https://reunir.unir.net/handle/123456789/17343 Thu, 31 Oct 2024 06:49:34 GMT 2024-10-31T06:49:34Z https://reunir.unir.net/handle/123456789/17347 Editor’s Note Mu-Yen, Chen; C. K. Hung, Patrick With (EC) the rise of global economy and Electronic Commerce (EC), efficient inter-organizational planning and deployment for value chain processes have become important. Radio-frequency Identification (RFID), Near Field Communication (NFC), and related wireless technologies are evaluated to be some of the most significant technological innovations in the twenty-first century. In the past few years, wireless and context-awareness technology have led to much hope and optimism. The mainstream press hails these innovations as the avant-garde in technology and business. The Internet of Everything (IoE) goal is the intelligent connection of people, process, data, and things. The IoE describes a world where billions of objects have sensors to detect, measure, and assess their status, all connected over public or private networks using standard and proprietary protocols. Hence, this special issue investigates the state-of-art AI and deep learning approaches for successful systems or applications in the IoE environment. In addition, this special issue also wants to understand the direct and indirect effects of using these smart technologies to build language information processing based on the Web of Things (WoT) around the smart cities and societies. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-04T07:42:11Z No. of bitstreams: 1 Editorial.pdf: 57324 bytes, checksum: 5223830d24ee8c02a8626180dc5aec60 (MD5); Made available in DSpace on 2024-09-04T07:42:11Z (GMT). No. of bitstreams: 1 Editorial.pdf: 57324 bytes, checksum: 5223830d24ee8c02a8626180dc5aec60 (MD5) https://reunir.unir.net/handle/123456789/17347 https://reunir.unir.net/handle/123456789/17346 Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation Acarer, Tayfun Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-03T15:34:28Z No. of bitstreams: 1 Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation.pdf: 1136398 bytes, checksum: c439f2ab4117c44a0ceb71f6494fcd27 (MD5); Made available in DSpace on 2024-09-03T15:34:28Z (GMT). No. of bitstreams: 1 Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks for Safer Maritime Transportation.pdf: 1136398 bytes, checksum: c439f2ab4117c44a0ceb71f6494fcd27 (MD5) https://reunir.unir.net/handle/123456789/17346 https://reunir.unir.net/handle/123456789/17345 Predicting Consumer Electronics E-Commerce: Technology Acceptance Model and Logistics Service Quality Wu, Cheng-Feng; Zhang, Kunkun; Lin, Meng-Chen; Chiou, Chei-Chang In online shopping for consumer electronics, information and physical flows are crucial determinants of consumer purchase intentions. This study examines these factors by integrating the Technology Acceptance Model with logistics service quality, analyzing the relationship between retailers and consumers in e-commerce. The focus is on how information and physical flows, as critical supply chain elements, affect consumers' decisions to purchase online. A structural model and machine learning algorithm with SHapley Additive exPlanations are employed to analyze the data, providing a comprehensive analysis of the Technology Acceptance Model in conjunction with logistics service quality. The findings reveal that attitude, perceived usefulness, and informativeness are the most influential factors affecting consumers' purchase intention. This study contributes to the understanding of consumer behavior in the context of e-commerce platforms for consumer electronic products by integrating the Technology Acceptance Model and logistics service quality theoretical perspectives and analyzing the data using innovative techniques, specifically, Shapley Additive Explanations. This research offers valuable insights into the significant role of various features in shaping consumers' purchase intention in the context of online e-commerce platforms for consumer electrical products. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-03T15:20:07Z No. of bitstreams: 1 Predicting Consumer Electronics E-Commerce Technology Acceptance Model and Logistics Service Quality.pdf: 693126 bytes, checksum: 87919cc7e704958addc421323ece5da9 (MD5); Made available in DSpace on 2024-09-03T15:20:07Z (GMT). No. of bitstreams: 1 Predicting Consumer Electronics E-Commerce Technology Acceptance Model and Logistics Service Quality.pdf: 693126 bytes, checksum: 87919cc7e704958addc421323ece5da9 (MD5) https://reunir.unir.net/handle/123456789/17345 https://reunir.unir.net/handle/123456789/17344 Design of Traffic Electronic Information Signal Acquisition System Based on Internet of Things Technology and Artificial Intelligence Hongling, Wang This study aims to devise a traffic electronic information signal acquisition system employing Internet of Things and artificial intelligence technologies, offering a novel approach to address prevailing challenges related to traffic congestion and safety. Initially, the hardware circuit for the high-speed signal acquisition control core is developed, leveraging Field-Programmable Gate Array technology. This facilitates wireless monitoring of signal acquisition. Subsequently, a comprehensive time signal acquisition system is formulated, encompassing modules for communication, acquisition, storage, adaptive measurement, and signal analysis. The geomagnetic acquisition module within this system is utilized for collecting geomagnetic signals, which are then translated into switch signals indicating the presence or absence of vehicles. These signals are subsequently transmitted to the geomagnetic signal processor. Experimental results pertaining to the signal acquisition system reveal a notable peak storage speed of 200KB/s, considering the utilization of one million sampling points. Across a series of tests, the maximum relative error of the obtained results ranges from 2.2% to 2.7%, underscoring the consistency and reliability of the measurements. In comparison to existing testing devices, the system exhibits heightened accuracy in test results, rendering it more apt for traffic signal acquisition applications. In conclusion, this study accomplishes the collection and dissemination of diverse traffic information, furnishing robust support for traffic control and ensuring safe operations. Submitted by Eva María Arévalo Cid (evamaria.arevalo@unir.net) on 2024-09-03T15:01:46Z No. of bitstreams: 1 Design of Traffic Electronic Information Signal Acquisition System Based on Internet of Things Technology and Artificial Intelligence.pdf: 413839 bytes, checksum: 60a21bee29bd7ed62a03a51d3a79749c (MD5); Made available in DSpace on 2024-09-03T15:01:46Z (GMT). No. of bitstreams: 1 Design of Traffic Electronic Information Signal Acquisition System Based on Internet of Things Technology and Artificial Intelligence.pdf: 413839 bytes, checksum: 60a21bee29bd7ed62a03a51d3a79749c (MD5) https://reunir.unir.net/handle/123456789/17344 https://reunir.unir.net/handle/123456789/17202 Semi-Supervised Machine Learning Approaches for Thyroid Disease Prediction and its Integration With the Internet of Everything Agraz, Melih Thyroid disorders are critical conditions that considerably affect a person’s general health, and may lead to additional health complications. Notably, these conditions often remain undetected in individuals who show "normal" results on traditional thyroid function tests. To enhance the diagnostic accuracy for thyroid disorders, such as hypothyroidism and hyperthyroidism, this study leveraged digital health records and explored semisupervised learning methods. We intentionally removed the labels from subjects initially categorized as "normal," incorporating them into our dataset as unlabeled data. The goal was to overcome the limitations of conventional diagnostic techniques, which may fail to detect subtle imbalances in thyroid hormones. In pursuit of this objective, we employed a combination of semi-supervised learning methods, namely FixMatch, Co-training, and self-training, in conjunction with supervised learning algorithms, specifically Naive Bayes and logistic regression. Our findings indicate that the FixMatch algorithm surpassed traditional supervised learning methods in various metrics, including accuracy (0.9054), sensitivity (0.9494), negative predictive value (0.9365), and F1 score (0.9146). Additionally, we propose a framework for integrating these diagnostic tools into the Internet of Everything (IoE) to promote early detection and facilitate improved healthcare outcomes. This research highlights the potential of semi-supervised learning techniques in the diagnosis of thyroid disorders and offers a roadmap for harnessing the IoE in healthcare advancement. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T15:34:48Z No. of bitstreams: 1 Semi-Supervised Machine Learning Approaches.pdf: 1416476 bytes, checksum: a99c331898f8c23044ac4ad09d770bf0 (MD5); Made available in DSpace on 2024-08-07T15:34:48Z (GMT). No. of bitstreams: 1 Semi-Supervised Machine Learning Approaches.pdf: 1416476 bytes, checksum: a99c331898f8c23044ac4ad09d770bf0 (MD5) https://reunir.unir.net/handle/123456789/17202 https://reunir.unir.net/handle/123456789/17201 Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining Yan, Lou; Ren, Zhipeng; Zhang, Yong; Tao, Zhonghui; Zhao, Yizu This research endeavors to address the persistent dissemination of public opinion within social networks, mitigate the propagation of inappropriate content on these platforms, and enhance the overall service quality of social networks. To achieve these objectives, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques are employed in this research to develop a predictive model for anticipating public opinion crises in social network mining. This model furnishes users with a valuable reference for subsequent decisionmaking processes. The initial phase of this research involves the collection of user behavior data from social networks using IoT technologies, serving as the basis for extensive big data analysis and neural network research. Subsequently, a social network text categorization model is constructed by amalgamating the Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture, elucidating the training procedures of deep learning methodologies within CNN and LSTM networks. The effectiveness of this approach is subsequently validated through comparisons with other deep learning techniques. Based on the obtained results and findings, the CNN-LSTM model demonstrates a noteworthy accuracy rate of 92.19% and an exceptionally low loss value of 0.4075. Of particular significance is the classification accuracy of the CNN-LSTM algorithm within social network datasets, which surpasses that of alternative algorithms, including CNN (by 6.31%), LSTM (by 4.43%), RNN (by 3.51%), Transformer (by 40.29%), and Generative Adversarial Network (GAN) (by 4.49%). This underscores the effectiveness of the CNN-LSTM algorithm in the realm of social network text classification. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T15:29:05Z No. of bitstreams: 1 Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining.pdf: 3327316 bytes, checksum: 4dc82685d20fce25bac3f02ec4bed882 (MD5); Made available in DSpace on 2024-08-07T15:29:05Z (GMT). No. of bitstreams: 1 Constructing the Public Opinion Crisis Prediction Model Using CNN and LSTM Techniques Based on Social Network Mining.pdf: 3327316 bytes, checksum: 4dc82685d20fce25bac3f02ec4bed882 (MD5) https://reunir.unir.net/handle/123456789/17201 https://reunir.unir.net/handle/123456789/17200 The Human Motion Behavior Recognition by Deep Learning Approach and the Internet of Things Li, Hui; Liu, Huayang; Zhao, Wei; Liu, Hao This paper is dedicated to exploring the practical implementation of deep learning and Internet of Things (IoT) technology within systems designed for recognizing human motion behavior. It places a particular emphasis on evaluating performance in complex environments, aiming to mitigate challenges such as poor robustness and high computational workload encountered in conventional human motion behavior recognition approaches by employing Convolutional Neural Networks (CNN). The primary focus is on enhancing the performance of human motion behavior recognition systems for real-world scenarios, optimizing them for real-time accuracy, and enhancing their suitability for practical applications. Specifically, the paper investigates human motion behavior recognition using CNN, where the parameters of the CNN model are fine-tuned to improve recognition performance. The paper commences by delineating the process and methodology employed for human motion recognition, followed by an in-depth exploration of the CNN model's application in recognizing human motion behavior. To acquire data depicting human motion behavior in authentic settings, the Internet of Things (IoT) is utilized for extracting relevant information from the living environment. The dataset chosen for human motion behavior recognition is the Royal Institute of Technology (KTH) database. The analysis demonstrates that the network training loss function reaches a minimum value of 0.0001. Leveraging the trained CNN model, the recognition accuracy for human motion behavior achieves peak performance, registering an average accuracy of 94.41%. Notably, the recognition accuracy for static motion behavior generally exceeds that for dynamic motion behavior across different models. The CNN-based human motion behavior recognition method exhibits promising results in both static and dynamic behavior recognition scenarios. Furthermore, the paper advocates for the use of IoT in collecting human motion behavior data in real-world living environments, contributing to the advancement of human motion behavior recognition technology and its application in diverse domains such as intelligent surveillance and health management. The research findings carry significant implications for furthering the development of human motion behavior recognition technology and enhancing its applications in areas such as intelligent surveillance and health management. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T15:15:54Z No. of bitstreams: 1 The Human Motion Behavior Recognition.pdf: 4269382 bytes, checksum: f53f0f7796ab666535653b73a3c92f2a (MD5); Made available in DSpace on 2024-08-07T15:15:54Z (GMT). No. of bitstreams: 1 The Human Motion Behavior Recognition.pdf: 4269382 bytes, checksum: f53f0f7796ab666535653b73a3c92f2a (MD5) https://reunir.unir.net/handle/123456789/17200 https://reunir.unir.net/handle/123456789/17174 Enhancing Tennis Serve Scoring Efficiency: An AI Deep Learning Approach Liu, Jing-Wei The playing field of a tennis competition is a dynamic and complex formative environment given the following preliminary knowledge: (a) the basic technical, tactical, situational, and special types of shots used by the opponent; (b) the hitting area of the tennis player; (c) the place of service; (d) the ball drop position; and (d) batting efficiency and other related information that may improve the chances of victory. In this study, we propose an AI classification model for tennis serve scores. Using a deep learning algorithm, the model automatically tracks and classifies the serve scores of professional tennis players from video data. We first defined the players’ techniques, volleys, and placements of strokes and serves. Subsequently, we defined the referee's tennis terms and the voice in deciding on a serve score. Finally, we developed a deep learning model to automatically classify the serving position, landing position, and use of tennis techniques. The methodology was applied in the context of 10 matches played by Roger Federer and Rafael Nadal. The proposed deep learning algorithm achieved a 98.27% accuracy in the automatic classification of serve scores, revealing that Nadal outscored Federer by 2.1% in terms of serve-scoring efficiency. These results are expected to facilitate the automatic comparison and classification of shots in future studies, enabling coaches to adjust tactics in a timely manner and thereby improve the chances of winning. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-08-07T09:57:58Z No. of bitstreams: 1 Enhancing Tennis Serve Scoring Efficiency.pdf: 605618 bytes, checksum: 3d89479d4e4a17f81ca2de5e1e9469e8 (MD5); Made available in DSpace on 2024-08-07T09:57:58Z (GMT). No. of bitstreams: 1 Enhancing Tennis Serve Scoring Efficiency.pdf: 605618 bytes, checksum: 3d89479d4e4a17f81ca2de5e1e9469e8 (MD5) https://reunir.unir.net/handle/123456789/17174 https://reunir.unir.net/handle/123456789/16569 Evaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques Su, Yu-Sheng; Hu, Yu-Cheng; Wu, Yun-Chin; Lo, Ching-Teng Over the past decade, excessive groundwater extraction has been the leading cause of land subsidence in Taiwan's Chuoshui River Alluvial Fan (CRAF) area. To effectively manage and monitor groundwater resources, assessing the effects of varying seasonal groundwater extraction on groundwater levels is necessary. This study focuses on the CRAF in Taiwan. We applied three artificial intelligence techniques for three predictive models: multiple linear regression (MLR), support vector regression (SVR), and Long Short-Term Memory Networks (LSTM). Each prediction model evaluated the extraction rate, considering temporal and spatial correlations. The study aimed to predict groundwater level variations by comparing the results of different models. This study used groundwater level and extraction data from the CRAF area in Taiwan. The dataset we constructed was the input variable for predicting groundwater level variations. The experimental results show that the LSTM method is the most suitable and stable deep learning model for predicting groundwater level variations in the CRAF, Taiwan, followed by the SVR method and finally the MLR method. Additionally, when considering different distances and depths of pumping data at groundwater level monitoring stations, it was found that the Guosheng and Hexing groundwater level monitoring stations are best predicted using pumping data within a distance of 20 kilometers and a depth of 20 meters. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-05-13T16:01:02Z No. of bitstreams: 1 Evaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques.pdf: 2739489 bytes, checksum: 8b91534008eeb41e6c20dd05d89810a1 (MD5); Made available in DSpace on 2024-05-13T16:01:02Z (GMT). No. of bitstreams: 1 Evaluating the Impact of Pumping on Groundwater Level Prediction in the Chuoshui River Alluvial Fan Using Artificial Intelligence Techniques.pdf: 2739489 bytes, checksum: 8b91534008eeb41e6c20dd05d89810a1 (MD5) https://reunir.unir.net/handle/123456789/16569 https://reunir.unir.net/handle/123456789/16264 What Drives IoT-Based Smart Pet Appliances Usage Intention? The Perspective of the Unified Theory of Acceptance and Use of Technology Model Chen, Chia-Chen; Lin, Chia-Pei The advancement of IOT (Internet of Things) has facilitated the development of smart pet appliances, and the market for these products has growing rapidly, this study seeks to identify key factors for pet owner adoption of “smart” pet appliances. The Unified Theory of Acceptance and Use of Technology (UTAUT) a wellestablished model in the field of IOT research is used as the main framework, integrating brand trust, perceived value and perceived enjoyment as the basis for hypothesis formulation and testing based on data collected through questionnaires distributed through online social platforms. Reliability analysis, validity analysis and structural equation model analysis were carried out through confirmatory factor analysis to test the variables and research hypotheses. Results for the UTAUT indicate that effort expectancy has a direct impact on performance expectancy, while performance expectancy, effort expectancy and facilitating condition all have a positive impact on intention. While social influence does not directly or significantly affect use intention, it can indirectly affect intention through perceived value and perceived enjoyment. Brand trust does not have a significant impact on use intention, but can indirectly affect use intention through perceived value. This study further compares user age and number of smart pet home appliances owned to better understand the impact of demographic factors. Findings indicate that, for users under the age of 30, effort expectancy has no significant impact on use intention, while brand trust has no significant impact on perceived value among users over 30. Among the research results based on age as a basis, the impact of hardships in the ethnic group in the age of 30 is not significant, nor do facilitating conditions or perceived value have significant impact on use intention. For users with one smart pet device at home, neither favorable conditions not perceived value have significant impact on use intention, while for users with two smart pet devices, perceived enjoyment does not significantly impact use intention. These finding have potential reference value for future related research in the IOT or smart pet home appliance research field. Submitted by Andrea Sala (andrea.sala@unir.net) on 2024-04-04T09:47:52Z No. of bitstreams: 1 What Drives IoT-Based Smart Pet Appliances Usage Intention.pdf: 643943 bytes, checksum: efc27e1ab5afdf7df22a3f0a26d725b9 (MD5); Made available in DSpace on 2024-04-04T09:47:52Z (GMT). No. of bitstreams: 1 What Drives IoT-Based Smart Pet Appliances Usage Intention.pdf: 643943 bytes, checksum: efc27e1ab5afdf7df22a3f0a26d725b9 (MD5) https://reunir.unir.net/handle/123456789/16264