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检索条件"主题词=CLASSIFICATION algorithms"

共 26354 条记录，以下是4971-4980 订阅

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Prediction of U2R attack in Wireless Networks using Novel CNN Alexnet in Comparison with Random Forest Algorithm

Prediction of U2R attack in Wireless Networks using Novel CN...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： V Chandra Sekar V Nagaraju Dept of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The main objective of proposed research is to employ DL (Deep Learning) models to predict User-to-Root (U2R) attack using CNN (Convolution Neural Network) Alexnet. In this study, the dataset used was obtained directly from the Github. The estimation of User-to-Root (U2R) attacks was based on a sample size of 22543, with 15 iterations that are from $1^{\text {st }}$ and $2^{\text {nd }}$ group respectively. The sample size for this experiment is determined with G-power analysis, with the power value for ($1-\beta$) is 0.8, where $\alpha$ and $\beta$ values are 0.05 and 0.2, respectively, with the 95% confidence interval. Both Novel CNN Alexnet and Random Forest were employed for the prediction of U2R attacks using the same sample size ($\mathrm{N}=15$). The accuracy of the suggested approach is validated at 92.4060%, surpassing the benchmarks set by Novel Convolutional Neural Network Alexnet (CNN Alexnet) and Random Forest, which achieve accuracies of 85.5180% and 92.4060%, respectively. Notably, there is a significant difference between the two methods. This observation is corroborated by a significance value ($\mathbf{p}$-value) of 0.261, determined through the Independent T-test samples, with the value $\mathrm{pf} \mathbf{p}$ greater than $\mathbf{0. 0 5}$. Thus, our results are suggesting that the algorithm’s performance is statistically not significant. This suggested study method results states that it has been determined that the produced Novel CNN Alexnet model performs higher than the RF classifier in the prediction of User-to-Root (U2R) attacks using data banks.

关键词： Accuracy Wireless networks Neural networks Predictive models Prediction algorithms Data models classification algorithms Convolutional neural networks Random forests Software development management

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Combining Transfer Learning and Genetic algorithms for Real-Time Gesture Recognition using EMG Signals

Combining Transfer Learning and Genetic Algorithms for Real-...

引用

Mechatronics and Robotics Engineering (ICMRE), International Conference on

作者： Genci Capi Kazuma Iizawa Shin-Ichiro Kaneko Department of Mechanical Engineering Hosei University Tokyo Japan Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical and Control Systems Engineering National Institute of Technology Toyama College Toyama Japan

ISBN: (数字)9798350394276

ISBN: (纸本)9798350394283

This paper proposes a hybrid method using Genetic Algorithm and Transfer Learning in Deep Neural Networks for gesture classification using surface EMG (sEMG) signals. The goal is to develop efficient Convolutional Neural Networks (CNN) that can be implemented in real-time situations to recognize gestures with a small number of EMG sensors. By “efficient” we mean good classification accuracy, short data acquisition time, and short CNN training time. The dataset to verify the performance of the proposed algorithm is created in our laboratory. We compare the performance of CNNs trained (a) from scratch, (b) using transfer learning, and (c) using transfer learning with GA-optimized parameters. The performance of the proposed hybrid method shows a good performance.

关键词： Training Transfer learning Training data Real-time systems Electromyography classification algorithms Sensors

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An Exclusive System for Labeling the Dog Breed for improved Accuracy using Convolution Neural Network in comparison with Random Forest

An Exclusive System for Labeling the Dog Breed for improved ...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： T Sai Deepak Reddy W. Deva Priya Dept of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Using a dataset of breeds of dogs that were handpicked, this research aims to improve the accuracy of Dog breed classification using a Convolutional Neural Network in comparison with Random Forest through the use of deep learning algorithms and a well-curated dataset. Both the Convolutional Neural Network and Random Forest used in this study contain 10055 dog images out of which 8044 images are used for training and the remaining 2011 images are used for testing. The G power test was used with a significance level of 0.05 and a power of 80%. Result: The results showed that CNN outperformed Random Forest with an accuracy of 93.94% compared to 91.39%. The statistical analysis showed a significance value of 0.002 (p<0.05), indicating that there is a significant difference between the two algorithms for the novel dog breed classification application. Conclusion: Therefore, it can be concluded that MobileNet is more accurate than Random Forest for dog breed classification

关键词： Deep learning Training Accuracy Statistical analysis Transfer learning Dogs classification algorithms Convolutional neural networks Random forests Testing

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Application of Machine Learning for Ensuring Water Quality in Aquaculture Ponds

Application of Machine Learning for Ensuring Water Quality i...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： Sv.Shri Bharathi Paidipati Lakshman Sai Paidipati Charan Ram Ayyappa Kakuru Jaya Praveen Reddy Gangireddy Sai Preetham Reddy Katuri Madhava Rajesh Department of Data Science and Business Systems SRMIST- KTR Chennai Tamil Nadu India Department of Computer Science and Engineering VIT Chennai Tamil Nadu India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Aquaculture plays a pivotal role in meeting the growing global demand for seafood. However, ensuring optimal water quality within aquaculture ponds is a pressing challenge. This project proposes a paradigm shift by integrating machine learning to enhance water quality assurance. The system leverages real-time monitoring, predictive analytics, and adaptable algorithms, contributing to sustainability and responsible aquaculture practices.

关键词： Support vector machines Machine learning algorithms Water quality Machine learning Prediction algorithms Real-time systems classification algorithms

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Edge Learning Optimization in Task-Oriented NOMA Communications for Autonomous Vehicle Perception

Edge Learning Optimization in Task-Oriented NOMA Communicati...

引用

IEEE Conference on Wireless Communications and Networking

作者： Carla E. Garcia Mario R. Camana Abuzar B. Mohammad Jorge Querol Symeon Chatzinotas Interdisciplinary Centre for Security Reliability and Trust (SnT) University of Luxembourg Luxembourg City Luxembourg School of Electrical Engineering Korea University Seoul South Korea

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The Internet of Vehicles (IoV) is undergoing swift advancements in capability and intelligence, poised to facilitate a diverse range of innovative applications. Within the IoV, edge learning empowers intelligent applications and services by leveraging data -driven tasks. Therefore, in this paper, we propose optimizing the edge learning error prediction within an edge-supported Non-Orthogonal Multiple Access (NOMA) in task-oriented communications. Specifically, we consider three autonomous vehicle perception tasks, called: object detection, traffic sign, and weather classification. For this purpose, we propose a novel approach based on the Particle Swarm Optimization (PSO) algorithm to jointly minimize the edge learning error and optimize power allocation variables. Moreover, we investigate alternative benchmark schemes, including Quantum Particle Swarm Optimization, Cuckoo Search, and Butterfly Op-timization algorithms. Satisfactorily, our simulations substantiate the superiority of the PSO algorithm over the baseline schemes, delivering superior performance with reduced computation time.

关键词： NOMA Image edge detection Simulation Prediction algorithms classification algorithms Resource management Task analysis

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Facial Emotion Recognition using YOLO based Deep Learning Classifier

Facial Emotion Recognition using YOLO based Deep Learning Cl...

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Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Sri Vaibhav Mohan Dev Vanamoju Moturi Veda Vineetha Hitesh Tekchandani Pallavi Joshi Praveen Kumar Shukla Anita Khanna Electronics and Communication Koneru Lakshmaiah Education Foundation Hyderabad India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Department of Computer Science Amrita Vishwa Vidyapeetham Mysuru Mysuru India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Bilaspur India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Understanding facial expressions is key to successful communication. It's like decoding a secret language that helps us know if someone is happy, sad, angry, surprised, or something else altogether. Harnessing facial emotion recognition (FER) in machines holds the potential to revolutionize human-computer interaction, improve customer service experiences and provide invaluable support in medical diagnosis. Machines can comprehend human's needs more effectively by reading the emotions from their facial expression and therefore this results in a more natural conversation between them and the machine. Various machine and deep learning methodologies have been implemented for FER. In this research work, the authors propose the state-of-the-art YOLO (You Only Look Once) based deep learning classifier for FER. The YOLO algorithm is a single-shot approach that efficiently classifies objects in a single pass. It achieves this by using a single neural network to predict bounding boxes and target class probabilities by utilizing the entire image as input. YOLOv8 is a recent iteration of the YOLO object detection algorithm is popular for its speed and accuracy which offers improvements over prior versions, making it a suitable choice for real-time intended task of FER. With the proposed classifier, the authors achieved test accuracy of 72.56% on the FER-2013 dataset in a seven-class FER task.

关键词： YOLO Deep learning Emotion recognition Accuracy Neural networks Signal processing algorithms Signal processing Prediction algorithms Real-time systems classification algorithms

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Research on Portable Surface Defect Detection System Based on Channel Pruning Technology

Research on Portable Surface Defect Detection System Based o...

引用

Sensors, Electronics and Computer Engineering (ICSECE), IEEE International Conference on

作者： Mingde Bi Yingchao Liang Yang Xu Wuhan Second Ship Design and Research Institute Wuhan China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

This study addresses the prevalent issue of surface defect detection in industrial manufacturing, particularly in high-precision domains such as semiconductor and automotive parts production, where traditional manual inspection methods fall short in terms of efficiency and accuracy. In response to these challenges, this paper introduces a portable surface defect detection system based on channel pruning technology. The research incorporates an enhanced YOLOv8 object detection algorithm and global channel pruning techniques to optimize algorithm performance, thereby enhancing the system's operational efficiency and detection accuracy under resource-constrained conditions. Key findings include the system's ability to effectively identify and classify various surface defects, significantly accelerating detection speed and accuracy. Additionally, the system's design facilitates seamless integration with existing manufacturing systems and offers considerable scalability.

关键词： Accuracy Scalability Production Quality control Reliability theory Sensor systems and applications classification algorithms Optimization Defect detection Smart manufacturing

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Methods for Mango and Ripeness Detection Using Improved YOLOv8

Methods for Mango and Ripeness Detection Using Improved YOLO...

引用

IEEE International Conference on Unmanned Systems (ICUS)

作者： Jiahuan Lu Xiangye Lin Chenglin Li Shan Zeng Xiwen Luo Jiehao Li School of Engineering South China Agricultural University Guangzhou China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Automated mango harvesting is a trending practice, yet recognizing mangoes and their ripeness remains a bottleneck due to uneven lighting, fruit overlap, and the similar colors of fruit and leaves. To address these challenges, this paper proposes a method for mango and ripeness detection based on an improved You Only Look Once version 8 (YOLOv8). This method incorporates attention mechanisms and lightweight enhancements to achieve accurate and rapid detection of mangoes and their ripeness under varying lighting conditions, different levels of occlusion, and backgrounds where the colors of fruits and leaves are similar. Validation on 2,742 samples demonstrates that the proposed method based on improved YOLOv8 achieves a mean Average Precision (mAP) of 96.6%, processing each sample in just 22.07 ms. Compared to YOLOv8, the proposed method improves mAP by 1.8% while reducing processing speed by 8.6%. This method provides a precise and rapid detection technology for the automated harvesting of mangoes and potentially other fruits.

关键词： YOLO Adaptation models Attention mechanisms Autonomous systems Computational modeling Lighting Color Real-time systems classification algorithms Load modeling

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Employee Attrition Analysis Using XGBoost

Employee Attrition Analysis Using XGBoost

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Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： Isha Nitin Thapliyal Sheetal Solanki Neeraj Kumar Pandey Sangita Papola Department of Computer Science Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

In today's time, employee attrition is a major problem faced by organisations. The most precious resource for a firm is its workforce. They are the ones that bring values both quantitatively and qualitatively. A steady decline in the workforce due to resignation, death or retirement is referred to as attrition. It is a matter of concern when employees leave their jobs of better opportunities, but this leads the company to face its consequences. Employee turnover and attrition are equivalent. Leaders can avoid it by implementing better retention techniques by mastering this concept. In this paper, we use dataset to analyse attrition and determine the root cause of employee resignation. We did analysis of the training dataset for effective data exploration. Machine Learning algorithms, such as Random Forest Classifier, Logistic Regression, XGBoost and ensemble stacking technique were used. On data exploration and enabling algorithm we discovered that XGBoost classifier with an accuracy of 88. % and precision 89% was the most appropriate algorithm for this dataset. We may also conclude that monthly income, age, distance from home can be a reason behind employees leaving their job and finding better opportunities.

关键词： Training Logistic regression Machine learning algorithms Stacking Companies Forestry classification algorithms

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A Comprehensive Review of Plant Disease Detection and Analysis Using Machine Learning Techniques

A Comprehensive Review of Plant Disease Detection and Analys...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Adil Thakur Shilpa Sharma Puneet Thapar School of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The identification of plant diseases plays a pivotal role in mitigating losses in agricultural yield and product quality. These diseases manifest as visually observable patterns on plant surfaces, and their timely detection is critical for sustainable agriculture. Plant pathology knowledge is required for the labour-intensive and time-consuming manual monitoring of plant diseases. To address these challenges, image processing techniques have emerged as valuable tools for automating disease detection. This thorough article investigates the methods used to identify and evaluate plant diseases by using leaf image data. Agriculture stands as one of India’s most ancient and essential occupations. Given that India’s economy is predominantly reliant on agricultural production, safeguarding food production is of paramount importance. Pests, including viruses, fungi, and bacteria, can inflict significant damage to crops, resulting in reduced quality and quantity of agricultural output. Therefore, it is essential to provide careful maintenance and early identification of plant diseases in order to protect the agricultural sector.

关键词： Deep learning Plant diseases Accuracy Reviews Image processing Clustering algorithms Crops Agriculture classification algorithms Surface treatment

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