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2025 international conference on multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raskar, Sandeep Dhasmana, Gulshan Lakshminarayana, M. Patil, Harshal Bhagya Shree, S. Natrayan, L. Terna Engineering College Department of Artificial Intelligence and Data Science Nerul Maharashtra Navi Mumbai400706 India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India M S Ramaiah Institute of Technology Department of Medical Electronics Engineering Karnataka Bengaluru560054 India School of Computer Science & Engineering Iilm University Uttar Pradesh Greater Noida201 306 India Brindavan College of Engineering Department of Physics Karnataka Bengaluru560063 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509828

Wireless Sensor Networks (WSNs) are essential for many applications, but because of their limited power resources, they confront major energy efficiency difficulties. This research offers a novel approach for improving WSN energy efficiency that makes use of deep learning, namely deep reinforcement learning. The framework uses sophisticated energy data transformation methods to pre-process sensor data, derive important information, and spot energy usage trends. In order to better comprehend the model and represent the distinct dynamics of WSNs, domain-specific features including residual energy, transmission power, and path length are designed. The system learns the best routing and energy-saving strategies by incorporating deep reinforcement learning, which strikes a balance between energy usage and data transmission efficiency. When compared to traditional techniques, evaluation metrics show that the suggested framework can greatly reduce energy depletion and increase network longevity. Deep learning has the ability to address WSN energy concerns by providing a scalable and adaptable solution for energy-efficient network operations. © 2025 IEEE.

关键词： Deep reinforcement learning

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to international Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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Property Price and Rent Prediction System

Property Price and Rent Prediction System

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Singh, Atharava Pratap Rai, Abhinav Karnwal, Devansh Vats, Anupam Pandey, Tripti Abes Engineering College Department of Information Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331529833

Machine Learning (ML) has revolutionized various sectors including speech recognition, product recommendations, and healthcare. This research focuses on applying ML techniques to address challenges in real estate market prediction. The unpredictable nature of property prices creates significant difficulties for buyers in making informed decisions. This research proposes a multi-algorithm housing price prediction model, integrating SVM, Random Forest, and GBM approaches, with Random Forest demonstrating superior predictive performance. The model considers various factors influencing property prices, enabling buyers to make data-driven decisions without excessive reliance on intermediaries. This paper presents the methodology, implementation, and comparative results of these algorithms in the context of real estate price prediction. © 2025 IEEE.

关键词： Prediction models

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Enhancing precision and efficiency: small object segmentation in high-resolution images using multi-task networks 4

Enhancing precision and efficiency: small object segmentatio...

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4th international conference on Machine Learning and computer Application, ICMLCA 2023

作者： Bian, Kaiwen Xu, Zhengguang University of Science and Technology Beijing Beijing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Semantic segmentation is a typical task in computer vision. To address the issue of poor performance for small objects in high-resolution images by existing semantic segmentation networks, a method based on a multi-task network is proposed in this paper. This approach jointly learns relevant tasks, enhancing the accuracy of small object segmentation. The methodology of upsampling in semantic segmentation has been refined through the amalgamation of sub-pixel convolution and conventional convolution techniques, ensuring the meticulous preservation of intricate details. Additionally, a sophisticated post-processing module has been introduced, effectively enhancing the accuracy of small object edge segmentation. The empirical results highlight the effectiveness of this approach, significantly enhancing the segmentation quality of small objects in high-resolution images, while simultaneously ensuring real-time computational efficiency. © 2024 SPIE.

关键词： Convolution

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Research on the steering of multi-legged wheeled robots 3

Research on the steering of multi-legged wheeled robots

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3rd international conference on Intelligent Mechanical and Human-computer Interaction Technology, IHCIT 2024

作者： Pang, Jianning Zhao, Dong Zhu, Xiaoxuan Zheng, Pengchao School of Mechanical Engineering University of Jinan Shandong Jinan250022 China

ISBN: (纸本)9781510683105

A simulation analysis was conducted on the steering of the multi-legged wheeled robot. The feasibility of the Ackermann steering principal usage in the multi-legged wheeled robot was analyzed in the paper. Through the co-simulation of ADAMS and MATLAB, the Ackermann steering principle used in the multi-legged wheeled robot was found out and improved. Based on the simulation results, experimental verification was conducted, and the results indicated that the Ackermann steering principle was suitable for multi-legged wheeled robots, but there were also many shortcomings to overcome in order to practical application. © 2024 SPIE.

关键词： MATLAB

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Data-Driven Diagnosis of multi-Class Open-Circuit Switch Faults in Three-Phase Front-End Converters

Data-Driven Diagnosis of Multi-Class Open-Circuit Switch Fau...

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2024 international conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Ghosh, Sourabh Singh, Vikash Singh, Asheesh Kumar Hassan, Ehtesham Singh, Sri Niwas Motilal Nehru National Institute of Technology Allahabad Electrical Engineering Department Prayagraj India Kuwait College of Science and Technology Department of Computer Science and Engineering Kuwait Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9798350388534

This paper analyzes the application of benchmark classification techniques for multi-class diagnosis of open-circuit switch faults (OCSFs) in three-phase front-end converters (TP-FECs). OCSFs result in harmonic distortion, secondary faults, and unscheduled shutdowns in power utilities. Moreover, due to the inherent difference between OCSFs and short-circuit switch faults, the same diagnosis techniques do not apply to both types of faults. This paper presents a detailed study on a two-level TP-FEC to generate single-, double-, and triple-switch OCSFs, resembling real-world scenarios. The single-moment fault data with 624 observations is captured through 13 distinct features to extract the fault characteristics during each of the 38 classes of OCSFs under varying load demands. Later, the paper analyzes the performance of neural networks (NNs), support vector machines (SVMs), ensemble bagged trees (EBTs), and k-nearest neighbours (KNNs) for multi-class diagnosis of OCSFs. The evaluation employed cross-validation with optimal hyperparameter combinations to train different models. Through rigorous experiments, we demonstrate the superiority of NNs in classifying multi-class OCSFs with a testing accuracy of 97.50%, achieving an improvement of 0.4%, 2.4%, and 3.8% compared to SVM, KNN, and EBT, respectively. Furthermore, our experiments underscore the efficacy of NNs and SVMs over other state-of-the-art methods for diagnosing OCSFs. © 2024 IEEE.

关键词： Harmonic distortion

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Research on the seismic signal classification algorithm based on SE-DenseNet 4

Research on the seismic signal classification algorithm base...

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4th international conference on Machine Learning and computer Application, ICMLCA 2023

作者： Wang, Yuan Huang, Hanming Ding, Weiwei Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Addressing the issue of seismic signal classification and recognition for natural earthquakes and artificial blasting, a deep learning model suitable for seismic signal classification and recognition is proposed. In this study, a dataset is created by selecting 14,001 natural earthquake waveforms and 12,870 artificial blasting waveforms from the seismic waveform data provided by the China National Earthquake science Data Sharing Centre for the years 2010-2016. Based on the DenseNet architecture, an attention mechanism is introduced into the original Dense Block, adaptively enhancing valuable feature channels, thereby improving the model's perception and expression capabilities for important information. The model achieves an accuracy of 96.25% on the test set, surpassing the compared methods. Experimental results demonstrate that the attention-enhanced DenseNet exhibits excellent classification effectiveness and generalization ability when applied to seismic signals. © 2024 SPIE.

关键词： Earthquakes

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Enhancing Quality of Service in Mobile Ad-Hoc Networks through Secure Routing with multi-Constrained Network Feature Approximation

Enhancing Quality of Service in Mobile Ad-Hoc Networks throu...

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2024 international conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India Srv Media Pvt. Ltd India Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the realm of Mobile Ad-Hoc Networks (MANETs), achieving optimal Quality of Service (QoS) amidst the challenges of security threats and dynamic network topology remains a paramount concern. This paper introduces a groundbreaking secure routing strategy empowered by a multi-constrained network feature approximation technique aimed at bolstering QoS in MANETs. The core innovation lies in the fusion of a multi-dimensional optimization framework with an advanced feature approximation algorithm, tailored to navigate the intricacies of MANETs' decentralized architecture and fluctuating conditions. By focusing on critical QoS parameters such as throughput, delay, packet delivery ratio, and jitter, alongside ensuring robust security measures, our proposed method significantly enhances the reliability and performance of data transmission within MANETs. A comprehensive simulation environment is utilized to evaluate the effectiveness of our approach, revealing substantial improvements in QoS metrics compared to conventional secure routing protocols. Through a meticulous examination of these results, the study underscores the proposed method's efficiency in managing the dual objectives of security and QoS optimization. This research not only marks a significant advancement in secure routing protocols for MANETs but also sets the groundwork for future explorations into integrating multi-constrained optimization techniques for network feature approximation in ad-hoc networks. © 2024 IEEE.

关键词： Constrained optimization

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multi-domain multi-scale DeepFake Detection for Generalization 4

Multi-domain Multi-scale DeepFake Detection for Generalizati...

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4th international conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Liu, Tianju Liu, Yuan Li, Ningyi Shi, Hui Liaoning Normal University School of Computer Science and Artificial Intelligence Liaoning Dalian China

ISBN: (纸本)9798331534622

As deepfake detection technology progresses and application scenarios broaden, it becomes challenging for detection models within a single domain or single scale to handle the complex application environments, leading to limited accuracy and generalization capabilities of the detection models. To address the above problems, we propose a multi-domain multi-scale DeepFake Detection model. First, we employ the RetinaFace framework to preprocess and augment the image data, which directs the model's attention to the relevant regions of the image. Next, we utilize the EfficientNet-B0 network for rapid coarse-grained feature extraction. Building on these coarse-grained features, we design two modules to capture forgery traces at multiple levels: a spatial-domain fine-grained feature extraction module based on multi-scale analysis and a frequency-domain fine-grained feature extraction module leveraging the Fast Fourier Transform. Finally, the dual-domain features are fused using a multi-channel attention mechanism, enabling the model to comprehensively analyze images from various perspectives. Additionally, we implement a Particle Swarm Optimization (PSO) strategy to optimize the hyper-parameters of the network model. The proposed model demonstrates significant advantages over other algorithms, particularly in cross-dataset detection, exhibiting strong generalization, robustness, and adaptability. When trained on the FF++ dataset and tested on the Celeb-DF dataset, it achieves an AUC of 91.7%. This represents a 14.8% improvement in cross-dataset performance compared to the best existing model. © 2024 IEEE.

关键词： Frequency domain analysis

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Legal Judgment Prediction with LLM and Graph Contrastive Learning Networks 24

Legal Judgment Prediction with LLM and Graph Contrastive Lea...

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8th international conference on computer science and Artificial Intelligence, CSAI 2024

作者： Xia, Yangbin Luo, Xudong School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin China Guangxi Key Lab of Multi-Source Information Mining Guangxi Normal University Guangxi Guilin China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9798400718182

Legal judgment prediction is a complex task that involves analysing case descriptions and relevant facts to predict outcomes. This paper presents LLG-Judger, a novel framework combining Large Language models and Graph neural networks for LJP. LLG-Judger integrates the strengths of rule-based divisions, Large Language Models (LLMs) for initial case predictions, and graph neural networks for structured fact representation. The proposed model first constructs a fact graph from case descriptions and then applies a graph encoder to capture syntactic and semantic relationships within the text. To enhance prediction accuracy, we employ a contrastive learning module that improves feature representation by focusing on both syntactic and global semantic neighbours. Additionally, we integrate ChatGPT’s predictions with the graph model’s results using KL divergence to optimise the final prediction. Experimental evaluations demonstrate the effectiveness of LLG-Judger, achieving superior performance over various baselines, especially in the legal domain. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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