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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Jeyakodi, G. Bala, P. Shanthi Department of Computer Science School of Engineering and Technology Pondicherry University Puducherry India

ISBN: (纸本)9783031736162

Pre-trained Language Model facilitates contextual relation classification by capturing contextual information, addressing word ambiguity, encoding global sentence context, enabling transfer learning, handling out-of-vocabulary words, and improving performance with limited labelled data. Existing pre-training approaches suffer in size, bias, interpretability, generalization, and the lack of domain specificity. To address this, HyPRETo, the hybrid model that combines the strength of token replacement and dynamic masking is proposed to achieve upgraded performance to increase classification accuracy. The Mosquito Vector Biocontrol Agents data is used for implementing the model for a contextual relation classification task. HyPRETo uses ontology to provide structured knowledge. HyPRETo is pre-trained by ELECTRA and fine-tuned by RoBERTa models. Feedforward and softmax activation function is used for classification. The Natural Language Processing technique and SQL database are used to develop an automated question-answering system. The HyPRETo was evaluated with state-of-art models and achieved 98.42% accuracy. As a contribution, the manually annotated input dataset on the mosquito vector control agent is prepared for the classification task. Subsequently, the enhanced model is developed. The interface for an automated question-answering system for mosquito vector biocontrol agents is developed to assist public health applications such as mosquito vector control, disease control, ecosystem management, environmental conservation, and so on. © IFIP International Federation for Information Processing 2025.

关键词： Vectors

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Virtual Learning Machine for Tiny Devices 20th

Virtual Learning Machine for Tiny Devices

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Kitagawa, Nozomi Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Recent developments in artificial intelligence (AI) have increased the demand for high-performance computational devices. However, edge devices are highly restricted in terms of computational power and memory capacity. To address this, a learning engine called swap kernel regression (SKR) [Masaharu et al. 2019] was developed that enables both inference and learning using an edge device. The key feature of SKR is ability to extend beyond the real physical capacity by collaborating with a second storage device. SKR thereby enables the virtual construction of a large-scale learning machine on a tiny device. We developed an improved version of SKR that maximizes performance by solving the thrashing problem and further reduces the computational cost. Using this improved version, we aim to achieve few-shot class-incremental learning (FSCIL), which continuously adapts to new tasks with limited samples, envisioned for on-site implementation. The proposed method uses a kernel machine that operates in a restricted environment in collaboration with a secondary storage system. The kernel parameters, which are not essential for calculating the output values for upcoming inputs, are stored in secondary storage to create a space in the main memory. The essential kernel parameters stored in secondary storage are loaded into main memory when required. Using this strategy, the system can realize recognition or regression tasks without compromising its generalization capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Predicting the Early Risk of Heart Disease using MABC with Self-Adaptive Bayesian Technique based on Lifestyle Factors 7

Predicting the Early Risk of Heart Disease using MABC with S...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Sudhakar, K. Mahaveerakannan, R. Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350340600

A heart attack occurs when the veins and arteries that provide blood to muscle get blocked. The hazards and obligations of modern life are shared equally by people of both sexes. When people have a heart attack, they often don't know what to do, so they put off getting help, which can make the injury worse and, in most cases, proves fatal. Medical diagnosis using data mining techniques has been explored by a number of researchers with promising outcomes. Different approaches can either predict a single disease or a large range of diseases. Improved health prediction is possible as well. This article discussed in depth the many methods of data categorization that are currently in use. In the field of IT, data categorization is common yet computationally demanding. In order to solve optimisation problems, metaheuristics are widely used. Metaheuristic methods can be used to increase a model's precision in computation. Artificial intelligence is widely used for early illness detection, severity appraisal, and prediction. To address this issue, our IoT platform employs an Optimised Self-Adaptive Bayesian algorithm (OSABA) to deliver more accurate diagnoses of HD. The MSABA is applied to the collected sensor data in order to ascertain its normalcy or abnormality. Patients, health care budgets, and disease progression can all benefit from this strategy. This research makes an effort to categorise and forecast human cardiac disease by using machine learning and metaheuristics. We may summarise the usefulness of the proposed OSABA by comparing it to already-existing models. Results metrics demonstrate the superiority of the proposed OSABA-based prediction system. Following the recommended procedure, we show that the OSABA outperforms state-of-the-art classifiers on the fullest datasets. © 2023 IEEE.

关键词： Diseases

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IDCE:Integrated Data Compression and Encryption for Enhanced Security and Efficiency

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computer Modeling in engineering & Sciences 2025年第4期143卷 1029-1048页

作者： Muhammad Usama Arshad Aziz Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev Department of Cyber Security Pakistan Navy Engineering CollegeNational University of Sciences and TechnologyKarachi75350Pakistan Department of Computer Science Main CampusIqra UniversityKarachi75500Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100170Uzbekistan

Data compression plays a vital role in datamanagement and information theory by reducing ***,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and *** the exponential growth of digital data,robust security measures are *** encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key *** their individual benefits,both require significant computational ***,performing them separately for the same data increases complexity and processing *** the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and *** on 128-bit block sizes and a 256-bit secret key *** combines Huffman coding for compression and a Tent map for ***,an iterative Arnold cat map further enhances cryptographic confusion *** analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.

关键词： Chaotic maps security data compression data encryption integrated compression and encryption

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Object Detection in Shelf Image of the Retail store using Single Stage Detector 2

Object Detection in Shelf Image of the Retail store using Si...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Bagyammal, T. Mahesh Madhav, K. Dhiraj Kumar, Y. Arthi Sri, N. Siva Srinivas, V. Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354461

The primary objective is to develop a robust system for precise object identification on retail shelves, accurate item counting, and product categorization through class detection. This multifaceted approach directly tackles critical issues in retail industry, encompassing inventory management and optimized product placement. The ultimate goal is to boost operational efficiency and elevate customer satisfaction within the retail sector. The proposed methodology involves comprehensive steps, including data collection, annotation, pre-processing, and the deployment of advanced object detection models based on YOLOv5, with a particular emphasis on YOLO due to its real-time capabilities and being known as a single stage detector. By achieving these milestones, the proposed model promises to provide valuable solutions for the retail industry, reducing out-of-stock situations, minimizing manual labour, and delivering enhanced product organization. Thus, ensuring good performance compared to its other object detectors of the same use case. We have modified the architecture at different levels of the State-of-the-art YOLOv5 (Backbone, Head) and compared the models implemented for inference. We found out that Soft-IoU layer added in the head has given the best performance in detecting object with mean average precision of 79.7 © 2024 IEEE.

关键词： Customer satisfaction

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Multi-Class SVM & Random Forest Based Intrusion Detection Using UNSW-NB15 Dataset 15

Multi-Class SVM & Random Forest Based Intrusion Detection Us...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jose, Anish Mathew Mukherjee, Avirup Saha, Joydeep Bhaumik, Anubhab Datta, Kamalesh Patra, Radhashyam Behura, Aradhana KIIT University BBSR School of Computer Engineering Odisha India VSSUT Department of Electronics and Telecommunication Engineering Sambalpur Odisha Burla India Universit BBSR School of Computer Engineering Odisha India

ISBN: (纸本)9798350370249

Intrusion Detection or malicious node detection (IDS) show a important role in safeguarding computer networks system from security threats. However, traditional IDS methods often struggle with high false positive rates and limited detection accuracy. This paper presents an advanced IDS that harnesses the power of a multi-class support vector machine (SVM) and integrates random forest technique for feature selection, specifically targeting the UNSW-NB15 dataset. The motivation behind this research is to enhance IDS performance by leveraging robust machine learning techniques. The research problem centers on improving the accuracy and efficiency of detecting various network attacks while reducing false positives. Our approach involves meticulously preprocessing network traffic data to extract relevant features, utilizing random forest to identify the most informative ones, and then employing these features to train a multi-class SVM classifier. This classifier effectively categorizes network traffic into different attack types. The results demonstrate a significant improvement in prediction accuracy and a decrease in rate of false positives, thereby enhancing the overall effectiveness of IDS in protecting computer networks against security threats. © 2024 IEEE.

关键词： Network intrusion

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Survey on Asclepius: A User-Centric Chatbot Bridging the Gap in Healthcare Integrated with VPN Technology 4

Survey on Asclepius: A User-Centric Chatbot Bridging the Gap...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shukla, Garima Sharma, Dolly Awasthi, Vanshaj Shekhawat, Deepika Shukla, Pavan Kumar Saranya, A. Amity University Mumbai Amity School of Engineering and Technology Department of Computer Science and Engineering Maharashtra India Graphic Era Hill University School of Computing Haldwani India Amity University Mumbai Department of Computer Science and Engineering Mumbai India Noida Institure of Engineering and Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350364729

This groundbreaking paper introduces Asclepius, a revolutionary software solution aimed at transforming early healthcare assistance through the seamless integration of cutting-edge technologies. At its core lies a sophisticated chatbot system, augmented with machine learning capabilities, facilitating swift and efficient connections between users and local medical professionals. With a strong emphasis on user anonymity and security, particularly concerning sensitive mental health concerns, Asclepius ensures utmost privacy throughout the interaction process. Constructed with meticulous attention to healthcare technology and security, Asclepius has been developed following extensive research across various domains, incorporating insights from acclaimed research articles to optimize and build an effective model. Central to its functionality is the utilization of state-of-the-art facial recognition technology and secure authentication protocols, instilling confidence in the system's reliability by enabling accurate user verification upon login. As of the publishing date, Asclepius has an 89% accuracy for its diagnoses by making use of a Supervised Decision tree-based Machine Learning Model. Through the incorporation of integrated virtual private network (VPN) technology, the project further establishes a secure communication channel between users and healthcare professionals, guaranteeing the protection of sensitive information and fostering a safe, confidential environment for seeking assistance. By seamlessly integrating a vast array of advanced technologies, Asclepius provides users with a comprehensive and user-friendly interface, prioritizing convenience and security in healthcare interactions and heralding a transformative shift in the landscape of early healthcare assistance. © 2024 IEEE.

关键词： Diagnosis

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Timestamp Based OTP and Enhanced RSA Key Exchange Scheme with SIT Encryption to Secure IoT Devices

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Journal of Cyber Security and Mobility 2023年第1期12卷 77-102页

作者： Kollipara, V.N. Hemanth Kalakota, Sai Koushik Chamarthi, Sujith Ramani, S. Malik, Preeti Karuppiah, Marimuthu School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering & Information Science Presidency University Karnataka Bengaluru560064 India

The Internet of Things (IoT) has become an emerging technology and is expected to connect billions of more devices to the internet in the near future. With time, more and more devices like wearables, intelligent home systems, and industrial automation devices are getting connected to the internet. IoT devices primarily transfer data using wireless communication networks, introducing more vulnerabilities like man-in-the-middle-attacks and eavesdropping. These security concerns are customary for any device communicating over the internet because of its intrinsic open nature. These problems are usually subdued by conventional cryptographic algorithms used in typical systems that are power-hungry and computationally intensive, making them infeasible to be used in IoT devices since they run on lowpowered chips, limiting performance, memory, and bandwidth. Hence, there is a requirement to adopt lightweight cryptographic algorithms that can abate the security issues while using low computational resources, which is the constraint in the given scenario. Hence, we propose an end-to-end secured IoT system that ensures the system's integrity is never compromised using lightweight cryptographic algorithms. We propose a three-module system, where the first module handles user authentication using a time-based one-time password, the second secures communication using lightweight enhanced RSA, and the third performs data encryption using Feistel-based enhanced SIT. This kind of system is designed to deal with security challenges in IoT devices, ensuring adequate data security while reducing the computational footprint using lightweight cryptography. © 2023 River Publishers.

关键词： Authentication

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Evaluation of Resonant Modes in Magneto-dielectric Circular Patch Antenna 10

Evaluation of Resonant Modes in Magneto-dielectric Circular ...

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10th IEEE Asia-Pacific Conference on Applied Electromagnetics, APACE 2024

作者： Asthan, Rheyuniarto Sahlendar Prasetyo, Agus Dwi Juhana, Tutun Munir, Achmad School of Electrical Engineering and Informatics ITB Radio Telecommunication and Microwave Laboratory Bandung Lampung Indonesia ITERA Department of Electrical Engineering Bandung Lampung Indonesia Telkom University School of Electrical Engineering Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Telematics Laboratory Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Radio Telecommunication and Microwave Laboratory Bandung Indonesia

ISBN: (纸本)9798350364163

This study evaluates the resonant modes of a magneto-dielectric (MD) circular patch antenna incorporated by a ferrite material without external magnetic bias. The ferrite material used is Yttrium Iron Garnet (YIG), which has a circular shape with the dimensions of 18.1 mm in diameter and 4.7 mm in thickness. This ferrite material is incorporated in the host dielectric substrate to realize an MD circular patch antenna. Meanwhile, the host dielectric material employs an FR-4 epoxy substrate with the thickness of 4.8 mm. The results shows that the MD circular patch antenna operates at the resonant frequency of 4.67 GHz, shifted from the fundamental frequency of 4.34 GHz, which is observed in the conventional circular patch antenna. Investigation on the distributions of electric field and surface current density indicates that the resonant frequency of MD circular patch antenna is closely corresponded to higher-order resonant modes. © 2024 IEEE.

关键词： Microstrip antennas

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