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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是4911-4920 订阅

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Steel Defect Detection Using a ResNet-Inspired Model and Fourier Transforms 4

Steel Defect Detection Using a ResNet-Inspired Model and Fou...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anand, Philip Sudharsan, Rohit Madhavan Ramachandran, Vishwath Prusty, Manas Ranjan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350343670

Defect detection in steel materials is a critical task to ensure product quality and reliability in various industrial applications. Traditional methods for detecting defects in steel surfaces are often labor-intensive, time-consuming, and prone to subjectivity and errors. However, the advent of deep learning-based models and techniques has provided a promising avenue for automating this process, significantly reducing error rates. In this research, the authors propose a Steel Defect Detection system with the help of a custom designed deep neural network inspired by the ResNet architecture. Notably, the model incorporates innovative attention layers not previously integrated into similar architectures, enhancing its predictive capabilities. The authors make use of data augmentation techniques to improve the model performance to generalize, and detect defects in imperceptible data. The novel multiclass semantic segmentation model has achieved over 91% accuracy and can be used to automate the Steel defect detection process, which can remarkably decrease the costs and time associated with manually inspecting the Steel sheets. © 2024 IEEE.

关键词： Attention layers augmentation Deep learning Residual Network Steel Defect

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Tomato Disease Detection using Multispectral Imaging with Deep learning Models. 7

Tomato Disease Detection using Multispectral Imaging with De...

引用

7th international conference on Artificial Intelligence, Big data, computing and data Communication Systems (ICABCD)

作者： De Silva, Malithi Brown, Dane Rhodes Univ Comp Sci Grahamstown South Africa

ISBN: (纸本)9798350387919;9798350387902

Identifying plant diseases plays a pivotal role in managing plant health. It poses significant challenges due to the diverse manifestations of diseases. This study investigates the impact of environmental conditions and imaging factors on analysing multispectral images captured through six filters spanning various portions of the Near Infrared spectrum for tomato disease identification. Two distinct datasets were compiled for analysis. dataset 1 comprised uniform images across all filters. dataset 2 incorporated variations in image capture to explore the influence of environmental and imaging factors on filter performance. Classification between healthy and diseased states was conducted on both datasets utilising the popular Convolutional Neural Network, Vision Transformer, Hybrid Vision Transformer, and Swin Transformer models. Among all the filters tested, K590 demonstrated the highest average accuracy, reaching 88.69% for dataset 1 and 93.31% for dataset 2. Furthermore, this filter consistently outperformed others in terms of precision and recall across both datasets. ViT-B16 emerged as the most effective model across all evaluation metrics, with an average accuracy of 89.92%. Furthermore, comparisons were drawn with prior literature, encompassing balanced and unbalanced datasets for tomato disease classification tasks. The findings indicated that environmental and imaging factors do not significantly influence disease classification using multispectral imaging.

关键词： Plant Disease Identification Convolutional Neural Network Models Multispectral Images Near Infrared Vision Transformer Models Hybrid Vision Transformer Models Swin Transformer Models

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MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED learning 49

MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARN...

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49th IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Software Engn Wuhan Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Australia Natl Engn Res Ctr Big Data Technol & Syst Beijing Peoples R China Serv Comp Technol & Syst Lab Wuhan Peoples R China Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China Hubei Engn Res Ctr Big Data Secur Wuhan Peoples R China Cluster & Grid Comp Lab Wuhan Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

关键词： Split Federated learning Poisoning Attack Robustness

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data Driven learning of Aperiodic Nonlinear Dynamic Systems Using Spike Based Reservoirs-in-Reservoir

Data Driven Learning of Aperiodic Nonlinear Dynamic Systems ...

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international Joint conference on Neural Networks (IJCNN)

作者： Paul, Ankita Kandasamy, Nagarajan Dandekar, Kapil Das, Anup Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA

ISBN: (纸本)9798350359329;9798350359312

Mimicking an aperiodic nonlinear dynamic system is challenging as it is difficult to represent it using closed-form equations. A feedback-driven spike-based recurrent spiking neural network is a powerful computational model that can mimic such dynamical systems. We propose reservoirs-in-reservoir (R-iR), a novel architecture to mimic the frequent pattern changes in space and time of an aperiodic nonlinear dynamic system. Here, a large reservoir is built by connecting multiple small reservoirs to a common output. These small reservoirs are individually specialized to mimic a portion of the input dynamic. The internal recurrent connections of each reservoir and its readout are trained using a recursive least squares (RLS)-based full first-order and reduced control error (full-FORCE) algorithm. To make the entire R-i-R architecture adaptable to the change in periodicity of an input, we implement a new cost function that incorporates a unique forgetting factor to control the fading and wind-up of the covariance matrix of each reservoir during training. We evaluate R-i-R using seven aperiodic nonlinear dynamic systems. We show that R-i-R with rate encoding reduces the error rate by an average 59% with 1.8X reduction in network size compared to state-of-the-art. To improve energy efficiency, we implement a time-to-first-spike encoding and show an average reduction of 39. 5% in the number of spikes.

关键词： Dynamic systems reservoir computing spiking neural networks non-linear systems machine learning

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Early Detection of ADHD in Children Using Two learning Approaches

Early Detection of ADHD in Children Using Two Learning Appro...

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2024 international conference on Advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Sherine, M. Selvi Rajendran, P. Hindustan Institute Of Technology And Science Dept Of Computer Science And Engineering Chennai603103 India

ISBN: (纸本)9798350364828

Children with Attention Deficit /Hyperactivity Disorder (ADHD) struggle to focus for longer than a few minutes at a time. The onset of symptoms can occur as early as age 3 or 12, and they may worsen until maturity. For symptoms to be minimised and behavioural techniques to be improved, early diagnosis is critical. Even though it was first identified in the 1940s, after the 1990s, awareness grew. ADHD detection may also be impacted by genetic issues. Boys are treated more often than girls, which makes them less attentive and makes it harder for them to develop objectives. Brain analyses and behavior-related questionnaires are two early diagnostic techniques. The proposed system composed of two phases of diagnosing the ADHD in children. The first phase uses the dataset obtained from screening test in which Random Forest gave the highest accuracy. The second phase uses the Deep Neural Network with attention (DNN-AM) model for the early identification of ADHD in children. It analyses images as a whole, tracking how children with ADHD interact with their surroundings. It employs many modules for unique patterns and a single DNN-AM network for feature extraction and classification. By employing a Local Binary Pattern image for analysis, the feature set is expanded, improving the extraction of ADHD patterns in children. The suggested method, DNN-AM, outperforms the trained network in predicting ADHD in children, obtaining the lowest error rate, higher accuracy, and F1-Score. With both learning models, RF and DNN-AM the ADHD in children is diagnosed. © 2024 IEEE.

关键词： Deep neural networks

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DiffGCA: A Diffusion Recommendation Model with Global Attention Mechanism 7

DiffGCA: A Diffusion Recommendation Model with Global Attent...

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7th international conference on data science and Information Technology, DSIT 2024

作者： Li, Jiatong Jia, Zhichun Xing, Xing Xin, Mindong Bohai University College of Information Science and Technology Jinzhou China

ISBN: (纸本)9798350384093

Sequential recommendation aims to predict users' future preferences by analyzing their historical interactions. Compared to traditional recommendation systems, sequential models emphasize capturing the dynamic changes in user interests to generate more personalized recommendations. Diffusion models introduce noise and denoising processes, providing a flexible generative mechanism for sequential recommendation, which better handles the uncertainty and diversity in user preferences. However, existing diffusion models struggle with capturing the global dependencies and multi-dimensional interests in user behaviors, especially when confronted with sparse data and rapidly changing user interests, leading to recommendations that often lack diversity and accuracy. To overcome these challenges, we propose the DiffGCA model, which integrates the Global Grouped Coordinate Attention module to enhance feature representation by leveraging global information. Extensive experiments on multiple real-world recommendation datasets demonstrate that DiffGCA significantly outperforms two representative baseline models in both recommendation accuracy and diversity, validating its effectiveness in addressing issues related to sparse data and dynamic user behavior. © 2024 IEEE.

关键词： Diffusion Model Recommender System Sequential Recommendation User Preference learning

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Spectral – Vegetative Indices fusion for Crop Yield Analysis 15

Spectral – Vegetative Indices fusion for Crop Yield Analysi...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sathvik, Jammula Durga Bala Mohanty, Monish Sushma, N. Raghav, Panchami Amudha, J. John, Maria Dept of Computer Science Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350370249

The use of remote sensing in smart farming is gaining popularity around the world. Due to the rising global demand for r food grains, assessing yield before actual production is critical in developing policies and making decisions in the agricultural production system. In this project, the aim is to develop a deep learning model which will predict an individual farm’s crop yield using remotely sensed satellite images. The study focuses on crop yield estimation of wheat, corn and soybean in the chosen study area in KBS-LIER, Michigan, United States. Four vegetative spectral indices, including NDVI and SAVI, etc. were calculated from the seven spectral bands collected over 11 years. Model-I, trained on spectral bands data, achieves an accuracy of 94.3%, outperforming Model-II, which utilizes vegetative indices data and achieves and accuracy of 88.8%]. ©2024 IEEE.

关键词： Crops

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Face based Attendance and Behaviour Tracking 3

Face based Attendance and Behaviour Tracking

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Vijayasekaran, G. Rajalakshmi, B. Anindhitha, M. Bhavani, B. Bhavya, S. New Horizon College of Engineering Department of Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9798350375190

A high-accuracy system for multi-face detection, recognition, one of the busiest uses of image processing is face recognition, which is essential to the industry. The ability to recognize a human face is a current concern for authentication, particularly when it comes to student attendance. A highaccuracy system for multi-face detection, recognition, and behaviour tracking is challenging task in the modern industry sector. The technology uses sophisticated face identification algorithms and recognition models by utilizing datasets and deep learning. The GUI application for the same improves user interaction, ensuring strong performance for real-world applications. This project's goal is to improve attendance accuracy, simultaneously recognizing several faces at once using MTCNN (Multi-task Cascaded Convolutional Neural Networks) algorithm and documenting the data. This study investigates how deep learning approaches can help with less time-consuming attendance tracking. © 2024 IEEE.

关键词： Python

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Research on Resource Allocation Method of Unmanned Aerial Vehicle Ad Hoc Network Based on Deep Reinforcement learning 9

Research on Resource Allocation Method of Unmanned Aerial Ve...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Tao Wang, Zijia Wang, Yupeng Jiang, Zhao School of Information Science and Engineering Southeast University Nanjing China Aviation Key Laboratory of Science and Technology on Electromagnetic Environmental Effects National Key Laboratory of Electromagnetic Information Control and Effects Shenyang China College of Electronic and Information Engineering Shenyang Aerospace University Liaoning Province Shenyang City China

ISBN: (纸本)9798350376548

With the rapid and continuous development of Unmanned Aerial Vehicle(UAV) and communication technology, UAV cluster have become a preferred solution for long-distance mission execution. Additionally, Unmanned Aerial Vehicles(UAVs) are no longer confined to individual or single-task combat operations. Instead, UAVs are coordinated to form an Ad Hoc network to accomplish multiple mission objectives. As the number of missions and electronic communication devices increases, the complexity of spectrum resource allocation becomes more and more complex. In order to address the spectrum allocation challenges faced by multiple onboard systems within the UAV Ad Hoc network during mobility flights, this paper proposes an implicit spectrum resource allocation algorithm based on deep reinforcement learning, where the ground station and UAVs are viewed as intelligent agents in the deep reinforcement learning, addressing communication issues among different onboard systems. By leveraging the past experience through Q-learning and utilizing deep learning to train the spectrum allocation strategies, the proposed algorithm aims to achieve a better system performance. Simulation results demonstrate that the proposed spectrum resource allocation algorithm based on deep reinforcement learning can reasonably allocate spectrum resources according to various onboard systems and communication environment with benefits on system data rate. © 2024 IEEE.

关键词： Antennas

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Classification of Multi-Class Skin Cancer using Deep learning Algorithm 3

Classification of Multi-Class Skin Cancer using Deep Learnin...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Priyadarshini, M. Khan, N. Zafar Ali School Of Computer Science And Engineering Presidency University Bangalore India

ISBN: (纸本)9798350318609

In recent years, skin cancer is a frequent and dangerous disease that requires rapid and accurate analysis to secure effective treatment. Accurate detection and diagnosis of skin lesions is challenging due to the similarities between various skin lesions such as nevi and melanoma, especially when analyzing color images of the skin. In this survey, various deep learning (DL) techniques are analyzed for multi-class skin cancer classification. This survey discussed the significant assumptions, limitations, and advantages analyzed in existing DL approaches. DL-based techniques such as feature extraction, segmentation, and classification are used for multi-class skin cancer classification. The performance of the existing methods was analyzed using various performance metrics such as Matthews Correlation Coefficient (MCC), recall, precision, fl-score, dice, precision, sensitivity, Jaccard, and time. This survey concludes that multivariate skin cancer classification over DL overcomes the drawbacks of poor data quality reduces time complexity and improves efficiency. © 2024 IEEE.

关键词： Classification (of information)

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