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2023 Intelligent Computing and Control for engineering and Business systems, ICCEBS 2023

作者： Chinnasamy, A. Selvakumari, P. School of Computing Srmist Department of Data Science and Business Systems kattankulathur campus Chennai India Chennai Institute of Technology Chennai Anna University Department of Computer Science and Engineering India

ISBN: (纸本)9798350394580

The DR becomes increasingly common, there is a need to automatically extract and classify disease severity. Due to diabetes problems, about 2% of people with this disease become completely blind, and DR complications make him 10% visually impaired if he has diabetes for 15 years. Furthermore, it plays an important role in the progression of blindness in middle-aged and older adults. If the disease is not recognized as soon as possible, patients can experience a severe stage of irreversible blindness. The shortage of ophthalmologists is a serious problem for the growing number of diabetic patients. Hard exudates must be found to screen and assist in disease monitoring and diagnosis. Therefore, Lloyd's clustering technique was used in this work. © 2023 IEEE.

关键词： Cat Swarm Optimization Diabetes Mellitus Diabetic Retinopathy Mean Squared Error

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Enhanced Monarch Butterfly Optimization Algorithm with Rank-Based Selection and Random Jump Strategy for Constrained engineering Problems

Enhanced Monarch Butterfly Optimization Algorithm with Rank-...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Kathiroli, Panimalar Kanmani, S. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India Puducherry Technological University Department of Information Technology Puducherry India

ISBN: (纸本)9798350333244

Nature-inspired algorithms have become increasingly prevalent in engineering and mathematical optimization due to their efficacy and adaptability. Among these algorithms, the Monarch Butterfly Optimization Algorithm (MBOA) is a population-based metaheuristic, characterized by its simple implementation and the utilization of the Migration Operator (MO) and Butterfly Adjusting Rate (BAR) to guide the search path of butterflies. However, the algorithm can suffer from susceptibility to local optima traps, early convergence, and inferior performance due to a lack of random factors. To address these challenges, we propose an Enhanced Monarch Butterfly Optimization Algorithm (E-MBOA) that incorporates Ranking-based Selection (RBS) and a Random Jumping strategy. The RBS prioritizes the search direction based on individuals' ranks in the population, enhancing the search process by encouraging the exploration of feasible areas with lower objective function values. By employing the Random Jumping strategy, the process can effectively investigate the search space and avoid local optima, thus mitigating early convergence issues. The proposed model is assessed on a variety of mechanical and engineering design problems. Results are contrasted to other optimization approaches published in the literature, demonstrating that the proposed model offers faster convergence to global optima and effectively avoids local optima traps. E-MBOA's performance validates its feasibility and effectiveness as a powerful tool for resolving complex engineering optimization problems. © 2023 IEEE.

关键词： Constrained optimization

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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Stochastic First-Order Methods with Non-smooth and Non-Euclidean Proximal Terms for Nonconvex High-Dimensional Stochastic Optimization

arXiv

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arXiv 2024年

作者： Xie, Yue Bi, Jiawen Liu, Hongcheng Department of Mathematics and Musketeers Foundation Institute of Data Science The University of Hong Kong Pokfulam Hong Kong Department of Mathematics The University of Hong Kong Pokfulam Hong Kong Department of Industrial and Systems Engineering University of Florida United States

When the nonconvex problem is complicated by stochasticity, the sample complexity of stochastic first-order methods may depend linearly on the problem dimension, which is undesirable for large-scale problems. In this work, we propose dimension-insensitive stochastic first-order methods (DISFOMs) to address nonconvex optimization with expected-valued objective function. Our algorithms allow for non-Euclidean and non-smooth distance functions as the proximal terms. Under mild assumptions, we show that DISFOM using minibatches to estimate the gradient enjoys sample complexity of O((log d)/ϵ4) to obtain an ϵ-stationary point. Furthermore, we prove that DISFOM employing variance reduction can sharpen this bound to O((log d)2/3/ϵ10/3), which perhaps leads to the best-known sample complexity result in terms of d. We provide two choices of the non-smooth distance functions, both of which allow for closed-form solutions to the proximal step. Numerical experiments are conducted to illustrate the dimension insensitive property of the proposed *** Codes 90C06, 90C15, 90C26, 90C30 Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramya, R.S. Daniel, Keren Lois Rajkumar, S. Madhavan, Sruthi S. Akshya, J. Choudhry, Mani Deepak Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India Sona College of Technology Department of Information Technology Salem India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision. © 2025 IEEE.

关键词： Decision making

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InfoNet: neural estimation of mutual information without test-time optimization 24

InfoNet: neural estimation of mutual information without tes...

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Proceedings of the 41st International Conference on Machine Learning

作者： Zhengyang Hu Song Kang Qunsong Zeng Kaibin Huang Yanchao Yang Department of Electrical and Electronic Engineering the University of Hong Kong School of Information Science and Technology University of Science and Technology of China and HKU Department of Electrical and Electronic Engineering and Musketeers Foundation Institute of Data Science the University of Hong Kong

Estimating mutual correlations between random variables or data streams is essential for intelligent behavior and decision-making. As a fundamental quantity for measuring statistical relationships, mutual information has been extensively studied and utilized for its generality and equitability. However, existing methods often lack the efficiency needed for real-time applications, such as test-time optimization of a neural network, or the differentiability required for end-to-end learning, like histograms. We introduce a neural network called InfoNet, which directly outputs mutual information estimations of data streams by leveraging the attention mechanism and the computational efficiency of deep learning infrastructures. By maximizing a dual formulation of mutual information through large-scale simulated training, our approach circumvents time-consuming test-time optimization and offers generalization ability. We evaluate the effectiveness and generalization of our proposed mutual information estimation scheme on various families of distributions and applications. Our results demonstrate that InfoNet and its training process provide a graceful efficiency-accuracy trade-off and order-preserving properties. Our code and models are available as a comprehensive toolbox to facilitate studies in different fields requiring real-time mutual information estimation.

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Monolithic 50 GHz tunable microwave signal generation based on erbium-doped lithium niobate dual lasers

Monolithic 50 GHz tunable microwave signal generation based ...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Cai, Minglu Zhang, Xujia Li, Tianyi Shi, Hao Li, Tieying Cao, Xianyi Cui, Zekun Chen, Jianping Wu, Kan Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

The monolithic erbium-doped lithium niobate lasers can achieve a tunable wavelength range of 56.25 GHz and generate a 50 GHz microwave signal with a side-mode suppression ratio of 35 dB by dual lasers. © Optica P... 详细信息

关键词： Monolithic microwave integrated circuits

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Monolithic 50 GHz tunable microwave signal generation based on erbium-doped lithium niobate dual lasers

Monolithic 50 GHz tunable microwave signal generation based ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Microwave generation

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High-resolution and High-rate Dual-comb FMCW Lidar with Coherent Comb Stitching

High-resolution and High-rate Dual-comb FMCW Lidar with Cohe...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Cao, Xianyi Wang, Long Li, Tianyi Cai, Minglu Wu, Guiling Chen, Jianping Wu, Kan Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

A dual-comb FMCW Lidar with coherent comb stitching (CCS) technique is firstly demonstrated. CCS can simultaneously achieve 65 GHz high frequency sweeping range, millimeter-level ranging resolution and 100 kHz high vo... 详细信息

ISBN: (纸本)9781957171395

关键词： Optical radar

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High-resolution and High-rate Dual-comb FMCW Lidar with Coherent Comb Stitching

High-resolution and High-rate Dual-comb FMCW Lidar with Cohe...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Pulse amplitude modulation

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