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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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Enhancing Intrusion Prevention with Explainable Convolutional Neural Networks: Recent Developments and Promising Applications 8

Enhancing Intrusion Prevention with Explainable Convolutiona...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Chitnis, Ajay Chandre, Pankaj Pathan, Shafi DevOps Architect Hcl Technologies Uk Limited Pune India Mit Art Design & Technology University Mit School of Computing Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350396638

the proliferation of cyberattacks in recent years has led to an increased demand for effective intrusion prevention systems (IPS). Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy of IPS, but their lack of interpretability hinders their wider adoption. Explainable AI (XAI) techniques have emerged to address this issue, allowing users to understand the reasoning behind a model's predictions. this survey paper provides an overview of recent developments in enhancing IPS with XAI techniques in CNNs. this study reviews state-of-the-art research on integrating interpretability into CNN models, including methods such as attention mechanisms, saliency maps, and LIME. Also, this study discusses about the promising applications of CNN-based IPS with XAI, including network traffic analysis, malware detection, and anomaly detection. Finally, we highlight some of the challenges and opportunities for future research in this area, including the need for standardized evaluation metrics and the potential for combining XAI techniques with other AI methods such as reinforcement learning. Overall, this survey paper aims to provide a comprehensive understanding of the recent advances in using CNNs with XAI for enhancing IPS, and to identify promising directions for future research in this field. © 2023 ieee.

关键词： Convolutional neural networks

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An Interactive Multi-task Learning Model for Aspect-Based Sentiment Analysis 8

An Interactive Multi-task Learning Model for Aspect-Based Se...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Qian, Bing Wei, Rongting Wu, Yuexin Ma, Chong China Telecom Corporation Limited Beijing Research Institute Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665456128

Aspect-based sentiment analysis (ABSA) aims to analyze and understand the sentiment attitude of reviews at the aspect level, and it consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). How-ever, most of the current work only focuses on the sub task of aspect polarity classification, without considering both sub tasks in combination. Besides, existing studies have paid less attention to the Chinese-oriented ABSA task. To address these challenges, we present a complete solution for ABSA by jointly training two subtasks. Compared with the former methods, our model can extract aspect terms and predict aspect polarity simultaneously;moreover, the model has the capacity to process both English and Chinese data. In this paper, we also propose a novel Local Context Focus framework for the APC subtask, which can effectively capture the contextual information of the aspect and improve the accuracy of polarity prediction. By integrating a parameter-sharing RoBERTa layer, our model accomplished two tasks perfectly, and we named it as LCF-RoBERTa. Experimental results on three English benchmark datasets and four Chinese datasets demonstrated the effectiveness of our method, the LCF-RoBERTa approached the state-of-the-art results. © 2023 ieee.

关键词： Sentiment analysis

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BuzzSort: A Linear-Time, Event-Driven Data Conversion and Sorting Framework for Approximate Computing Architectures 8

BuzzSort: A Linear-Time, Event-Driven Data Conversion and So...

引用

8th ieee international conference on Rebooting Computing, ICRC 2023

作者： Bhattacharyya, Swagat Yang, Linhao Hasler, Jennifer O. Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States

ISBN: (纸本)9798350382044

Analog computational primitives, such as vector-matrix multipliers (VMMs), are foreseen to play a pivotal role in economizing computing;however, to improve the viability of general-purpose accelerators, there is a need for efficient data conversion and sorting during readout. this work introduces 'BuzzSort,' an event-driven framework that simultaneously converts and sorts data from analog systems. BuzzSort acquires analog data, retrieves sorting indices, and produces a sorted output vector in linear time. We experimentally demonstrate and characterize the efficacy of BuzzSort with a field-programmable analog array (FPAA) in a 350 nm process and a field-programmable gate array (FPGA). © 2023 ieee.

关键词： Sorting

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Misbehavior Detection in the Internet of Vehicles using Machine Learning based Situation Identification 8

Misbehavior Detection in the Internet of Vehicles using Mach...

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8th international conference on Communication and Electronics systems, ICCES 2023

作者： Kelagadi, Hemantaraj M. Chandra Sekhar, Jampani Rawat, Ruchira Dhaliwal, Navdeep Singh, Swati Saravana, B. Shanthi Kle Technological University School of Electronics and Communication Engineering Karnataka Hubballi India Nri Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Guntur India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Division of Research and Development Punjab Phagwara India Uttaranchal University Division of Research and Innovation Uttarakhand Dehradun India Adhiparasakthi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Kalavai India

ISBN: (纸本)9798350396638

To improve communications network efficiency, situation identification is essential. the requirement for real-time scenario identification of vehicular communications is growing more pressing provided a certain vehicle movement makes vehicle interaction circumstances vibrant and that there are significant variations in signal propagation, vehicle speeds, and vehicular concentrations among possible situations. On the other hand, it has been demonstrated that cryptography methods are less effective at preventing insider attacks that originate inside the automotive communication network. A misbehaviour detection system has been found to be more effective in identifying and preventing insider attacks. this research study presents a misbehaviour detection system based on machine learning and is learned utilizing datasets produced by numerical simulations in a genuine vehicular distributed system. Vehicle communication systems may choose the proper spatial multiplexing and system integration by correctly detecting the current conditions in order to maintain an efficient and reliable operating condition. A back-propagation neural network (BPNN) is trained, and a scenario identification model is developed, using datasets that comprise channel properties acquired from channel measurements in various scenarios. Additionally, the proposed identification model's best performance-enabling model configuration technique is analysed and offered. the identification accuracy is then verified using validation information from the relevant circumstances. © 2023 ieee.

关键词： Vehicles

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An Unsupervised Convolutional Adversarial Anomaly Detection Model for IoT Data Infrastructure 24

An Unsupervised Convolutional Adversarial Anomaly Detection ...

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24th ieee international conference on High Performance Computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wen, Peian Yang, Zhenyu Wu, Lei Qi, Sibo Chen, Juan School of Computer and Software Engineering Xihua University Chengdu China School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350319934

Anomaly detection is critical to ensure the IoT (Internet of things) data infrastructures' Quality of Service. However, due to the complexity of incon-spicuous(indistinct) anomalies, high dynamicity, and lack of anomaly labels in the operational IoT systems and cloud infrastructures, multivariate time series anomaly detection becomes more difficult. Existing approaches are rarely effective in meeting these challenges. In this paper, We propose a novel convolutional adversarial model based on CNN adversarial training with POT (Peak Over threshold) dynamic threshold selection (CAT-POT) for multivariate time series anomaly detection in the IoT data. We build on the original autoencoder, replacing the fundamental linear changes with a convolutional neural network and adding a convolutional decoder to make an adversarial training architecture. the ability of the model to identify 'slight' anomalies is enhanced through adversarial training, combined with POT dynamically selected thresholds to detect anomalies. We conduct ex-periments on five subsets of various open datasets, and the overall performance of CAT-POT is better than that of the baseline method. the average F1 value of CAT-POT on all datasets is 0.889, which is 5.1% higher than the suboptimal model, with the best improvement reaching 21.5%. © 2022 ieee.

关键词： Internet of things

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Design and Analysis of HDMA-assisted Visible Light Communication System Based on Transmitting and Reflecting RIS 8

Design and Analysis of HDMA-assisted Visible Light Communica...

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8th international conference on computer and Communication systems, ICCCS 2023

作者： Yu, Lisu Lv, Xinxin Qian, Jiajia Liu, Chaoliang Wang, Yuhao Wang, Zhenghai Zheng, Lei School of Information Engineering Nanchang University Nanchang330031 China School of Electronic and Communication Engineering Guangzhou University Guangzhou510006 China

ISBN: (纸本)9781665456128

For this article, a hybrid domain multiple access (HDMA)-assisted visible light communication (VLC) system based on transmitting and reflecting reconfigurable intelligent surface (TR-RIS) is studied. HDMA is a new multiple access technology enjoying multi-domain signal information, which can effectively improve the system performance by carrying more users' information on the light emitting diodes (LEDs). Contrapose to the problem of multi-LED interference in the system, this paper introduces a transmitting RIS (T-RIS) angle diversity receiver (RADR) model, using the electronically controlled refractive index characteristics of the T-RIS and the angle diversity of the spatial optical signal, which can effectively alleviate interference problem between multiple LEDs. Furthermore, a system optimization model is proposed in this paper. Considering various constraints of the system, an optimization objective is established to maximize the communication capacity by optimizing the power distribution ratio and reflecting RIS (R-RIS) unit. the simulation results show that the proposed scheme can effectively improve the VLC system capacity compared to the existing ones. © 2023 ieee.

关键词： Visible light communication

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Authentication Scheme Based on Non-Interactive Zero-Knowledge Proof for Mobile Health 24

Authentication Scheme Based on Non-Interactive Zero-Knowledg...

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作者： Acheampong, Edward Mensah Zhou, Shijie Liao, Yongjian Antwi-Boasiako, Emmanuel Obiri, Isaac Amankona School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350319934

Digital technologies, such as wireless body area networks (WBANs) for mobile health (mHealth) applications, are expected to enhance the quality of the public health care system. Although mHealth can improve patients' quality of health by offering outpatient real-time health monitoring systems instead of being stuck in the hospital all the time to monitor chronic diseases. the major challenge in adopting mHealth is data security and privacy. the health data routed on the internet from the patient monitoring device to the health center for remote monitoring is vulnerable to confidentiality attacks. To handle this issue, we present an authentication scheme based on non-interactive zero-knowledge proof (NIZKP), which issues certificates and authenticates monitoring devices each time performing transactions without revealing sensitive information. Our authentication scheme provides a high level of security with a low computational cost, which is lightweight for WBANs. © 2022 ieee.

关键词： Authentication

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A Study and Recognition of Alzheimer's Disease Using Machine Learning 8

A Study and Recognition of Alzheimer's Disease Using Machine...

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8th ieee international conference for Convergence in Technology, I2CT 2023

作者： Basha, S.K. Masthan Ashalatha, G. Vara Prasad, Srisailapu D Al-Hameed, Mazin R. Zearah, Sajad Ali Saikumar, K. Vignan's Institute of Management and Technology for Women ECE Department Hyderabad India VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science-CS-DS&AI&DS Telangana Hyderabad India GITAM Deemed to Be University Computer Science and Engineering School of Technology Telangana Hyderabad India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (纸本)9798350334012

Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. their everyday activities may measure this disorder, and how they speak about it shows how concerned they are about it. the way we live today is different from how it was in the past because of our working environment and the lack of opportunities to interact with people. WHO estimates that about 10 billion individuals perish each year due to Alzheimer's disease since not been appropriately diagnosed and treated. An advanced Machine Learning-based recognition system design performs in this research work. the proposed model has been designed based on the human being EEG dataset(testing) and the Kaggle Alzheimer dataset (training). the developed intelligent system will be updated with data collection and pre-processing through the Random Forest machine model to locate our health condition and recognize daily routine changes. Only by developing a machine-based approach will we be able to discover Alzheimer's patients who previously had no idea that they had the illness and are now unable to reverse the progression of their condition. the designed application has attained a sensitivity of 93.01%, an accuracy of 91.32%, a recall of 97.28%, and a detection rate of 98.91%. the implemented application is beneficial to investigators and surgeons for easy treatment. the proposed RFO Machine learning design competes with present technology and outperforms methodology. © 2023 ieee.

关键词： Intelligent systems

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Handwritten Character Recognition of Telugu Characters 8

Handwritten Character Recognition of Telugu Characters

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8th ieee international conference for Convergence in Technology, I2CT 2023

作者： Wasalwar, Yash Prashant Singh Bagga, Kishan Bhogendra Rao, P.V.R.R. Dongre, Snehlata G H Raisoni College of Engineering Dept. of Computer Science and Engineerng Nagpur India DRDO Kanchanbagh Defence Research and Development Laboratory Hyderabad India

ISBN: (纸本)9798350334012

Given the cursive structure of the writing and the similarity in shape of the letters, Telugu handwritten character identification is an interesting topic. the lack of Telugu-related handwritten datasets has slowed the development of handwritten word recognizers and forced researchers to compare various approaches. Modern deep neural networks find it difficult because they often need hundreds or thousands of photos per class. It has been demonstrated that learning important aspects of machine learning systems can be computationally expensive and challenging when there is a limited amount of data available. this research analysis work proposes a use case on the pre-existing model called EfficientNet and on top of that a custom pooling layer is added to check the trend as the dataset size increases of Telugu characters. the dataset has been divided into three categories, namely, Vowels only dataset, Consonant only dataset, and All character dataset. Proposed model was trained with a considerable amount of dataset containing half a thousand of handwritten Telugu characters and has produced some fascinating results which were worth observing. the accuracies had followed a certain trend. the model was tested on the dataset collected, which were filtered out to record any performance improvement and improvement was observed, where average accuracy went from 55% to 92%. © 2023 ieee.

关键词： Character recognition

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Architecture and Implementation of a patient criticality aware Edge-Cloud offloading technique 8

Architecture and Implementation of a patient criticality awa...

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8th ieee international conference for Convergence in Technology, I2CT 2023

作者： Menon, Athira B Rajeev, Krishna Jyothika, S. Ratheesh, Karthika Pathinarupothi, Rahul Krishnan Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India Amritapuri India

ISBN: (纸本)9798350334012

the Internet of things (IoT)-based remote health monitoring is one of the most promising technological interventions that is emerging to address the unique challenges of affordability, accessibility, and availability in global health, which denote equitable access to healthcare, particularly in remote-rural-developing regions. IoT devices can monitor multiple vital signs, which are then used for AI-assisted decision-making systems, which in turn assist physicians in forecasting remotely. However, there are many difficulties, like bandwidth issues, data loss, and overburdened doctors due to the massive amount of data. Shifting data from cloud to edge improves performance, cost efficiency, privacy, reduces communication between distant servers and the edge, resulting in less processing delay. We developed a clinical response requirement based cloud-to-edge offloading technique. As a use case, we developed an edge AI application for acute hypotensive episode prediction and compared its performance both in the cloud as well as on the edge. © 2023 ieee.

关键词： Internet of things

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