LSTM-DSSAE) model. Finally, the dipper-throated optimization algorithm (DTOA) was utilized for optimal hyperparameter selection of the LSTM-DSSAE method. To highlight better results of the IMFOHDL-ID model, a series of simulation analyses were performed. Extensive comparative results stated the improved outcome of the IMFOHDL-ID technique over existing approaches.

关键词： Internet of Things Feature extraction Optimization Cyberattack Long short term memory Safety Intrusion detection Computer security detection algorithms Cybersecurity intrusion detection system feature selection hybrid deep learning

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Low-Complexity MIMO detection Based on DRSD With ISMMSE-PIC

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IEEE ACCESS 2024年 12卷 70235-70244页

作者： Shin, Sang-Sik Jae Kim, Ho Kim, Hyoung-Nam Pusan Natl Univ Dept Elect Engn Busan 46241 South Korea

This paper introduces a low-complexity multiple-input multiple-output (MIMO) detection method based on dimension-reduction-soft-demodulation (DRSD) with iterative soft minimum-mean-squared-error parallel interference cancellation (ISMMSE-PIC). The conventional ISMMSE-PIC detection method exhibits several drawbacks, such as inordinate dependence on decoder performance and modulation-order sensitivity. Therefore, a DRSD-based strategy is presented to search over a part of the set of transmitted symbols for calculating log-likelihood ratio (LLR) values, such as maximum likelihood detection, to improve detection performance. The scheme for the rest of the symbols directly utilizes ISMMSE-PIC detection for computing the corresponding LLR values. The computational complexity of the proposed scheme is similar to that of ISMMSE-PIC, whereas its bit error rate performance is comparatively higher. The complexity analysis and simulation results validate the proposed algorithm.

关键词： Symbols MIMO communication Vectors Detectors Transmitting antennas Receiving antennas Computational complexity detection algorithms Multiple-input multiple-output iterative soft minimum-mean-squared-error parallel interference cancellation dimension reduction soft demodulation geometric symmetry

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MoVAE: Multi-Omics Variational Auto-Encoder for Cancer Subtype detection

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IEEE ACCESS 2024年 12卷 133617-133631页

作者： Rahmanian, Mohsen Mansoori, Eghbal G. Shiraz Univ Sch Elect & Comp Engn Shiraz *** Iran

With the advent of advanced genomic data extraction methods, numerous studies have been utilized these data to identify cancer subtypes. Given the complexity of cancer subtyping and the limitations of single-omics data, multi-omics approaches have emerged as a promising solution. Despite the challenges of multi-omics data integration, these methods have shown acceptable results. Various strategies, including matrix decomposition, kernel-based fusion, and deep learning, have been proposed for data fusion and subtyping. As a deep model, we present the Multi-omics Variational Auto Encoder (MoVAE), an innovative strategy for integrating multi-omics data. MoVAE facilitates adaptive fusion by extracting unique and synergistic features from high-dimensional multi-omics data using an AggreEncoder network. Unlike previous methods, MoVAE effectively handles the integration of heterogeneous data, accommodates missing omics, and manages high-dimensionality. As a variational autoencoder, MoVAE encodes each multi-omics input into a low-dimensional latent space while extracting the omics-dependency and complementary information of multi-omics data according to a prior joint distribution. Using the mined knowledge in the cancer subtype detection model, its prediction efficiency would be improved. By decoding the latent vectors, MoVAE reconstructs the input multi-omics data as accurately as possible. To demonstrate MoVAE's effectiveness in identifying biologically meaningful and clinically relevant cancer subtypes, we conducted several experiments and compared the results with state-of-the-art VAE-based methods.

关键词： Cancer Data models Feature extraction Data mining Genomics Computational modeling Bioinformatics detection algorithms Autoencoders Cancer subtype detection encoder aggregator multi-omics data variation auto-encoder

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TransMixer-AF: Advanced Real-Time detection of Atrial Fibrillation Utilizing Single-Lead Electrocardiogram Signals

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IEEE ACCESS 2024年 12卷 143149-143162页

作者： Mahim, S. M. Emamul Hossen, Md Al Hasan, Shakib Islam, Md Khairul Iqbal, Zafar Alibakhshikenari, Mohammad Collotta, Mario Miah, Md Sipon Islamic Univ Dept Biomed Engn Kushtia 7003 Bangladesh King Saud Univ Coll Med Dept Gen Surg Riyadh 11472 Saudi Arabia Univ Roma Tor Vergata Dept Elect Engn I-00133 Rome Italy Kore Univ Enna Dept Engn & Architecture I-94100 Enna Italy Carlos III Univ Madrid UC3M Dept Signal Theory & Commun Leganes 28911 Madrid Spain Islamic Univ Dept Informat & Commun Technol Kushtia 7003 Bangladesh

Atrial Fibrillation (AF) is a prevalent cardiac arrhythmia that carries an increased risk of stroke and heart failure. The manual interpretation of Electrocardiograms (ECGs) for diagnosing AF is time-consuming and subject to observer variability. Deep Learning (DL) techniques offer a promising solution for robust AF detection. This paper introduces a novel DL model that leverages ConvMixer and Transformer architecture, combining convolutional neural networks and attention mechanism to extract both local and global features from ECG data. It excels in accurately classifying ECG rhythms into two categories: normal and atrial fibrillation. Additionally, the model utilizes GradCAM++ visualization to provide interpretability. The model was evaluated using the PhysioNet/CinC 2017 Database, achieving accuracies of 91.66% on original data and 96.59% on preprocessed data. When applied to the the MIT-BIH Arrhythmia Database, it achieved accuracies of 95.86% on original data and 98.78% on preprocessed data, employing a ten-fold cross-validation strategy. This innovative approach has the potential to assist clinicians in the real-time detection of common atrial fibrillation during routine ECG screening, significantly enhancing the efficiency and accuracy of diagnosis. With a strong focus on improving patient outcomes and reducing the burden on healthcare professionals, this research represents a crucial step forward in cardiac arrhythmia detection.

关键词： Atrial fibrillation deep learning TransMixer-AF detection algorithms signal detection Atrial fibrillation deep learning TransMixer-AF detection algorithms signal detection

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How to Design a Cheap Music detection System Using a Simple Multilayer Perceptron With Temporal Integration

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IEEE SIGNAL PROCESSING MAGAZINE 2024年第4期41卷 83-88页

作者： Rafii, Zafar Wold, Erling Boulderstone, Richard Audible Mag Los Gatos CA 95032 USA

We show how to design a cheap system for detecting when music is present in audio recordings. We make use of a small neural network consisting of a simple multilayer perceptron (MLP) along with compact features derive... 详细信息

关键词： Neural networks Multilayer perceptrons Audio recording Multiple signal classification Music Design methodology detection algorithms Spectrogram

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A Comprehensive Review on COVID-19 detection Based on Cough Sounds, Symptoms, CXR, and CT Images

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IEEE ACCESS 2024年 12卷 75412-75425页

作者： Mahanty, Chandrakanta Patro, S. Gopal Krishna Rathor, Sandeep Rachapudi, Venubabu Mohanty, Jnanaranjan Muzammil, Khursheed Islam, Saiful Khan, Wahaj Ahmad GITAM Deemed Univ GITAM Sch Technol Dept Comp Sci & Engn Visakhapatnam 530045 India Woxsen Univ Sch Technol Hyderabad 502345 Telangana India GLA Univ Dept CEA Mathura 281406 India Dept Comp Sci & Engn Dept Comp Sci & Engn Guntur 522302 Andhra Pradesh India Parala Maharaja Engn Coll Govt Dept Comp Sci & Engn Berhampur 761003 Odisha India King Khalid Univ Coll Appl Med Sci Dept Publ Hlth Khamis Mushait Campus Abha 61421 Saudi Arabia King Khalid Univ Coll Engn Civil Engn Dept Abha 61421 Saudi Arabia Dire Dawa Univ Inst Technol Sch Civil Engn & Architecture Dire Dawa 1362 Ethiopia

The worldwide spread of the coronavirus illness has led to the requirement of creating machine-based technologies to identify the diseases. The worldwide pandemic caused by new coronaviruses has resulted in a significant loss of life and necessitates the development of several affordable diagnostic methods to detect the presence of COVID-19 infection. Thankfully, the current era of advanced technology, including transfer learning (TL) approaches, has improved several areas of human health and enabled the identification of chronic and communicable diseases. There is a need for thorough investigation in order to combat the transmission of this alarming virus via the use of evidence-based intelligence models and implementation of preventive measures. The present systematic review focuses on the examination of TL and fuzzy ensemble techniques that have been described in the literature pertaining to strategies for detecting COVID-19. Multiple studies have used cough sounds, CT scans, X-ray images, and symptoms information to identify cases of COVID-19. The application of DL/ML, TL, fuzzy ensemble, and fuzzy inference approaches for COVID-19 identification is discussed in this paper.

关键词： COVID-19 Computed tomography Convolutional neural networks Residual neural networks Noise measurement X-ray imaging Mel frequency cepstral coefficient Acoustics Ensemble learning Predictive models detection algorithms Prediction cough sounds CXR images CT images symptoms ensemble

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Wildfire detection With Deep Learning-A Case Study for the CICLOPE Project

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IEEE ACCESS 2024年 12卷 82095-82110页

作者： Goncalves, Afonso M. Brandao, Tomas Ferreira, Joao C. Lovelytics Arlington VA 22201 USA ISTAR Inst Univ Lisboa ISCTE IUL P-1649026 Lisbon Portugal ISTAR INOV INESC Inovacao Inst Novas Tecnol P-1000029 Lisbon Portugal

In recent years, Portugal has seen wide variability in wildfire damage associated to high unpredictability of climatic events such as severe heatwaves and drier summers. Therefore, timely and accurate detection of forest and rural wildfires is of great importance for successful fire containment and suppression efforts, as wildfires exponentially increase their spread rate from the moment of ignition. In the field of early smoke detection, the CICLOPE project currently trailblazes in the employment of a network of Remote Acquisition Towers for wildfire prevention and observation, along with a rule-based automatic smoke detection system, covering over 2, 700, 000 hectares of wildland and rural area in continental Portugal. However, the inherent challenges of automatic smoke detection raise issues of high false alarm rates that affect the system's prediction quality and overwhelm the Management and Control Centers with numerous false alarms. The research work presented in this paper evaluates the potential improvement in wildfire smoke detection accuracy and specificity using deep learning-based architectures. It proposes a solution based on a Dual-Channel CNN that can be deployed as a secondary prediction confirmation layer to further refine the CICLOPE automatic smoke detection system. The proposed solution takes advantage of the high true alarm coverage of the current detection system by taking only the predicted alarm images and respective bounding box coordinates as inputs. The Dual-Channel network combines the widely used DenseNet architecture with a novel detail selective network with spatial and channel attention modules trained separately with image data obtained from CICLOPE, fusing the extracted features from both networks in a concatenation layer. The results demonstrate that the proposed Dual-Channel CNN outperforms both single-channel networks, achieving an accuracy of 99.7% and a low false alarm rate of 0.20% when re-examining the alarms produced by the

关键词： Feature extraction Wildfires Computer architecture Image classification Computational modeling YOLO Computer vision Convolutional neural networks Deep learning detection algorithms convolutional neural networks deep learning smoke detection wildfire detection

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Self-Supervised Graph Transformer for Deepfake detection

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IEEE ACCESS 2024年 12卷 58114-58127页

作者： Khormali, Aminollah Yuan, Jiann-Shiun Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with unseen samples. As a result, a reliable deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. Despite various attempts to enhance cross-dataset generalization, the problem remains challenging, particularly when testing against common post-processing perturbations, such as video compression or blur. Hence, this study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability, withstanding common corruptions and enabling feature explainability. The framework comprises three key components: a feature extractor based on vision Transformer architecture that is pre-trained via self-supervised contrastive learning methodology, a graph convolution network coupled with a Transformer discriminator, and a graph Transformer relevancy map that provides a better understanding of manipulated regions and further explains the model's decision. To assess the effectiveness of the proposed framework, several challenging experiments are conducted, including in-data distribution performance, cross-dataset & cross-manipulation generalization, and robustness against common post-production perturbations. The results achieved demonstrate the remarkable effectiveness of the proposed deepfake detection framework, surpassing the current state-of-the-art approaches.

关键词： Deepfakes Transformers Feature extraction Self-supervised learning Visualization Task analysis Rendering (computer graphics) detection algorithms Convolutional neural networks Deepfake detection graph convolution networks self-supervised contrastive learning vision transformer

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A Novel Early detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System

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IEEE ACCESS 2024年 12卷 26683-26695页

作者： Ramesh, B. Lakshmanna, Kuruva Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamil Nadu India

Diabetes is the "mother of all diseases" as it affects multiple organs of body of an individual in some way. Its timely detection and management are critically important. Otherwise, the long run, it can cause several complications in a diabetic. Heart disease is one of the major complications of *** work proposed an Optimal Scrutiny Boosted Graph Convolutional LSTM (O-SBGC-LSTM), SBGC-LSTM enhanced by Eurygaster Optimization Algorithm (EOA) to tune hyperparameters for early prevention and detection of diabetes disease. This work proposed an Optimal Scrutiny Boosted Graph Convolutional LSTM (O-SBGC-LSTM), SBGC-LSTM enhanced by Eurygaster Optimization Algorithm (EOA) to tune hyperparameters for early prevention and detection of diabetes disease. This method not only captures discriminative features in spatial configuration and temporal dynamics but also explore the co-occurrence relationship between spatial and temporal domains. This method also presents a temporal hierarchical architecture to increase temporal receptive fields of top SBGC-LSTM layer, which boosts the ability to learn high-level semantic representation and significantly reduces computation cost. The performance of O-SBGC-LSTM was found overall to be satisfactory, reaching >98% accuracy in most studies. In comparison with classic machine learning approaches, proposed hybrid DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Furthermore, prevention is better than cure. Additionally, employed fuzzy based inference techniques to enhance the prevention procedure using suggestion table.

关键词： Diabetes Diseases Heart Fuzzy logic Predictive models Prediction algorithms Training detection algorithms Coronary arteriosclerosis Convolutional neural networks Long short term memory Fuzzy systems Inference algorithms detection prevention coronary heart disease optimal scrutiny boosted graph convolutional LSTM deep learning fuzzy inference systems

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