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ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers

Materials Today: Proceedings 2023年 80卷 3769-3776页

作者： Gite, Pratik Chouhan, Kuldeep Murali Krishna, K. Kumar Nayak, Chinmaya Soni, Mukesh Shrivastava, Amit Department of Computer Science and Engineering IPSA-IES Indore AB Road MP Indore India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Faculty of Emerging Technology Sri Sri University Odisha751001 India Department of Computer Science & Engineering Jagran Lakecity University Bhopal India Department of Computer Science Engineering Swami Vivekanand College of Engineering Khandwa Road Near Tolnaka Vivekanand Knowledge City Indore India

A key component for preserving a WSN's integrity is designing an intrusion detection system (IDS). This article covers several sorts of security threats that may occur in a WSN. It proposes a detection of malicious nodes in a WSN. This system identifies four common assaults (i.e., black hole, wormhole, gray hole, and DDoS attacks) and is based on a Base Station machine learning (ML) algorithm (BS). The suggested ML algorithm continually analyzes data patterns from each node. Based on this technique, BS recognizes the network's harmful behavior and provides notifications to neighbor nodes to prevent attacker. First, the various assaults are examined and their characteristics are derived in terms of network parameters. Furthermore, the gathered pattern trains an ML algorithm. Then, the attacker node is effectively and properly classified within the BS. The NS2 simulator simulated the needed secure WSN. The experimental findings revealed excellent accuracy of the suggested method for attack detection. This precision can enhance network performance in terms of energy usage and packet delivery (PDR). © 2021

关键词： Base stations

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective 7

Toward Digital Transformation Adoption: A Conceptual Framewo...

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7th Information systems International Conference, ISICO 2023

作者： Marcel Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey. © 2023 The Authors. Published by Elsevier B.V.

关键词： Failure rate

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Railway Track Fault Detection Using Mel Frequency Cepstral Coefficient from Acoustic Data using Chi Square

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Chinese Journal of Electronics 2023年

作者： Furqan Rustam Abid Ishaq Muhammad Shadab Alam Hashmi Hafeez-Ur-Rehman Siddiqui Imran Ashraf School of Computer Science University College Dublin Faculty of Computer Science and Information Technology Khawaja Fareed University of Engineering and Information Technology Department of Information and Communication Engineering Yeungnam University

Spatial, temporal, and weather elements like ballast, loose nuts, misalignment, and cracks due to rain, snow, and earthquakes may lead to railway accidents and cause human and financial loss. Manual inspection is erroneous, labor-intensive, and *** automatic inspection provides a fast, reliable, and unbiased solution in this regard, however, ensuring high accuracy for fault detection is challenging due to the lack of public datasets, noisy data, high computer processing requirements, and inefficient models. This study presents an approach that uses Mel frequency cepstral coefficient features from the acoustic data. The dataset gathered using a customized railway cart from our previous research is used for experiments. The focus of the study is to increase the fault detection performance using selective features from the acoustic data. This study employs Chisquare(Chi2) for the selection of important features and involves performance analysis of machine learning and deep learning models using selected features. Experimental results suggest that using 60 features, 40 original features, and 20 Chi2 features, produces optimal results both regarding accuracy and computational complexity. A 100%accuracy can be obtained using the proposed approach with machine learning models. Moreover, this performance is significantly better than existing approaches.

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A New Technique of Soluble NPK Nutrient Detection system Using Deep Learning-Based Recurrent Neural Networks (RNN) 12

A New Technique of Soluble NPK Nutrient Detection System Usi...

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12th International Electrical engineering Congress, iEECON 2024

作者： Tipwong, Wallop Sirikham, Adisorn Konpang, Jessada Kirdpipat, Putthiphong Chongjarearn, Yutana Preutisrunyanont, Oradee Rajamangala University Of Technology Krungthep Department Of Electrical And Telecommunication Engineering Bangkok Thailand Thammasat School Of Engineering Thammasat University Faculty Of Engineering Department Of Electrical And Computer Engineering Pathum Thani Thailand Dhurakij Pundit University Department Of Robotic Engineering And Automation System Bangkok Thailand

ISBN: (纸本)9798350383591

Modern agriculture relies heavily on effective process monitoring, control, and remote data collection, making accurate and cost-effective sensors crucial for innovation. This research focuses on the development of a spectroscopy-based device for real-time and remote monitoring of nutrient levels in hydroponic systems. The device utilizes IoT architecture, incorporating principles of light absorbance to quantify concentrations of essential nutrients during crop growth. Three deep learning techniques, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) were adopted for data analysis. Results demonstrate that tailored implementation of deep learning can significantly enhance the performance of sensor network system (SNS). Specifically, the RNN format achieved an impressive 98% model accuracy during both training and testing phases. The technological advancements in addressing contract variation and photometric levels contribute to increased accuracy as more sensors become available. The findings serve as a comprehensive guide for collecting extensive agricultural data, encompassing environmental factors throughout the entire cultivation period, using state-of-the-art remote control sensor equipment. This work provides a practical, efficient, and cost-effective solution for precise nutrient control and continuous data monitoring in agricultural production. © 2024 IEEE.

关键词： Cost effectiveness

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Design and Analysis of High-Speed communication systems for 5G and Beyond 9

Design and Analysis of High-Speed Communication Systems for ...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Jegadeesan, R. Srinivas, Dava Deivakani, M. Joshi, Sunita Dinesh, P.M. Kumar, M. Jogendra Jyothishmathi Institute of Technology And Science Department of Computer Science And Engineering Telangana Karimnagar505481 India Psna College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Dindigul624622 India Manav Rachna International Institute of Research And Studies Department of Faculty of Computer Applications Haryana Faridabad121004 India Sona College of Technology Department of Electronics And Communication Engineering Tamil Nadu Salem636005 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350365092

Fifth Era Innovation (5G) is the late portable network in wireless communications. This article presents an assessment of the advancements in portable communications. The different issues that arise at each stage are reported in our guide to state-of-the-art wearable networks. The fifth era (5G) of portable networks offers everybody admittance to high-speed web at whatever point they need, any place they are. Since to its inventive qualities, such connecting individuals and controlling contraptions, machines, and items, 5G is somewhat unique. Various levels of execution and capacities presented by the 5G versatile innovation will make new client encounters and connection new organizations. It is pivotal to comprehend where the business can utilize5G's benefits. This work examines and models a microstrip fix antenna working at 29 GHz for impending 5G communication innovation. The result of this reproduction is better than past work that was finished. It can then be used as a significant source of strength for 5G wireless technologies. The ongoing logic diary demonstrates that the proposed antenna performs better than those currently in use. This antenna may therefore be adequate for his 5G wireless connection architecture. © 2024 IEEE.

关键词： 5G mobile communication systems

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Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1140-1149页

作者： Mian Imtiaz ul Haq Ruhul Amin Khalil Muhannad Almutiry Ahmad Sawalmeh Tanveer Ahmad Nasir Saeed Faculty of Electrical Engineering Swedish College of Engineering and TechnologyWah CanttPakistan Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawarPakistan Department of Electrical Engineering Remote Sensing UnitNorthern Border UniversityArarSaudi Arabia Data Science and Artificial Intelligence Department‐College of Science and Information Technology Irbid National UniversityIrbadJordan Innovation Education and Research Center for On‐Device AI Software(Bk21) Department of Computer Science and EngineeringChungnam National UniversityDaejeonRepublic of Korea Department of Electrical and Communication Engineering United Arab Emirates University(UAEU)Al AinUnited Arab Emirates

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various ***,a novel localisation algorithm is proposed for noisy range measurements in IIoT *** position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position ***,the AMs selection algorithm for the localisation of BMs in the IIoT network is *** those AMs will participate in the localisation process,which increases the accuracy of the final location ***,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement *** results are compared with the state‐of‐the‐art and verified through numerous simulations.

关键词： Cramér‐Rao lower bound greedy successive anchorization industrial internet of things localization

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Leveraging AI to Unlock the Potential of Data Science 15

Leveraging AI to Unlock the Potential of Data Science

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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Multi Area Dynamic Model for Continental Europe Synchronous Area Splitting 23

Multi Area Dynamic Model for Continental Europe Synchronous ...

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23rd International Scientific Conference on Electric Power engineering, EPE 2023

作者： Koudelka, Jan Batora, Branislav Toman, Petr Macejko, Stanislav Haba, Tomas Maslo, Karel Brno University of Technology Faculty of Electrical Engineering and Communication Department of Electrical Power Engineering Brno Czech Republic ČEPS A.S Transmission System Analysis Department Prague Czech Republic

ISBN: (纸本)9798350335934

The paper deals with frequency stability simulations. It presents twenty areas equivalent dynamic model of the Continental Europe synchronous area. The model is suitable to simulate different system splitting scenarios and assess the dynamic behaviour of the power system. The network simulator MODES is used as a simulation tool. © 2023 IEEE.

关键词： Frequency stability

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Grid Frequency Support from Electric Vehicle Charging Stations 23

Grid Frequency Support from Electric Vehicle Charging Statio...

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23rd International Scientific Conference on Electric Power engineering, EPE 2023

作者： Koudelka, Jan Batora, Branislav Toman, Petr Haba, Tomas Macejko, Stanislav Maslo, Karel Brno University of Technology Faculty of Electrical Engineering and Communication Department of Electrical Power Engineering Brno Czech Republic ČEPS A.s. Transmission System Analysis Department Prague Czech Republic

ISBN: (纸本)9798350335934

The paper presents the use of electric vehicle charging stations for frequency control in the network emergency state (when the frequency drops below 49.8 Hz) to prevent the frequency from falling further to the critical value of 49 Hz (operation of underfrequency load shedding). It compares three different frequency responses using the simplified twenty areas dynamic models. © 2023 IEEE.

关键词： Frequency response

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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