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IEEE Electronic Power Grid (eGrid)

作者： Hussain Sarwar Khan Ihab S. Mohamed Kimmo Kauhaniemi Lantao Liu School of Technology and Innovations University of Vaasa Vaasa Finland Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (纸本)9781665449809

The rapid growth of renewable energy technology enables the concept of microgrid (MG) to be widely accepted in the power systems. Due to the advantages of the DC distribution system such as easy integration of energy storage and less system loss, DC MG attracts significant attention nowadays. The linear controller such as PI or PID is matured and extensively used by the power electronics industry, but their performance is not optimal as system parameters are changed. In this study, an artificial neural network (ANN) based voltage control strategy is proposed for the DC-DC boost converter. In this paper, the model predictive control (MPC) is used as an expert, which provides the data to train the proposed ANN. As ANN is tuned finely, then it is utilized directly to control the step-up DC converter. The main advantage of the ANN is that the neural network system identification decreases the inaccuracy of the system model even with inaccurate parameters and has less computational burden compared to MPC due to its parallel structure. To validate the performance of the proposed ANN, extensive MATLAB/Simulink simulations are carried out. The simulation results show that the ANN-based control strategy has better performance under different loading conditions comparison to the PI controller. The accuracy of the trained ANN model is about 97%, which makes it suitable to be used for DC microgrid applications.

关键词： Simulation Computational modeling Training data Artificial neural networks Microgrids Mathematical models Voltage control

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Enhancing Phishing Universal Resource Locator Detection Systems Using Hybrid Machine Learning Classifiers and Data Balancing Techniques

Enhancing Phishing Universal Resource Locator Detection Syst...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Mohammed Ibrahim Bello Muhammed Hadi Muhammed Basheer Jasser Samuel-Soma M. Ajibade Athirah Mohd Ramly Yoke Leng Yong Bayan Issa Dept. of Cyber Security Nigerian Defence Academy Kaduna Nigeria School of Engineering and Technology Sunway University Bandar Sunway Selangor Darul Ehsan Malaysia School of Architecture Computing and Engineering University of East London University Way Docklands Campus London UK School of Information and Communication Technology HELP University Persiaran Cakerawala Subang Bestari Seksyen U4 Shah Alam Malaysia Faculty of Informatics Engineering University of Aleppo Syria

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Phishing attacks have become a significant threat to online security, with cybercriminals using sophisticated tactics to evade detection. Traditional phishing detection systems often relied on rule-based approaches, which can be limited in their effectiveness. This research uses machine learning (ML) to detect phishing universal resource locator (URL) by evaluating the performance of advanced classifier for URL phishing detection using both original and balanced datasets. The classifier assessed is Random Forest. In conjunction with data balancing techniques like SMOTE and Resample, on the original dataset, it achieved an accuracy of $\mathbf{9 8. 1 0 \%}$ with a Kappa statistic of $\mathbf{9 6. 2 0\%}$, requiring 1.93 seconds for training and 0.05 seconds for testing. For the balanced dataset, the performance slightly improved, achieving an accuracy of $\mathbf{9 8. 2 0 \%}$ and a Kappa statistic of $\mathbf{9 6. 4 0\%}$, with reduced training time of $\mathbf{1. 3 0}$ seconds and testing time of $\mathbf{0. 0 3}$ seconds. Hence, balancing original dataset can significantly improve URL phishing detections using ML.

关键词： Uniform resource locators Training Accuracy Phishing Classification algorithms Security Reliability Particle swarm optimization Random forests Testing

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Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Insect Identification in Pulsed Lidar Images Using Changepoi...

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Intermountain Engineering, technology and computing (IETC)

作者： Nathaniel Sweeney Caroline Xu Joseph A. Shaw Toby D. Hocking Bradley M. Whitaker Electrical and Computer Engineering Department Montana State University USA Electrical Engineering and Computer Science Department University of Michigan USA Optical Technology Center Montana State University USA School of Informatics Computing and Cyber Systems Northern Arizona University USA

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training.

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Android Based Heart-rate Monitoring System using ECG Sensor 2

Android Based Heart-rate Monitoring System using ECG Sensor

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2nd International Conference on Sustainable Engineering and Creative computing, ICSECC 2020

作者： Rosalina Selina, Grace Mandala, Rila Faculty of Computing Information Technology Study Program President University Bekasi Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728175881

This research aims to help the user monitor their heart’s condition and notify other people in case of an emergency due to an abnormal heartbeat (normal heartbeat around 60-100 beats per minute). From the heart rate graph, the user could do a consultation with an expert in the nearest hospital using a map feature that will show the nearest hospital with a 1 KM radius from the user’s current location. The heart is undeniably an essential part of the human body system. The heart's missions are to pump blood to the lungs to expel the waste carbon dioxide that resulted from the respiration and pump blood that will deliver oxygen throughout the human body's circulation system. While the blood that carries oxygen being pumped throughout the human body, the heart will provide the oxygen to all cells. Body cells need to use oxygen to get the energy from food through a process called cellular respiration. Monitoring our heartbeat and pulse rate can increase people’s awareness about their heart’s health, motivating, and encouraging people to maintain a healthy eating plan and exercise. This research will be implemented ECG Sensor. The sensor will then send the data to the android application via HC-06 Bluetooth module. Monitoring the heart rate and analyzing the user’s heartbeat will be done in the android with a microcontroller device's help. © 2020 IEEE.

关键词： Electrocardiograms

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Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

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Computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

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A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

arXiv

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arXiv 2024年

作者： Hu, Junwei Bewong, Michael Kwashie, Selasi Zhang, Wen Nofong, Vincent M. Wu, Guangsheng Feng, Zaiwen College of Informatics Huazhong Agricultural University Hubei Wuhan China School of Computing Mathematics & Engineering Charles Sturt University Wagga WaggaNSW Australia Artificial Intelligence & Cyber Futures Institute Charles Sturt University BathurstNSW Australia Faculty of Engineering University of Mines and Technology Tarkwa Ghana School of Mathematics and Computer Science Xinyu University China

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://***/Zaiwen/HNCL-DTI. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning

Real Time Prediction Model for Air Pollution and Air Quality...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Rakesh Kumar Bhisham Sharma Shashi Shekhar Imed Ben Dhaou Saurabh Singhal Department of Computer Engineering & Applications GLA University Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals’ wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city.

关键词： Technological innovation Smart cities Atmospheric modeling Predictive models Air pollution Real-time systems Indexes

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RIGOROUS DERIVATION OF THE DEGENERATE PARABOLIC-ELLIPTIC KELLER-SEGEL SYSTEM FROM A MODERATELY INTERACTING STOCHASTIC PARTICLE SYSTEM. PART II PROPAGATION OF CHAOS

arXiv

引用

arXiv 2023年

作者： Chen, Li Gvozdik, Veniamin Holzinger, Alexandra Li, Yue School of Business Informatics and Mathematics Universität Mannheim Mannheim68131 Germany Institute for Analysis and Scientific Computing Vienna University of Technology Wiedner Hauptstraße 8–10 Wien1040 Austria Department of Mathematics Nanjing University Nanjing210093 China

This work is a series of two articles. The main goal is to rigorously derive the degenerate parabolic-elliptic Keller-Segel system in the sub-critical regime from a moderately interacting stochastic particle system. In the first article [7], we establish the classical solution theory of the degenerate parabolic-elliptic Keller-Segel system and its non-local version. In the second article, which is the current one, we derive a propagation of chaos result, where the classical solution theory obtained in the first article is used to derive required estimates for the particle system. Due to the degeneracy of the non-linear diffusion and the singular aggregation effect in the system, we perform an approximation of the stochastic particle system by using a cut-offed interacting potential. An additional linear diffusion on the particle level is used as a parabolic regularization of the system. We present the propagation of chaos result with logarithmic scalings. Consequently, the propagation of chaos follows directly from convergence in the sense of expectation and the vanishing viscosity argument of the Keller-Segel system. Copyright © 2023, The Authors. All rights reserved.

关键词： Chaos theory

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Dynamic Clustering Convolutional Neural Network 39

Dynamic Clustering Convolutional Neural Network

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Tanzhe Zhang, Baochang Lyu, Jiayi Zheng, Xiawu Guo, Guodong Jin, Taisong Key Laboratory of Multimedia Trusted Perception and Effcient Computing Ministry of Education of China Xiamen University China School of Informatics Xiamen University China Hangzhou Research Institute School of Artificial Intelligence Beihang University China Nanchang Institute of Technology China School of Engineering Science University of Chinese Academy of Sciences China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China

ISBN: (纸本)157735897X

Convolutional neural networks (CNNs) have been playing a dominant role in computer vision. However, the existing approaches of using local window modeling in popular CNNs lack flexibility and hinder their ability to capture long-range dependencies of objects in an image. To overcome these limitations, we propose a novel CNN architecture, termed Dynamic Clustering Convolutional Neural Network (DCCNeXt). The proposed DCCNeXt takes a unique approach by employing global clustering to group image patches with similar semantics into clusters that are then convolved using the shared convolution kernels. To address the high computational complexity of global clustering, the feature vectors from each patch's subspace are extracted for efficient clustering, which makes the proposed model widely compatible with the downstream vision tasks. The extensive experiments of image classification, object detection, instance segmentation, and semantic segmentation on the benchmark datasets demonstrate that the proposed DCCNeXt outperforms the mainstream Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Vision Multi-layer Per-ceptrons (MLPs), Vision Graph Neural Networks (GNNs), and Vision Mambas (ViMs). We anticipate that this study will provide a new perspective and a promising avenue for the design of convolutional neural networks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convolutional neural networks

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Differentiated Relevance Embedding for Group-based Referring Expression Comprehension

arXiv

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arXiv 2022年

作者： Chen, Fuhai Ge, Xuri Sun, Xiaoshuai Gao, Yue Liu, Jianzhuang Chen, Fufeng Li, Wenjie Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computing Science University of Glasgow United Kingdom Department of Artificial Intelligence School of Informatics Xiamen University China School of Software Tsinghua University China Huawei Noahs Ark Lab Shenzhen China Fuzhou Institute for Data Technology China

Referring expression comprehension aims to locate a specific object in an image referred by a natural language expression, where the key lies in capturing the cross-modal relevance between the expressions and their corresponding objects. Existing works typically model the cross-modal relevance by estimating the attribute-oriented triplet ranking loss in each image, where the anchor object/expression and their positive expression/object have the same attribute as the negative expression/object, but with different attribute values, e.g. red and black for the attribute clothing color. These objects/expressions are exclusively utilized to learn the implicit representation of the attribute by a pair of different values, which however impedes the accuracies of the attribute representations, expression/object representations, and their cross-modal relevances since each anchor object/expression usually has multiple attributes while each attribute usually has multiple potential values. To effectively learn the cross-modal relevance, we investigate a novel REC problem named Group-based REC, where each object/expression is simultaneously employed to construct the multiple triplets among the semantically similar images. To tackle the explosion of the negatives and the differentiation of the anchor-negative relevance scores, we propose a novel multi-group self-paced relevance learning schema (termed MSRL) for the cross-modal alignment, where the within-group object-expression pairs are adaptively assigned with different priorities based on their cross-modal relevance scores during model training. Since the average cross-modal relevance varies a lot across different groups, we further design an across-group relevance constraint to balance the bias of the group priority. Quantitative experiments on three widely-used REC benchmarks (RefCOCO, RefCOCO+, and RefCOCOg) demonstrate the superiority of the proposed MSRL method against the state-of-the-art methods. Extensive ablative exper

关键词： Natural language processing systems

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