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Robust Vehicle Detection Based on Improved You Look Only Once

computers, Materials & Continua 2023年第2期74卷 3561-3577页

作者： Sunil Kumar Manisha Jailia Sudeep Varshney Nitish Pathak Shabana Urooj Nouf Abd Elmunim Department of Computer Science Banasthali VidyapithRajasthan304022India Department of Computer Science&Engineering School of Engineering&TechnologySharda UniversityGreater Noida201310India Department of Information Technology Bhagwan Parshuram Institute of Technology(BPIT)GGSIPUNew Delhi110078India Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia

Vehicle detection is still challenging for intelligent transportation systems(ITS)to achieve satisfactory *** existing methods based on one stage and two-stage have intrinsic weakness in obtaining high vehicle detection *** to advancements in detection technology,deep learning-based methods for vehicle detection have become more popular because of their higher detection accuracy and speed than the existing *** paper presents a robust vehicle detection technique based on Improved You Look Only Once(RVD-YOLOv5)to enhance vehicle detection *** proposed method works in three phases;in the first phase,the K-means algorithm performs data clustering on datasets to generate the classes of the ***,in the second phase,the YOLOv5 is applied to create the bounding box,and the Non-Maximum Suppression(NMS)technique is used to eliminate the overlapping of the bounding boxes of the ***,the loss function CIoU is employed to obtain the accurate regression bounding box of the vehicle in the third *** simulation results show that the proposed method achieves better results when compared with other state-of-art techniques,namely LightweightDilated Convolutional Neural Network(LD-CNN),Single Shot Detector(SSD),YOLOv3 and YOLOv4 on the performance metric like precision,recall,mAP and *** simulation and analysis are carried out on PASCAL VOC 2007,2012 and MS COCO 2017 datasets to obtain better performance for vehicle ***,the RVD-YOLOv5 obtains the results with an mAP of 98.6%and Precision,Recall,and F1-Score are 98%,96.2%and 97.09%,respectively.

关键词： Image-processing K-means clustering CNN YOLOv5 loss function deep-learning

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Neural Network-Based Analysis of Mammography Images for Identifying Breast Cancer Histological Subtypes

Neural Network-Based Analysis of Mammography Images for Iden...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Alqaaf, Muhammad Technology, Ahmad Kamal Nasution Graduate school Of science And Supriyanti, Retno Asaduzzaman, Abu Ono, Naoaki Altaf-Ul-Amin, Md. Kanaya, Shigehiko Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Jenderal Soedirman University Department of Electrical Engineering Banyumas Indonesia Wichita State University Department of Electrical and Computer Engineering Wichita United States

ISBN: (纸本)9798350391213

Breast cancer (BC) is a complex disease with multiple histological subtypes that exhibits distinct biological and clinical characteristics. Accurate identification of these subtypes is crucial for the implementation of personalized treatment strategies and the improvement of patient outcomes. This study aimed to leverage the potential of neural networks and mammography data to identify specific histological subtypes of BC, with a focus on distinguishing between in situ and invasive carcinoma. We used the Digital Database for Screening Mammography (DDSM) and its curated subset, CBIS-DDSM, which provides mammography images of normal and malignant cases with verified pathological information. A convolutional neural network (CNN) architecture was designed to extract relevant features from mammography images. The model was trained using data augmentation techniques to enhance diversity and mitigate overfitting, resulting in training and validation accuracies ranging from 0.98 to 1.0. The features extracted by the trained model were then used for clustering analysis using Kmeans on PCA (KM-PCA), which identified nine well-separated clusters with minimal overlap. The clustering results were validated using a subset of mammography images with confirmed invasive carcinoma types from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Two validation approaches were incorporated to strengthen the hypothesis and further validate the results. Overall, this study demonstrates the potential of combining neural networks and mammography data to accurately identify BC histological subtypes, which could pave the way for personalized and effective treatment strategies. © 2024 IEEE.

关键词： Lung cancer

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Bidirectional Gated Recurrent Unit Based-Grey Wolf Optimizer for Interval Prediction of Voltage Margin Stability Index in Power Systems

Bidirectional Gated Recurrent Unit Based-Grey Wolf Optimizer...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Massaoudi, Mohamed Refaat, Shady S. Ghrayeb, Ali Abu-Rub, Haitham Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States University of Hertfordshire School of Physics Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9781665490719

Despite the importance of Photovoltaic (PV) generation systems for smart grids, the uncertainty of PV power outputs due to weather variations can cause power stability problems leading up to voltage collapse in power systems. Governed by load dynamics, data-driven short-term Voltage Stability Margin (VSM) assessment is a critical technique for power system design and operation. This paper addresses improving the VSM accuracy and quantifying the uncertainties associated with the predicted values using deep learning. Interval estimation of VSM is significant for monitoring short-term voltage stability in real-time against fast voltage collapse and sustained low voltage without recovery. The proposed solution lies in the use of a Bidirectional Gated Recurrent Unit (GRU) optimized by a multi-objective Grey Wolf optimizer (GWO) algorithm. Time domain simulation results are conducted using IEEE59 and IEEE Nordic-44 bus systems to provide the training samples and maintain a high level of grid observability for the grid stability evaluation. The generated N-l contingency test cases data were conducted using Power System Simulator for engineering (PSS/E)-based time domain simulations. The generated features from fault-induced voltage events include the post-disturbance voltage magnitude, angles, frequencies, and active and reactive power trajectories of the system buses. The obtained results show that the proposed method can enable timely stability assessment with higher effectiveness. © 2023 IEEE.

关键词： Smart power grids

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Cross Society Special Issue (IFETC & ISCAS) on Flexible Hybrid Electronics: Advancing Next-Generation Applications

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第12期3卷 524-525页

作者： Shao, Leilai Ogras, Umit Y. Tong, Ge McFarlane, Nicole Nathan, Arokia Shanghai Jiao Tong University School of Mechanical Engineering Shanghai 200240 China University of Wisconsin-Madison Department of Electrical and Computer Engineering Madison 53706 WI United States Nanyang Technological University School of Electrical & Electronic Engineering Jurong West 639798 Singapore The University of Tennessee Department of Electrical and Computer Science Knoxville 37996 TN United States Shandong University School of Information Science Jinan 250100 China

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Multivariate design and data analysis for plasmonic sensing

Multivariate design and data analysis for plasmonic sensing

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Smart Photonic and Optoelectronic Integrated Circuits 2024

作者： Korsa, Matiyas T. Etxebarria-Elezgarai, Jaione Roggero, Ursula F.S. Hernández-Figueroa, Hugo E. Adam, Jost Seifert, Andreas Department of Electrical Engineering and Computer Science University of Kassel Kassel34121 Germany CIC nanoGUNE BRTA San Sebastián20018 Spain School of Electrical and Computer Engineering UNICAMP São Paulo Brazil IKERBASQUE Basque Foundation for Science Bilbao48009 Spain

ISBN: (纸本)9781510670402

Plasmonic sensing using surface plasmon resonances remains an area with unclear detection limits. The introduction of metallic nanostructures as nanoantennas can improve plasmonic sensing through localized surface plasmon resonances (LSPR), complex lattice effects, and simultaneous propagating plasmons. The combination of plasmonic effects through near or far-field coupling lead to more complex phenomena called plasmonic hybridization. Multivariate analysis methods significantly improve the sensing performance in terms of figures of merit by exploiting multiple features of the resonance curves. Likewise, multivariate design approaches provide optimized plasmonic substrates through electromagnetic simulations when the same curve features are used as in multivariate data analysis to achieve optimized performance metrics. Here, we demonstrate with one-dimensional gold nanogratings that multivariate design concepts lead to optimized performance in plasmonic sensing, which is also confirmed experimentally. © 2024 SPIE.

关键词： Surface plasmon resonance

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Tunable Hybrid Plasmonic-Semiconductor Laser Incorporating ENZ Material

Tunable Hybrid Plasmonic-Semiconductor Laser Incorporating E...

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2023 Photonics North, PN 2023

作者： Saeidi, Shayan Cheben, Pavel Schmid, Jens H. Berini, Pierre University of Ottawa School of Electrical Engineering and Computer Science Nexqt Institute Ottawa Canada National Research Council of Canada Ottawa Canada Nexqt Institute University of Ottawa School of Electrical Engineering and Computer Science Department of Physics Ottawa Canada

ISBN: (纸本)9798350326734

In this paper, we suggest a new approach to achieve tunability in semiconductor lasers, based on mode loss perturbation. We propose a hybrid plasmonic-semiconductor laser, in which we add a metal oxide semiconductor (MOS) on top of a conventional Fabry-Perot (FP) InP-based diode laser. This hybrid laser experiences significant change in the imaginary part of the mode effective index when the MOS operates in the accumulation region, which constitutes the basis of our tuning mechanism. © 2023 IEEE.

关键词： Semiconductor lasers

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Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles

Supervised Deep Learning Models for Detecting GPS Spoofing A...

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Khoei, Tala Talaei Aissou, Ghilas Al Shamaileh, Khair Devabhaktuni, Vijaya Kumar Kaabouch, Naima School of Electrical Engineering and Computer Science University of North Dakota Grand ForksND58202 United States Purdue University Northwest Electrical and Computer Engineering Department HammondIN46323 United States The University of Maine Electrical and Computer Engineering Department OronoME04469 United States

ISBN: (纸本)9781665493765

Unmanned Aerial Networks (UAVs) are prone to several cyber-atttacks, including Global Positioining Spoofing attacks. For this purpose, numerous studies have been conducted to detect, classify, and mitigate these attacks, using Artificial Intelligence technqiues;howver, most of these studies provided techniques with low detection, high misdetection, and high bias rates. To fill this gap, in this paper, we propose three supervised deep learning techniques, namely Deep Neural Network, U Neural Network, and Long Short Term Memory. These models are evaluated in terms of Accuracy, Detection Rate, Misdetection Rate, False Alarm Rate, Training Time per Sample, Prediction Time, and Memory Size. The simulation results indicated that the U Neural Network outperforms other models with accuracy of 98.80%, a probability of detection of 98.85%, a misdetection of 1.15%, a false alarm of 1.8%, a training time per sample of 0.22 seconds, a prediction time of 0.2 seconds, and a memory size of 199.87 MiB. In addition, these results depicted that the Long Short Term Memory model provides the lowest performance among other models for detecting these attacks on UAVs. © 2023 IEEE.

关键词： Long short-term memory

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Can AI Keep You Safe? A Study of Large Language Models for Phishing Detection 14

Can AI Keep You Safe? A Study of Large Language Models for P...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Chataut, Robin Gyawali, Prashnna Kumar Usman, Yusuf Quinnipiac University School of Computing and Engineering HamdenCT06518 United States Department of Computer Science and Electrical Engineering MorgantownWV26506 United States

ISBN: (纸本)9798350360134

Phishing attacks continue to be a pervasive challenge in cybersecurity, with threat actors constantly developing new strategies to penetrate email inboxes and compromise sensitive data. In this study, we investigate the effectiveness of Large Language Models (LLMs) in the crucial task of phishing email detection. With the growing sophistication of these attacks, we assess the performance of three distinct LLMs: GPT-3.5, GPT-4, and a customized ChatGPT, against a carefully curated dataset containing both phishing and legitimate emails. Our research reveals the proficiency of LLMs in identifying phishing emails, with each model showing varying levels of success. The paper outlines the strengths and limitations of GPT-3.5, GPT-4, and the custom ChatGPT, illuminating their respective suitability for practical applications in email security. These results underscore the potential of LLMs in effectively identifying phishing emails and their significant implications for enhancing cybersecurity measures and safeguarding users from the risks of online fraud. © 2024 IEEE.

关键词： Electronic mail

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Measurement of the ratio of the scalar polarizability to the vector polarizability for the 6sS1/22→7sS1/22 transition in cesium

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Physical Review A 2024年第6期109卷 062809-062809页

作者： Jonah A. Quirk Carol E. Tanner D. S. Elliott Department of Physics and Astronomy Purdue Quantum Science and Engineering Institute School of Electrical and Computer Engineering

We report measurements of the ratio of the scalar polarizability α to the vector polarizability β for the 6sS1/22→7sS1/22 transition in atomic cesium. These measurements are motivated by a discrepancy between the values of the vector transition polarizability as determined using two separate methods. In the present measurement, we use a two-pathway, coherent-control technique in which we observe the interference between a two-photon interaction driven by infrared light at 1079 nm and a linear Stark-induced interaction driven by the mutually coherent second harmonic of this infrared beam at 540 nm. The result of our measurements is α/β=−9.902(9), in good agreement with the previous determination of this ratio. This measurement, critical to the study of atomic parity violation in cesium, does not reduce the discrepancy between the two methods for the determination of the vector polarizability β for this transition.

关键词： Coherent control Electronic structure of atoms & molecules Stark effect

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