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Intelligent Intrusion Detection System for Industrial Internet of Things Environment

computer systems science & Engineering 2023年第2期44卷 1567-1582页

作者： R.Gopi R.Sheeba K.Anguraj T.Chelladurai Haya Mesfer Alshahrani Nadhem Nemri Tarek Lamoudan Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Mathematics College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival *** classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and *** resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 *** CCSOA-OWKELM technique focuses on the design of feature selection with classiﬁcation approach to achieve minimum computation complex-ity and maximum detection *** CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with ***,the OWKELM technique is applied for the intrusion detection and classiﬁcation *** addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunﬂower optimization(SFO)*** utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better *** order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

关键词： Intrusion detection system artiﬁcial intelligence machine learning industry 4.0 internet of things

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Utilization of Database Spatial Queries for Region of Interest Extraction in Stored Images

Utilization of Database Spatial Queries for Region of Intere...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Valdez, Julian Troy C. Tan, Wilson M. University of the Philippines Diliman Department of Computer Science Networks and Distributed Systems Group Quezon City Philippines

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Image processing, specifically extracting regions of interest, being a costly operation would benefit from a method of storing its output region and input image data in a database for future querying so as to prevent re-execution of the processing operation. This study investigates the performance of using built-in spatial queries found in a couple of modern Database systems to store and retrieve specific regions of image data. After testing two widely used Database software: MongoDB and Postgresql, results have shown that MongoDB performs better in terms of average and tail latency as well as efficiency in disk space consumption. © 2022 IEEE.

关键词： Query processing

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CloudyPages: A Secure and Scalable Serverless Web-Based Blogging Application 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prakash, Vijay Jaiswal, Shrestha Purohit, Saumya Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MsidaMSD 2080 Malta

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

The CloudyPages app is a precise and trustworthy platform for bloggers to create and effectively manage blogs. The CloudyPages application provides bloggers a platform for creating and managing their blogs. The application can achieve remarkable accuracy, precision, and review rates by utilizing AWS services, assuring minimal errors and enhancing the discoverability and accessibility of bloggers’ content. Collectively, these metrics contribute to an optimized user experience. AWS WAF (Amazon Web Services Web Application Firewall) provides robust protection against common web application vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) attacks. By implementing this security layer, the application effectively protects the data of bloggers and readers from potential attacks. The AWS Web Application Firewall (WAF) protects against known attack patterns and continuously monitors and detects harmful activities to prevent unauthorized access. The application's serverless architecture lets bloggers focus on content creation and administration instead of server maintenance. The application's affordable price makes it an appealing, adaptable, and inclusive solution. The application's serverless design and AWS WAF and AWS Open make it flexible, affordable, and accessible for bloggers of all sizes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blogs

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Performance Analysis and Early Detection of Lung Cancer with Spiking Neural Networks 5

Performance Analysis and Early Detection of Lung Cancer with...

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5th International Conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Ghatage, Annasab Krishna Sumitha, V. S. Priya Srinivasan, P. Muhammed Anshad, P.Y. Mallaiah, Badepally Murugan, A. Durai M.S. Ichalkaranji India Kg College of Arts and Science Department of Electronics and Communication Systems Tamilnadu Coimbatore India Sona College of Technology Department of Electronics and Communication Engineering Tamilnadu Salem India Mes Institute of Technology & Management Department of Computer Science Kerala India Cvr College of Engineering Department of Information Technology Telangana Hyderabad India M.Kumarasamy College of Engineering Department of Computer Science And Business Systems Karur India

ISBN: (纸本)9798331522100

Cancer of the lung occurs when non-native lung cells grow uncontrollably. The increased incidence of this disease has raised both sexes mortality rates. Lung cancer is a disease that cannot be eradicated, although its risk can be diminished. The survival rate of lung cancer is greatly affected by its early identification. Training the model, extracting features, binarizing, decreasing noise, and converting to grayscale are suggested steps. We convert RGB photographs to grayscale first. Noise reduction removes background noise. A threshold value binarizes grayscale pictures into binary. Feature extraction uses directional gradient histograms, the Gabor filter, and standard deviation. Training the model with the Spiking Neural Network (SNN) ensures accuracy and durability. Compared to Alex Net and CNN, the suggested model is more accurate. It greatly increases lung cancer detection with 91.14% accuracy. This emphasizes early detection's role in lung cancer survival. The proposed method combines state-of-the-art feature extraction with model training to diagnose lung cancer accurately. Due to its precision, this approach may improve diagnostics and health outcomes. © 2025 IEEE.

关键词： Convolutional neural networks

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Comparative Study between KNN and CNN's Techniques for Kidney Stone Detection

Comparative Study between KNN and CNN's Techniques for Kidne...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Al-Tbenawey, Fahdah M. Alotaibi, Shoayee Salem, Marwa Mir, Shamaila Mir, Zahida Computer Science and Engineering College University of Ha'il Department of Computer Science and Information Hail Saudi Arabia Faculty of Engineering Department of Electrical Communication and Electronics Systems Engineering Cairo Egypt

ISBN: (纸本)9798350347050

This paper aims to determine the better technique for kidney stone detection between K-Nearest Neighbor (KNN) and Convolutional Neural Networks (CNNs). As well known, the presence of kidney stones is an important topic that deserves discussion regarding expected medical outcomes. Most of the detection measures comprise CT scans, ultrasounds, and abdominal X-rays. These measures have varying effectiveness levels, hence used in the detection to prepare patients for specific treatment options. Patients' treatment options comprise percutaneous nephrolithotomy (PCNL), extracorporeal shockwave lithotripsy (ESWL), and ureteroscopy. The different detection techniques carried out for the patients are essential to test whether the patient has small or large kidney stones. One essential technique is using conventional neural networks (CNNs) technology. In this paper kidney, stone detection will be carried out using both of K-Nearest Neighbor (KNN) and CNN's techniques. A comparison between both techniques will be carried out to figure out the better. All of the simulations are carried out using MATLAB. © 2023 IEEE.

关键词： MATLAB

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Machine Learning-based Tamil Sign Language Recognition Tool for the Deaf and Hard-of-Hearing Community.

Machine Learning-based Tamil Sign Language Recognition Tool ...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Prabhu, D. Dhivyadharshini, K. Lokeswari, S. Pavithra Devi, T. Harithaa, S. Preethi, S. Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India Sri Sairam Institute of Technology Department of Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350348910

This paper proposes a machine learning-based Tamil sign language recognition tool for the deaf and hard-of-hearing community. The tool uses computer vision techniques to capture the hand gestures of sign language users and processes them using convolutional neural networks (CNN) to recognize the corresponding Tamil alphabet or word. The proposed system consists of two main stages: data collection and model training. During the data collection stage, a dataset of Tamil sign language gestures is created by recording videos of sign language users performing various gestures. In the model training stage, the dataset is preprocessed and augmented, and a CNN is trained to recognize the gestures. The proposed system achieves a recognition accuracy of 85% on the test dataset, which is a significant improvement over the existing system. The proposed tool can be deployed on a mobile device or a web application, making it easily accessible to the deaf and hard-of-hearing community. © 2023 IEEE.

关键词： Statistical tests

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A Study of Two Types of Introductory Exercise Problems for Network Programming Learning with Python 23

A Study of Two Types of Introductory Exercise Problems for N...

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11th International Conference on computer and communications Management, ICCCM 2023

作者： Lu, Xiqin Funabiki, Nobuo Ueda, Kiyoshi Kao, Wen-Chung Department of Information and Communication Systems Distributed System Design Lab. Okayama University Okayama Japan Department of Computer Science Control Software Lab. Nihon University Koriyama Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798400707735

Network programming is the act of writing computer codes for communications between programs or processes in different computers across networks. It is crucial for applications and services using networks, including e-commerce, gaming, and social media. However, university courses of educating it are very limited. As a result, it has become important to develop and offer IT tools to support self-studies of network programming by novices. In this paper, we present a study of two types of introductory exercise problems, namely, Grammar-concept Understanding Problem (GUP) and Comment Insertion Problem (CIP), for self-studies of five basic network protocols of TCP/IP, ARP, SMTP, POP3, and SSH with Python. A GUP instance requests to answer the keyword such as a library function in the given source code that corresponds to the grammar concept described in the question. A CIP instance requests to insert the comment at each blank of the code. String matching is adopted to mark any answer at the browser-based answer interface. Through solving them, it is expected to understand the meaning of important words and the code structure for network programming. As preliminary evaluations, we generated six GUP and CIP instances respectively, and assigned them to 10 students in Okayama University, Japan. The results confirmed that the proposal is proper for novices to learn network programming with Python. © 2023 ACM.

关键词： Python

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ZigBee Network for AGV communication in Industrial Environments 10

ZigBee Network for AGV Communication in Industrial Environme...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Flak, Jaroslaw Skowron, Tomasz Cupek, Rafal Fojcik, Marcin Caban, Dariusz Domanski, Adam Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering Bergen Norway

ISBN: (纸本)9798350345032

Automated Guided Vehicles (AGVs) are a key component of many modern industrial systems. AGVs are supposed to communicate with each other in real time using wireless networks. In this article, the advantages and disadvantages of the ZigBee wireless network related to the control of AGVs are considered. We analyze the performance of the ZigBee network programmed with both C# and Python libraries to control ZigBee devices. The throughput and signal strength are presented and discussed depending on the transmission speed of the serial port, the payload size, and the presence and distance from the obstacles. The results of the experiments show the effective values of these parameters, the methods of using C# and Python, and the reliability of the throughput up to a certain point in network devices. © 2023 IEEE.

关键词： Python

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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