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Supervised machine learning for intrusion detection: A survey and performance evaluation

Supervised machine learning for intrusion detection: A surve...

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international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Chkirbene, Zina Hamila, Ridha College of Engineering Qatar University Qatar

ISBN: (纸本)9781032803722

Cloud computing is a cost-effective way to host and deliver services over the internet, but the large volume of data transmitted makes the cloud network a target for malicious attacks. To protect against these threats, various intrusion detection systems (IDSs) have been developed to identify different types of attacks on the network. However, some of these systems are not always efficient due to the level of human involvement they require. Machine learning approaches, which have been widely used in IDSs, have the potential to improve efficiency by building well-trained models for anomaly detection using large datasets with a variety of attack types. In this paper, we examine the current state of research on using machine learning techniques for IDSs and evaluate the suitability of different algorithms using the UNSW dataset. We compare the accuracy rate and classification performance of several algorithms to determine their effectiveness. © 2025 The Author(s).

关键词： Network intrusion

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Integrating Machine Learning and Deep Learning Approaches for Accurate Cardiovascular Disease Prediction from Electronic Health Records

Integrating Machine Learning and Deep Learning Approaches fo...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Natchathirah, V.S. Meera, T. Devi, S. Padma Velammal College of Engineering And Technology Department of Cse Madurai India

ISBN: (纸本)9798331509828

Cardiovascular diseases (CVDs) are a leading cause of death globally, making early prediction and identification of high-risk patients critical for improving treatment outcomes. This study examines the effectiveness of various machine learning and deep learning algorithms in predicting CVD using electronic health records (EHRs). We evaluate the performance of several ensemble methods, including Gradient Boosting, CatBoost, Random Forest, AdaBoost, and LightGBM (LGBM), alongside deep learning architectures such as Long Short-Term Memory (LSTM) and TABNET, utilizing a large EHR dataset. Our results reveal that CatBoost outperforms other ensemble methods, while TABNET achieves the highest accuracy among the deep learning models assessed. Furthermore, we investigate feature importance and the influence of hyperparameter tuning on model performance, providing insights into optimizing predictive capabilities. This research underscores the potential of advanced algorithms in enhancing early detection of CVD and supporting clinical decision-making. © 2025 IEEE.

关键词： Electronic health record

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IYAL: Real-Time Voice to Text Communication for the Deaf

IYAL: Real-Time Voice to Text Communication for the Deaf

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Balamani, T. Subiksha, K. Swathi, D. Vennila, V. Vishnu Bharathi, J. M Kumarasamy College of Engineering Department of Electronics and Communication Engineering India

ISBN: (纸本)9798331509828

The Iyal app is a real-time communication tool designed to assist those who are hard of hearing or deaf in daily situations, much like the Nagish app. Through speech-to- text translation and vice versa, Iyal ensures seamless communication in both personal and professional contexts. Because the software is bilingual, users may communicate with ease regardless of the language being spoken. Its advanced AI speech recognition and text-to-speech algorithms produce accurate translations in real time. Reversible font sizes and background colors are among the adjustable accessibility options that Iyal provides to further meet various needs. This user-friendly program can assist the deaf population become more connected and autonomous, which will increase social engagement and facilitate conversations. © 2025 IEEE.

关键词： Speech recognition

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The Power of PDF-to-Audio Summaries in Modern Learning 4

The Power of PDF-to-Audio Summaries in Modern Learning

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Kawde, Dhanshri Mendhe, Sameer Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

PDF -to-audio based summarization technology is designed to enhance information accessibility and efficiency by transforming long written reports into short, audible summaries. AI, NLP, and TTS technologies that have developed recently have made it possible to extract main points without losing context. Yet, current systems have various shortcomings such as inability to process intricate PDF layouts like tables, charts, and annotations, contextual summarization inaccuracies, robotic or unhuman speech synthesis, and limited customization. Furthermore, the absence of multilingual support and easy integration into various platforms hinders wider adoption. These issues are addressed in this paper with a better approach that employs sophisticated NLP methods, context-sensitive summarization algorithms, and adaptive TTS systems. Our goal is to create a strong, user-oriented solution that provides greater summarization accuracy, personalized speech output, and improved accessibility for a variety of users, such as professionals, students, and the disabled. © 2025 IEEE.

关键词： Web Design

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

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6th IEEE international conference on Image Processing, Applications and systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Water Leakage Control Device in Metro Water Pipeline

Water Leakage Control Device in Metro Water Pipeline

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Nivishna, S. Kalaivani, C. Hemamalini, S. Arul Arumugam, R. M. Kumarasamy college of Engineering Department of Electronics and communication Engineering Tamilnadu Karur India Krishnaswamy college of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Cuddalore India Anna University Tamilnadu Chennai India Annapoorana Engineering College Department of Electronics and communication Engineering Tamilnadu Salem India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

The need for effective water management systems has grown as a result of the population expansion and increasing urbanization of metropolitan areas. Water leaks in metro water pipelines, however, present a serious problem since they cause financial losses, structural damage, and water loss. As a result, creating a Smart Water Leakage Controller (SWLC) becomes essential for immediately identifying and addressing leaks. The main elements and features of a SWLC system created especially for metro water pipelines are described in this abstract. In order to continually monitor the pipeline network, the SWLC system integrates cutting-edge sensor technology, including acoustic, pressure, and flow sensors. These sensors pick up on irregularities that could be signs of leaks, like sharp reductions in pressure or very high flow rates. The sensors send real-time data to a central control unit that has sophisticated algorithms for data processing and decision-making. The SWLC system takes rapid action to minimize water loss and stop more damage after detecting a leakage occurrence. To ensure system stability, these steps can entail alerting maintenance staff, isolating the impacted pipeline portion, and modifying water flow. © 2025 IEEE.

关键词： Decision making

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Geometrically reconfigurable Silk-Based Electronic Implants 38

Geometrically Reconfigurable Silk-Based Electronic Implants

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38th IEEE international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Ni, Siyuan Zhu, Ziyi Yan, Zhiwen Liang, Zhengyu Jiang, Jianbo Zou, Dujuan Yang, Huiran Zhou, Zhitao Sun, Liuyang Tao, Tiger H. Qian, Yun Wei, Xiaoling Liu, Keyin State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China Shanghai Sixth People's Hospital Shanghai Jiao Tong University School of Medicine China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China ShanghaiTech University China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Jiangxi Nanchang China Guangdong Institute of Intelligence Science and Technology Zhuhai Guangdong Hengqin China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

ISBN: (纸本)9798331508890

We report a geometrically reconfigurable silk-based electronic implant designed for peripheral nerve monitoring. Metallic conductive structures were fabricated on the surface of a geometrically reconfigurable membrane using Micro-Electro-Mechanical systems (MEMS) technology. By controlling the cutting angles of the membrane, we achieved water-responsive self-wrapping spiral electrodes, which exhibit excellent geometric adaptability to nerve tissues. In vitro tests demonstrate that our electrodes possess good mechanical, biocompatibility, and electrical properties. In vivo applications confirmed that our electrodes can effectively record compound nerve action potentials (CNAP). Our approach offers a new method for developing implantable electronic devices that require mechanical and geometrical adaptability. © 2025 IEEE.

关键词： Silk

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Machine Learning for Signal Processing: Kernel-extreme Learning Machines with Augmented Complex-valued and Mixture Correntropy Techniques 16

Machine Learning for Signal Processing: Kernel-extreme Learn...

引用

16th international conference on Signal Processing systems, ICSPS 2024

作者： Lin, Bingqing Wang, Yan Guan, Yunhe Qian, Guobing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781510689251

In this paper, we propose novel machine learning algorithms for signal processing applications based on kernel-extreme learning machines (KELM) and its variants. Specifically, we focus on the use of complex domain and mixture correntropy techniques to enhance the performance and generalization ability of KELM. Two models are derived by augmenting a complex-valued extreme learning machine with kernel functions, minimizing human intervention while enhancing accuracy. Furthermore, we extend the mixture correntropy-based kernel extreme learning machine (MCKELM) to complex domains, resulting in the MCKCELM algorithm, capable of solving prediction problems in complex domains. To further improve the performance of MCKCELM, we apply an augmented matrix to derive two variant models, and demonstrate their convergence through experiments. All models presented in this paper are regularized models specifically designed for signal processing tasks. © 2025 SPIE.

关键词： Adversarial machine learning

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Decentralized Target Localization in Distributed MIMO Radars 16

Decentralized Target Localization in Distributed MIMO Radars

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16th international conference on Signal Processing systems, ICSPS 2024

作者： Qiu, Qinhui Li, Jie Xi, Feng School of Electronic and Optical Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781510689251

This paper addresses the problem of target estimation in distributed MIMO radars, focusing on the challenge of requiring a robust fusion center to process all the collected data. To overcome this limitation, we propose a solution based on the Alternating Direction Method of Multipliers (ADMM) that eliminates the need for a centralized fusion center. By leveraging the sparsity of target location information within the detection range, the target localization problem is reformulated as a sparse recovery problem, which is then efficiently solved using ADMM. The key innovation of the proposed approach is the transformation of the average value computation, typically required in centralized methods, into a consensus averaging problem, and subsequently into a consensus optimization problem. This transformation allows for fully decentralized processing, where each node only communicates with its neighboring nodes to reach a consensus on the estimation of target location. Simulation results demonstrate that the proposed distributed algorithm achieves performance comparable to that of semi-distributed algorithms while fully exploiting the advantages of decentralization. © 2025 SPIE.

关键词： Consensus algorithm

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Advanced HLS for reconfigurable Image Processing in Big Data systems 12th

Advanced HLS for Reconfigurable Image Processing in Big Da...

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12th international conference on Big Data and Artificial Intelligence, BDA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, Vivek Noida201309 India

ISBN: (纸本)9783031818202

This paper introduces novel HLS techniques for reconfigurable and memory-efficient image processing within deep learning frameworks, addressing inherent limitations of current deep learning accelerators (DLAs) due to their dependency on the host processor, leading to pipeline delays and reduced throughput. The proposed approach emphasizes parallelization, on-chip buffering, and scalability, enhancing computational efficiency and throughput for AI applications in big data and edge computing environments. Methodologically, the paper emphasizes mathematical analysis, hardware architecture specification, HLS modeling, Verilog RTL implementation. By addressing these challenges, this work contributes to advancing the performance capabilities of DLAs in deep learning inference tasks. The algorithms integrates seamlessly with DLA IP subsystems, such as AXI interfaces, external (DRAM) and internal (SRAM) memory interfaces, and activation/pooling engines. Key contributions include advanced algorithm design to enhance memory efficiency and reconfigurability, essential for adapting to diverse hardware architectures. Experimental validation demonstrates substantial improvements in computational speed and resource utilization compared to traditional methods, making it suitable for intensive AI tasks in big data analytics. In conclusion, this research presents a framework for integrating HLS techniques into hardware accelerators, facilitating efficient image processing across diverse applications in big data and edge computing. These advancements aim to bolster DLAs’ capabilities in handling complex AI workloads effectively, promising enhanced solutions for AI-driven domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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