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Information technology, Applied Mathematics and Statistics (ICITAMS), International Conference on

作者： Ekram Hakim Dhiah Al-Shammary Ahmed M. Mahdi Dep. Computer Science College of computer science and information technology University of Al-Qadisiyah Diwaniyah Iraq

ISBN: (数字)9798350335118

ISBN: (纸本)9798350335125

This paper proposes a design and implementation of a highly efficient feature selection based on Minkowski’s mathematical similarity for machine learning. Moreover, the development of the Minkowski feature selection can be general for intelligent datasets and machine learning. Intelligent optimizers and feature selection represent an advanced stage in machine learning and innovative computer applications by reducing the overall number of features to be included in the classification function process. This paper analyses Minkowski’s selection of best factors for EEG Bonn University data. Minkowski cancels the random movement and systematically limits the number of features and its point-to-point movement in order to promote exploration and reduce the probability of falling into stagnation. The main measures to analyze this research are Accuracy, precision, Recall, and F1score assessment. Moreover, the accuracy of Minkowski’s selection provided better classification results than some of the other approaches (100%)

关键词： Machine learning algorithms Limiting Machine learning computer applications Feature extraction Mathematical models Electroencephalography

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The Realization Factors of Taiwan's Low Altitude Airspace Economy and UAM: Analysis and Evaluation 20

The Realization Factors of Taiwan's Low Altitude Airspace Ec...

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20th IEEE International Conference on e-Business Engineering, ICEBE 2024

作者： Wu, Tung-Ling Kung, Chih-Ming Lin, Guan-Ting Tsai, Kuen-Yu Meng, Guang-Yun Huang, Yu-Che Cheng, Wan-Shu Institute of Transportation at Ministry of Transportation and Communications Taiwan Shin Chin Univeristy Kaohsiung Campus Dep. of Information Technology&Communication Taiwan Providence University Dep. of Computer Science and Information Management Taichung Taiwan National Taiwan Univeristy Department of Electrical Engineering Taipei Taiwan Executive Officer Center for Research on High-Performance Remote Sensing and Urban Informatics Ntu Taiwan

ISBN: (纸本)9798350365856

Low altitude airspace economy applications will be of highly effective in a wide range of civil activities and will be a critical element in the smart cities of the future. In view of the main features of unmanned aerial vehicle (UAV), such as automatic piloting system, access to uninhabited areas, low cost compared to road and manned aircraft, shorter delivery time, and the ability to carry heavy payloads, it is important to recognize that UAVs can be used for a wide range of civil activities. With the development of these applications in full swing around the world, in addition to advanced communications technology and other related infrastructure, business demand and a favorable investment climate, clarity, definiteness and feasible regulations in the Taiwanese environment are also essential. Based on the premise of local economic strength, the technical level of the telecommunication industry, and commercial demands, Taiwan should establish a rational standard for the UAV regulatory system as soon as possible, so as to facilitate the development of UAM and to ensure the international competitiveness of the related technologies for market development © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method

arXiv

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arXiv 2024年

作者： Jeeveswaran, Kishaan Arani, Elahe Zonooz, Bahram Dep. of Mathematics and Computer Science Eindhoven University of Technology Netherlands Wayve Technologies Ltd London United Kingdom

Domain incremental learning (DIL) poses a significant challenge in real-world scenarios, as models need to be sequentially trained on diverse domains over time, all the while avoiding catastrophic forgetting. Mitigating representation drift, which refers to the phenomenon of learned representations undergoing changes as the model adapts to new tasks, can help alleviate catastrophic forgetting. In this study, we propose a novel DIL method named DARE, featuring a three-stage training process: Divergence, Adaptation, and REfinement. This process gradually adapts the representations associated with new tasks into the feature space spanned by samples from previous tasks, simultaneously integrating task-specific decision boundaries. Additionally, we introduce a novel strategy for buffer sampling and demonstrate the effectiveness of our proposed method, combined with this sampling strategy, in reducing representation drift within the feature encoder. This contribution effectively alleviates catastrophic forgetting across multiple DIL benchmarks. Furthermore, our approach prevents sudden representation drift at task boundaries, resulting in a well-calibrated DIL model that maintains the performance on previous tasks. 1 © 2024, CC BY-SA.

关键词： Learning systems

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Alert system with IoT Techniques for Stray Children 2

Alert system with IoT Techniques for Stray Children

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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Pramkeaw, Patiyuth Ganokratanaa, Thittaporn Pinthong, Thidarat Kitprasert, Napathsorn King Mongkut's University of Technology Thonburi Media Technology Program Bangkok Thailand King Mongkut's University of Technology Thonburi Applied Computer Science Program Bangkok Thailand Phetchaburi Rajabhat University Dep. of Business Information Management Phetchaburi Thailand

ISBN: (纸本)9798350336573

The purpose of this research was to design, produce and evaluate a stray child warning system. The device has been designed by using 2 ESP8266 boards to receive signals as specified. The system will notify the information via Line Notify to let users know how far the board that acts as a Client is from the board that acts as an Access Point. Expressed in meters (m) in the notification line notify process. The test results of the system in terms of signal transmission between the Access Point and the Client can work well. Buzzer speaker and LED notification light according to the specified distance every time. Notifications to LINE Notify to mobile phones can be done every time. © 2023 IEEE.

关键词： Internet of things

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model 3

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Elshabrawy, Mahmoud Eid, Marwa M. Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Delta Higher Institute of Engineering and Technology Dept. of Communications & Electronics Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Ain Shams University Faculty of Computer and Information Sciences Dep. of Computer Science Cairo Egypt University Mansoura Faculty of Engineering Computer Engineering and Control Systems Dept. Egypt

ISBN: (纸本)9798350323511

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. © 2023 IEEE.

关键词： Ant colony optimization

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor

Logistic Regression Classification for Assessing the Risk of...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Dalia Alzu'bi Rabia Emhamed Al Mamlook Ahmad Nasayreh Mohammad Aljaidi Rami AlAzab Hasan Gharaibeh Qais Al-Na'amneh Dep. Information and Systems Engineering Concordia University Montreal Canada Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Surgery and Urology Jordan University of Science and Technology Irbid Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses

关键词： Logistic regression Analytical models Accuracy Predictive models Genetics History Reliability

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Longitudinal Evolution of Coachees’ Behavioural Responses to Interaction Ruptures in Robotic Positive Psychology Coaching

Longitudinal Evolution of Coachees’ Behavioural Responses t...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Micol Spitale Minja Axelsson Neval Kara Hatice Gunes University of Cambridge Cankaya University Dep. of Computer Science & Technology AFAR Lab University of Cambridge

Robotic mental well-being coaches could be used to help people maintain their well-being, and improve access to mental healthcare. In coaching, the alliance between the coach and coachee is important for the success of the practice. However, this alliance might be negatively affected by interaction ruptures (e.g., the robot making mistakes and the user feeling awkward) that still commonly occur in human-robot interactions. Therefore, robotic coaches should be able to recognize ruptures occurring during their interactions with human users to guarantee the success of the well-being practice. To this aim, we analyse coachee behavioural responses to interaction ruptures during a robotic positive psychology coaching practice and how these behavioural cues evolve over time. We focus our analysis on a dataset we collected in a previous work, where 26 participants interacted with either a QTrobot or a Misty II robot at their workplace over 4 weeks. We undertake a longitudinal analysis of coachees’ multimodal nonverbal cues (i.e., facial expressions, vocal acoustic features, and body pose features) to investigate the contribution of individual modalities for detecting interaction ruptures. Our results show that coachees: i) displayed facial cues of rupture (e.g, laughing at the robot) and suspicion more in the first week than in the last week; ii) talked more and were less silent in the last week than in the previous weeks; and iii) exhibited a higher number of hand-over-face gestures (a cue for self-disclosure) in the last week than in the previous weeks. Our findings aim to inform the development of AI models for multi-modal detection of interaction ruptures which can be used to improve the effectiveness and the success of robotic well-being coaching.

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TIoT: A Trust by Design Architecture for Secure and Private Internet of Things (IoT) 21

TIoT: A Trust by Design Architecture for Secure and Private ...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alsheakh, H. Mahdi, Tareq Nasser Kadhm, Mustafa S. Al Shammari, Abeer Abdulsahib, Fatimah I. Taher, Hazeem B Algriree, Waleed Alobaidi, Marwa Q. Mohammed, Hayder Dhafer College of Science Mustansiriyah University Department of Computer Science Baghdad Iraq College of Pharmacy Mustansiriyah University Clinical Laboratory Science Baghdad Iraq Ministry of Science and Technology Baghdad Iraq Szechenyi Istvan Egyetem Department of Computer Science Gyor9026 Hungary University of Thi-Qar Department of Computer Science Thi-Qar Iraq University Putra Malaysia Dep. of Wireless Communication and Network Engineering Selangor Malaysia National Engineering School of Sfax Sfax University Sfax Tunisia College of Pharmacy Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9798350374131

The Internet of Things (loT) facilitates the interconnection of a vast array of devices, services, and individuals for data exchange in multi-domain loT ecosystems, the social, physical, and cyber domains. That emerges various security, privacy, and trust issues, such as vulnerabilities, threats, and attacks. Addressing these challenges requires innovative solutions. New controls (solutions) are needed. Trust by design can be one of these controls, supporting security and privacy (among other system components). This paper proposes a Trusted loT (TloT), a trust-by-design model, and highlights the author's principal contributions. First, it advocates for implementing Tlo T through trust services. Second, it outlines an architecture for using trust by design in loT, including the design of its top-level structure and activities. Minor contributions include overviews of loT and trust, as well as looking at security and privacy in terms of security services and privacy services, respectively. © 2024 IEEE.

关键词： Internet of things

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Implementation and Design of Physical/Digital Switches Modified from Sonoff Product Based on Internet of Things 4

Implementation and Design of Physical/Digital Switches Modif...

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4th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Tsao, Yung-Chung Kuo, Yaw-Wen Wu, Chia-Chun Tsai, Yin-Te Hsu, Chihcheng Li, Chiyang Wang, Han-Ching National Chi Nan University Dep. of Electrical Engineering Nantou County Taiwan National Quemoy University Dep. of Industrial Engineering and Management Kinmen County Taiwan Providence University Dep. of Computer Science and Communication Engineering Taichung City Taiwan National Central University Dep. of Information Management Taoyuan Country Taiwan College of Science and Technology National Chi Nan University Nantou County Taiwan Xinhe Elementary School Xindian District New Taipei City Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

While more home appliances are designed and manufactured, Sonoff serial home assistant alliances are designed and manufactured by eWeLink for many cheap and convenient devices controlled with exclusive APPs. Sonoff home assistant alliances are popular and well-known to attract many developers who can develop unofficial software to enhance the closed APPs designed by eWeLink and make flexible applications based on original hardware such as Tasmota and parameterized open-source APPs. We follow the above-mentioned developers to design a firmware that controls and is controlled based on message queue telemetry transport (MQTT) protocol from a cloud platform and the internet of thing (IoT) based architecture. The Sonoff home assistant alliances installed by the designed firmware can be controlled with one or more groups through a clouding platform by existing physical power-sockets from one-to-one controlling by eWeLink APPs. Under the proposed architecture, those Sonoff serial home assistant alliances are easily used like traditional power sockets for old-age users, who only use physical switches with touch-panel with MCU-based controllers to control power sockets. We designed digital switches on the website by browsers and physical touch panels as traditional switches work indep.ndently and coexist to meet most users with software-based APPs. The proposed architecture can be implemented and dep.oyed as a proof of concept (PoC) for the usability and validity. © 2022 IEEE.

关键词： Internet of things

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AI-driven Risk Assessment and Decision-making in Healthcare Applications Using Machine Learning: A Case Study on Customer Twitter Feeds

AI-driven Risk Assessment and Decision-making in Healthcare ...

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Computational Intelligence and Network Systems (CINS), International Conference on

作者： Abdelaziz Darwiesh Mohamed Elhoseny A.H. El-Baz Reem Atassi Mathematics Dep. Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. Faculty of Computing and Info. University of Sharjah Sharjah UAE Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt Higher Colleges of Technology Dubai UAE

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

This study presents an AI-driven strategy for enhancing decision-making in healthcare applications by assessing and managing potential risks through the analysis of customer interactions on social media platforms, specifically Twitter. We propose the use of machine learning models to outperform traditional lexical approaches, addressing the complexities inherent in risk detection. Natural language processing methods are employed to efficiently process and analyze textual data, enabling healthcare institutions to make informed decisions about risk management. By leveraging customer feedback from the X platform, our approach aids in the development of effective healthcare programs by identifying and classifying risks. We create three specialized datasets for risk analysis, risk assessment, and hazard classification to optimize the training and dep.oyment phases. In our case study, which focuses on tweets related to healthcare for US consumers, we find that over 50% of the sample identifies potential risks, with nearly 80% indicating medium levels of risk. Commonly identified risks include financial, operational, and strategic threats. The proposed methodology demonstrates an average accuracy of 82.66% in risk detection, identification, and assessment, confirming its effectiveness in supporting AI-driven decision-making in healthcare application development.

关键词： Training Accuracy Social networking (online) Computational modeling Decision making Blogs Medical services Natural language processing Complexity theory Risk management

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