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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Kharbas, Vikas Kumar Mahesh, T.R. Upadhyay, Satish Vivekananda Global University Department of Computer Science & Application Jaipur India Faculty of Engineering and Technology Department of Computer Science Engineering Bangalore India Atlas SkillTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

Due to the complexity and urgency of information exchange in modern companies, optimizing communication protocols in the workplace is essential. Using methods from machine learning, this research takes a fresh tack toward optimizing the performance of communication protocols. The suggested approach integrates Reinforcement Learning (RL), Convolutional Neural Networks (CNNs), and Genetic Algorithms (GAs) to provide a self-optimizing system with dynamic behavior. The RL feature facilitates this protocol's flexibility in the face of dynamic network circumstances by providing rapid feedback. CNNs are used for real-time anomaly detection to improve safety and dependability. GAs methodically explores combinations of protocol parameters to determine the optimal collection that matches specified performance criteria. The recommended technique was compared to the usual methodology using a variety of efficiency and reliability measures. The findings demonstrate that the proposed approach frequently outperforms the state-of-the-art methods in a broad range of commercial settings with regards to efficiency, dependability, and adaptability. Understanding how to enhance industrial communication protocols is crucial in the age of Industry 4.0 and the IIoT. The recommended solution offers an autonomous, adaptable, and secure tool for boosting communication in dynamic production situations. © 2024 IEEE.

关键词： Convolutional neural networks

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Playing Against Deep-Neural-Network-Based Object Detectors: A Novel Bidirectional Adversarial Attack Approach

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第1期3卷 20-28页

作者： Li, Xiang Jiang, Yuchen Liu, Chenglin Liu, Shaochong Luo, Hao Yin, Shen Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Mechanical and Industrial Engineering Faculty of Engineering Norwegian University of Science and Technology Trondheim Norway

In the fields of deep learning and computer vision, the security of object detection models has received extensive attention. Revealing the security vulnerabilities resulting from adversarial attacks has become one of the most important research directions. Existing studies show that object detection models can also be threatened by adversarial examples, just like other deep-neural-network-based models, e.g., those for classification. In this article, we propose a bidirectional adversarial attack method. First, the added perturbation pushes the prediction results given by the object detectors far away from the ground-truth class while getting close to the background class. Second, a confidence loss function is designed for the region proposal network to reduce the foreground scores. Third, the adversarial examples are generated by a pretrained autoencoder, and the model is trained using an adversarial approach, which can enhance the similarity between the adversarial examples and the original image and speed up algorithm convergence. The proposed method was verified on the most popular two-stage detection framework (Faster R-CNN), and 55.1% drop in the mean average precision (mAP-drop) was obtained. In addition, the adversarial examples have superior transferability, migrating which to the common one-stage detection framework (YOLOv3) gets a 39.5% mAP-drop. © 2020 IEEE.

关键词： Object detection

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IoT-Based Smart Farmland Using Deep Learning 2

IoT-Based Smart Farmland Using Deep Learning

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Uma, J. Shobana, M. Abhiniti, G. Rahul, R. Kavinkumar, S.P. Hirithik Roshan, S. M. Kumarasamy College of Engineering Electrical and Electronics Engineering Karur India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Karur India

ISBN: (纸本)9798350349221

The IoT based Smart farmland using deep learning is a system for tracking animals on agricultural land combines surveillance cameras, drones, an Arduino controller, IR sensors, and an LCD display to detect and tally the animal's presence. Upon detection, the system utilizes AI methods to categorize the animal type and send immediate SMS notifications to farmers through a GSM module. It incorporates an animal-repellant buzzer and a gate control mechanism. Through the use of transfer learning with CNN models, it accurately identifies four specific animal species: elephants, cows, goats, and pigs. Continuous updates and training with new data maintain its precision, serving the agricultural and wildlife protection domains. This technology significantly aids farmers in safeguarding their crops, offering crucial insights into intrusions and amplifying crop yield. Its primary purpose revolves around simplifying the identification of animals on agricultural land. © 2024 IEEE.

关键词： Crops

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Minimum Epsilon-Kernel Computation for Large-Scale Data Processing

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Journal of computer science & Technology 2022年第6期37卷 1398-1411页

作者： Hong-Jie Guo Jian-Zhong Li Hong Gao School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Kernel is a kind of data summary which is elaborately extracted from a large *** a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate *** is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive *** this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data *** the open problem presented by Wang et *** whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its *** to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve *** prove that the MC problem and the MK problem can be Turing-reduced to each ***,we discuss the update of MK under insertion and deletion operations,***,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of *** efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic *** show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,*** speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic ***-SCMK runs up to three times faster than SCMK on synthetic datasets.

关键词： approximate query processing kernel large-scale dataset NP-hard

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Recursive Probability Mass Function Method to Calculate Probability Distributions of Pulse-Shaped Signals

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IEICE Transactions on Fundamentals of Electronics, Communications and computer sciences 2023年第10期E106.A卷 1286-1296页

作者： Fukami, Tomoya Saito, Hirobumi Hirose, Akira Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo113-8656 Japan Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

This paper proposes an accurate and efficient method to calculate probability distributions of pulse-shaped complex signals. We showthat the distribution over the in-phase and quadrature-phase (I/Q) complex plane is obtained by a recursive probability mass function of the accumulator for a pulse-shaping filter. In contrast to existing analytical methods, the proposed method provides complex-plane distributions in addition to instantaneous power distributions. Since digital signal processing generally deals with complex amplitude rather than power, the complex-plane distributions are more useful when considering digital signal processing. In addition, our approach is free from the derivation of signal-dependent functions. This fact results in its easy application to arbitrary constellations and pulse-shaping filters like Monte Carlo simulations. Since the proposed method works without numerical integrals and calculations of transcendental functions, the accuracy degradation caused by floating-point arithmetic is inherently reduced. Even though our method is faster than Monte Carlo simulations, the obtained distributions are more accurate. These features of the proposed method realize a novel framework for evaluating the characteristics of pulse-shaped signals, leading to new modulation, predistortion and peak-to-average power ratio (PAPR) reduction schemes. © 2023 The Institute of Electronics.

关键词： Digital signal processing

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on Biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Decision trees

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Prototype Proposal of an 18 kW Non-Isolated Bidirectional Converter for Battery Energy Storage System 22

Prototype Proposal of an 18 kW Non-Isolated Bidirectional Co...

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22nd International Symposium on Power Electronics, Ee 2023

作者： Brandis, Andrej Knol, Kristian Pelin, Denis Topic, Danijel Computer Science and Information Technology Osijek Faculty of Electricity Engineering Department of Electromechanical Engineering Osijek Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering Department of Power Engineering Osijek Croatia

ISBN: (纸本)9798350343175

In this paper, a brief overview of converter topologies used in stationary battery energy storage systems is given. A simulation model of converter was developed in MATLAB Simulink. A simulation is conducted to provide a preliminary understanding of the converter's bidirectional nature. The results indicate that the state of charge (SOC) increases during battery charging and decreases during discharging. The topology of non-isolated two-stage bidirectional converter is presented for proposed real-world prototype, including the high-level electrical schematic and mechanical layout. An 18-kW prototype converter for stationary battery energy storage systems was developed and presented. The converter housing with all installed ancillary components for proper operation is also described. At the time of writing this paper, the actual converter is still in the development phase. © 2023 IEEE.

关键词： Charging (batteries)

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Design and Implementation of a Slant Polarized Wideband Vivaldi Antenna Array Feed for Monopulse Radar Reflectors

Design and Implementation of a Slant Polarized Wideband Viva...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Masoumi, Reza Kazemi, Robab Fathy, Aly E. University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Traditionally, conical ridge horn antennas are used for feeding large reflectors, but they can cause grating lobes in arrays. This paper introduces a compact Vivaldi antenna for monopulse radar, featuring a planar feed structure that offers a wider bandwidth and simplifies manufacturing. The innovative design of the Vivaldi antenna incorporates strategically placed grooves and a circular dielectric lens to enhance gain and reduce sidelobe levels. Operating from 6 GHz to 18 GHz with proper impedance matching, the antenna is 23 mm wide (0.9λ at 12 GHz), making it over 66% smaller than traditional designs. The antennas are angled in 2 × 2 array configuration to overlap, with a center-to-center spacing of 11.3 mm (0.68λ at 18 GHz), ensuring that no grating lobes are present across the entire bandwidth. This innovative antenna effectively feeds an elliptical reflector, providing high gain, broadband capabilities, and significant null depth, ensuring accurate and efficient performance in monopulse radar applications. © 2025 IEEE.

关键词： Monopulse radar

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