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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Noguchi, Shunta Yoshikawa, Yuta Okai, Takayuki Oya, Hidetoshi Rahman, Md. Masudur Yoshida, Minoru Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Tokyo City University Department of Computer Science Tokyo Japan Noakhali Science and Technology University Department of Information and Communication Engineering Bangladesh Kanagawa University Department of Applied Systems and Mathematics Kanagawa Japan

ISBN: (纸本)9798331540319

In this paper, we propose a prediction system of the effect of electrical defibrillation. In order to develop the proposed system, we firstly analyze from pre-shock (immediately before defibrillation) ECGs (ElectroCardioGrams) by using Gabor Wavelet Transform, Poincaré plot analysis, Spectral entropy, and feature parameters are extracted by those extracted analysis results, we focus on time-transition feature parameter. Next, effective feature parameters are selected based on χ2 -test. Finally, 'Effective' and 'Ineffective' for electrical defibrillation are classified by using SVM (Support Vector Machine) with three kernels (Linear, Gaussian, and Polynomial) and regularization. In this way, we shows the effectiveness of the proposed feature extraction methods and prediction system. © 2024 IEEE.

关键词： Support vector machines

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An Analysis of Signature-Based Components in Hybrid Intrusion Detection Systems

An Analysis of Signature-Based Components in Hybrid Intrusio...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Agoramoorthy, Moorthy Ali, Ahamed Sujatha, D. Michael Raj, T.F. Ramesh, G. Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Computer Science and Engineering Easwari Engineering College Chennai Ramapuram India St.Peters College of Engineering and Technology Department of Computer Science and Engineering Chennai Avadi India Simats Department of Computer Applications Sclas Tamilnadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivagangai - Tamilnadu Pottapalayam India

ISBN: (纸本)9798350394580

In cybersecurity, Intrusion Detection Systems (IDS) protect against emerging cyber threats. Combining signature-based and anomaly-based detection methods may improve IDS accuracy and reduce false positives. This research analyzes hybrid intrusion detection systems' signature-based components' performance and limitations. The paper begins with a detailed history of signature-based detection methods responding to changing threat situations. This research analyzes signature databases to determine their capacity to identify and guard against current threats and cover known vulnerabilities. The paper also examines the intricate relationship between signature-based detection and anomaly-based techniques in hybrid IDS systems. This investigation examines how these two methodologies work together to uncover old and new attack strategies, focusing on zero-day vulnerabilities and polymorphic malware. A diverse dataset of network traffic and attack scenarios is used to test. Detection, false positives, and response times assess signature-based components. Comparative examinations investigate how signature-based detection affects system accuracy and efficiency. This research illuminates the role of signature-based aspects in hybrid intrusion detection systems. This study recommends integrating signature-based detection techniques with anomaly-based methods to improve hybrid intrusion detection systems (IDS) at recognizing and mitigating various cyber threats. © 2023 IEEE.

关键词： Anomaly-Based Detection Cybersecurity False Positives Hybrid IDS Intrusion Detection Systems Network Security Polymorphic Malware Signature-Based Detection Threat Detection Zero-Day Exploits

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Hyperspectral Remote Sensing Image Classification Using Improved Metaheuristic with Deep Learning

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computer Systems science & engineering 2023年第8期46卷 1673-1688页

作者： S.Rajalakshmi S.Nalini Ahmed Alkhayyat Rami Q.Malik Department of Information Technology Velalar College of Engineering and TechnologyErode638012India Department of Computer Science&Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India College of Technical Engineering The Islamic UniversityNajafIraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq

Remote sensing image(RSI)classifier roles a vital play in earth observation technology utilizing Remote sensing(RS)data are extremely exploited from both military and civil *** recently,as novel DL approaches develop,techniques for RSI classifiers with DL have attained important breakthroughs,providing a new opportunity for the research and development of RSI *** study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification(ISMOGCN-HRSC)*** ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of *** the presented ISMOGCN-HRSC model,the synergic deep learning(SDL)model is exploited to produce feature *** GCN model is utilized for image classification purposes to identify the proper class labels of the *** ISMO algorithm is used to enhance the classification efficiency of the GCN method,which is derived by integrating chaotic concepts into the SMO *** experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset.

关键词： Deep learning remote sensing images image classification slime mould optimization parameter tuning

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Quantum Prototype Clustering: Advancing AI Enhanced Machine Learning 9

Quantum Prototype Clustering: Advancing AI Enhanced Machine ...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Bangroo, Ishan Shivansh Kumar, Ravi Department of Computer and Information Science and Engineering University of Florida FL United States Department of Electronics and Communication Engineering Jaypee University of Engineering & Technology M.P. India

ISBN: (纸本)9798350383201

The quantization reconstruction of classical machine learning algorithms is an important research direction in quantum machine learning. Clustering, a widely applied algorithm in machine learning, also holds high research value when quantized. Currently, most quantum machine learning algorithms, including those employing AI techniques, face challenges such as difficulty in replication and the lack of intuitive comparisons with classical algorithms. A quantum prototype clustering algorithm (QPC) is proposed to address these issues, which can be easily deployed on existing general-purpose quantum computing devices. This approach first utilizes single-qubit rotation properties to find the feature mapping with minimal information loss, creating single-qubit rotations from two-dimensional feature data. Then, based on the characteristics of multi-qubit entanglement and entanglement system collapse, a quantum circuit for generating specific quantum entanglement systems and measuring the collapse results of entanglement systems is designed. By establishing the relationship between controlled qubit rotation angles in the entanglement system and the collapse results of the entanglement system and combining the definition of Minkowski distance, a quantum distance is derived for evaluating the similarity of input samples. The quantum distance measurement module, with the same input-output format as the distance calculation module in classical computers, can directly replace the Minkowski distance calculation in prototype clustering without modification, thus reconstructing classical prototype clustering algorithms, with AI included, into QPC. Multiple repeated experiments on publicly available datasets from Kaggle and Scikitlearn demonstrate that with AI integration, QPC exhibits no significant differences in evaluation metrics, such as average sample centre distance, compared to classical prototype clustering algorithms. © 2023 IEEE.

关键词： Clustering algorithms

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A Multi-Agent-System framework for flooding events 23

A Multi-Agent-System framework for flooding events

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23rd Workshop "From Objects to Agents", WOA 2022

作者： Rafanelli, Andrea Costantini, Stefania De Gasperis, Giovanni Department of Computer Science University of Pisa Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy

This paper presents the potential capabilities offered by an integrated multi-agent system comprising logical agents and a neural network, specialized in monitoring flood events for civil protection purposes Here we describe the idea of a framework – at the moment only partially developed – consisting of a set of intelligent agents, which perform various tasks and communicate with each other to efficiently generate alerts during flood crisis events, collaborating with a neural network, derived from the PSP-Net model, which is dedicated to the inspection and analysis of satellite images. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Multi agent systems

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Design and mechanism insight on SiC quantum dots sensitized inverse opal TiO_(2) with superior photocatalytic activities under sunlight

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Frontiers of Chemical science and engineering 2023年第12期17卷 1913-1924页

作者： Yingchao He Qiong Sun Likun Sun Zhixing Gan Liyan Yu Lifeng Dong College of Materials Science and Engineering Qingdao University of Science and TechnologyQingdao 266042China Center for Future Optoelectronic Functional Materials School of Computer and Electronic InformationNanjing Normal UniversityNanjing 210023China Department of Physics Hamline UniversitySt.PaulMN 55104USA

The combination of SiC quantum dots sensitized inverse opal TiO_(2) photocatalyst is designed in this work and then applied in wastewater purification under simulated *** various spectroscopic techniques,it is found that electrons transfer directionally from SiC quantum dots to inverse opal TiO_(2),and the energy difference between their conduction/valence bands can reduce the recombination rate of photogenerated carriers and provide a pathway with low interfacial resistance for charge transfer inside the *** a result,a typical type-II mechanism is proved to dominate the photoinduced charge transfer ***,the composite achieves excellent photocatalytic performances(the highest apparent kinetic constant of 0.037 min^(-1)),which is 6.2 times(0.006 min^(-1))and 2.1 times(0.018 min^(-1))of the bare inverse opal TiO_(2) and commercial P25 ***,the stability and non-toxicity of SiC quantum dots sensitized inverse opal TiO_(2) composite enables it with great potential in practical photocatalytic applications.

关键词： inverse opal TiO_(2) silicon carbide quantum dots quantum dot sensitized photocatalyst type-II charge transfer route

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Intelligent Additive Manufacturing Architecture for Enhancing Uniformity of Surface Roughness and Mechanical Properties of Laser Powder Bed Fusion Components

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IEEE Transactions on Automation science and engineering 2023年第4期20卷 2527-2538页

作者： Tran, Hong-Chuong Lo, Yu-Lung Yang, Haw-Ching Hsiao, Hung-Chang Cheng, Fan-Tien Kuo, Tsung-Han Southern Taiwan University of Science and Technology Department of Mechanical Engineering Tainan71005 Taiwan National Cheng Kung University Department of Mechanical Engineering Tainan70101 Taiwan National Kaohsiung First University of Science and Technology Department of Electrical Engineering Kaohsiung82445 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan70101 Taiwan

In the Laser Powder Bed Fusion (L-PBF) process, 3D components with complex geometries are fabricated in a layer-by-layer fashion by using a controlled laser beam to selectively melt particular regions of the metal powder bed. However, due to the stochastic nature of the L-PBF process, the top surface roughness of each solidified layer tends to be different even when the optimal processing conditions for the different positions on the build plate are employed. As a result, the mechanical properties of the built components frequently vary from one component to the next. Accordingly, this study proposes an Intelligent Additive Manufacturing Architecture (IAMA) for controlling the surface roughness of each build layer through an appropriate adjustment of the laser re-melting parameters. The IAMA architecture comprises five modules, namely In-Situ Metrology (ISM), Ex-Situ Metrology (ESM), Automatic Virtual Metrology (AVM), Additive Manufacturing Simulation (AMS) and Intelligent Compensator (IC). The feasibility of the proposed architecture is demonstrated by comparing the top surface roughness of cubic and mechanical strengths of tensile test samples built using the proposed method with those built using a traditional L-PBF approach without surface roughness control. It is found that the samples fabricated using the IAMA approach have an average top surface roughness of 1.6μm and a standard deviation is 0.7μm. By contrast, the samples produced using the traditional L-PBF approach have an average surface roughness of 13.45μm and a standard deviation of 2.5μm. In addition, the specimens produced with the assistance of IAMA architecture have an average tensile strength of 1013 MPa with a standard deviation of 69.5 MPa, while those printed without surface roughness control have an average tensile strength of 903 MPa with a standard deviation of 101.4 MPa Note to Practitioners - As L-PBF produce part in a layer-by-layer manner, therefore, the roughness on the top surface of p

关键词： Surface roughness

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An Interactive Visual Presentation of Core Database Design Concepts 2024

An Interactive Visual Presentation of Core Database Design C...

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1st ACM Virtual Global Computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Abdelaziz, Noha Farghally, Mohammed Mohammed, Mostafa Soliman, Taysir Faculty of Computers and Information Assiut University Assiut Egypt Department of Computer Science Virginia Tech BlacksburgVA24061 United States Department of Computer Science and Engineering The State University of New York at Buffalo BuffaloNY14260 United States

ISBN: (纸本)9798400705984

Database design is a core topic in computer science (CS) curricula at the university level. Students often encounter difficulties and misconceptions while learning these concepts. Previous research attempted to address these learning difficulties through interactive visual demonstrations. However, most of these resources are not well integrated into the curriculum, and lack a proper educational evaluation. In this paper, we present a set of online interactive visualizations that we name DataBase Visualizations (DBVs), that address common database design learning difficulties in an introductory undergraduate database course. Core database design concepts are visualized step-by-step, facilitating a deep understanding of relationship establishment and mapping onto a relational schema. DBVs could be easily embedded in an online eTextbook facilitating integration with the existing curriculum. We present our findings from an evaluation study of the effectiveness of DBVs when applied to a semester-long undergraduate database course in a large public institution in the middle east. Results indicate that intervention group students had significantly higher scores on a post-test offered as part of the final compared to control group students using primarily traditional textual content. Furthermore, intervention group students were surveyed at the end of the semester asking them about the value of DBVs to their learning process and suggestions for improvement. Survey results indicate that DBVs were clear, engaging, and easy to use. We believe that DBVs will be helpful to undergraduate database instructors in their teaching of basic database design concepts. © 2024 ACM.

关键词： Students

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Cloud Integration in the Educational Sector Using Amazon Web Services 3

Cloud Integration in the Educational Sector Using Amazon Web...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Dubey, Parul Tiwari, Arvind Kumar Dr. C v Raman University Department of Computer Science and Engineering Bilaspur India Dr. C v Raman University Department of Computer Science and Information Technology Bilaspur India

ISBN: (纸本)9781665494007

Web Services (AWS) is a prominent cloud service provider in the information technology industry. Businesses in the contemporary period may benefit from its propensity to develop and expand, as well as from its scalability and flexibility. However, it has not yet achieved widespread acceptance in the academic community. In this article, we will cover the fundamentals of AWS, such as interfacing with EC2 instances, and then compare the costs of deploying in the cloud vs on-premises. This will be useful in understanding why AWS is not more widely used in the academic community. The results of this comparison show that when the time frame of the demand is short and the number of individuals served is low, implementation on AWS is more cost-effective than on premises. When the quantity and duration of users in the education sector grow, the consequences change. © 2023 IEEE.

关键词： Web services

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Enhancing Federated Learning with Globally Shared Model: A Modified FedAVG Approach (GSM-FedAVG) 6

Enhancing Federated Learning with Globally Shared Model: A M...

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6th International Conference on Electrical information and Communication Technology, EICT 2023

作者： Tanmoy, Oudarja Barman Mamun, Md. Al Hasan, Sakib Anwar, Adnan Rajshahi University of Engineering & Technology Department of Computer Science and Engineering Rajshahi6204 Bangladesh Deakin University School of Information Technology Melbourne Australia

ISBN: (纸本)9798350370836

The wireless communications industry is interested in using data-driven machine learning solutions to supplement traditional model-driven design processes. Decentralized ML algorithms that maintain data in its original location are more alluring due to the lack of accessibility of private data and significant communication overhead. Federated learning is crucial for wireless applications to protect user privacy. The Federated Averaging Algorithm is a technique used in the Federated Learning process, which cooperatively learns an ML model by using the simple arithmetic average. In this paper, we propose a technique for enhancing the robustness of FedAVG, which involves employing a globally shared model with each clients in addition to each client's local model. This method is referred to as the GSM-FedAVG approach. The GSM approach with FedAVG has shown promising results in improving the accuracy and robustness of the ML models used as global model in FL setting. The effectiveness of the proposed method is validated using a number of benchmark datasets. © 2023 IEEE.

关键词： Recurrent neural networks

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