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7th IEEE information technology and Mechatronics engineering Conference, ITOEC 2023

作者： Ma, Xiaolin Peng, Yi Yang, Qingqing Kunming University of Science and Technology College of Information Engineering and Automation Kunming China Kunming University of Science and Technology Yunnan Key Laboratory of Computer Science Kunming China

ISBN: (纸本)9798350334197

Aiming at the problem that the total throughput of secondary users is not maximized, considering the fairness between secondary users and the interference of primary users to secondary users, a power allocation strategy to maximize the throughput of CR-NOMA is proposed. Firstly, the elementary matrix is established according to the equivalent channel gain, and the secondary user is selected and multiplexed to the primary user sub-channel. Then, for the selected secondary users, the minimum transmission rate is dynamically adjusted based on fairness, and the optimal power scaling factor under the maximization of the total throughput of the secondary users is calculated. Finally, power distribution is performed. The simulation results show that the proposed algorithm can effectively reduce the complexity of the algorithm and maximize the total throughput of the secondary users compared with the HPPA and CR-OMA algorithms. © 2023 IEEE.

关键词： Cognitive radio

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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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An Overview of Deployment of information and Communications technology (ICT) in Disaster Risk Management

An Overview of Deployment of Information and Communications ...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Nachappa, M.N. Sharma, Himanshu Pal, Ritu Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350344387

Efficient disaster management necessitates the prompt and timely distribution of information to provide quick provision of emergency assistance to the affected population. The rapid growth of "information and communication technologies"has made it easy to acquire real-time information from many sources. The utilization of ICT in many phases of disaster management is being examined. The analysis includes an examination of current solutions as well as an investigation into the integration of developing ICT networks, services, and applications. Various evacuation techniques are being examined. Following a comparison of various ICT-based technologies, a model for evacuation is proposed. © 2023 IEEE.

关键词： Disaster Management Early warning systems ICT Remote sensing Risk

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LayOut Loud: An AI-Powered Augmented Reality and Mobile Application for Room Interior Design and Layout Optimization 4

LayOut Loud: An AI-Powered Augmented Reality and Mobile Appl...

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4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Cuevas, John Raphael G. Rili, John Kenneth R. Santillan, Anny Neolicia G. Agustin, Vivien A. Gonzales, Mary Grace N. Centeno, Criselle J. Regala, Richard C. College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Computer Science Manila Philippines Pamantasan ng Lungsod ng Maynila Faculty of College of Engineering and Technology Department of Information Technology Manila Philippines College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Department of Information Technology Manila Philippines Pamantasan ng Lungsod ng Maynila College of Engineering and Technology Department of Computer Science Manila Philippines

ISBN: (纸本)9798331506490

LayOut Loud is an AI-powered augmented reality (AR) and mobile application designed to revolutionize room interior design by offering tailored, real-time solutions for layout optimization. The primary objective of this tool is to design AI-powered features that analyze user-provided room dimensions, furniture types, and style preferences. By leveraging advanced algorithms, LayOut Loud generates personalized design suggestions that cater to the individual needs and aesthetic tastes of users, ensuring that each interior design experience is customized and unique. A key goal of the application is to enhance user experience and decision-making through a clustering algorithm. This algorithm categorizes furniture pieces based on their suitability and appearance in living spaces, streamlining the selection process for users. By enabling the visualization of these items in a 3D model, users can make informed decisions about their interior design choices, ensuring that each piece contributes to the overall aesthetic and functional harmony of the space. Additionally, LayOut Loud focuses on improving its AR capabilities by allowing users to capture their room layouts and view virtual furniture arrangements in real time. This feature provides practical, interactive design solutions, empowering users to experiment with different configurations and instantly see how potential changes would appear in their actual living environments. Ultimately, LayOut Loud offers a user-friendly, immersive platform that transforms the way individuals approach interior design. © 2024 IEEE.

关键词： Architectural design

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IOT and Machine Learning Based Real-Time Protection Against Cyber-Physical Threat Mitigation 4

IOT and Machine Learning Based Real-Time Protection Against ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saini, Harnit Mohammad, Faz Singh, Manoj Kumar Sharma, Kapil Kumar Shukla, Meenu Singh, Ramendra Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Ims Engineering College Department of Computer Science & Engineering Ghaziabad India Ims Engineering College Department of Mca Ghaziabad India Maharishi University of Information Technology School of Engineering and Technology Noida India Ghaziabad India

ISBN: (纸本)9798350360165

An investigation is carried out into the dynamic relationship between machine learning algorithms used in IoT security, with detailed scrutiny of performance indicators to give a fuller view of what happens. The study pays particular attention to metrics like accuracy, precision, recall, and F1 score when ten varied samples are being examined. As a result, all the algorithms exhibit excellent performance with 95.5 %-98.2 % accuracy values, inspiring high reliability under diverse scenarios. Precision and recall metrics also depict the models' capabilities. Balancing precision and recall can be observed in F1 scores that are between 95.2% and 98.0%, and these numbers show that the approach based on this understanding of trade-offs is characterized by its operationalization. At the other end, results obtained from performance metrics go beyond numerical benchmarks to give a holistic appreciation of the algorithms' strength and areas for possible enhancement. The paper also does not provide only a theoretical approach to machine learning related to security in the IoT environment but also provides information about security considerations for the successful deployment of powerful, adaptive models into real-life scenarios. In this case, findings of the research help in understanding how to make sure that smart and effective measures are implemented during the process of developing security solutions considering all related issues in connection with the IoT technology landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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science China(information sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Accurate detection of fresh and old vertebral compression fractures on CT images using ensemble YOLOR

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Multimedia Tools and Applications 2024年第41期83卷 89375-89391页

作者： Hsieh, Min-Hong Chang, Chuan-Yu Hsu, Shao-Min Department of Orthopedics Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation Chiayi Taiwan School of Medicine Tzu Chi University Hualien Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

Vertebral compression fractures resulting from osteoporosis contribute to pain and disability among older people, necessitating early detection and treatment. While MRI provides effective diagnosis, its higher cost poses a challenge. Conversely, CT imaging offers a more cost-effective alternative. However, CT-based detection of vertebral fractures may lack the accuracy of MRI. This paper presents a novel YOLO-based object detection method for localizing old and fresh fractures on spine CT images to expedite diagnosis and identify the optimal treatment window. We propose replacing the CSPDarknet53 backbone in the native YOLOR model with MobileViT and EfficientNet_NS, training three separate YOLOR models with different backbones. Subsequently, an ensemble approach is employed to leverage the enhanced feature extraction capabilities of the three models. Experimental results show individual model accuracies of 89%, 89.8%, and 89.2%, respectively. Furthermore, by replacing the convolution layer with the Involution layer and integrating the models using the ensemble method, we achieve a remarkable accuracy rate of 93.4%. The proposed Ensemble YOLOR model surpasses other state-of-the-art approaches, offering a fast and precise solution. This advancement provides valuable insights and guidance for physicians in diagnosing compression between old and new fractures. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： computerized tomography

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Risk Analysis of Android Applications using Static Permissions and Convolutional Neural Network 2023

Risk Analysis of Android Applications using Static Permissio...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Chauhan, Kshamta Gandotra, Ekta Department of Computer Science & Engineering Jaypee University of Information Technology Solan H.P. Waknaghat India

ISBN: (纸本)9798400700224

Today, Android is the most popular mobile platform for users, vendors, and developers. As the number of Android applications grows, the risk of malware on these devices also increases. Mobile apps do collect a tonne of information about users and their devices, however, not all of it is technically necessary for the operation of the app (e.g., user profiles and habits). App developers are required by privacy laws to disclose to consumers the data usage policies they have implemented (such as what data is gathered and why). Thus, they end up installing apps without realising how that may affect their privacy. In this paper, static permissions are used to detect the risk of the applications. A convolutional neural network is used to predict the probability of the benign and malware classes. The risk of the application is categorised into four parts: no risk, low risk, medium risk, and high risk. Additionally, a graphical user interface is created, upon which the Android app may be uploaded in order to determine its risk using the proposed approach. After the experimental results, the accuracy of the proposed model is 97.02%. © 2023 ACM.

关键词： Risk assessment

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The Performance Analysis of Distributed Denial of Service Attacks in Wireless Networks 2

The Performance Analysis of Distributed Denial of Service At...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Agrawal, Trapty Raghavendra, R. Singh, K. Ranjith Rajiv Gandhi, N. Dhabliya, Dharmesh Katariya, Jitendra Kumar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350308846

Complete studies paper that examines the outcomes that malicious 0.33-celebration actors will have on Wi-Fi networks via dispensed denial of carrier (DDoS) attacks. This paper explores the available numerous assault models and the potential countermeasures that may be applied if you want to mitigate the damage due to such assaults. In addition, this paper focuses on the capability blessings related to deploying preventative measures to lessen the impact of such assaults. Moreover, the paper investigates existing attack trends. It affords an outline of the safety techniques to be had for limiting the harm caused by DDoS attacks in wireless networks. By comparing and analyzing the performance of diverse protection mechanisms, this paper offers an evidenceprimarily based evaluation of the effectiveness of DDoS attack safety answers. ultimately, this paper concludes with the aid of supplying recommended measures for strengthening and ensuring the safety of Wi-Fi networks against malicious actors. © 2023 IEEE.

关键词： Evaluation Measures Performance Recommended Strengthening

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Leveraging a Randomized Key Matrix to Enhance the Security of Symmetric Substitution Ciphers

Leveraging a Randomized Key Matrix to Enhance the Security o...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Gandhi, Shubham Khare, Om Dravid, Mihika Sanghvi, Mihika Mane, Sunil Gajaralwar, Aadesh Gandhi, Saloni Coep Technological University Department of Computer Science and Engineering India BRACT's Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350341072

An innovative strategy to enhance the security of symmetric substitution ciphers is presented, through the implementation of a randomized key matrix suitable for various file formats, including but not limited to binary and text files. Despite their historical relevance, symmetric substitution ciphers have been limited by vulnerabilities to cryptanalytic methods like frequency analysis and known plaintext attacks. The aim of our research is to mitigate these vulnerabilities by employing a polyalphabetic substitution strategy that incorporates a distinct randomized key matrix. This matrix plays a pivotal role in generating a unique random key, comprising characters, encompassing both uppercase and lowercase letters, numeric, and special characters, to derive the corresponding ciphertext. The effectiveness of the proposed methodology in enhancing the security of conventional substitution methods for file encryption and decryption is supported by comprehensive testing and analysis, which encompass computational speed, frequency analysis, keyspace examination, Kasiski test, entropy analysis, and the utilization of a large language model. © 2023 IEEE.

关键词： Cryptography

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