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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 811 条记录，以下是381-390 订阅

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DeepTweet: Leveraging Transformer-based Models for Enhanced Fake News Detection in Twitter Sentiment Analysis 7

DeepTweet: Leveraging Transformer-based Models for Enhanced ...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Vallileka, N. Sundaravadivel, P. Karthikeyan, U. Krishnan, R. Santhana Narayanan, K. Lakshmi Sundararajan, S. Psna College of Engineering and Technology Department of Computer Applications Dindigul India Saveetha Engineering College Department of Artificial Intelligence & Machine Learning Thandalam India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India

ISBN: (纸本)9798350341485

With the increase in the popularity of the social media sites, there arises lot of adverse effects including the spread of fake news. this research study proposes a novel algorithm, called DeepTweet, which leverages transformer-based models for enhanced fake news detection in Twitter sentiment analysis. the aim is to improve the accuracy and efficiency of identifying fake news in real-time by exploiting the contextual information and semantic representations captured by the transformer-based models. the proposed DeepTweet algorithm utilizes a pre-trained transformer model, such as BERT or GPT, to encode the textual content of tweets and extract rich contextual embeddings. these embeddings capture both local and global dependencies, enabling a more comprehensive understanding of the sentiment expressed in a tweet. Furthermore, a novel attention mechanism tailored for fake news detection is introduced to focus on key textual features that are indicative of potential misinformation. To assess the efficiency of DeepTweet, extensive simulations using a large dataset of labelled tweets containing both genuine and fake news are done. Further, this study compares the performance of DeepTweet with existing algorithms, including LSTM, CNN, and traditional machine learning approaches. Various simulation metric measures such as accuracy, precision, recall, and F1 score are employed to analyze the algorithm's performance. the simulation grades demonstrate that DeepTweet outperforms the prevailing algorithms by means of accuracy and overall detection performance. the proposed attention mechanism proves to be particularly effective in identifying subtle linguistic cues and deceptive patterns associated with fake news. the enhanced contextual embeddings captured by the transformer-based models contribute to improved sentiment analysis and subsequently lead to more accurate fake news detection. © 2023 IEEE.

关键词： Embeddings

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Pomegranate Leaf Fruit Disease Prediction Using machine learning 4th

Pomegranate Leaf Fruit Disease Prediction Using Machine Lear...

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4th international conference on machine learning, Advances in computing, Renewable Energy and Communication, MARC 2023

作者： Mokal, Atul Bhimrao Avhad, Vaishnavi Jadhav, Om Khamkar, Vishal Japtap, Mansi Ingle, Sumedh S. Department of Computer Engineering Sanjivani College of Engineering Kopargaon A. Nagar Kopargaon India

ISBN: (纸本)9789819752300

the impact of agricultural plant diseases on farmers’ economic losses is significant. these diseases can harm various parts of the plant, including the roots, fruits, leaves, and stems. Early detection of these diseases is crucial for improving agricultural productivity. In traditional agricultural systems, disease recognition relies on the expertise of agricultural professionals and experienced farmers, but this often results in lower accuracy, leading to losses for farmers. To address this issue, numerous researchers have been exploring the use of soft computing and expert systems for more accurate plant disease recognition. Visual identification of plant diseases is less reliable, as some diseases may not exhibit visible symptoms, or their symptoms may only become apparent late in the growing season, near the time of harvest. Fortunately, modern technology has the potential to significantly enhance agricultural production and sustainability. this paper offers a comprehensive review of techniques for detecting diseases in pomegranate plants. the study covers various stages of the disease detection process, including preprocessing, segmentation, feature extraction, and classification. Additionally, the paper provides a comparison of existing methods and highlights their limitations. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Expert systems

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Crime Rate Prediction Using machine learning Techniques 1

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6th international conference on soft computing and Signal Processing, ICSCSP 2023

作者： Yashwanth, Yarasam Mahitha, Kamarapu Shah, Bhavesh Shaik, Subhani Sriman Narayana Iyengar, N. Ch. Telangana Hyderabad India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

Crime is a pervasive and concerning issue in modern society, with a multitude of crimes being committed on a daily basis, causing great unrest among the general populace. Preventing crime is therefore an essential task. In recent times, the significance of artificial intelligence has been observed in almost every field, including crime prediction. To achieve this, it is necessary to maintain a comprehensive data of past crimes, as this information can be utilized for future reference. Predicting the likelihood of future crimes can help law enforcement agencies prevent them before they occur, providing valuable strategic information based on factors such as time and location. However, accurately predicting crime is a challenging task, given the increasing rate at which crimes are being committed (Dutta in Random forests regression in python [1]). therefore, the development of effective crime prediction and analysis methods is crucial in detecting and reducing future crimes. To this end, numerous researchers have conducted experiments to predict crimes using various machine learning techniques and input parameters such as random forest and decision trees. the ultimate goal is to demonstrate the value and efficiency of machine learning in predicting crimes occurring in a particular place, so that it can be utilized by the police to reduce crime rates and promote societal well-being. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Modified InceptionV3 Using soft Attention for the Grading of Diabetic Retinopathy 1

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Proceedings of the 7th international conference on Advances in computing and Data Sciences, ICACDS 2023

作者： Goswami, Shroddha Ashwini, K. Dash, Ratnakar National Institute of Technology Rourkela Rourkela India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Diabetic retinopathy (DR) is an eye ailment affecting retinal blood vessels. Many deep learning methods for detecting DR have been presented as manual diagnosis is time-consuming and inconvenient. InceptionV3 architecture was modified using a soft attention module in this proposed framework. the attention technique’s basic concept is to concentrate on specific relevant parts by assigning the weights accordingly. Contrast Limited Adaptive Histogram Equalization (CLAHE) is the pre-processing method applied initially to the fundus images to improve the contrast level. Along with this, augmentation has been done to increase the number of images, which are then trained and validated using a modified InceptionV3 model. the experimental results show that the suggested model better diagnoses all stages of DR than existing techniques and outperforms the existing model on the IDRiD and DDR datasets. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Grading

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Automated Road Surveillance System Using machine learning 7th

Automated Road Surveillance System Using Machine Learning

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7th international conference on Big Data and Cloud computing Challenges, ICBCC 2022

作者： Vishnu, Ashish Sushmitha, S. Jacob, Tina Susan David Maxim Gururaj, A. Dhanasekar, S. School of Advanced Sciences Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819910502

Road safety requires an understanding of traffic rules. It is also not just the responsibility of oneself but the coordination of every individual on the road to be aware and alert to avoid accidents. the objective of the paper is to analyze the impact of the accident and identify the vehicle which is being prone to accidents using image classification through machine learning. machine learning provides the system with an ability to automatically learn and improve from the given dataset without human intervention or assistance. It looks for patterns in the data and takes a decision accordingly. the training process involves the following steps: collecting the images, annotating the image, data ingestion, and data processing. this paper follows the convolutional neural network algorithm that takes image inputs, assigns various aspects to images, and differentiates them from one another. the image recognition model automatically determines whether the incident in the given image is an accident with the help of bounding boxes. these bounding boxes surround themselves on vehicles which are prone to accidents. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Accidents

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Automated Plant Disease Detection: CNN for Corn Maize, Tomato, and Potato 7th

Automated Plant Disease Detection: CNN for Corn Maize, Tomat...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

作者： Angeline, R. Aruneshwaran, S. Department of Computer Science and Engineering with Specialization in Artificial Intelligence and Machine Learning SRM Institute of Science and Technology Ramapuram India

ISBN: (纸本)9789819720880

Plant ailments pose present a significant challenge to the worldwide food security and the agricultural sector. Swift and precise detection of these diseases is pivotal for effectively managing them and preventing crop yield reductions. Lately, advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), have exhibited encouraging outcomes across various tasks involving image recognition. this undertaking strives to create and execute a model founded on CNNs to prognosticate plant diseases through leaf images. the proposed strategy encompasses three main phases: compiling and preparing the data, developing the model architecture, and assessing performance. Initially, an extensive dataset of plant leaf images, encompassing leaves afflicted by diverse diseases, is assembled. the images undergo preprocessing to heighten quality and eliminate disturbances, ensuring a dependable model training process. Subsequently, a CNN structure is devised and trained to employ the dataset. the chosen CNN model adheres to a sequential design, where each layer possesses precisely one input and output. these layers are arranged sequentially to construct the entire network and incorporate multiple convolutional layers such as Conv2D, MaxPooling2D, Flatten, and Dense, enabling the learning of features from the input images. the findings underscore that the CNN-centered model for forecasting plant diseases attains remarkable training precision of 99.65%, accompanied by a testing precision of 99.44% and a validation precision of 98.61%, proficiently identifying prevalent ailments like common rust disease in corn plants, bacterial spot infection in tomato crops, and the early blight ailment in potato plants. In conclusion, the proposed CNN-driven prognostic model for plant diseases manifests encouraging outcomes in precisely recognizing these diseases from leaf images. the efficacious application of this model can assist farmers and agricultural specialists in inform

关键词： Fruits

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Unveiling Powerful machine learning Strategies for Detecting Malware in Modern Digital Environment 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Ravindra Babu, S. Leisha, R. Medows, Katelyn Jade Sreekumar, Krishna Girish, G.P. Bengaluru India Bengaluru India Hyderabad India

ISBN: (数字)9783031508875

ISBN: (纸本)9783031508868

machine learning has emerged as formidable instrument in realm of malware detection exhibiting capacity to dynamically adapt to ever-shifting topography of digital hazards. this study presents an exhaustive comparative analysis of four intricate machine learning algorithms namely XGBoost Classifier, K-Nearest Neighbors (KNN) Classifier, Binomial Logistic Regression and Random Forest with primary objective of assessing their effectiveness in domain of malware detection. Conventional signature-based detection methodologies have struggled to synchronize with rapid mutations exhibited by malware variants. In sharp contrast machine learning algorithms proffer data-centric approach adept at unraveling intricate data patterns thereby enabling identification of both well-known and hitherto uncharted threats. To meticulously appraise efficacy of these machine learning models we employ stringent set of evaluation metrics. Precision, recall, F1 Score, testing accuracy and training accuracy are meticulously scrutinized to ascertain distinctive strengths and frailties of these algorithms. By providing comparative analysis of machine learning algorithms within milieu of malware detection this research engenders significant contribution to ongoing endeavor of fortifying cybersecurity. Resultant analysis elucidates that each algorithm possesses its unique competencies. XGBoost Classifier showcases remarkable precision (Benign files: 99%, Malicious files: 99%), recall (Benign files: 97%, Malicious files: 99%) and F1 Score (Benign files: 98%, Malicious files: 99%) implying its aptitude for precise malware identification. KNN Classifier excels in discerning benign software exhibiting precision (Benign files: 90%) and recall (Benign files: 91%) to mitigate likelihood of erroneous positives. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Logistic regression

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Benchmarking Classifiers for Loan Default Prediction using Archetypal Analysis 7

Benchmarking Classifiers for Loan Default Prediction using A...

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7th international conference on Algorithms, computing and Systems, ICACS 2023

作者： Antoniadis, Anastasios Georgiou, Konstantinos Charmanas, Konstantinos Mittas, Nikolaos Angelis, Lefteris School of Informatics Aristotle University Greece Department of Chemistry International Hellenic University Greece

ISBN: (纸本)9798400709098

the prediction of loan default is a critical process for the successful development of financial institutions. To effectively manage credit risk, numerous machine learning models have been employed to distinguish creditworthy from high-risk applicants. However, determining an optimal model remains a challenge. To address this, in the current study, we explore an alternative approach for model benchmarking. the main concept involves the usage of a pipeline that constructs different classifiers for loan prediction and compares their performance across several evaluation metrics. To achieve this goal, we deploy an approach based on a multivariate statistical method, known as Archetypal Analysis (AA). the proposed methodology is applied to four datasets with diverse structural characteristics. the findings demonstrate that advanced classifiers like Random Forests (RF) and Artificial Neural Networks (ANN), with oversampling, simple parameter tuning, and feature selection consistently outperform traditional classifiers across most evaluation criteria. In conclusion, the results showcase the ability of AA to intuitively identify the best and worst models for each unique scenario. © 2023 Copyright held by the owner/author(s).

关键词： Forecasting

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7th international conference on Smart computing and Communication, SmartCom 2022

7th International Conference on Smart Computing and Communic...

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7th international conference on Smart computing and Communication, SmartCom 2022

ISBN: (纸本)9783031281235

the proceedings contain 64 papers. the special focus in this conference is on Smart computing and Communication. the topics include: Equipment Health Assessment Based on Node Embedding;Improvement of ERP Cost Accounting System with Big Data;OpenVenus: An Open Service Interface for HPC Environment Based on SLURM;design and Analysis of Two Efficient Socialist Millionaires’ Protocols for Privacy Protection;ABODE-Net: An Attention-based Deep learning Model for Non-intrusive Building Occupancy Detection Using Smart Meter Data;Confidentially computing DNA Matching Against Malicious Adversaries;research on Action Recognition Based on Zero-shot learning;DPSD: Dynamic Private Spatial Decomposition Based on Spatial and Temporal Correlations;Scheduling Algorithm for Low Energy Consumable Parallel Task Application Based on DVFS;GenGLAD: A Generated Graph Based Log Anomaly Detection Framework;research of Duplicate Requirement Detection Method;bayesian Causal Mediation Analysis with Longitudinal Data;Track Obstacle Real-Time Detection of Underground Electric Locomotive Based on Improved YOLOX;Research on Sharding Strategy of Blockchain Based on TOPSIS;parallel Pileup Correction for Nuclear Spectrometric Data on Many-Core Accelerators;component Extraction for Deep learning through Progressive Method;lowFreqAttack: An Frequency Attack Method in Time Series Prediction;a Novel machine learning-Based Model for Reentrant Vulnerabilities Detection;deep learning Object Detection;a Decision-Making Method for Blockchain Platforms Using Axiomatic Design;a New Digital-Currency Model Based on Certificates;An Auxiliary Classifier GAN-Based DDoS Defense Solution in Blockchain-Based software Defined Industrial Network;blockchain-Based Fairness-Enhanced Federated learning Scheme Against Data Poisoning Attack;cryptography of Blockchain;an Efficient Detection Model for Smart Contract Reentrancy Vulnerabilities;using Convolutional Neural Network to Redress Outliers in Clustering Based Side-Channel A

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Multi-source Heterogeneous Data Fusion Algorithm Based on Federated learning 7th

Multi-source Heterogeneous Data Fusion Algorithm Based on Fe...

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7th international conference on soft computing in Data Science (SCDS)

作者： Zhou, Jincheng Lei, Yang Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Guizhou Peoples R China

ISBN: (纸本)9789819904044;9789819904051

With the rapid advancement of science and technology, the number of edge devices with computing and storage capabilities, as well as the generated data traffic, continues to increase, making it difficult for the centralized processing mode centered on cloud computing to efficiently process the data generated by edge devices. Moreover, multimodal data is abundant due to the diversity of edge network devices and the ongoing improvement of data representation techniques. To make full use of heterogeneous data on edge devices and to tackle the "data communication barrier" problem caused by data privacy in edge computing, a multi-source heterogeneous data fusion technique based on Tucker decomposition is developed. the algorithm introduces tensor Tucker decomposition theory and realizes federated learning by constructing a high-order tensorwith heterogeneous spatial dimension characteristics to capture the high-dimensional features of heterogeneous data and solve the fusion problem of heterogeneous data assuming no interaction. Combining and remembering. this method, unlike its predecessors, can efficiently integrate multi-source heterogeneous data without data transfer, therefore overcoming privacy and security-related data communication concerns. Finally, the effectiveness of the technique is confirmed using the MOSI data set, and the shortcomings of the original federated learning algorithm are solved by the updated federated weighted average algorithm. Subjective criteria are used to calculate the data quality for the improved federated average approach based on AHP. We present Influence, an improved federated weighted average method for dealing with multi-source data from a data quality perspective.

关键词： Edge computing Federated learning Deep learning Tensor theory Heterogeneous data fusion

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