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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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A Cognitive Memory-Augmented Network for Visual Anomaly Detection

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1296-1307页

作者： Tian Wang Xing Xu Fumin Shen Yang Yang the Center for Future Multimedia and School of Computer Science and Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China the Center for Future Multimedia and School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731 the Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808China

With the rapid development of automated visual analysis,visual analysis systems have become a popular research topic in the field of computer vision and automated *** analysis systems can assist humans to detect anomalous events(e.g.,fighting,walking alone on the grass,etc).In general,the existing methods for visual anomaly detection are usually based on an autoencoder architecture,i.e.,reconstructing the current frame or predicting the future ***,the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or *** flaws of the existing methods are that abnormal samples can also be reconstructed *** this paper,inspired by the human memory ability,we propose a novel deep neural network(DNN)based model termed cognitive memory-augmented network(CMAN)for the visual anomaly detection *** proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected ***,in the proposed CMAN model,we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data *** reconstruction errors and the novelty scores are used to distinguish abnormal events from *** addition,we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve *** experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts *** implementation code of our CMAN method can be accessed at https://***/CMANcode/CMAN_pytorch.

关键词： Cognitive computing density estimation memory visual analysis systems visual anomaly detection.

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Multi-histogram Reversible Data Hiding with Contrast Enhancement

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 198-208页

作者： Weng, Shaowei Zhou, Ye You, Bingbing Zhang, Tiancong Yang, Caijie Zhang, Chunyu Shi, Yunqing School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China College of information engineering Xizang Minzu university Xianyang712082 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07103 United States

The basic principle of reversible data hiding with contrast enhancement ab-breviated as RDH-CE is to enhance the perceived contrast of stego images using an reversible data-embedding manner. Considering that existing RDH-CE methods can only enhance the global contrast of the image but ignore or may even reduce the local contrast, an RDH-CE method combining K-means clustering with multiple features is proposed in this paper. K-means clustering with multiple features is employed to yield multiple histograms. Adaptive contrast enhancement based on the local properties of multiple histograms is applied to enhance the local contrast as well as global contrast while achieving satisfactory embedding capacity. The experimental results also demonstrate the effectiveness of the proposed method. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： K-means clustering

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Forecasting Continuous Air Pollution with Cloud-Based Big Data Platform using Spatial-Temporal Visualization

Forecasting Continuous Air Pollution with Cloud-Based Big Da...

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International Conference for Convergence of technology (I2CT)

作者： Yue Shan Chang Satheesh Abimananan Saurabh Shukla Dhivyadharsini Satheesh Department of Computer Science and Information Engineering National Taipei University New Taipei City Taiwan Department of Computer Science and Engineering Amity University Maharashtra Mumbai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

In today’s era of rapidly growing data, having a well-designed platform for analyzing and visualizing big data is becoming increasingly important. This study presents a cloud-based big data platform for analyzing and visualizing continuous spatial-temporal air pollution. The platform combines a Spark Hadoop-based machine learning environment with a TensorFlow-based deep learning framework for forecasting and a map for demonstration and visualization of the results. The platform uses the Inverse Distance Weighted (IDW) method to estimate the concentration of particulate matter in the atmosphere and a cloud based non-SQL database to store the pre-calculated air quality images. The platform also features a sliding window mechanism, which allows users to easily visualize the results over time. This work provides an instant overview of air quality in Taiwan and evaluates the performance of the platform, concluding that it is efficient and capable of providing continuous spatial-temporal results. This platform represents a significant step forward in the field of air quality monitoring and forecasting.

关键词： Deep learning Databases Clouds Data visualization Big Data Air pollution Sparks

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CLIP-Driven Semantic Discovery Network for Visible-Infrared Person Re-Identification

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IEEE Transactions on Multimedia 2025年

作者： Yu, Xiaoyan Dong, Neng Zhu, Liehuang Peng, Hao Tao, Dapeng Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Beihang University School of Cyber Science and Technology Beijing100191 China Yunnan University Fist Laboratory School of Information Science and Engineering Kunming650091 China

Visible-infrared person re-identification (VIReID) primarily deals with matching identities across person images from different modalities. Due to the modality gap between visible and infrared images, cross-modality identity matching poses significant challenges. Recognizing that high-level semantics of pedestrian appearance, such as gender, shape, and clothing style, remain consistent across modalities, this paper intends to bridge the modality gap by infusing visual features with high-level semantics. Given the capability of Contrastive Language-Image Pre-training (CLIP) to sense high-level semantic information corresponding to visual representations, we explore the application of CLIP within the domain of VIReID. Consequently, we propose a CLIP-Driven Semantic Discovery Network (CSDN) that consists of Modality-specific Prompt Learner, Semantic information Integration (SII), and High-level Semantic Embedding (HSE). Specifically, considering the diversity stemming from modality discrepancies in language descriptions, we devise bimodal learnable text tokens to capture modality-private semantic information for visible and infrared images, respectively. Additionally, acknowledging the complementary nature of semantic details across different modalities, we integrate text features from the bimodal language descriptions to achieve comprehensive semantics. Finally, we establish a connection between the integrated text features and the visual features across modalities. This process embed rich high-level semantic information into visual representations, thereby promoting the modality invariance of visual representations. The effectiveness and superiority of our proposed CSDN over existing methods have been substantiated through experimental evaluations on multiple widely used benchmarks. The code has been released at https://***/nengdong96/CSDN. © 1999-2012 IEEE.

关键词： Alignment

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Fake Review Classification Using Supervised Machine Learning 25th

Fake Review Classification Using Supervised Machine Learning

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Khan, Hanif Asghar, Muhammad Usama Asghar, Muhammad Zubair Srivastava, Gautam Maddikunta, Praveen Kumar Reddy Gadekallu, Thippa Reddy Pakistan Department of Math and Computer Science Brandon University BrandonMB Canada School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9783030687984

The revolution of social media has propelled the online community to take advantage of online reviews for not only posting feedback about the products, services, and other issues but also assists individuals to analyze user’s feedback for making purchase decisions, and companies for improving the quality of manufactured goods. However, the propagation of fake reviews has become an alarming issue, as it deceives online users while purchasing and promotes or demotes the reputation of competing brands. In this work, we propose a supervised learning-based technique for the detection of fake reviews from the online textual content. The study employs machine learning classifiers for bifurcating fake and genuine reviews. Experimental results are evaluated against different evaluation measures and the performance of the proposed system is compared with baseline works. © 2021, Springer Nature Switzerland AG.

关键词： Fake detection

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Comparative analysis of machine learning algorithms for Classification of US Airlines Tweets 10

Comparative analysis of machine learning algorithms for Clas...

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10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Sharma, Subham Mittal, Usha Wadhawan, Ankita Kansal, Nancy School of Computer Science and Engineering Lovely Professional University Punjab India Information Technology Noida Institute of Information and Technology Gr. Noida India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

Sentiment classification (SC) task is well favoured in e-commerce field as the reviews posted by users, classified into positive and negative categories, help the business holders to understand their current market and other's market strategy and settings as well. This task of SC is widely encouraged by various researchers for different domains such as movie reviews, political reviews, product reviews, airlines and so on. This paper aims to classify the tweets that are shared by the American flight travellers into positive and negative class so that the other people can utilize this information to take their journey related decisions. The task has been accomplished using three machine learning classifiers to classify the tweets data that is collected from Kaggle site. It is seen from the experimental results that SVM outperforms the LR and MNB baselines by 0.9% and 6.23% accuracy respectively. © 2022 IEEE.

关键词： Sentiment analysis

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Proposed Privacy Preservation Technique for Color Medical Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network 38

SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Re...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Guo, Weiyu Sun, Ying Xu, Yijie Qiao, Ziyue Yang, Yongkui Xiong, Hui Thrust of Artificial Intelligence HKUST Guangzhou China School of Computing and Information Technology Great Bay University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Department of Computer Science and Engineering HKUST Hong Kong

Surface electromyography (sEMG) based gesture recognition offers a natural and intuitive interaction modality for wearable devices. Despite significant advancements in sEMG-based gesture recognition models, existing methods often suffer from high computational latency and increased energy consumption. Additionally, the inherent instability of sEMG signals, combined with their sensitivity to distribution shifts in real-world settings, compromises model robustness. To tackle these challenges, we propose a novel SpGesture framework based on Spiking Neural Networks, which possesses several unique merits compared with existing methods: (1) Robustness: By utilizing membrane potential as a memory list, we pioneer the introduction of Source-Free Domain Adaptation into SNN for the first time. This enables SpGesture to mitigate the accuracy degradation caused by distribution shifts. (2) High Accuracy: With a novel Spiking Jaccard Attention, SpGesture enhances the SNNs' ability to represent sEMG features, leading to a notable rise in system accuracy. To validate SpGesture's performance, we collected a new sEMG gesture dataset which has different forearm postures, where SpGesture achieved the highest accuracy among the baselines (89.26%). Moreover, the actual deployment on the CPU demonstrated a latency below 100ms, well within real-time requirements. This impressive performance showcases SpGesture's potential to enhance the applicability of sEMG in real-world scenarios. The code is available at https://***/guoweiyu/SpGesture/. © 2024 Neural information processing systems foundation. All rights reserved.

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