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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme learning machine 4

Efficient Method for Tomato Leaf Disease Detection and Class...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Chaturvedi, Abhay Yagnasree, Sirivella Chhabra, Gunjan Chidambara Rajan, P. Chauhan, Amit Maranan, Ramya Gla University Department of Electronics and Communication Engineering Uttar Pradesh Mathura India Lovely Professional University Department of Electronics & Communication Engineering Punjab Phagwara India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Dr. N. G. P. Arts and Science College Department of Biotechnology Tamilnadu Coimbatore India Department of Life Sciences Karnataka Bengaluru India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. the solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. the agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. this proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. that improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. the GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. the proposed approach to outperforms other two models such as CNN and ELM. © 2023 IEEE.

关键词： K-means clustering

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Channel Estimation for Intelligent Reflecting Surface-Assisted Wireless Communication Systems Based on Deep learning 4

Channel Estimation for Intelligent Reflecting Surface-Assist...

引用

4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Zhang, Xuhui Xu, Yan Zhang, Xiaoqi School of Electronics and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510672505

Intelligent Reflecting Surface (IRS) is capable of performing phase and amplitude transformations on incoming signals, thereby achieving precise signal transmission and improving coverage and transmission efficiency. However, these advantages can only be achieved when the precise Channel State Information (CSI) is known. Due to the passive nature of IRS elements, obtaining accurate CSI is challenging. therefore, a combination of Compressive Sensing (CS) algorithm and Deep learning (DL) is proposed to address this issue. Firstly, joint-link structure is used to optimize the traditional CS algorithm, enabling better Normalized Mean Square Error (NMSE) performance at lower pilot overhead and Signal-to-Noise Ratio (SNR). Secondly, the channel estimation problem is regarded as a denoising problem, where the output of the optimized CS algorithm is treated as noisy CSI. A multi-layered deep denoising block network is utilized to further denoise the CSI, resulting in more accurate estimation. Finally, experimental results demonstrate that the proposed algorithm outperforms the comparison algorithms under the same SNR, yielding better accuracy. © 2023 SPIE.

关键词： Channel state information

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A Robust Image Registration Using Multi-Scale Feature Extraction Techniques Using Deep learning 4

A Robust Image Registration Using Multi-Scale Feature Extrac...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Madasamy, Sathiyakeerthi Nelson Kennedy Babu, C. Sumathi, V. Chokkalingam, Arun Pilvi Systems Inc Solution Architect LewisvilleTX75057 United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Ram Engineering College Department of Mathematics Tamil Nadu Chennai44 India Rmk College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

Image registration is a significant assignment in computer visualization and medical imaging, which involves aligning two or more descriptions of the same division or object taken from different viewpoints or at dissimilar epochs. Deep learning-based approaches have recently demonstrated promising outcomes in several image-processing applications, including image registration. Using multi-scale feature extraction methods and deep learning, the study provides a reliable image registration method in this study. three primary steps make up the suggested method: feature extraction, feature matching, and transformation estimation. A deep convolutional neural network (CNN) is utilized in the feature extraction process to extract multi-scale information from the input images. the CNN architecture is built to manage large-scale deformations by extracting features at various scales. Following feature extraction, a strong matching technique based on the nearest neighbor distance ratio test is utilized to match the extracted features. the proposed method uses the Random Sample Consensus (RANSAC) algorithm to estimate the transformation parameters robustly, which helps to handle outliers and noise in the feature-matching process. the proposed method is assessed on several benchmark datasets, including the Eth-80 dataset and the TUM RGB-D dataset, and compared with state-of-the-art approaches. the investigational consequences demonstrate that the proposed method outstrips prevailing approaches in terms of accuracy and robustness, especially for images with large-scale deformations. the proposed technique also demonstrates good performance in the presence of noise and outliers, which further demonstrates its robustness. © 2023 IEEE.

关键词： Feature extraction

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learning Algorithms for Breast Cancer Classification and Diagnosis 1

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4th international conference on Optimization learning Algorithms and applications

作者： Miranda Valentin, Ana Beatriz Bressan, Glaucia Maria Lizzi, Elisangela Ap da Silva Lopes, Fabricio Martins Univ Tecnol Fed Parana UTFPR Grad Program Bioinformat Ave Alberto Carazzai 1640 BR-86300000 Cornelio Procopio PR Brazil

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

the comprehension about the key features of breast tumors that lead to their classification as benign or malignant is fundamental to improving the detection and diagnosis of breast cancer, contributing significantly to survival rates and treatment effectiveness. this study proposes a multidisciplinary approach that combines analytical methods and graphical visualizations to classify breast tumors as benign or malignant, using supervised and unsupervised learning algorithms. the adopted dataset in this study is from the repository of the University of Wisconsin (USA). It comprises 569 breast tumor biopsy samples, with 32 features measured from digitized images of biopsy slides. Initially, for unsupervised learning, Pearson correlation was used as a similarity metric for hierarchical grouping, resulting in the formation of six clusters through a dendrogram. In supervised learning, the Principal Component Analysis (PCA) technique was performed to reduce the number of features, m achieving the 10 most relevant. the Support Vector machine (SVM) model was applied with and without the PCA results. the comparison between hierarchical grouping and SVM methods demonstrated a notable advantage of SVM in terms of accuracy in classifying breast tumors. the use of cross-validation showed the superiority of SVM over clustering for this specific purpose. the analysis of breast tumor features and the classification approaches offer important perspectives on improving breast cancer diagnosis and treatment practices. the use of classifiers such as SVM, together with dimensionality reduction techniques such as PCA, can result in significant improvements in diagnostic accuracy and effectiveness, directly benefiting patient care in this critical area of medicine.

关键词： Breast cancer Tumor classification Feature selection efficient diagnosis

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Effective Prediction Analysis for Cardiovascular Using Various machine learning Algorithms 4th

Effective Prediction Analysis for Cardiovascular Using Vario...

引用

4th international conference on computer and Communication Technologies, IC3T 2022

作者： Shanmuga Sundari, M. Dyva Sugnana Rao, M. Kumar, Ch Anil BVRIT HYDERABAD College of Engineering for Women Bachupally Hyderabad India

ISBN: (纸本)9789811985621

Healthy life leads to healthy growth in a human’s life. Health is more important than all other things in our life. the heart is the backbone of our life. Many types of research are going on in the medical field that finds new treatments in the healthcare industry. machine learning is an important technology that helps predict the accuracy of disease. machine learning takes the attributes from the health industry and analyzes the data using many algorithms. Based on the trained data, the experiment will explore the analysis prediction in the given dataset. It helps in many ways for humans in their life. In this research, we predicted cardiovascular disease before the early stage. We collected healthcare data and applied various machine learning algorithms. We analyzed the accuracy of the support vector machine (SVM), logistic regression (LR), and stochastic gradient descent (SGD) algorithms. Finally, research proves support vector machines as the best algorithm for predicting cardiovascular disease in the early stage of our lifespan. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Defect Detection in Printed Circuit Boards Based on EdgeML and computer Vision 4

Defect Detection in Printed Circuit Boards Based on EdgeML a...

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4th international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Rocha, Felipe G. F. Silva, Hyago V. L. B. Vimieiro, Rodrigo B. De Figueiredo, Felipe A. P. Inatel Wai Lab Santa Rita do Sapucaí Brazil São Carlos School of Engineering University of São Paulo São Carlos Brazil

ISBN: (纸本)9798331506490

the production of electronic boards is a common activity in the industrial environment, and ensuring their quality is essential for obtaining reliable products. this work presents a comparative study of the performance of three machine learning architectures: YoloV8, FOMO, and MobileNet. the dataset was created based on four classes for assembly fault detection. the model that achieved the best results was FOMO, with precision, recall, and Fl-Score above 95 %, as well as processing 10.5 frames per second and having a total size of 152 kB. © 2024 IEEE.

关键词： Adversarial machine learning

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Explainable Process Deviance Discovery with Data-Efficient Deep learning 4th

Explainable Process Deviance Discovery with Data-Efficient ...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Folino, Francesco Folino, Gianluigi Guarascio, Massimo Pontieri, Luigi Rende87036 Italy

ISBN: (纸本)9783031812408

Detecting deviant execution instances of a business process is an important issue in modern enterprises and organizations. Recently, learning deep Deviance Detection Models (DDMs) out of process traces via (semi-)supervised methods was shown to outperform traditional approaches based on standard machine learning solutions. However, deep models need to be trained with large enough amounts of data, which may not be available in several real-life settings, especially in Green-AI applications where restrictions to data access and data processing operations must be obeyed. To better suit such settings, a novel approach for discovering a deep DDM is proposed that compensates for the scarcity of training data by leveraging a complementary self-supervised learning task. Besides minimizing the deviance classification loss, the reconstruction loss of an auxiliary auto-encoder sub-net is exploited as an additional (self-)supervision signal. For the sake of explanation, the neural network is provided with a flat pattern-based encoding, on top of which two parallel stacks of feature-representation layers are learnt efficiently and robustly by leveraging residual-like skip connections. the approach effectively deals with the challenging combination of data-efficiency and explainability requirements on a case study concerning the execution traces of a real-life process. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Self-Renewal machine learning Approach for Fast Wireless Network Optimization 20

Self-Renewal Machine Learning Approach for Fast Wireless Net...

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20th IEEE international conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Ajayi, Oluwaseun T. Cao, Xianghui Shan, Hangguan Cheng, Yu Illinois Institute of Technology Department of Electrical and Computer Engineering Chicago60616 United States Southeast University School of Automation Nanjing210096 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9798350324334

the throughput maximization in multi-hop wireless networks is largely limited by interference due to the reuse of the channel resources. Although machine learning (ML) can accelerate the optimization of wireless network capacity, the existing system can become limited because of insufficient knowledge from available data. We propose a self-renewal ML (SRML) method that incrementally improves the throughput maximization of future optimization instances through the design of a data selection algorithm for scheduling structure classification and application identification model retraining. With one round of implementation, the SRML method outperforms the fixed ML (FML) method, random (RAND) method and Greedy Heuristic method in the multi-commodity flow deployment setting with an average achievable throughput of 100% for small flows and at least 79% for large flows, relative to the delayed column generation (DCG) benchmark algorithm, while reducing the computational complexity and achieving a high solution efficiency. By leveraging the transfer learning of parameters during self-renewal, the computational cost of model training is reduced by at least 71.09%. © 2023 IEEE.

关键词： Heuristic methods

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7th international conference on Mobile Internet Security, MobiSec 2023

7th International Conference on Mobile Internet Security, Mo...

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7th international conference on Mobile Internet Security, MobiSec 2023

ISBN: (纸本)9789819744640

the proceedings contain 21 papers. the special focus in this conference is on Mobile Internet Security. the topics include: Computing Resource Allocation Based on Multi-base Station and Multi-user Scenario in Mobile Edge Computing;lattice-Based Multi-entity Identification Protocols;Hardware Implementation of SM4 Encryption Algorithm with Hybrid Stochastic Computing;Privacy-Preserving Fair Outsourcing Polynomial Computation Without FHE and FPR;SFPDML: Securer and Faster Privacy-Preserving Distributed machine learning Based on MKTFHE;existence of Equivalent Keys in a Random Number Generator with Field Trace and a Dynamic Mapping;a Heterogeneous Computing Framework for Accelerating Fully Homomorphic Encryption;generic Construction of Forward-Secure Chameleon Hash Function;exploring Leakage Characteristics and Attacks through Profiles of Screaming Channels;Revisiting an Extension of Kannan’s Embedding for Ring-LWE;revisiting the Algorithms for the Quaternion -Isogeny Path Problems;the Impact of Data Scaling Approaches on Deep learning, Random Forest and Nearest Neighbour-Based Network Intrusion Detection Systems for DoS Detection in IoT Networks;integrating machine learning Models into the Linux Kernel: Opportunities and Challenges;early Weather Warning System with Real-Time Monitoring by IoT Sensors Considering Scalability and Reliability;privacy Preserving Multi Factor Authentication Protocol for Next Generation Grids Deployed in Smart Cities;research Directions in Formal Verification of Network Configurations Toward Verification of Mobile Networks;Destructive Malwares on MITRE ATT&CK Tactics for Cyber Warfare: A Brief Survey and Analysis;intelligent Cooperative Routing for Services in Computing Integration Networking;A Biometric-Based Data Protection Scheme for RSDs.

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qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation 4

qgym: A Gym for Training and Benchmarking RL-Based Quantum C...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Van Der Linde, Stan De Kok, Willem Bontekoe, Tariq Feld, Sebastian Applied Cryptography & Quantum Algorithms The Hague Netherlands Bernoulli Institute University of Groningen Faculty of Science and Engineering Groningen Netherlands QuTech Delft University of Technology Quantum & Computer Engineering Delft Netherlands

ISBN: (纸本)9798350343236

Compiling a quantum circuit for specific quantum hardware is a challenging task. Moreover, current quantum computers have severe hardware limitations. To make the most use of the limited resources, the compilation process should be optimized. To improve currents methods, Reinforcement learning (RL), a technique in which an agent interacts with an environment to learn complex policies to attain a specific goal, can be used. In this work, we present qgym, a software framework derived from the OpenAI gym, together with environments that are specifically tailored towards quantum compilation. the goal of qgym is to connect the research fields of Artificial Intelligence (AI) with quantum compilation by abstracting parts of the process that are irrelevant to either domain. It can be used to train and benchmark RL agents and algorithms in highly customizable environments. © 2023 IEEE.

关键词： Reinforcement learning

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