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Resonant Beam Communications:A New Design Paradigm and Challenges

Journal of Communications and Information networks 2024年第1期9卷 80-87页

作者： Yuanming Tian Dongxu Li Chuan Huang Qingwen Liu Shengli Zhou Future Network of Intelligence Institute and School of Science and Engineering the Chinese University of Hong KongShenzhen 518172China School of Science and Engineering and the Future Network of Intelligence Institute the Chinese University of Hong KongShenzhen 518172China College of Electronics and Information Engineering Tongji UniversityShanghai 201804China Department of Electrical and Computer Engineering University of ConnecticutStorrsCT 06250USA

Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently,the performance of the proposed RBCom is evaluated and compared with that of visible light communications(VLC)and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.

关键词： resonant beam communications(RBCom) wireless optical communications(WOC) echo interference elimination mobility multiple access

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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Detect and Prevent URL Phishing through Li-Fi technology via FSO Channel 16

Detect and Prevent URL Phishing through Li-Fi technology via...

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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Ahmed, Mohammed M. Alnajjar, Satea H. Al-Iraqia University Department of Computer Engineering Baghdad Iraq Al-Iraqia University Department of Network Engineering Baghdad Iraq

ISBN: (纸本)9798331506896

Due to the pressing demand for advanced 6G and beyond communications technology that can effectively handle large-scale requirements with utmost security and efficiency. With the growth and spread of companies and institutions that deal with a large amount of data, protecting data and ensuring privacy has become a basic requirement. Actual reality and recent research have shown a significant increase in phishing scams. This research seeks to develop the security level of Li-Fi technology by detecting and preventing phishing activities. It is adopted to inspect URL links transmitted over the network before they reach the end user. The proposed system was tested using two-layer algorithms to detect and prevent hacking and phishing. The performance of the proposed system was examined on Li-Fi technology via FSO under different weather conditions. The proposed system has demonstrated ambitious results in implementing tasks successfully and effectively. © 2024 IEEE.

关键词： Phishing

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Proactive Phishing Defense: A URL Classification System Using Machine Learning 1

Proactive Phishing Defense: A URL Classification System Usin...

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1st Asian Conference on Communication and networks, ASIANComNet 2024

作者： Jawad, Samer Kadhim Alnajjar, Satea Hikmat Al-Iraqia University Department of Computer Engineering Baghdad Iraq Al-Iraqia University Department of Network Engineering Baghdad Iraq

ISBN: (纸本)9798350367003

Phishing attacks are the most common cyberattacks nowadays. Phishing attacks rely on social engineering concepts to trick victims into reaching the goals of malicious attackers. In addition, phishing attacks are the largest vector for various cyberattacks. However, URLs are a fulcrum for phishing attacks. The difficulty distinguishing between legitimate and phishing URLs is the reason for the increased success rates of these attacks. An integrated framework is proposed in this study to detect phishing attacks based on classifying URLs into phishing or legitimate URLs through machine learning models such as decision tree (DT) and random forest (RF), which have high power and prediction accuracy in binary classification tasks. The RF model, using the cross validation (CV) technique, achieved an accuracy score of 9 8.2. This methodology is embedded in a web application with a graphical user interface to provide ease of handling and show alerts in real time and visually. This contributes to providing the field of cybersecurity with a highly accurate verification system to reduce users falling victim to these dangerous attacks. © 2024 IEEE.

关键词： Phishing

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AlFloos: a balanced dataset for the sixth issue of the Saudi Arabian currency banknote*

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Journal of Umm Al-Qura University for engineering and Architecture 2024年第4期15卷 446-454页

作者： Bati, Ghassan F. Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

Artificial Intelligence, especially machine learning, is considered by many researchers the new electricity and an important building block of the fourth industrial revolution. Saudi Arabia is amongst the first nations worldwide to realize this importance and apply it in its school systems at various levels. This interest motivates researchers to create Arabic datasets for research and educational purposes, particularly with the popularity of English sources and the absence of Arabic sources. This study attempts to lessen this gap by creating for the first time ever a dataset for the sixth issue of the Saudi Arabian currency and analyzing it using the famous machine learning tools: Orange Data Mining, Google Teachable Machine, and ***, which require no coding. The importance of the study to the community is as follows: 1—It provides the first work ever to establish a balanced dataset for the sixth issue of the Saudi Arabian currency banknote, which contains images for the banknote denominations and the tabular data generated using deep learning. 2—It is the first scientific work that uses shallow machine learning and deep learning models to create good-performing models for classifying the sixth issue of the Saudi Arabian currency without coding. This work paves the way for researchers and those interested from various fields and backgrounds to develop machine learning applications to classify the sixth issue of the Saudi Arabian currency, especially in mobile phones or in microcontrollers, to inspire multiple IoT and Tiny machine learning applications like currency recognition and classification for special needs people, automatic counting and sorting of banknotes in banks and stores, and fake currency detection. © The Author(s) 2024.

关键词： Deep learning Image classification Image embedding Saudi Arabian Currency Classification Supervised machine learning

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Reinforcing Artificial Neural networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

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computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

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Method of Fundamental Solutions for Doubly Periodic Potential Problems 10th

Method of Fundamental Solutions for Doubly Periodic Potentia...

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10th International Conference on Mathematics and Computing, ICMC 2024

作者： Ogata, Hidenori Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Chofu182-8585 Japan

ISBN: (纸本)9789819720651

In this paper, we propose a method of fundamental solution for two-dimensional doubly periodic problems, especially potential flow problems with a doubly periodic array of obstacles. In the proposed method, we approximate the solution, which involves doubly periodic functions, by a linear combination of the logarithmic potentials consisting of the theta functions. The method inherits the efficiency of the ordinary method of fundamental solutions and gives an approximate solution that has the same periodicity as the one of the exact solution. Numerical examples show the efficiency of the presented method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Efficiency

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ENHANCING HEALTH CARE THROUGH REMOTE SERVICES AND INTERACTIVE TECHNOLOGIES 9

ENHANCING HEALTH CARE THROUGH REMOTE SERVICES AND INTERACTIV...

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on computer Science and Information Systems 2024, MCCSIS 2024

作者： Lu, Ssu-Hsuan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan

ISBN: (纸本)9789898704597

With the aging population, health issues have become a significant challenge for society. The increasing demand for remote medical services stems from changing interests in reducing healthcare costs and the sudden outbreak of COVID-19 globally. As the Internet of Things (IoT) and cloud technology advance rapidly worldwide, this trend has driven the digital transformation of biotechnology and pharmaceutical-related industries. Health care is shifting from a focus on medical institutions to a decentralized approach, emphasizing care facilities and home-based services. However, remote medical care brings about certain challenges, such as limited appointment availability due to network constraints, concerns about internet security, data encryption, privacy issues, and lack of awareness among people in remote areas. With the continuous development and widespread adoption of technology, smart home systems have become an integral part of modern life. In this project, the system emphasizes interaction with the elderly and utilizes the multifunctionality of touch screens. It provides various features through a simple and user-friendly interface, including health monitoring, outpatient services, life assistance, and social interaction functions. In the future, the system can incorporate more interactive features for the elderly, such as mini-games to enhance cognitive abilities, hand-eye coordination, or rehabilitation training. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Touch screens

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Image Recognition Technology Supported Environment Monitoring Smart Fan 7

Image Recognition Technology Supported Environment Monitorin...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Yang, Sheng-Yuan Lee, Hsiu-Chia Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan Taiwan

ISBN: (纸本)9798350307207

Owing to the development of the Internet of Things (IoT), smart electrical appliances bring convenience to life. However, smart home appliances are too cumbersome as their interface operations are mostly not intuitive with less practicability than expected. Taiwan has an island climate, and the summer weather is scorching. An easy-to-use and practical electric fan is indispensable for cooling off the heat, which is the research object of this study to make users feel comfortable. In this study, image recognition technology was introduced. The user turns on the fan manually or through the mobile APP, and selects the tracking mode through the mode function. The camera with the fan detects the user's position and then starts to track the user. The device is equipped with a smoke sensor and a camera to monitor the user's home. The fan proves the feasibility of the conceptual architecture. © 2024 IEEE.

关键词： Image recognition

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A Resource-Efficient Multi-core Multi-thread RISC-V-based System-on-Chip 21

A Resource-Efficient Multi-core Multi-thread RISC-V-based Sy...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Kieu-Do-Nguyen, Binh Nguyen, Khai-Duy Binh, Nguyen The Dang, Tuan-Kiet Le, Duc-Hung Pham-Quoc, Cuong Tran, Ngoc-Thinh Pham, Cong-Kha Hoang, Trong-Thuc Department of Computer and Network Engineering Tokyo Japan Faculty of Computer Science and Engineering Hochiminh Viet Nam Faculty of Electronics and Telecommunications Hochiminh Viet Nam

ISBN: (纸本)9798350377088

In the past, a small size microcontroller was needed to control and gather data from resource-constrained electronic devices aimed at Internet of Things (IoT) applications. While the server nodes receive the requests with a high demand for calculation. However, to minimize network traffic transactions and the overall workload that the server node must handle, a portion of these operations must now be completed locally. Additionally, multiple tasks must be processed simultaneously by IoT devices. In this study, we suggested a multi-core, multi-thread enable System-on-Chip (SoC) that consume small resources. The suggested architecture achieves a notable speed increase over single-core design by utilizing the advantages of RISC-V Instruction Set Architecture (ISA). © 2024 IEEE.

关键词： System-on-chip

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