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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是281-290 订阅

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Ethereum Decentralized peer-to-peer Contracts Mechanism 3

Ethereum Decentralized peer-to-peer Contracts Mechanism

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Singh, Rajender Bansal, Naveen Vivekananda Global University Department of Mechanical Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application India

ISBN: (纸本)9798350399264

With its dedicated money Ether, Ethereum offers regionalized Ethernet simulated computers to manage as an open-source public blockchain, peer-to-peer agreementsability to perform clever contracts. And since Ethereum is the second largest blockchain, transaction data volumes are growing rapidly. However, at this point, most analyses of Ethereum data are focused on the statistical characteristics of Ethereum nodes and do not examine how transactions between them behave. In this research, leverage transaction data from existing blocks to categorize users and smart contracts into groups and Applies machine learning to Ethereum analytics for the first time. The identification data of the accessible Ethereum users and smart contracts is used to examine the clustering findings. © 2023 IEEE.

关键词： Ethereum

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Comparative Analysis of Crop Diseases Detection Using machine learning Algorithm 3

Comparative Analysis of Crop Diseases Detection Using Machin...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Jha, Pradeep Dembla, Deepak Dubey, Widhi Jecrc University Department of Cse Rajasthan Jaipur India Jecrc University Department of Computer Application Rajasthan Jaipur India Jecrc University Department of Botany Rajasthan Jaipur India

ISBN: (纸本)9781665462167

Nursery workers have a wide scope of diversity to select the reasonable crops. In any case, due to the absence of information, farmers are stunned by the effect of the diseases in farmland. Numerous farmers struggle and waste through a lot of their time harvesting diseased crops. Farming assumes an imperative part in the Indian economy. Farming is the major occupation across the globe. Nearly 70% of Indian population depends on agriculture. This article presents the different techniques and algorithm of crop disease detection. Also, presents a comparative analysis for the detection of crop diseases by using different algorithms and techniques with their obtained accuracy. © 2023 IEEE.

关键词： Crops

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IoT and machine learning-Based Monitoring of the Growth of Crops Using Blockchain Technology 3rd

IoT and Machine Learning-Based Monitoring of the Growth of C...

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Yadav, Randheer Mishra, Ashish Kumar Pandey, Mahima Shanker Pandey, Brijesh REC Ambedkar Nagar Dept IT Ambedkar Nagar Uttar Pradesh India Sharda Univ Dept CS & Applicat Greater Noida Uttar Pradesh India Lovely Profess Univ Dept CSE Jalandhar Punjab India

ISBN: (纸本)9783031640759;9783031640766

Agriculture is the cultivation of soil, the cultivation of crops, and the raising of livestock. Agriculture is essential to the economic development of any country. Despite significant advancement in the service sector, agriculture remains India's most important employer and source of income. The agricultural sector has great potential to improve food needs and provide healthy and nutritious food. But nowadays, the situation is not favoring the farmers. Farmers have to bear a lot of loss due to diseases, pests, and lack of moisture in soil, destroying the crop. Detecting insects and diseases in plants is one of the most challenging tasks for farmers, as large portions of crops are damaged, and quality is affected as a result. Farmers have so far adopted conventional farming methods. These techniques were imprecise, slowing productivity and taking a lot of time. Precision agriculture helps improve productivity by identifying steps that need to be taken at different phases of crop production. Precision farming uses advanced techniques such as collecting data, training systems, and predicting outcomes using IoT, data mining, data analytics, machine learning, and more. With the help of emerging technologies, precise farming reduces manual work and increases productivity. The application of machine learning with IoT data analytics in the agricultural sector brings new benefits to improve the quantity and quality of production from crop fields and meet the growing demand for food. The proposed model will predict the task needed to be performed at the time of disease in the crop, lack of moisture in the soil, in case of pests, etc. The goal of this work is to enable individuals to grow crops efficiently and achieve high productivity at low cost.

关键词： Agriculture IoT machine learning Blockchain Disease Detection Pest Control

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An Extensive Examination of Methods for Detecting and Classifying Malignant and Benign Skin Cancers Using Deep learning Techniques 3

An Extensive Examination of Methods for Detecting and Classi...

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

作者： Pasumarthy, Rambabu Gondi, Lakshmeeswari Gitam School Of Technology Gitam Deemed To Be University Department Of Computer Science And Engineering Andhra Pradesh Vishakhapatnam India

ISBN: (纸本)9798350318609

Skin cancer is a pressing health concern responsible for numerous fatalities in today's world, often diagnosed in its advanced stages, making treatment planning and patient survival challenging. Medical image interpretation, such as MRI as well as CT scans, is crucial for skin cancer diagnosis, but the manual analysis is costly, time-consuming, and can be influenced by biases. DL (Deep learning), a cutting-edge technology, is gaining attention as it allows for the automatic evaluation and interpretation of medical images, reducing the need for human intervention. Deep learning approaches have demonstrated remarkable outcomes in various domains by automatically extracting intrinsic image features, excluding the need for manual feature extraction. This review delves into the application of DL approaches in the diagnosis & detection of cancer, with a particular focus on skin cancer. Detecting melanoma from samples of dermoscopic skin is a formidable task, but employing a machine vision tool, like a deep learning approach, can address some of these challenges. The study introduces an automated melanoma classifier on the basis of DCNN (Deep Convolutional Neural Network) to accurately differentiate malignant from benign melanoma. It also outlines the various deep learning models and steps involved in utilizing these models for skin cancer detection. Recent advancements in the skin cancer detection Deep learning (DL) methods are explored and summarized, shedding light on the critical challenges that have been faced in accurately detecting the cancer of skin in its early stages. For this research, dermoscopic images from the international Skin Imaging Collaboration datastores (ISIC 2016, ISIC 2017, and ISIC 2020), containing diverse cancer samples, were obtained and evaluated based on metrics like precision, accuracy, specificity, recall, and F1-score. © 2024 IEEE.

关键词： Convolutional neural networks

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Advanced statistical and machine learning methods for multi-step multivariate time series forecasting in predictive maintenance 3

Advanced statistical and machine learning methods for multi-...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Tessoni, Valentina Amoretti, Michele Sidel Via La Spezia 241a I-43126 Parma Italy Univ Parma Parco Area Sci 181a I-43124 Parma Italy

The accurate prediction of failure events is of central interest to the field of predictive maintenance, where the role of forecasting is of paramount importance. In this paper, we present and compare some advanced statistical and machine learning methods for multi-step multivariate time series forecasting. Regarding statistical methods, we considered VAR, VMA, VARMA and Theta. The machine learning approaches we selected are variants of the Recurrent Neural Network model, namely ERNN, LSTM and GRU. All the considered methods have been evaluated in terms of accuracy, using 5 public datasets. As an extra contribution, we have implemented the multivariate version of the Theta method. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： predictive maintenance multi-step multivariate time series forecasting statistical methods machine learning

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

3rd Biennial International Conference on Future Learning Asp...

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

ISBN: (纸本)9789819947577

The proceedings contain 70 papers. The special focus in this conference is on Future learning Aspects of Mechanical Engineering. The topics include: Importance of Performance and Emission Characteristics in Biodiesel;Experimental Investigation of Parallel Fan Arrangement for Variable Air Volume in HVAC Systems;comparison of Basalt/Kevlar/Glass Fibre Duralumin Laminate-Reinforced Composite Using Nanoclay Filler;comprehensive Study on Solar Adsorption Cooling System;absorption Cooling System Powered by a Low Concentration Collector: A Case Study;analyzing the Factors Influencing the Market Feasibility of Alternative Fuel-Based Vehicles;hydrogen as a Fuel For Power Generation—A Review;mix Oil Biodiesel Blend’s Performance Characteristics with Energy Audit;portable Solar Stirling Engine;analyzing Barriers of Industry 4.0 Enabled Sustainable Manufacturing to Achieve Circular Economy;prioritization of Factors for Robust Manufacturing System: A Case of Disk Brake;maintaining Comfort Air Conditioning System Inside a Four Wheeler Using Phase Change Material;comparative Study of Phenotypic and Genotypic Methods for Biofilm Detection on Medical Devices: An Empirical Approach;synthesis and Analysis of Vital Social Sustainability Indicators Using Pareto Analysis;numerical Analysis of Cavity Flow at Different Angles of Attack;investigation of Carbon Nanotubes and Titanium Dioxide Doped Biodiesel on the Performance and Emission Characteristics of Four-Stroke Diesel Engine;a Study of the Effects of Low Quantity Lubrication on machine Efficiency;a Time Saving Image Segmentation Technique and Its application to Detect Cotton Leaf Spot;COMSOL Multiphysics Simulation of Microwave System-Based Household Welding Approach;Influence of Nanoparticles in application of Solar Collector-Based Energy Harvesting (SCBEH): A Review;a Review on Compliant Microgripper.

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Using Unsupervised learning to detect Fraud in Cryptocurrency 3

Using Unsupervised Learning to detect Fraud in Cryptocurrenc...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Chordia, Sankalp Nilesh Shinde, Sandeep Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350354348

Bitcoin, the most popular form of cryptocurrency, operates independently of central banks or governmental entities. The emphasis on anonymity complicates the task of tracing cryptocurrency payments linked to illicit activities or criminal transactions. Consequently, ransomware developers exploit this environment to extort ransom by encrypting valuable user information. Numerous research endeavors have explored the application of machine learning techniques to detect fraudulent activities within cryptocurrency transactions. However, there is a notable absence of studies showcasing the practical implementation of such methods using real-time blockchain data. The present study is structured into two main sections. Firstly, we delineate the development of various features utilized for training purposes. Subsequently, our approach will be validated using real-time Bitcoin data. The other section highlights the use of open source investigation tool (OSINT) to carry forward the cryptocurrency investigation by taking the input from previous findings. OSINT involves the retrieval of information from a range of publicly accessible data sources, while offering a viable approach to address the difficulties associated with extracting and aggregating intelligence from diverse publicly available information channels and social platforms. Thus narrowing down the search investigation by exploiting the pseudonymity of Bitcoin and tracing back the wallet and IP addresses' digital and physical identity. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning Techniques For Software Component Reusability 3

Machine Learning Techniques For Software Component Reusabili...

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3rd international conference for Emerging Technology, INCET 2022

作者： Khan, Feroz Lingala, Gowtham Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science and Engineering Andhra Pradesh Guntur522502 India

ISBN: (纸本)9781665494991

Software reuse has considerably reduced the products time to market with increased reliability and productivity. Widespread use of software reuse, agile software development, User stories and component-based development methods can be observed nowadays. To reap the advantages of software reuse developers can access it from the repositories. The repositories are generated through different techniques used in data mining. In order to predict the reusability of a component machine learning techniques is used. This article provides a method of integration of agile, user stories and component-based developments methods using artificial neural network. The factors that determine the reusability of software are studied and corresponding software metrics are input to the proposed classifier. The metrics used for the study are Cyclomatic Complexity (CC), Design Structure Quality Index (DSQI), Cohesion, weighted method per class (WMP), Bugs, Coupling. Depth Inheritance Tree (DIT), Number of children (NOC). The classifier was tested with some standard classifiers namely Random Forest, Logit Boost, J48 and ESRT used in machine learning and the accuracy achieved is 91.8%. © 2022 IEEE.

关键词： computer software reusability

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Abnormal Signal Recognition Method of Wearable Sensor Based on machine learning 3rd

Abnormal Signal Recognition Method of Wearable Sensor Based ...

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3rd EAI international conference on IoT and Big Data Technologies for Health Care, IotCARE 2022

作者： Li, Chao Zhang, Xuan Department of Information Engineering Tongling Polytechnic Anhui Tongling244000 China Monroe College New RochelleNY10801 United States

ISBN: (纸本)9783031335440

The recognition of abnormal signal of wearable sensor is of great significance to the application value of the device. In order to improve the accuracy of abnormal signal recognition of wearable sensors and indirectly ensure the safety of wearable sensor devices, a method of abnormal signal recognition of wearable sensors based on machine learning was proposed. According to the different abnormal types and principles of wearable sensors, the signal abnormal judgment criteria are set. The wearable sensor signal is collected, and the initial signal is preprocessed by Kalman filtering, normalization and weighted fusion. The machine learning algorithm is used to extract the features of sensor signals, and the recognition results of the abnormal type, abnormal semaphore and abnormal location of sensor signals are obtained through feature matching. Through the identification performance test experiment, it is obtained that the average abnormal type error detection rate of the optimization design identification method is 0.86%, and the average statistical error of abnormal semaphore is 0.22 db, lower than the preset value. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Health Insurance Cost Prediction using machine learning 3

Health Insurance Cost Prediction using Machine Learning

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3rd international conference for Emerging Technology, INCET 2022

作者： Bhatia, Kashish Gill, Shabeg Singh Kamboj, Navneet Kumar, Manish Bhatia, Rajesh Kumar Punjabi University Department of Computer Science and Engineering Patiala India Indraprastha Institute of Information Technology Department of Computer Science and Engineering Delhi India Amazon Big Data Engineer Punjab Engineering College Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9781665494991

This paper represents a machine learning-based health insurance prediction system. Recently, many attempts have been made to solve this problem, as after Covid-19 pandemic, health insurance has become one of the most prominent areas of research. We have used the USA's medical cost personal dataset from kaggle, having 1338 entries. Features in the dataset that are used for the prediction of insurance cost include: Age, Gender, BMI, Smoking Habit, number of children etc. We used linear regression and also determined the relation between price and these features. We trained the system using a 70-30 split and achieved an accuracy of 81.3%. © 2022 IEEE.

关键词： Health insurance

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