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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and Engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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Non-Contact Blood Pressure Estimation Using Accurate Cardiac Movements Extracted by Hidden Semi-Markov Model

Non-Contact Blood Pressure Estimation Using Accurate Cardiac...

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IEEE International Conference on Communications (ICC)

作者： Shengze Wang Mondher Boazizi Tomoaki Ohtsuki Graduate School of Science and Technology Keio University Yokohama Japan Department of Information and Computer Science Faculty of Science and Technology Keio University Yokohama Japan

This paper presents a radar-based, non-contact Blood Pressure (BP) estimation model based on accurate detection of cardiac activities, which enables BP monitoring to be performed in a touch-free and continuous manner. Cardiac movements have been regarded as essential factors for BP estimation. However, accurately obtaining these movements by a radar system remains a challenging problem because these movements are too inconspicuous and could be easily hindered by respiration and random noise. In this paper, we propose a method that mainly focuses on cardiac feature extraction in radar-based BP monitoring. First, we employ an integrated-spectrum waveform. It is derived from short-time Fourier transform (STFT) and is capable of recording and preserving minor cardiac activities. Compared with the pulse-wave signals used in previous works, the integrated-spectrum focuses on energy changes introduced by short and high-frequency vibrations. It can eliminate the interference of respiration and random noise, and cardiac contractile movement can be reserved accurately. Second, we propose a cardiac features estimation method in which a hidden semi-Markov model (HSMM) is applied to the integrated-spectrum for feature extraction. Compared to the pulse wave signal, the Root-Mean-Square Error (RMSE) of the estimated interbeat intervals (IBI), Systolic time, and Diastolic time is reduced by 44.2%, 73.3%, and 76.7% respectively. The estimated accurate cardiac features are further used as inputs for a Random Forest model for BP prediction. Though previous work required the subject to hold one's breath, we achieved a comparable prediction accuracy even when our subject is breathing normally. The Diastolic BP (DBP) error of our model is $4.27\pm 5.84$ mmHg (Mean Absolute Difference ± Standard Deviation), and the Systolic BP (SBP) error is $6.63\pm 8.95$ mmHg.

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Sociotechnical Transformation in the Decade of Healthy Ageing to empower the Silver Economy: Bridging the Silver Divide through Social and Digital Inclusion 24

Sociotechnical Transformation in the Decade of Healthy Agein...

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25th Annual International Conference on Digital Government Research, DGO 2024

作者： Nikiforova, Anastasija Tseng, Hsien-Lee Butt, Sidra Azmat Daheim, Dirk Liu, Li-Chuan University of Tartu Faculty of Science and Technology Institute of Computer Science Tartu Estonia National University of Tainan Department of Public Administration and Management Tainan Taiwan Tallinn University of Technology Information Systems Group Tallinn Estonia National Taitung University Department of Public and Cultural Affairs Taitung Taiwan

ISBN: (纸本)9798400709883

The current decade has been marked as the Decade of Healthy Ageing, where increased participation, social and digital inclusion for the silver population within and for the Silver Economy has become imperative. This panel discusses current activities in the regions where the largest numbers of older people live - Asia and Europe seeking to gain a diverse perspective on current development in the area from two leaders in the field of ICT and digital government – Taiwan and Estonia (with a more diverse view of the Baltic Sea region). The panel discusses the feasibility and effectiveness of these developments, identifies the obstacles these projects face, and examine their limitations. It also explores alternative solutions to address these gaps and the potential for adopting similar projects in other regions, thereby contributing to the guiding principles of"The plan for a Decade of Healthy Ageing 2021-2030" and the Sustainable Development Goals (SDGs). © 2024 Copyright held by the owner/author(s).

关键词： Silver

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Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems

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IEEE Internet of Things Journal 2023年第19期10卷 17254-17264页

作者： Guan, Jiwei Pan, Lei Wang, Chen Yu, Shui Gao, Longxiang Zheng, Xi Macquarie University School of Computing SydneyNSW2109 Australia CSIRO Data61 SydneyNSW1710 Australia Deakin University School of Information Technology Waurn PondsVIC3125 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Qilu University of Technology Shangdong Computer Science Center Jinan250316 China

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this article, we propose the multimodal fusion framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using expected calibration errors, we measure calibration errors and Monte Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for advanced driver assistance systems. © 2014 IEEE.

关键词： Speech recognition

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Preliminary Evaluation of Kyokko for Inter-FPGA Communication Framework CIRCUS

Preliminary Evaluation of Kyokko for Inter-FPGA Communicatio...

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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Kaito Kitazume Norihisa Fujita Ryohei Kobayashi Taisuke Boku Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Center for Computational Sciences University of Tsukuba

In recent years, Field-programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customiz... 详细信息

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

关键词： Conferences High performance computing Circuits Cluster computing Low latency communication Field programmable gate arrays

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Data mining for epidemiology: The correlation of typhoid fever occurrence and environmental factors 7

Data mining for epidemiology: The correlation of typhoid fev...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Asadi, Faisal Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Typhoid fever is an endemic disease that burdens Indonesia and has a potentially fatal infection multisystem. Salmonella typhi bacterium is responsible for typhoid fever disease. Poor sanitation, crowding, and slums are the main factors of increasing typhoid fever incidences. Environmental factors directly connected to meteorological factors are the main factor in breeding the Salmonella typhi bacterium. This study aims to identify the correlation between meteorological parameters and typhoid fever disease occurrence. The study was carried out in Jakarta, Indonesia, and the Bureau of Meteorological, Climatology, and Geophysics (BMKG) provided the meteorological parameter data. In addition, the Jakarta health surveillance office provided information on typhoid fever hospitalizations from 2019 to 2021. Pearson's concept was utilized d to investigate the correlation between typhoid fever incidences and the meteorological parameters. Humidity, precipitation, and wind speed are the meteorological parameters that significantly affect in contribute to the occurrence of typhoid fever disease. These findings might be used as a reference for Indonesia's government in making public policy to prevent typhoid fever in Indonesia. © 2022 Elsevier B.V.. All rights reserved.

关键词： Data mining

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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computer science, information technology and Engineering (ICCoSITE), International Conference on

作者： Raymond Sunardi Oetama Yanfi Yanfi Masagus M. Ikhsan Assiddiq U.P Sani Muhamad Isa Information System DepartmentFaculty of Engineering and Informatics Universitas Multimedia Nusantara Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.

关键词： Support vector machines Training Sentiment analysis Vocabulary Dictionaries Social networking (online) Blogs

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Securing the Sustainable 5G Enabled IoMT-Fog Computing Environment: A Blockchain-Based Approach 1st

Securing the Sustainable 5G Enabled IoMT-Fog Computing Envir...

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1st International Conference on Sustainable Development through Machine Learning, Artificial Intelligence, and IoT, ICSD 2023

作者： Rajawat, Anand Singh Goyal, S.B. Chee, Ming Wei Kautish, Sandeep School of Computer Science and Engineering Sandip University Nashik4222133 India Faculty of Information Technology City University Petaling Jaya Malaysia City Graduate School City University Petaling Jaya Malaysia LBEF Campus Kathmandu Nepal

ISBN: (纸本)9783031470547

Recent breakthroughs in fog computing and Fog’s Internet of Things (Fog-IoT) technologies are focused on data analysis and medical procedures that use AI. Recent changes in the field of fog computing made it possible for these things to happen. Even at the level of fog computing, this paradigm is very open to many types of cyber-attacks and threats. This is its main flaw. In this set-up, the edge (sensing) layer, the fog (processing) layer, and the public (storage and management) layer could all be in danger (cloud). The Internet of Things is an important part of today’s complex healthcare systems, which rely heavily on it (Fog-IoT). Medical equipment that is part of the Internet of Things (IoT) has problems with scalability, data security, and data storage. The Fog-IoT was made to solve these problems. But it would be very hard to manage such a large and complicated medical IoT system with the current Single Cloud platforms (CP), especially since the number of IoT-based medical devices has grown so quickly. We showed a flexible FC built on Blockchain technology for an IoT platform that can connect to 5G networks. In this research, a secure Blockchain-based Fog-BM-IoMT communication mechanism is shown that works on a fluid computing (FC) architecture with low overhead and safe storage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Customer Segment Classification Prediction in the Australian Retail Based on Machine Learning Algorithms 4

Customer Segment Classification Prediction in the Australian...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Luo, Yiming Zhang, Rui Wang, Fei Wei, Tao Business Schoo The University of Sydney Sydney Australia Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China School of Chemical and Environmental Engineering Sichuan University of Science and Engineering Zigong China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment. © 2023 ACM.

关键词： Customer satisfaction

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Covert Channel Detection and Generation Techniques: A Survey

Covert Channel Detection and Generation Techniques: A Survey

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Nesmah A. AL-Khulaidi Ammar T. Zahary Muneer A.S Hazaa Adel A. Nasser Department of Computer Networks Yemen Academy for Graduate Studies Sana'a Yemen Department of Computer Science Faculty of Computing and IT University of Science and Technology Sana'a Yemen Department of Computer Science Faculty of Computer and Information System Thamar University Thamar Yemen Department of Information Systems and Computer Science Faculty of Sciences Sa'adah University Sa'adah Yemen

The use of covert communications has become more widespread recently as a solution to the information security issue. information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. System security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment.

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