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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Lee, Cho-Yuan Wang, Kuan-Chen Liu, Kai-Chun Wang, Yu-Te Lu, Xugang Yeh, Ping-Cheng Tsao, Yu National Yang Ming Chiao Tung University School of Medicine Taipei Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei Taiwan National Taiwan University Graduate Institute of Communication Engineering Taipei Taiwan University of Massachusetts College of Information and Computer Sciences AmherstMA01003 United States National Institute of Information and Communications Technology Tokyo Japan

ISBN: (纸本)9798350371499

In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the quality of real-world sEMG data more effectively, this study proposes QASE-net, a new non-intrusive model that predicts the SNR of sEMG signals. QASE-net integrates a one-dimensional Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BLSTM) layer and attention mechanisms, following an end-to-end training strategy. Our experimental framework utilizes real-world sEMG and ECG data from two open-access databases, the Non-Invasive Adaptive Prosthetics Database and the MIT-BIH Normal Sinus Rhythm Database, respectively. The experimental results demonstrate the superiority of QASE-net over the baseline method, exhibiting significantly reduced prediction errors and notably higher linear correlations with the ground truth. These findings show the potential of QASE-net to substantially enhance the reliability and precision of sEMG quality assessment in practical applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigation of Big Data for Human Emotion Detection using Deep Learning 1

Investigation of Big Data for Human Emotion Detection using ...

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1st IEEE International Conference on Emerging research in Computational Science, ICERCS 2023

作者： Ali Hussien, Naseer Shnawa, Ammar H. Jaffer, Zain Wasmi Zaydan, Nabaa Rabea Hussam, Ragheed Noori, Mohammed Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

The significance of this study relies upon inside the numerous fields it has the potential to impact positively;these fields consist of human-computer interaction, medicinal drug, advertising and marketing, and academia. The study intends to improve user experience, targeted actions, and make a contribution to the creation of emotionally wise structures by deciphering complex emotional states within extensive datasets. Data privacy issues, algorithmic biases, and the inaccessibility of deep mastering models represent merely some of the problems that the advised Human Emotion Detection technique seeks to solve. To correctly negotiate moral concerns and assure sturdy emotion detection in a huge variety of situations, Human Emotion Detection (HED) prioritizes a balance among model complexity and transparency. The counseled technique of Deep Learning-assisted Robust Human Emotion Detection (DL-RHED) makes use of sophisticated deep learning architectures, which includes attention mechanisms and recurrent neural networks, to hit upon nuances in expression over time. This technique seeks to offer an all-encompassing information of human emotions, permitting the version to dynamically adjust to new emotional displays. Validation of the proposed DL-RHED approach using simulation analysis is a fundamental a part of this research. The effectiveness and consistency of DL-RHED across distinct datasets are established inside the conceptual, which incorporates details about the design of the experiment, evaluation measures, and simulation consequences. These investigations provide real proof for the practicality and scalability of the proposed method. © 2023 IEEE.

关键词： Big data

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Fostering Air Pollution using Cloud Computing and IoT-based Methods 1

Fostering Air Pollution using Cloud Computing and IoT-based ...

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1st IEEE International Conference on Emerging research in Computational Science, ICERCS 2023

作者： Majeed, Maryam Ghassan Nazar Dawood, Mustafa Taha, Ahmed Hussien, Naseer Ali Lafta, Alaa M. Noori, Mohammed Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

Each year, the number of people dying from pollution rises, and air pollution is the main factor in these deaths. Car pollution is a key contributor to air pollution (AP) caused by various sources. Most cities have poor air quality that does not meet health standards. The major issue in the contemporary civilised world is AP, which hurts human health and the environment. With numerous emission sources, motor vehicles and industrial processes are the principal causes of air pollution. Both short-term and long-term exposure to airborne toxicants can have various toxicological consequences on people, including long-term chronic disorders including cancer, neuropsychiatric issues, skin diseases, eye irritation, and respiratory and cardiovascular illnesses. Using the Internet of Things (IoT), track air pollutants in a given area and analyse and forecast air quality. This article will combine cloud computing with the AP-IoT strategy to deliver these services more swiftly and in real time. The area of town where the intended system will be installed is very polluted. The concentrations of each hazardous contaminant are monitored regularly. A mechanism is in place for monitoring and reducing traffic in areas with high pollution levels. The answer in this situation is a sensor-based hardware module that can be positioned beside roads. These gadgets mounted on lampposts may wirelessly send information about air quality to a remote server. For traffic control, this information may be used. This technology will be developed further so consumers can get weekly or monthly air quality reports via an easier-to-use mobile app. Commuters can choose routes where the air quality is good thanks to a smartphone application that provides information about it. The experimental results show that, in comparison to other approaches already in use, the suggested AP-IoT model improves prediction ratios of 98.9%, accuracy ratios of 95.6%, stability ratios of 97.85, efficiency ratios of 96.7%, an

关键词： Diseases

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Study on Host-based Security System Advancement in ICT-Enabled Smart Cities 1

Study on Host-based Security System Advancement in ICT-Enabl...

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1st IEEE International Conference on Emerging research in Computational Science, ICERCS 2023

作者： Fouad, Laith Hussien, Naseer Ali Hussian, Ahmed Alwan, Adil Abbas Qasim Abbas, Saad Abdulaziz, Ismail Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Scientific Research Center Al-Ayen University Information and Communication Technology Research Group Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

Modern ICTs (Information and Communication technology) are used in Smart Cities (SC) to boost productivity, share data with other users, deliver better federal programs, and improve customer satisfaction. Most technological schemas and operational information are readily available and can be used to expand the reach of civic intelligence. Security and privacy concerns, however, remain a major obstacle when faced with the difficulties of meeting security standards. 5G-enabled technologies, self-driving cars, and the proliferation of communication equipment increase the opportunities for malicious actors to launch attacks. Host-level security tools are known as Host Security (HS). Modern technology used in smart cities meets residents' requirements and solves urban problems. An intelligent city achieves many things and gives its residents a voice. With host-based protection, it is feasible to escape the battle of security upgrades, allowing IT executives to focus on the most important changes. For the smart city against cyber-attacks protection, this article employs ICT enabled Smart City based Host security (ICT-SC-HS). A single infected machine could destroy the enterprise's systems without host-based protection. A network security application installed on a computer or networked device is a host-based barrier. Using these firewalls can control the transmission of dangerous infestations across the network. Individual hosts may be safeguarded from viruses and malware. A host-based firewall, which is installed on each server, decides its accessibility to a certain device and also monitors and regulates inbound and outbound traffic. Hence, compared with existing research, the proposed article presents malware detection in different attacks as 72.1%, evaluation of host networks as 69.7%, absolute errors of security analysis as 82.8%, probability of network as 75.4%, and accuracy as 67.2%. © 2023 IEEE.

关键词： Smart city

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Moving Scene Classification And Human Action Recognition Based On Artificial Intelligence 1

Moving Scene Classification And Human Action Recognition Bas...

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1st IEEE International Conference on Emerging research in Computational Science, ICERCS 2023

作者： Hassan, Ahmed R. Taha, Ahmed Hussien, Naseer Ali Khalid Hussain, Angham Ali, Aqeel Matti, Imad National University of Science and Technology Engineering Technologies Department of Medical Devices Nasiriyah Dhi Qar Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

In friendly correspondence, human behavior was generally a basic part. All signs that permit an assessment of the human way of behaving are human action and activity acknowledgment. Recognizing human action is a huge issue in different applications, including human-PC cooperation and innovative video observing, to further develop security in many fields. Often, recognizing human exercises with the single perception of the human body and the elements of the climate and the related items is conceivable. Look at this subject and proposition as a system for distinguishing human action that incorporates various utilitarian channels from different things, like items, views, and individuals. This paper sums up current headways in acknowledgment of human movement. By and large, distinguishing human exercises with the single perception of the human body and the highlights of the climate and comparing items is conceivable. This study offers artificial intelligence-based human action recognition (AI-HAR). Given the rising notoriety and late forward leaps in human conduct acknowledgment because of manufactured reasoning, the review gives information on the latest things and possible core values for future logical endeavors. © 2023 IEEE.

关键词： Behavioral research

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Prevention and Management of Natural Disasters with Integrated Multidisciplinary Climatic Sensitivity and Big Data Model 1

Prevention and Management of Natural Disasters with Integrat...

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1st IEEE International Conference on Emerging research in Computational Science, ICERCS 2023

作者： Hussian, Ahmed Hussien, Naseer Ali Al-Chilibi, Haider. Alsalhy, Mohammed Jameel Abdul Jaleel Maktoof, Mohammed Matti, Imad Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al- Turath University College Computer Technologies Engineering Baghdad Iraq Al- Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

The comprehension and predictability of seasonal, yearly, decadal, and hundred-year climatic performances are improved by using climate models. Models observe the level of climatic change attributable to natural variability, human activities, or a combination of both. The problematic aspects of climatic considerations include heat-related disease and death, rising industrial losses, shortages, and damaged infrastructure, which are crucial components. To sustain their way of life, an Integrated Multidisciplinary Climatic Sensitivity utilizing Big Data Model (IMCSBDM) has been suggested in this article, which would exacerbate societal issues like hunger and poverty. research on social and climate issues has been carried out to gather information on the climate, environment, and social interactions. A superposed epoch resolution analysis is made to provide trading grounds with adaptive potential. In this suggested work, the analytical test of the simulation is carried out based on security, effectiveness, and anonymity. © 2023 IEEE.

关键词： Big data

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Exploring Image Generation for UAV Change Detection

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1061-1072页

作者： Xuan Li Haibin Duan Yonglin Tian Fei-Yue Wang Peng Cheng Laboratory Shenzhen 518000China IEEE State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijing 100083 Department of Automation University of Science and Technology of ChinaHefei 230027 State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense TechnologyChangsha 410073China

Change detection(CD)is becoming indispensable for unmanned aerial vehicles(UAVs),especially in the domain of water landing,rescue and ***,even the most advanced models require large amounts of data for model training and ***,sufficient labeled images with different imaging conditions are *** by computer graphics,we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated *** simulated dataset consists of six challenges to test the effects of dynamic background,weather,and noise on change detection ***,we propose an image translation framework that translates simulated images to synthetic *** framework uses shared parameters(encoder and generator)and 22×22 receptive fields(discriminator)to generate realistic synthetic images as model training *** experimental results indicate that:1)different imaging challenges affect the performance of change detection models;2)compared with simulated images,synthetic images can effectively improve the accuracy of supervised models.

关键词： Change detection computer graphics image translation simulated images synthetic images unmanned aerial vehicles(UAVs)

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover

Efficient Relaxed Functional Dependency Discovery with Minim...

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International Conference on Data engineering

作者： Xiaoou Ding Yida Liu Hongzhi Wang Chen Wang Yichen Song Donghua Yang Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2 m-l ) to O(1.5 m-1 ). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks.

关键词： Data integrity Soft sensors Transforms Data engineering Cleaning Data governance Data mining

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A Remote Fatigue Driving Detection System for Ship Supervision based on Physiological Response Features 23

A Remote Fatigue Driving Detection System for Ship Supervisi...

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Tong, Jinming Cheng, Wei Li, Chen Wang, Xuming Chen, Guanglin School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Tianjin Navigation Instruments Research Institute Tianjin300131 China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan430063 China National Engineering Research Center for Water Transport Safety Wuhan University of Technology Wuhan430063 China

ISBN: (纸本)9798400700705

Fatigue driving is one of the main influential factors causing maritime accidents, traditional physiological signal detection method has disadvantages such as poor stability and practicability, it has great individual differences and always interferes with the driver operation. This paper proposes a remote fatigue driving detection system based on physiological response features. By fusing different physiological response features such as head posture and eye closure, a fatigue detection model is constructed. As a supplement to single EAR detection for reducing the missed retrieval of eye closure behavior, Single Shot Multi Box Detector is applied to improve the accuracy and robustness of the system. The PERCLOS value is approximately solved by the number of frames with eye closure, and the abnormal head posture angle and the P80 standard have been used to evaluate the fatigue state. Experimental result shows that the detection accuracy has reached 96.9548%, it could meet the demand of ship supervision for driving behavior and fatigue detection which has prosperous application value in seafarers' training and maritime management fields. © 2023 ACM.

关键词： Ships

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TSDDISCOVER: Discovering Data Dependency for Time Series Data

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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International Conference on Data engineering

作者： Xiaoou Ding Yingze Li Hongzhi Wang Chen Wang Yida Liu Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods.

关键词： Data integrity Time series analysis Semantics Maintenance engineering Data engineering Cognition Cleaning

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