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European Control Conference (ECC)

作者： Weilin Wang Yanwei Zang Wenbiao Zhang Yang Xu Jiayuan Liang Hanran Zhang Lachlan Andrew Hai Vu Chaohui Gong Department of Control Science and Engineering University of Shanghai for Sciece and Technology Huadong Engineering Corporation Limited School of Computing and Information Systems University of Melbourne Institute of Transport Studies Monash University

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

A traffic light control protocol is proposed, which builds compact platoons of vehicles to make efficient use of green times. It forms short platoons at multiple links and times traffic lights to concatenate these to form a single dense platoon, in which vehicles mostly passes many intersections without stopping. The protocol minimizes the number of times vehicles stop; by simulation, it roughly halves that of greenwave under near saturated demand.

关键词： Protocols Roads Europe Vehicle dynamics Junctions

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Automated Detection and Classification of Cotton Leaf Diseases: A Computer Vision Approach

Automated Detection and Classification of Cotton Leaf Diseas...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： Swastick Ganguly Jathin S M Mohak M Sanjay BM Somashekhar N Shobharani Department of Computer Science Amrita School of Computing Mysuru Campus Dept of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru Campus

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability

关键词： Deep learning Training Plant diseases Pathogens Accuracy Computational modeling Crops

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Dimensionality Reduction on Fish Dataset with Different Machine Learning Algorithms Comparison

Dimensionality Reduction on Fish Dataset with Different Mach...

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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Sugunadevi C Roshni M Balakrishnan Department of Information Science & Engineering CMR Institute of Technology Bengaluru India Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidhyapeedham Bengaluru India

Classification of fish is a complex task in the current situation exhibited by the great diversity of available species, even though it is used in agricultural and commercial sectors. In this work, the data is classified with the Supervised Machine Learning algorithms such as K-Nearest Neighbour, Naïve Bayes, and Decision Tree, observing the results before and after applying dimensionality reduction techniques PCA, LDA, and Truncate SVD. One of the well-known dimensionality reduction techniques Linear Discriminant Analysis LDA achieves the highest accuracy for K-NN, Naïve Bayes 93.7%, and Decision Tree 96.8%.

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Learning to Optimize Resource Allocation in Dynamic Wireless Environments: Embracing the New While Engaging the Old

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Zhenrong Li, Yang Wu, Yik-Chung Gong, Yi The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Southern University of Science and Technology Department of Electrical and Electronic Engineering Shenzhen518055 China Shenzhen Research Institute of Big Data Shenzhen518172 China Great Bay University School of Computing and Information Technology Dongguan523000 China

Wireless resource allocation is a critical component in modern communication systems, and deep neural networks (DNNs) have shown great promise in addressing this challenge. However, the conventional DNNs assume that testing data follows the same distribution as that of the training data, which is incongruent with the dynamic nature of real-world wireless environments. This paper introduces a new training algorithm designed specifically for dynamic wireless environments where channel distribution exhibits variability. This method helps DNNs adapt to new environments while preserving previously learned information. The proposed approach distinguishes itself by updating the DNN parameters in the null space of the low-rank covariance of previous data, which reduces memory needs and boosts training efficiency. Additionally, to counter the problem of DNNs hitting their model capacity during continuous adaptation, a selective forgetting mechanism is proposed. This mechanism allows DNNs to discard the unimportant knowledge over time, freeing up model capacity for more effective adaptation. The effectiveness of the algorithm is validated by integrating it with graph neural networks and multilayer perceptrons for weighted sum-rate maximization. Through a comprehensive evaluation that includes synthetic and ray-tracing-based datasets, superior performance is demonstrated compared to existing methods. © 2002-2012 IEEE.

关键词： Deep learning dynamic environments low-rank null space wireless resource allocation

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A Bibliometric Analysis on Fintech and Digital Finance Using Visualizing Network

A Bibliometric Analysis on Fintech and Digital Finance Using...

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Sustainable computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Aryan Upadhyay Vijay K. Yadav Vikas Tyagi Varya Agarwal School of Management & Commerce Dev Bhoomi Uttarakhand University Dehradun Lloyd Institute of Engineering & Technology Greater Noida Department of Basic computing Quantum University

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Financial technology (FinTech) the new innovation in technological and financial field that is revolutionizing and improving the automation of delivery and use of financial services. FinTech has attracted wide attention for the benefits that's come hand in hand with this new technology and has also gained wide recognition because of the increase in financial literacy over the last 5 years. From 2018 to 2022, the services of fintech shifted toward being consumer-oriented. This new industry now includes sectors like retail banking, investment management, education, and non-profit organizations, Fintech is also developing to adaptive to be used for crypto-currencies and NFT's. The paper will expand on the argument of taking a proactive role in adoption of FinTech and how it will benefit the an average individual.

关键词： Industries Hands Visualization Technological innovation Fintech Education Finance Organizations Cryptocurrency Investment

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UDE-III: An Enhanced Unified Differential Evolution Algorithm for Constrained Optimization Problems

arXiv

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arXiv 2024年

作者： Trivedi, Anupam Chauhan, Dikshit The Department of Electrical and Computer Engineering The National University of Singapore Singapore The Department of Mathematics & Computing Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

In this paper, an enhanced unified differential evolution algorithm, named UDE-III, is presented for real parameter-constrained optimization problems. The proposed UDE-III is a significantly enhanced version of the Improved UDE (i.e., IUDE or UDE-II), which secured the 1st rank in the CEC 2018 competition on constrained real parameter optimization. UDE-II was inspired by the following state-of-the-art DE variants - CoDE, JADE, SaDE, DE with ranking-based mutation operator, SHADE, and C2oDE. To design UDE-III, we extensively analyzed and targeted the weaknesses of UDE-II. Specifically, UDE-III uses three trial vector generation strategies - DE/rand/1, DE/current-to-rand/1, and DE/current-to-pbest/1. It is based on a dual population approach, and for each generation, it divides the current population into two sub-populations. The top sub-population employs all three trial vector generation strategies on each target vector, just like in CoDE. On the other hand, in the bottom sub-population, one trial vector generation strategy is implemented on each target vector. The bottom sub-population employs strategy adaptation, wherein the probability of the three trial vector generation strategies is adapted in every generation by learning from their experiences in generating promising solutions in the previous learning period. The mutation operation in UDE-III is based on ranking-based mutation. Further, it employs the parameter adaptation principle of SHADE. The constraint handling principle in UDE-III is based on a combination of the feasibility rule and epsilon-constraint handling technique as proposed in C2oDE. We observed that stagnation is a major weakness of UDE-II. To overcome this weakness, we took inspiration from the best-discarded vector selection (BDVS) strategy proposed in the literature and integrated a novel strategy in UDE-III to address stagnation. Additionally, UDE-III considers the size of two sub-populations to be a design element, whereas in UDE-II, the t

关键词： Vectors

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Smart Multi-Building Energy Monitoring System

Smart Multi-Building Energy Monitoring System

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IEEE International Conference on Electro-Information technology

作者： Saurabh Saluja Colin Prochnow Grant Couper Filip Zivko Xinrui Yu Mikhail Gromov Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

This paper presents an intelligent system for monitoring energy usage in smart buildings. It functions as a bridge between the smart grid and smart buildings. A web-based platform is designed to monitor sensor data and track power consumption on the grid. AI technology provides tips for energy conservation based on factors such as power usage patterns and weather conditions. The adaptability of our approach allows for the tuning of automated protocols based on evolving power usage patterns and grid conditions. Data is transmitted between the client devices and the central server via a Zigbee network organized in a star formation, allowing for mutual communication. Our implementation results in the improvement of the overall energy efficiency of supported infrastructure while ensuring continuous and optimized operation in case of a bulk grid blackout.

关键词： Smart buildings Energy conservation Zigbee Stars Smart homes Energy efficiency Smart grids

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Binary and Ternary Human Gait Phase Classification Using Machine Learning Algorithms

Binary and Ternary Human Gait Phase Classification Using Mac...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Mekni, Amal Narayan, Jyotindra Gritli, Hassène ENIT University of Tunis El Manar Tunis Borj Cedria1164 Tunisia Department of Mechanical Engineering Thapar Institute of Engineering and Technology Punjab Patiala147004 India Department of Computing Imperial College LondonSWS 2AZ United Kingdom ENIT University of Tunis El Manar Le Belvédère Tunis1002 Tunisia ISTIC University of Carthage Borj Cedria Tunis1164 Tunisia

ISBN: (纸本)9798331533137

Accurately identifying gait phases offers vital insights into medical diagnostics, requiring precise biomechanical assessment. Despite various machine learning (ML) methods, achieving high precision is challenging. This study uses ML techniques to classify the gait phases of 100 individuals (average age 41.91 ± 5.3 years). Classification algorithms considered in this work are: k-Nearest Neighbor (k-NN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayesian (NB). The performance of these algorithms is evaluated using two training methods while classifying binary (stance/swing) and ternary (stance-I/stance-II/swing) subphases. In the first method, 80% of the data from binary or ternary phases is randomly selected for training and the rest for testing. In the second method, data is divided by participants, with 80% used for training and 20% for testing. The RF algorithm consistently outperforms others in both training methods, achieving 99.5% (first method) and 99.9% (second method) accuracy in binary classification and 99.4% (first method) and 99.5% (second method) in ternary classification. Despite the reduced performance while transitioning from binary to ternary classification, consistency is maintained for all ML algorithms. Moreover, the second training method is more effective for classifying binary and ternary gait phases. ©2024 IEEE.

关键词： Adversarial machine learning

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Exploring Eye Gaze Patterns in Three Dimensions: An Innovative Visualization Dashboard

Exploring Eye Gaze Patterns in Three Dimensions: An Innovati...

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Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Akshay S Yashwanth M V Manohar N Tian Jipeng Amudha J Department of Computer Science Amrita Vishwa Vidyapeetham School of Computing Mysore Karnataka India Department of Computer Science Computer Science College Zhongyuan University of Technology China Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Bengaluru Karnataka India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

Eye tracking is a method of measuring and analyzing the movements of the eyes as they move and focus on different objects in the visual field. This technique has been used in a wide range of fields, including psychology, neuroscience and human-computer interaction. Eye tracking provides valuable insights into how individuals process visual information, including attentional biases, cognitive processing, and decision-making. Traditionally, eye tracking has relied on licensed software and specialized hardware, making it costly and less accessible for some researchers and applications. However, with recent technological advancements and by leveraging the capabilities of webcams, researchers, and practitioners can now perform eye-tracking experiments without the need for expensive equipment. By utilizing webcams, researchers can conduct eye-tracking experiments in a cost-effective manner, making the method more accessible to a broader range of researchers and practitioners.

关键词： Visualization Three-dimensional displays Neuroscience Webcams Data visualization Psychology Gaze tracking

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NHC-mediated photocatalytic para-selective C–H acylation of aryl alcohols: regioselectivity control via remote radical spiro cyclization

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Science China Chemistry 2025年

作者： Tinglei Zhang Lala Wang Xiaolin Peng Tianhui Liao Dongmei Chen Ting Tu Donghan Liu Ziye Cheng Shiqing Huang Shi-Chao Ren Xinglong Zhang Yonggui Robin Chi State Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural Bioengineering Ministry of Education Guizhou University Qiannan Medical College for Nationalities School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Department of Chemistry The Chinese University of Hong Kong Institute of High Performance Computing Agency for Science Technology and Research

Friedel-Crafts reaction and transition-metal catalysis represent primary methodologies for arene acylation; however, suffer from certain limitations, such as electronic effect-dependent reactivity, regioselectivity issues, the necessity of directing group, and relatively exceptionally limited exploration in meta-and para-acylations. N-heterocyclic carbene(NHC) catalysis has recently emerged as a promising alternative strategy for arene acylation, offering distinct regioselectivity profiles that are challenging to achieve through known methods. Herein, an NHC-mediated photo-catalytic para-selective C–H acylation of aryl alcohols is disclosed. The reaction employs a remote radical-induced temporary spiro cyclization strategy, proceeding via tandem radical dearomatization, radical acylation, rearomatization processes. Combined experimental and computational mechanistic studies support the proposed reaction pathway and observe an exclusive para-selectivity regardless of the nature of the substituents on the substrates. Despite the less satisfactory yields, the current method exhibits non-electronic nature-based reactivity and regioselectivity, enabling quick access to acylated arenes that are difficult or impossible for reported methods.

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