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An enhanced strategy for minority class detection using bidirectional GRU employing penalized cross-entropy and self-attention mechanisms for imbalance network traffic

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European Physical Journal Plus 2024年第6期139卷 530页

作者： Abbasi, Asima Akber Zameer, Aneela Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Nilore 45650 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

The emergence of the Internet as a global communication tool has brought significant transformations across various aspects of our everyday lives. However, it has also resulted in new technical challenges, such as the risk of personal information exposure, network congestion, and limited availability of network addresses. The cybersecurity field has greatly benefited from the implementation of artificial intelligence and machine learning to model network activity and improve the detection of potential threats. Intrusion detection systems are specifically unable to tackle the challenges of high-class imbalance and the varying significance of misclassifications specifically for minority classes. The proposed SABiG (CPL) model targets the high dimensional imbalance data set CIC-IDS2017 by employing the evolutionary computing framework of genetic algorithms utilizing random forest GA(RF) for optimal feature selection. For attack classification, a powerful deep learning technique of bidirectional gated recurrent unit (Bi-GRU) with a self-attention mechanism is exploited. Our proposed model features a custom penalized cross entropy strategy by assigning three penalty values according to class imbalanced ratios to address the misclassification issue. The experimental results have demonstrated enhanced detection accuracy for all classes in general and minority classes in particular, such as infiltration, and web attacks. Comparison with the existing techniques in terms of all standard performance indices further evaluates our proposed intrusion detection system for imbalanced data. The generalization of the proposed model is evaluated with the recent CIC-CSE-IDS2018 web attack dataset. © The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024.

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Parallel differential evolution paradigm for multilayer electromechanical device optimization

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Modern Physics Letters B 2024年第31期38卷

作者： Zameer, Aneela Naz, Sidra Zahoor Raja, Muhammad Asif Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences (PIEAS) Nilore Islamabad 45650 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan

Design optimization of multilayer piezoelectric transducers is intended for efficient and practical usage of wideband transducers for fault diagnosis, biomedical, and underwater applications through adjusting layer thicknesses and volume fraction of piezoelectric material in each layer. In this context, we propose a parallel differential evolution (PDE) algorithm to mitigate the complexities of multivariate optimization as well as the computation time to achieve an optimized wideband transducer for the particular application. For lead magnesium niobate-lead titanate (PMN PT)- and PZT5h-based piezoelectric materials, the fitness function is formulated based on uniformity of mechanical pressure at the first three harmonics to achieve wide bandwidth in the required functional frequency range. It is carried out using a one-dimensional model (ODM), while input layer thicknesses and volume fractions of active material are evaluated using PDE. The simulation is performed on a parallel computing platform utilizing three different host machines to reduce computational time. Results of the proposed methodology for PDE are statistically represented in the form of minimum, maximum, mean, and standard deviation of fitness value, while graphically represented in terms of speedup and time. It can be observed that the execution time for parallel DE decreases with the increasing number of cores. © World Scientific Publishing Company.

关键词： di®erential evolution parallel computing piezocomposite piezoelectric ceramic Transducer

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Atomic physics-inspired atom search optimization heuristics integrated with chaotic maps for identification of electro-hydraulic actuator systems

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Modern Physics Letters B 2024年第30期38卷

作者： Mehmood, Khizer Chaudhary, Naveed Ishtiaq Khan, Zeshan Aslam Cheema, Khalid Mehmood Zahoor Raja, Muhammad Asif Department of Electrical and Computer Engineering International Islamic University Islamabad 44000 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Department of Electronic Engineering Fatima Jinnah Women University Rawalpindi 46000 Pakistan

Electro-hydraulic actuator system (EHAS) has imposed a challenge in the research community for accurate mathematical modeling and identification due to non-linearities. In this paper, autoregressive exogenous (ARX) structure is used for EHAS modeling and identification is performed by exploiting the competency of atomic physics-based chaotic atom search optimization (CASO) that adapts ten chaotic maps (Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal and Tent) in position update of atom search optimization (ASO). The fitness/merit function of the EHAS model is developed in mean-square error (MSE) sense between desired and approximated values. Simulations and analysis show that ASO with a chaotic logistic map (CASO5) performs better than the ASO and its other chaotic variants, as well as other recently introduced metaheuristics for diverse variations in the system model. Statistics based on MSE, learning plots, results of autonomous trials and average fitness analyses verify the consistency and reliability of the CASO5 for the identification of the EHAS model. © 2024 World Scientific Publishing Company.

关键词： ARX model Atom search optimization chaotic maps electro-hydraulic actuator system

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Enhancing Weakly Supervised Semantic Segmentation with Multi-modal Foundation Models: An End-to-End Approach

arXiv

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arXiv 2024年

作者： Ravanbakhsh, Elham Niu, Cheng Liang, Yongqing Ramanujam, J. Li, Xin Louisiana State University Baton RougeLA70803 United States Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States Section of Visual Computing and Interactive Media Texas A&M University College StationTX77843 United States

Semantic segmentation is a core computer vision problem, but the high costs of data annotation have hindered its wide application. Weakly-Supervised Semantic Segmentation (WSSS) offers a cost-efficient workaround to extensive labeling in comparison to fully-supervised methods by using partial or incomplete labels. Existing WSSS methods have difficulties in learning the boundaries of objects leading to poor segmentation results. We propose a novel and effective framework that addresses these issues by leveraging visual foundation models inside the bounding box. Adopting a two-stage WSSS framework, our proposed network consists of a pseudo-label generation module and a segmentation module. The first stage leverages Segment Anything Model (SAM) to generate high-quality pseudo-labels. To alleviate the problem of delineating precise boundaries, we adopt SAM inside the bounding box with the help of another pre-trained foundation model (e.g., Grounding-DINO). Furthermore, we eliminate the necessity of using the supervision of image labels, by employing CLIP in classification. Then in the second stage, the generated high-quality pseudo-labels are used to train an off-the-shelf segmenter that achieves the state-of-the-art performance on PASCAL VOC 2012 and MS COCO 2014. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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An Overview of Violence Detection Techniques: Current Challenges and Future Directions

arXiv

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arXiv 2022年

作者： Mumtaz, Nadia Ejaz, Naveed Habib, Shabana Mohsin, Syed Muhammad Tiwari, Prayag Band, Shahab Shamshir Kumar, Neeraj Department of Computer Science Iqra University Islamabad Campus Pakistan Department of Information Technology College of Computer Qassim University Buraydah52571 Saudi Arabia Department of Computer Science COMSATS University Islamabad45550 Pakistan School of Information Technology Halmstad University Sweden Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

The Big Video Data generated in today’s smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis a difficult task in terms of computation and preciseness. Violence Detection (VD), broadly plunging under Action and Activity recognition domain, is used to analyze Big Video data for anomalous actions incurred due to humans. The VD literature is traditionally based on manually engineered features, though advancements to deep learning based standalone models are developed for real-time VD analysis. This paper focuses on overview of deep sequence learning approaches along with localization strategies of the detected violence. This overview also dives into the initial image processing and machine learning-based VD literature and their possible advantages such as efficiency against the current complex models. Furthermore,the datasets are discussed, to provide an analysis of the current models, explaining their pros and cons with future directions in VD domain derived from an in-depth analysis of the previous methods. © 2022, CC BY.

关键词： Anomaly detection

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PointCG: Self-supervised Point Cloud Learning via Joint Completion and Generation

arXiv

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arXiv 2024年

作者： Liu, Yun Li, Peng Yan, Xuefeng Nan, Liangliang Wang, Bing Chen, Honghua Gong, Lina Zhao, Wei Wei, Mingqiang The School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Shenzhen Institute of Research Nanjing University of Aeronautics and Astronautics Shenzhen China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Urban Data Science section Delft University of Technology Delft Netherlands The Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong

The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects effectively. In this paper, we integrate two prevalent methods, masked point modeling (MPM) and 3D-to-2D generation, as pretext tasks within a pre-training framework. We leverage the spatial awareness and precise supervision offered by these two methods to address their respective limitations: ambiguous supervision signals and insensitivity to geometric information. Specifically, the proposed framework, abbreviated as PointCG, consists of a Hidden Point Completion (HPC) module and an Arbitrary-view Image Generation (AIG) module. We first capture visible points from arbitrary views as inputs by removing hidden points. Then, HPC extracts representations of the inputs with an encoder and completes the entire shape with a decoder, while AIG is used to generate rendered images based on the visible points' representations. Extensive experiments demonstrate the superiority of the proposed method over the baselines in various downstream tasks. Our code will be made available upon acceptance. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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A comparative recognition research on excretory organism in medical applications using neural networks

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Multimedia Tools and Applications 2023年

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Tamil Nadu Chennai600124 India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

In the current generation, most individuals are facing many health problems even in the elderly stages and one of the severe touch is caused by to digestive system. Usually, the problems in the digestive system transpire due to improper innards that are present inside it. Even though many experts have described effective solutions to overcome such problems no proper outcomes are achieved. Therefore, the proposed method builds an appropriate device that detects the harmful contents in the entire digestive system which records and stores the data for a continuous period. Additionally, the designed device is incorporated with the neural network using five defined layers where the data is divided into mini segments. This type of data segmentation of input images will reduce the amount of loss in the system thus making the system to be operated at reduced cost. Further equal weights are allocated in the hidden layer using reduced control parameters thereby achieving concentrated attack at several periods. To analyze the effectiveness of neural networks parametric cases are performed and the results prove to be very effective in comparison cases for about 68 percentage. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Medical applications

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Tomography of ultrabroadband polarization-frequency hyperentangled photons

Tomography of ultrabroadband polarization-frequency hyperent...

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IEEE Photonics Conference (IPC)

作者： Hsuan-Hao Lu Muneer Alshowkan Karthik V. Myilswamy Andrew M. Weiner Joseph M. Lukens Nicholas A. Peters Quantum Information Science Section Oak Ridge National Laboratory Oak Ridge Tennessee USA School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona USA

We generate photon pairs entangled in both frequency and polarization spanning the telecom C- and L-bands, characterize high-fidelity states in multiple wavelength channels, and show full tomography in 16- and 32-dime...

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On-chip pulse shaping of entangled photons

arXiv

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arXiv 2024年

作者： Wu, Kaiyi Cohen, Lucas M. Myilswamy, Karthik V. Lingaraju, Navin B. Lu, Hsuan-Hao Lukens, Joseph M. Weiner, Andrew M. Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States The Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States

We demonstrate spectral shaping of entangled photons with a six-channel microring-resonatorbased silicon photonic pulse shaper. Through precise calibration of thermal phase shifters in a microresonator-based pulse shaper, we demonstrate line-by-line phase control on a 3 GHz grid for two frequency-bin-entangled qudits, corresponding to Hilbert spaces of up to 6×6 (3×3) dimensions for shared (independent) signal-idler filters. The pulse shaper's fine spectral resolution enables control of nanosecond-scale temporal features, which are observed by direct coincidence detection of biphoton correlation functions that show excellent agreement with theory. This work marks, to our knowledge, the first demonstration of biphoton pulse shaping using an integrated spectral shaper and holds significant promise for applications in quantum information processing. Copyright © 2024, The Authors. All rights reserved.

关键词： Hilbert spaces

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Phase Optimization and Relay Selection for Joint Relay and IRS-Assisted Communication

arXiv

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arXiv 2024年

作者： Uyoata, Uyoata E. Akinsolu, Mobayode O. Obayiuwana, Enoruwa Sangodoyin, Abimbola Adeogun, Ramoni Wireless Communication Network Section Aalborg University Aalborg Denmark Faculty of Arts Computing and Engineering Wrexham University WrexhamLL11 2AW United Kingdom Department of Electronic and Electrical Engineering Obafemi Awolowo University Ife Nigeria School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom

The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)-assisted communication is considered to investigate the gains of optimizing both the phase angles and selection of relays. The combination of successive refinement and reinforcement learning is proposed. Successive refinement algorithm is used for phase optimization and reinforcement learning is used for relay selection. Experimental results indicate that the proposed approach offers improved achievable rate performance and scales better with number of relays compared to considered benchmark approaches. © 2024, CC BY.

关键词： Reinforcement learning

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