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7th IEEE International Conference on Electronic information and Communication technology, ICEICT 2024

作者： Hu, Yiting Feng, Botao Chung, Kwok L. Ding, Xiao Gu, Wenzhe Deng, Li Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Huizhou University School of Computer Science and Engineering Huizhou China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China

In this paper, we present a novel dual broadband antenna tailored for vehicular applications. The antenna design incorporates circular and square patches, metal cylinders, and conductive vias. Our simulated results de... 详细信息

ISBN: (纸本)9798350384437

关键词： Advanced driver assistance systems

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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain ...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

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Attack Detection in IoT using RF and ANN 4

Attack Detection in IoT using RF and ANN

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4th IEEE International Conference on Smart information Systems and Technologies, SIST 2024

作者： Adamova, Aigul Zhukabayeva, Tamara Adamov, Nurgalym Department of Computer Engineering Astana IT University Astana Kazakhstan Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Astana International University Astana Kazakhstan

ISBN: (纸本)9798350374865

Internet of Things (IoT) devices are the weak link in organizing a Wireless Sensor Network. Various Attacks on IoT devices can lead to different complex consequences. Real applications of the IoT generate a large amount of data every second, the confidentiality of which is of very high value. Therefore, detecting attacks in IoT interactions is of paramount interest from both science and industry. Among various attack detection approaches, machine learning methods show great potential due to their early detection ability. The paper presents a methodology for detecting attacks based on two machine learning methods: Random Forest (RF) and Artificial Neural Network (ANN). To conduct the experiment, various data sets were considered, the descriptions of which are given in the work. The experiment was carried out using open data sets obtained from real IoT devices. As a result, RF demonstrated a high accuracy of 98.6%. © 2024 IEEE.

关键词： Adversarial machine learning

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Development of Image Processing Incorporated Real-Time Smart Water Quality Monitoring System to Detect Algae and Debris for a Sustainable Environment 5

Development of Image Processing Incorporated Real-Time Smart...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Harischandra, Madusha Madhushan, Sanoj Dilruksha, Sachintha Kahatapitiya, Nipun Shantha Wijenayake, Udaya Silva, Bhagya Nathali Wijesinghe, Ruchire Eranga University of Sri Jayewardenepura Faculty of Technology Dept. of Materials and Mechanical Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Technology Dept. of Civil and Environmental Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Dept. of Computer Engineering Nugegoda Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Dept. of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Access to clean and safe drinking water is critical to human well-being while ensuring water quality remains an ongoing challenge. Despite the water board's best attempts at filtration, the potential for contamination remains due to factors such as inappropriate storage, deteriorating pipelines, or unintentional mixing during transit to houses. To address this issue, this study introduces an image processing incorporated real-time smart water quality Monitoring system to detect Algae and debris for a sustainable environment. This micro controller-based device consists of sensors, cameras, and image processing technologies to deliver real-time data for analysis. An object detection and localization model were trained using YOLO V5 architecture with 73.4% accuracy to assess the existence of Algae and debris. The developed user-friendly and accurate portable system will undoubtedly benefit the developing field of water quality monitoring and safeguarding public health. With further technological enhancements, the developed method will enable the assessment of numerous qualitative and quantitative parameters of water to contribute to environmental sustainability. © 2023 IEEE.

关键词： Potable water

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Advanced BERT and CNN-Based Computational Model for Phishing Detection in Enterprise Systems

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computer Modeling in engineering & Sciences 2024年第12期141卷 2165-2183页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon College of Business Administration Management DepartmentPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information ***’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing *** an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing *** approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.

关键词： Phishing BERT convolutional neural networks email security deep learning

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Pre-trained Self-Attention Framework: An Efficient Mechanism for Source Separation 13th

Pre-trained Self-Attention Framework: An Efficient Mechanism...

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13th International Conference on information technology and its Applications, CITA 2024

作者： Minh Tan, Ha Fhadli, Muhammad Kim Quoc, Nguyen Vu, Duc-Quang Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh City Viet Nam Department of Informatics Faculty of Engineering Khairun University Ternate City Indonesia Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam

ISBN: (纸本)9783031741265

In this work, the pre-trained model of the self-attention framework is proposed for single-channel speech separation. Firstly, all layers in the pre-trained self-attention framework are frozen. The model is then retrained through three stages using the scheduling mechanism for learning rates and the layers of the framework are unlocked following the schedule. This way, the model is relearned, enhanced, and updated from previous knowledge. This is an effective way to improve the advanced model performance while significantly reducing the time and cost of a training model. This method is beneficial in applying existing models to perform a similar task or enhancing model performance. In this strategy, the pre-trained system outperforms the non-pre-trained system since the following phases of the model’s training repurpose characteristics extracted through the previously trained early phases. The suggested method is tested and assessed on a conventional dataset. The findings from experiments suggest that the methodology has higher performance than the baseline framework and outperforms current methods for the monaural speech separation task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： fine-tuning framework pre-trained model self-attention mechanism speech separation time masking framework Transformer

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An Improved Technique for Preliminary Diagnosis of COVID-19 via Cough Audio Analysis 4th

An Improved Technique for Preliminary Diagnosis of COVID-1...

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4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021

作者： Pandhi, Tanya Kapoor, Teghdeep Gupta, Bharat Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida201309 India Faculty of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida201309 India

ISBN: (纸本)9783031070044

Humanity has suffered catastrophically due to the COVID-19 pandemic. One of the most reliable diagnoses of COVID-19 is RT-PCR (Reverse-Transcription Polymer Chain Reaction) testing. This method, however, has its limitations. It is time-consuming and requires scalability. This research work carries out a preliminary prognosis of COVID-19, which is scalable and less time-consuming. The research carried out a competitive analysis of four machine learning models namely, Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory, and VGG-19 with Support Vector Machines. Out of these models, Multilayer Perceptron outperformed with higher specificity of 94.5% and accuracy of 96.8%. The results show that Multilayer Perceptron was able to distinguish between positive and negative COVID-19 coughs by a robust feature embedding technique. © 2022, Springer Nature Switzerland AG.

关键词： Multilayer neural networks

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AI Bot to Increase the Accuracy and Efficiency of Hiring Process of Business Organizations

AI Bot to Increase the Accuracy and Efficiency of Hiring Pro...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Dilusha, N.H.A.S. Weerasinghe, E.J.K.D. Chamuditha, S.M.A. Wijekoon, K.W.M. Thelijjagoda, Samantha Giguruwa, Nishantha Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer System Engineering Malabe Sri Lanka College of Sustainability and Tourism Ritsumeikan Asia Pacific University Oita Prefecture Beppu Japan

ISBN: (纸本)9798331543617

Recruitment in most organizations is normally a rather inefficient process where it takes quite a long time to match the right candidate to a given job. In this research, AI-based hiring system is recommended to improve the efficiency and accuracy of the hiring process with the help of NLP and machine learning algorithms. Employment enabling processes such as CV filtering, skill evaluation and personality profiling as well as job matching are achieved by the system. An NLP-based tool is used for short listing of CVs, assessment quizzes are used to evaluate different skills and qualifications of the candidates and chatbot enables communication between candidates and HRs, while an AI bot is used to complete different document verifications' processes. Recruitment operation is made easier and efficient, and matching of candidates to the available jobs is done better since it is less time-consuming and energy-demanding, giving a solution that may be implemented on a large scale. Further improvements are to be made in the fine tuning of algorithms, enlargement of comprehensiveness of systems and the implementation of actual sample cases before the introduction into the marketplace. © 2024 IEEE.

关键词： Bot (Internet)

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Identification of selection criteria for blockchain platforms

Identification of selection criteria for blockchain platform...

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2022 International Conference on Frontiers of Communications, information System and Data Science, CISDS 2022

作者： Muradi, Shahr Banoo Kia Chiam, Yin Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665464468

Blockchain technology is widely used to develop software systems in different industries such as finance, healthcare, supply chain management, data management, Internet of Things (IoT). To adopt blockchain, some critical challenges such as performance, interoperability, availability, security attacks, sustainability, and latency need to be taken into consideration. If the blockchain platform selected is not appropriate, the functionality and quality of the blockchain-based systems will be affected. Additionally, numerous blockchain platforms with unique properties and specifications are available in the market, and the task of selecting the right platform that best meets the project needs is getting complicated. It is crucial to define significant criteria that can help to shortlist the blockchain platforms for further comparison and selection using multicriteria decision-making methods. This research proposes a set of selection criteria that cover both features and quality attributes of the blockchain platforms. A survey was conducted to get the opinions of blockchain practitioners on the suitability of the selection criteria for the evaluation and selection of blockchain platforms. © 2022 IEEE.

关键词： Blockchain

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Effective Density-Based Concept Drift Detection for Evolving Data Streams 24th

Effective Density-Based Concept Drift Detection for Evolving...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Cui, Zelin Tian, Hui Shen, Hong Institute of Information Security Engineering Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819982103

Concept drift is a common phenomenon appearing in evolving data streams of a wide range of applications including credit card fraud protection, weather forecast, network monitoring, etc. For online data streams it is difficult to determine a proper size of the sliding window for detection of concept drift, making the existing dataset-distance based algorithms not effective in application. In this paper, we propose a novel framework of Density-based Concept Drift Detection (DCDD) for detecting concept drifts in data streams using density-based clustering on a variable-size sliding window through dynamically adjusting the size of the sliding window. Our DCDD uses XGBoost (eXtreme Gradient Boosting) to predict the amount of data in the same concept and adjusts the size of the sliding window dynamically based on the collected information about concept drifting. To detect concept drift between two datasets, DCDD calculates the distance between the datasets using a new detection formula that considers the attribute of time as the weight for old data and calculates the distance between the data in the current sliding window and all data in the current concept rather than between two adjacent windows as used in the exiting work DCDA [2]. This yields an observable improvement on the detection accuracy and a significant improvement on the detection efficiency. Experimental results have shown that our framework detects the concept drift more accurately and efficiently than the existing work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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