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检索条件"任意字段=International Multi-Conference in Computer Science and Computer Engineering"

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Vision-based UAV Detection in Complex Backgrounds and Rainy Conditions 2

Vision-based UAV Detection in Complex Backgrounds and Rainy ...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Singh, Pranita Gupta, Keshav Jain, Amit Kumar Vishakha Jain, Abhishek Jain, Arpit Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Grd Institute of Management and Technology Department of Computer Science and Engineering Dehradun India Coer University Department of Cyber Security Roorkee India Uttaranchal University Department of Computer Science and Engineering Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350371055

Studies aimed at detecting UAVs in real time using processer vision and deep learning are in their infancy. Although there are many possible advantages to using unmanned aerial vehicles (UAVs), some people are concerned about the risks and misuse of these machines. All potential threats to safety, privacy, and security fall under this category. It is common practice to combine hardware components, such as cameras, with software components when developing vision-based detection systems. Under difficult circumstances such as changing experiences, varying UAV sizes, complicated related settings, and light to heavy rain, this research evaluates the performance of popular and cutting-edge vision-based object-identification algorithms in detecting unmanned aerial vehicles (UAVs). Two datasets were created;one utilized the sky and the other had a more complex background. The study's overarching goal was to evaluate how various methods fared in this particular context. In YOLOv5, YOLOv8, Retina Net, and Faster-RCNN, we examine and evaluate indicators with one stage and two stages. The results of this research could provide light on the performance of the chosen models in difficult settings and lead to the creation of more trustworthy UAV detection systems. © 2024 IEEE.

关键词： Cameras UAV sizes Unmanned aerial vehicles Vision-based YOLOv5 and Faster-RCNN

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Research on Intelligent Question-Answering Systems for Electrical Knowledge Based on Large Model Technology 24

Research on Intelligent Question-Answering Systems for Elect...

引用

5th international conference on computer science and Management Technology, ICCSMT 2024

作者： Kong, Weiquan Ge, Jike Yang, Xiaolu Wang, Zining Tan, Jie Yu, Wencheng Chongqing University of Science and Technology Chongqing China

ISBN: (纸本)9798400709999

The intelligent question-answering system for electrical knowledge can be used to popularize electrical knowledge and increase public understanding of the power *** can also serve as an auxiliary tool for training and evaluating the knowledge and skills of industry professionals, improving their *** electrical knowledge question-answering systems often rely on general models and lack specialized optimization for the electrical field, making it challenging for the system to generate more accurate and detailed answers based on domain-specific *** paper proposes PowerGPT, an intelligent question-answering system based on large language models and an electrical knowledge base, which provides knowledge enhancement for answer text *** system uses ChatGLM2-6B as the foundational model and fine-tunes it using ***, the system employs a multi-expert model approach to subdivide the question-answering *** experiments show that PowerGPT outperforms question-answering systems lacking knowledge enhancement, model fine-tuning, and multi-expert models in the field of electrical *** demonstrates different answering strategies for various types of questions, mitigates the issue of model hallucination, and improves the accuracy and automation of electrical knowledge question-answering. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Intelligent Question-Answering System

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Enhanced Detection of Brain Tumors using Fusion-Based Feature Optimization and Stacking Ensemble Algorithm

Enhanced Detection of Brain Tumors using Fusion-Based Featur...

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2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Raut, Sweta Thakur, Uma Shelke, Nilesh Borkar, Pradnya Motghare, Manish Jhulelal Institute of Technology Department of Computer Science and Engineering Nagpur India Pune India G.H.Raisoni College of Engineering and Management Department of Artificial Intelligence Nagpur India

ISBN: (纸本)9798331517953

The variation of the appearance of brain tumors is a challenging detection task-the interpretation of cells due to abnormal growth in dimension decreases the accuracy of the detection algorithm. Recently, several algorithms have been applied based on transform-based and machine learning to detect brain tumors. The mapping of retrieved brain tumor features in the classification algorithms is the main drawback of these techniques. This paper deployed a feature of a fusion-based approach for detecting brain tumours-the feature of a fusion approach using transform-based function and a heuristic-based algorithm. The fused feature passes through the proposed machine learning algorithm for detection-the proposed detection using a multi-kernel. © 2024 IEEE.

关键词： Heuristic algorithms

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Attention mechanism-based vulnerability detection method for graph neural network source code 3

Attention mechanism-based vulnerability detection method for...

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3rd international conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Cheng, Zekai Li, Xiaoke Hu, Jiahao Guo, Yongkang School of Computer Science and Technology Anhui University of Technology Anhui Ma'anshan China

ISBN: (纸本)9781510680449

With the development of artificial intelligence and machine learning, automatic code vulnerability detection methods based on machine learning and deep learning have been gradually applied. However, the false alarm rate and leakage rate of vulnerability detection are still a concern. To this end, a graph neural network source code vulnerability detection method based on the attention mechanism is proposed, which fully captures the syntactic and semantic information related to the vulnerability through the code attribute graph, and at the same time combines the attention mechanism to enhance the model's ability to understand the features. Experimental results show that compared with existing methods, our accuracy and precision are improved by 5.7% and 4.2%, respectively, which significantly improves the vulnerability detection capability. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Semantics

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COR-MFS: A Correlation-Based multi-Objective Feature Selection on EEG Signals 13

COR-MFS: A Correlation-Based Multi-Objective Feature Selecti...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Sutradhar, Ananda Bidgoli, Azam Asilian Rahnamayan, Shahryar Daffodil International University Department of Computer Science & Engineering Dhaka Bangladesh Wilfrid Laurier University Department of Computer Science WaterlooON Canada Brock University Department of Computer Science St. CatharinesON Canada

ISBN: (纸本)9798350308365

Feature selection is a crucial step in the model-building pipeline in machine learning (ML) applications such as Electroencephalogram (EEG) signal processing, providing benefits on model performance and computational efficiency. EEG signals play a pivotal role in elucidating human nature through promising ML mechanisms. However, extracting a large number of features from the EEG signals can be a challenge of efficient EEG processing. A multi-objective feature selection strategy applied to the extracted features from EEG signals can simultaneously improve the accuracy of the process and reduce the number of features. However, the high dimensionality of the EEG feature vectors actually diminishes the exploration capabilities of multi-objective algorithms, impeding their real-world applicability. Hence, we propose a novel correlation-based multi-objective feature selection (COR-MFS) method, that aims to reduce dimensionality before applying the multi-objective algorithm. In the initial phase, a correlation-based dimension re-duction method is applied to filter the most relevant features with highest correlation with the class label. Subsequently, a multi-objective feature selection algorithm is applied to the shrunk search space enhancing optimization efficiency and facilitating the search process. We evaluated the proposed COR-MFS method using six large-scale EEG datasets and observed significant improvements compared to the stand-alone multi-objective feature selection. This underscores the effectiveness of our innovative framework in providing more accurate solutions with fewer number of features for extensive EEG-based classification tasks. © 2024 IEEE.

关键词： Decision trees

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Unlocking Efficiency in Agricultural Supply Chains: A Secure and Transparent Approach through Blockchain Technology

Unlocking Efficiency in Agricultural Supply Chains: A Secure...

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2024 IEEE international conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Lohith, J.J. Shreya, S. Hamsa Priya, J.L. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India D.S.C.E Dept. of Information Science and Engineering Bangalore India B.M.S. College of Engineering Dept. of Electronics and Telecommunications Bangalore India

ISBN: (纸本)9798350372502

Supply Chain Management (SCM) involves orchestrating various processes that extend from acquiring raw materials to delivering finished products to end consumers. The key objectives of a proposed supply chain in agriculture, emphasizes information-driven processes, cost reduction, and customer impact. It creates a versatile system capable of managing the supply chain for diverse commodities within a country, while also addressing the crucial need for increased transparency and traceability in data throughout the chain of activities. In our proposed approach to agricultural supply chain management (SCM), the focus is on creating a secure, transparent, and traceable SCM. To achieve this, we leverage cutting-edge technologies like the Ethereum blockchain and the Interplanetary File System (IPFS). The resulting software is a Decentralized Application (DApp) that orchestrates a series of activities within the agricultural industry, using cryptocurrencies to establish a seamless and efficient supply chain management system. This software platform is fortified by the secure algorithms of blockchain, providing an added layer of reliability. Additionally, our application incorporates transparency and traceability, ensuring user-friendly functionality and making it more accessible for users. © 2024 IEEE.

关键词： Blockchain

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A Support System Development for Healthcare System using FL and FC Techniques 4

A Support System Development for Healthcare System using FL ...

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4th IEEE international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Sreedevi, M. Brahmareddy, A. Devi, S. Balamurugan, S. Ghani, Ruqayyah Haider Al-Hilali, Aqeel Hashem, Amjad Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Department of Computer Science and Engineering Telangana Hyderabad500043 India Department of Mathematics Tamil Nadu Dindigul624622 India Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Avadi India National University of Science and Technology Collage of Dentist Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq Alkunooze University College Computer Engineer Department Basra Iraq

ISBN: (纸本)9798350360165

This has been increasing in difficulty for accomplishing time-consuming and complex tasks with the rise in the number of Internet of Things (IoT) devices and the generated big data, along with problems of expanding concerns often in the form of latency and use of power. Fog computing is the potential solution advanced by a dispersed form of computing and has a solution to these issues. IoT-Fog applications are limited in meeting time constraints to reduce service latency and energy consumption, due to the limited processing capacity that fog computing devices have. Based on this factor, this paper now proposes an alternative approach by using Fuzzy Logic to find the solution based on Dynamic Integer Linear Programming to reduce the workload in the clinical decision support system by efficient allocation of resources to the IoT devices from the Fog compute layer while accounting for the system constraints and resource availability. The proposed task-priority scheme is integrated into the proposed clinical decision support system and reduces the latency and energy consumption in fog nodes. Experiments show that the proposed method outperforms the benchmark methods in terms of energy consumption and service delay. © 2024 IEEE.

关键词： Fog computing

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Dynamic Constrained Time-Domain Robust Optimization with Noises 4

Dynamic Constrained Time-Domain Robust Optimization with Noi...

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2023 4th international conference on computer engineering and Intelligent Control, ICCEIC 2023

作者： Zhang, Bin Shi, Xuhua Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350308877

In the field of engineering optimization design, there are many optimization problems with time-varying objective functions. Tracking motion optimization (TMO) and time domain Robust optimization (ROOT) are two widely used methods to solve this problem. The purpose of TMO is to find the optimal solution before the environment changes, while the purpose of ROOT is to find a solution that can adapt to changes over a long period of time. However, both TMO and ROOT approaches have some defects. First of all, the existing engineering optimization problems have constraints and will change with time. Second, in the optimizing environment, the noises in most environments will affect the candidates. Therefore, a dynamic constrained time-domain robust optimization algorithm (DCRO/SCNO) considering noises and switching costs is proposed in this paper. Switching cost and robustness are considered as multi-objective optimization, and the idea of double elite is introduced to divide the population into three groups to collaborate for the problem of non-robustness solutions in noisy environment. Benchmark experiments show that DCRO/SCNO can find non-dominant solutions in different noise environments, which confirms the validity of the dual elite idea. © 2023 IEEE.

关键词： Constrained optimization

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Machine Learning Approach for Evaluating Industry-Based Employer Ranking and Financial Stability 5

Machine Learning Approach for Evaluating Industry-Based Empl...

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5th IEEE international conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Swain, Prakash Chandra Balan, Seema Lakshmi, S.R. Choudhary, Ankur Patjoshi, Pramod Kumar Raja, J. School of Social Financial & Human Sciences Odisha Bhubaneswar India Rajalakshmi Engineering College Department of Management Studies Chennai India Christ University Department of Business & Management Karnataka Bangalore560073 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Centurion University of Technology and Management BBSR Campus School of Management India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350351378

Using the computational prowess of machine learning, this study presents a fresh method for assessing the relative standing and fiscal health of employers across different sectors. The research makes use of a wide variety of data, including financial reports, statistics on the labor market, employee evaluations, and indicators unique to the business, to arrive at in-depth judgements. The financial stability assessment applies a linear regression model, whereas employer ranking is predicted using a logistic regression model. Financial data, employment market dynamics, and sentiment research are used as foundational characteristics for these models. Company A is more financially stable than Company B, yet it is anticipated to be ranked lower as an employer. This highlights the difficulty of judging businesses. The implications of these results for job-seekers, investors, and businesses are varied. The study also highlights the significance of ethics, openness, and addressing biases in assessment. This study paves the way for future advancements in this crucial subject and provides a basis for data-driven, well-informed decision-making in the ever-changing landscapes of contemporary industrial evaluations. © 2024 IEEE.

关键词： Commerce

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Next-Generation Cyber Defense: Deep Q-Learning-Based Adversarial Detection and Prevention in Autonomous Vehicle Networks 4

Next-Generation Cyber Defense: Deep Q-Learning-Based Adversa...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Srivastava, Shubhi Bhargav, H.K. Gurani, Vijayakumar Chaturvedi, Arpana Gandhi, Ankita Dharangutti, Yogesh New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Karnatak University Department of Computer Science Dharwad India New Delhi Institute of Management Department of IT/AI/ML New Delhi India Parul Institute of Engineering and Technology Department of Computer science and Engineering Gujarat India Faculty of Law Pune India

ISBN: (纸本)9798350352931

This paper presents a novel AI-powered super-resolution technique for real-time video processing, which significantly enhances the visual quality of low-resolution video frames. The proposed framework employs a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs) to reconstruct high-resolution frames with minimal computational overhead. Experimental results on benchmark video datasets show that the proposed method achieves a peak signal-to-noise ratio (PSNR) of 34.56 dB and a structural similarity index (SSIM) of 0.94, outperforming state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM. Additionally, the system demonstrates an average processing speed of 30 frames per second (fps) on a GTX 1080Ti GPU, making it suitable for real-time applications. The qualitative and quantitative results validate the effectiveness of the proposed method in preserving fine details, reducing artifacts, and enhancing visual clarity in various real-time video processing scenarios. © 2024 IEEE.

关键词： Generative adversarial networks

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