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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Kuntichod, Watcharet Lee, Wilaiporn Prayote, Akara Department of Computer and Information Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

ISBN: (纸本)9789819606917

Named Entities Recognition (NER), which is a task to identify and classify named entities within text, has gained significant popularity in recent years. This task often requires pre-labeled data and large datasets to achieve high accuracy. A key challenge in NER is extracting unseen entities that have not been previously labeled or are new terms, posing difficulties in adapting to rapidly changing data and the emergence of new vocabularies. In this article, we propose a model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) for boundary extraction. The model aims to enhance the detection and labeling of unseen entities by leveraging Bi-LSTM's strengths in capturing contextual information and accurately identifying entities boundaries. The model demonstrates high potential in identifying unseen names without pre-labeling. Our study demonstrates the model's showcasing its effectiveness in discovering unlabeled words and unseen entities in Thai language datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Labeled data

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Improving Prediction Accuracy in IoT Network Security Against DDoS Attacks by Combining RIPPER and KNN Algorithms 8

Improving Prediction Accuracy in IoT Network Security Agains...

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8th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Juliansyah, Handrino Sihombing, Poltak Fahmi, Fahmi Universitas Sumatera Utara Faculty of Computer Science and Information Technology North Sumatera Medan Indonesia Universitas Sumatera Utara Faculty of Engineering Electrical Engineering Dept North Sumatera Medan Indonesia

ISBN: (纸本)9798331531249

The Internet of Things (IoT) is a network of linked devices comprising interconnected devices that communicate through internet, integrating sensors, software and other technologies. As the number of connected devices grows, ensuring IoT security has become increasingly critical, with cyber threats and other related risks arising. A prevalent threat is the Distributed Denial of Service (DDoS) attack, which seeks to incapacitate server systems by overwhelming the network with excessive traffic. This study explores machine learning approach to improve intrusion detection systems (IDS) performance, such as accuracy in identifying and mitigating DDoS attacks. Specifically, it employs a hybrid approach combining the well-known RIPPER algorithm or Repeated Incremental Pruning to Produce Error Reduction and the K-Nearest Neighbors (KNN) algorithm as detection models. The study utilizes the Network Security Layer-Knowledge Discovery in Database (NSL-KDD) dataset, which contains 41 features for classifying DDoS attacks. To optimize the model's performance, the data underwent preprocessing to eliminate irrelevant and redundant features while retaining essential information. The classification results demonstrate a peak accuracy of 97.32% with k=6 and an average accuracy of 96.30% across 15 trials with varying k values from 1 to 15. These findings highlight the effectiveness of the RIPPER-KNN approach in improving the prediction accuracy of DDoS attack detection in IoT networks. © 2024 IEEE.

关键词： Denial-of-service attack

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OntoDSO: an ontological-based dolphin swarm optimization (DSO) approach to perform energy efficient routing in Wireless Sensor Networks (WSNs)

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International Journal of information technology (Singapore) 2024年第3期16卷 1551-1557页

作者： Boopathi, Mythili Parikh, Swapnil Awasthi, Aishwary Malviya, Ashwini Nachappa, M.N. Mishra, Awakash Shyam, Gopal Krishna Narula, Gagandeep Singh Department of Information Technology Vellore Institute of Technology Tamil Nadu Vellore India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Mechanical Engineering Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Computer Science Engineering Presidency University Bengaluru India Guru Gobind Singh Indraprastha University Delhi Dwarka India

The aim of the paper is to perform an energy-efficient routing for moving nodes in Wireless Sensor Networks (WSNs) by using an ontological-based dolphin swarm optimization (DSO) approach. It makes use of echolocation and ontology to locate and represent cluster nodes close to the sink thereby maintaining optimal energy consumption during simulation. A comparative analysis for 20 samples (N = 10 each) is performed between OntoDSO, Ad-hoc On-demand Distance Vector (AODV) algorithm and existing literature review studies by taking several parameters namely mean delay, mean packet delivery ratio and mean energy consumption into consideration. The simulation of results is performed using MATLAB Simulink with implementation parameters such as number of nodes (n) and initial battery life. As a result, it is found that the OntoDSO approach identifies a set of optimized routes that can satisfy delay constraints and consume less energy, thereby achieving higher performance than AODV and existing studies in the context of WSNs. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Ad-hoc On-demand Distance Vector (AODV) algorithm Dolphin Swarm Optimization (DSO) algorithm Packet delivery ratio and energy consumption Wireless Sensor Network (WSN)

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An Improved Convolutional Neural Network Model for DNA Classification

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computers, Materials & Continua 2022年第3期70卷 5907-5927页

作者： Naglaa.F.Soliman Samia M.Abd-Alhalem Walid El-Shafai Salah Eldin S.E.Abdulrahman N.Ismaiel El-Sayed M.El-Rabaie Abeer D.Algarni Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DNA classification CNN downsampling hyperparameters DL 2D DT 2D RP

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Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

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computer Systems Science & engineering 2023年第1期44卷 535-544页

作者： Nguyen Tu Trung Tran Thi Ngan Tran Manh Tuan To Huu Nguyen Faculty of Computer Science and Engineering Thuyloi University175 Tay SonDong DaHanoi010000Vietnam University of Information and Communication Technology Thai Nguyen UniversityThai Nguyen240000Vietnam

Fusing medical images is a topic of interest in processing medical *** is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis *** fusion aims to improve the image quality and preserve the specific *** methods of medical image fusion generally use knowledge in many differentfields such as clinical medicine,computer vision,digital imaging,machine learning,pattern recognition to fuse different medical *** are two main approaches in fusing image,including spatial domain approach and transform domain *** paper proposes a new algorithm to fusion multimodal *** algorithm is based on Entropy optimization and the Sobel *** transform is used to split the input images into components over the low and high frequency ***,two fusion rules are used for obtaining the fusing *** rule,based on the Sobel operator,is used for high frequency *** second rule,based on Entropy optimization by using Particle Swarm Optimization(PSO)algorithm,is used for low frequency *** algorithm is implemented on the images related to central nervous system *** experimental results of the paper show that the proposed algorithm is better than some recent methods in term of brightness level,the contrast,the entropy,the gradient and visual informationfidelity for fusion(VIFF),Feature Mutual information(FMI)indices.

关键词： Medical image fusion wavelet entropy optimization PSO Sobel operator

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Control the Interval Estimation to Enhance the Performance of Optimized Link State Routing 4

Control the Interval Estimation to Enhance the Performance o...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Ishqeem, Omar Ammar Sarreb, Rebah Daw Hatif Libya Company Libyan Post Telecommunications & Information Technology Company Libya Sabratha university Faculty of Engineering Rigdalin Dept. of Computer Engineering Libya

ISBN: (纸本)9798350372632

This paper investigates the creation of self-organizing Mobile Ad hoc Networks (MANETs) employing Optimized Link State Routing Protocol (OLSR) with Multipoint Relay (MPR). The routing protocol in MANET controls the management of node mobility by using metrics to evaluate the quality of link states. The primary concept of OLSR is to choose the MPR that identifies the route from the source to the destination node. The MPR relays Hello and Topology Control (TC) messages between nodes used to discover and distribute link state information. The Hello and TC messages interval plays a critical role in the link state in the network. Accordingly, link failure for a few seconds may lead to a significant performance deterioration. This work enhances the performance of OLSR by estimating the efficient Hello and TC message interval for faster route recovery. Many experiments are simulated and analyzed by a set of parameters such as MPR count, TC traffic sent, throughput, delay, and data dropped. All our results revealed that the proposed Control of the Interval Estimation-Optimized Link State Routing (CIE-OLSR) has better performance than the traditional OLSR. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Image Contrast Enhancement using Fuzzy Logic with Metaheuristics 24

Image Contrast Enhancement using Fuzzy Logic with Metaheuris...

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11th International Conference on Networking, Systems and Security, NSysS 2024

作者： Kabir, Mohimenul Mobin, Jaiaid Hassanat, Ahmad Sohel Rahman, M. Department of Computer Science Bangladesh University of Engineering and Technology Dhaka Bangladesh Rochester Institute of Technology Rochester United States Faculty of Information Technology Mutah University Karak Mutah Jordan Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400711589

In this work, we present a method to enhance image contrast using a fuzzy logic-based transformation function. Our goal is to develop an image-specific transformation by tuning the fuzzy system. We employ Hill Climbing and Genetic Algorithms in various configurations to evolve the fuzzy system, conducting multiple experiments. Different variants of our method are tested on several images, and the two variants with the highest fitness scores are selected. Additionally, we compare against existing contrast enhancement technique and conduct a survey to evaluate the visual improvements made by these two variants. The survey results indicate that one method effectively enhances image contrast visually. © 2024 Copyright held by the owner/author(s).

关键词： computer circuits

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Expert Phishing Detection System

Expert Phishing Detection System

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology, IAICT 2024

作者： Alsarhan, Ayoub Al-Aiash, Issa Al-Fraihat, Dimah Aljaidi, Mohammad Abu Laila, Dena Abd Al Hakem Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Cybersecurity Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa13133 Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman11622 Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Cyber Security Faculty of Information Technology Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (纸本)9798350353464

Phishing attacks are a threat that continuously emphasizes the need for effective detection methods. This paper presents a machine learning detection method that employs a random forest classifier, which is an adaptable algorithm that can handle a variety of feature sets. The dataset used in this work offers a range of features extracted from various URLs, addressing elements such as URL length, domain structure, and keyword presence. We deployed a computational approach for feature engineering. The random forest classifier is employed for both prediction and to assess feature importance, guiding an automated feature selection process. By doing this, the model’s performance is optimized, and the most important features are utilized. The findings of this study show the effectiveness of our method with an impressive accuracy of 98.45% on a test set. The classifier exhibits balanced precision and recall for both phishing and legitimate instances. Moreover, the automated feature selection process improves the interpretation of the model, emphasizing key indicators of phishing attacks. The proposed approach not only produces outstanding outcomes but also offers a flexible and scalable approach to address the dynamically evolving threat landscape of phishing attempts. ©2024 IEEE.

关键词： Cyber-security Phishing attacks Phishing URLs

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Industrial Teleassistance with the Help of Augmented Reality 23

Industrial Teleassistance with the Help of Augmented Reality

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Bobeica, Valentin Popescu, Dan Angelescu, Nicoleta Ichim, Loretta National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Valahia University of Targoviste Faculty of Electrical Engineering Electronics and Information Technology Targoviste Romania

ISBN: (纸本)9798331540388

This paper details the integration of augmented reality (AR) in a new factory, focusing on enhancing maintenance efficiency and optimization. The innovative approach involves combining AR technology, an Android application, and Siemens PLCs (Programmable Logic Controller), for efficient automation. The robustness of the PLC enables seamless integration within manufacturing environments, accommodating diverse programming paradigms. The software aspect features Human Machine Interface (HMI) configuration using Siemens' WinCC software, offering dynamic visualizations, alarm handling, and data logging. WinCC's scalability suits applications of varying sizes. The HMI architecture is designed for easy parameter display. The Android application, developed with Java and Python, leverages their respective strengths in Android app development and image processing. Data exchange is facilitated by OPC-UA, supporting multiple programming languages for seamless communication. The AR Teleassistance is highlighted as a revolutionary solution, fostering real-Time communication among global experts, technicians, and operators. Beyond gaming origins, AR teleassistance enhances efficiency in various sectors, minimizing downtime and facilitating global knowledge transfer. The integration of Teleassistance promotes effective collaboration between human expertise and automated systems, crucial for optimal performance in modern industrial settings. Continuous development is suggested through the addition of more 3D mechanical drawings and real-Time stock information for parts in the application, aiming for future optimization and performance enhancement. © 2024 IEEE.

关键词： augmented reality data exchange global knowledge image processing manufacturing environment production processes smart industrial

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ATmega2560 MCU Space Vector Modulation Implementation on Three-phase Inverter and Validation via Typhoon HIL 402 5

ATmega2560 MCU Space Vector Modulation Implementation on Thr...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Brandis, Andrej Pelin, Denis Šimić, Zvonimir Vidošić, Ivan Department of Electromechanical Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

In this paper, a space vector modulation implementation is done via ATmega2560 microcontroller with the Arduino development board. The control parameters are defined by the user in MATLAB software and sent to Arduino to start or stop the controller or to change the controller parameters. Space vector switching times for chosen parameters are calculated and given. The implemented algorithm is validated via Typhoon HIL 402 system on a three-phase inverter model for two different operation points. Voltage and current waveforms are obtained in Typhoon HIL. Line and phase voltage, as well as the phase current spectrum analysis is done via Typhoon HIL and processed in MATLAB for given system parameters. The measurement results validates the implemented space vector control algorithm. © 2022 IEEE.

关键词： Microcontrollers

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