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共 20255 条记录，以下是461-470 订阅

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Mulberry Leaf Disease Detection System 3

Mulberry Leaf Disease Detection System

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mekala, V. Muhilan, R. Ragul, R.K. Rithika, C. Department of Electronics and Communication Engineering Kongu Engineering College Perundurai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

The early detection of plant diseases is critical for minimizing crop damage and ensuring sustainable agricultural practices. In the case of mulberry plants, which are essential for the sericulture industry, diseases such as leaf spot, leaf rust, and powdery mildew pose significant threats to crop health and productivity. Traditional disease detection methods are often inefficient, costly, and labor-intensive, making it difficult for farmers to identify and manage infections in a timely manner. This project presents a novel approach to automating mulberry leaf disease detection using deep learning techniques. The system utilizes a combination of two widely recognized convolutional neural networks (CNNs): ResNet50 and VGG19. These models are employed to extract rich feature representations from leaf images and are then fused to improve classification accuracy. The integration of these models allows for the detection of multiple disease types with greater robustness and precision compared to using a single model alone. The proposed solution is designed to be part of a mobile system capable of navigating through mulberry fields, capturing leaf images, and performing real-time disease analysis. The system is integrated with the human interface software with the disease identification system programmed to identify the disease. This system’s performance is evaluated based on metrics such as accuracy, precision, and recall, demonstrating its effectiveness in detecting the targeted leaf diseases. By leveraging the power of deep learning, this project aims to offer a cost-effective, scalable tool for farmers to protect their crops and increase yields. Ultimately, the implementation of this system could lead to healthier crops, reduced pesticide usage, and a more sustainable approach to agriculture. © 2025 IEEE.

关键词： Diseases

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Maximizing Throughput and Minimizing Latency: Evolutionary Algorithms for Optimizing Wireless Cellular Networks 3

Maximizing Throughput and Minimizing Latency: Evolutionary A...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pechetti, Jyoshitha Yadav, Sneha Tarunya, G. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

In the rapidly evolving world of wireless cellular network, optimizing key parameters like data throughput and latency is of critical importance for ensuring high quality communication services. The proposed presents a novel approach to multi-objective optimization to enhance overall network performance using Genetic Algorithms (GA), Pareto, and Particle Swarm Optimization (PSO). By employing optimization algorithms, a balance is sought in terms of trade-off of other parameters while maximizing data throughput and minimizing latency. Simulation layers indicate the comparative study further solidifies the efficacy of the value of evolutionary algorithms in optimizing wireless networks to satisfy the growing needs of high speed and less latency services in today’s communication landscape. © 2025 IEEE.

关键词： Optimization algorithms

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Harnessing AI: Enhancing English Language Teaching through Innovative Tools 3

Harnessing AI: Enhancing English Language Teaching through I...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Adlin Jerusha, J. Rajakumari, R. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of English Tamil Nadu Chennai602105 India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

The use of Artificial Intelligence (AI) into education, namely English language instruction, offers significant possibilities for transformation. This research investigates a range of AI-powered tools and approaches that greatly improve learning results. This study conducts a thorough examination of the replies obtained from an online questionnaire poll to identify the primary domains in which AI applications demonstrate the highest level of effectiveness. These domains include customized learning environments, automated grading systems, and adaptive content delivery. An in-depth analysis is conducted to completely assess the efficacy of AI in enhancing language proficiency in areas such as grammar, vocabulary, pronunciation, and listening comprehension. In addition, the research examines the function of natural language processing (NLP) and machine learning in developing flexible learning modules that adjust to the unique learning speeds of individuals. The article also covers challenges related to data protection, ethical issues, and accessibility, offering a thorough examination of the practical consequences and constraints of AI in language instruction. This project intends to enhance the use of AI technologies in educational environments by highlighting their advantages and examining their limits. The goal is to improve language learning experiences by making them more interesting, effective, and inclusive. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Stroke Prediction in Machine learning by Addressing data Imbalance 3

Optimizing Stroke Prediction in Machine Learning by Addressi...

引用

3rd international Signal Processing, Communications and engineering Management conference, ISPCEM 2023

作者： Wang, Jiacheng Gu, Hanwen Gu, Hong School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China University of Washington Department of Bioengineering Seattle United States

ISBN: (纸本)9798350307023

Brain strokes, responsible for about 11% of global deaths annually according to the World Health Organization, pose significant health challenges. Machine learning aids in early stroke risk prediction and personalized assessment, optimizing medical resource allocation. However, the low frequency of stroke cases in datasets poses challenges for accurate stroke prediction by these models. Addressing this data imbalance is key for effective prediction. This article focuses on: (i) analyzing machine learning outcomes on imbalanced stroke dataset;(ii) comparing methods to address data imbalances;and (iii) using automated Machine learning (AutoML) techniques to achieve high accuracy while realistically identifying stroke incidents. The results demonstrate that the application of Random Under Sampling (RUS) was instrumental in identifying the top-performing model, which showed superior performance in stroke prediction. © 2023 IEEE.

关键词： data handling

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Predicting Road Accident Severity and Identifying High-Risk Areas Using Tree-based learning: A Case Study from the UK 3

Predicting Road Accident Severity and Identifying High-Risk ...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Akarsha, D.P. Mohan, S.G. Nagendra Bharadwaj, D.G. Pandu Kumar, A.N. Manoj, V.S. Nitte Meenakshi Institute of Technology Department of Information Sciences and Engineering Bengaluru India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528201

Road accidents are a pressing global issue, often resulting in injuries ranging from minor to fatal. This study leverages machine learning algorithms to predict the severity and location of road accidents, with the aim of enhancing road safety measures. Utilizing accident data from the UK, we developed predictive models that classify the severity of accidents on a scale from 1 to 5, where 1 represents the least severe and 5 represents the most severe outcomes. The model's predictions have been integrated into a user-friendly application, providing visual insights into accident severity and highlighting high-risk areas on a map. By identifying and visualizing accident-prone locations, this study offers a valuable tool for authorities to implement targeted interventions, potentially reducing the frequency and impact of road accidents. The findings underscore the potential of machine learning in proactive road safety management, contributing to efforts that could save lives and minimize the societal costs associated with road accidents. © 2024 IEEE.

关键词： Adaptation models Visualization Road accidents Machine learning algorithms Costs Machine learning Predictive models Road safety data models Frequency measurement

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Deep reinforcement learning strategy in automated trading systems 3

Deep reinforcement learning strategy in automated trading sy...

引用

3rd international conference on Innovative Research in Applied Science, engineering and Technology, IRASET 2023

作者： Itri, Bouzgarne Mohamed, Youssfi Mohammed, Qbadou Omar, Bouattane Mohamed, Touil Enset Mohammedia University Hassan 2 2IACS Laboratory Casablanca Morocco Enset Mohammedia University Hassan 2 Eeis Laboratory Casablanca Morocco

ISBN: (纸本)9798350398366

Every day, a considerable amount of money is traded in the form of derivatives in global financial markets. Artificial intelligence (AI) software is now the main competitor of traditional investors;it allows market players to anticipate price variations. Adopting a strategy in the stock market using machine learning is essential to make a profit when the markets are depressed. Thus, this study proposes a new approach to train the model of an intelligent trading agent based on deep reinforcement learning (DRL), highlighting the dilemma of the exploitation and exploration phase during the training model as well as its impact on the model performance on a separate or combined portfolio asset. This study shows a clear improvement by backtesting a portfolio composed of three stock market indices, illustrating the advantages and gains obtained by deep reinforcement learning strategy in financial markets. © 2023 IEEE.

关键词： Financial markets

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A Hybrid Slime Mould Meta Heuristic Algorithm and Machine learning Technique for Intrusion Detection System 3

A Hybrid Slime Mould Meta Heuristic Algorithm and Machine Le...

引用

3rd IEEE international conference on intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Umarani, V. Swetha, K. Computer Science and Engineering Saveetha Engineering College Thandalam TamilNadu Chennai India

ISBN: (纸本)9798350361186

Network anomaly prediction is a crucial aspect of network security, as it helps identify unusual or potentially malicious activities within a computer network. There are several approaches and techniques used for network anomaly prediction, and Machine learning (ML) is often employed due to its ability to detect patterns and anomalies in large datasets. In this research, network anomaly detection is carried out by three stages. In first stage, data set from IEEE data port is collected and LRC, RFC models are trained for prediction. In second stage, dimension reduction algorithm PCA is used for preprocessing and ML models are trained for prediction. In third stage, the hybrid PCA +SMA is proposed along with ML models for prediction. From this analysis, the hybrid PCA+SMA+ML provide better performance (accuracy 96) compared with previous three stages. Finally, the hybrid PCA+SMA+RFC is selected for final deployment. © 2024 IEEE.

关键词： Machine learning

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Advances in Remote Sensing Technologies for Ecological Monitoring and Applications in Carbon Stock Estimation 3

Advances in Remote Sensing Technologies for Ecological Monit...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sadhasivam, Shankaripriya Gayathri, G. Poongothai, P. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This review explores recent advancements that are made in remote sensing, enabling detailed analysis of our ecosystem through high resolution and multispectral imaging. The recent advancements in remote sensing includes developments useful for generation of synthetic images using GAN such as StyleGAN3 and for estimation of carbon stocks in forest using OBIA. Ecotones like mangroves and saltmarsh are crucial for carbon sequestration but mapping them accurately was challenging due to spectral similarity and seasonal variations. Focusing on coastal protection by this review examines the mapping methodologies such as random forest classification, OBIA and FID for habitat distinction, canopy area assessment and analysis. Through these technologies data accuracy can be enhanced for better monitoring. This review concludes with a discussion of AI-powered image synthesis and classification. © 2025 IEEE.

关键词： Carbon sequestration

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Privacy-Preserving of Edge Intelligence using Homomorphic Encryption 3

Privacy-Preserving of Edge Intelligence using Homomorphic En...

引用

3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Jerang, Ronaldo Nayak, Sumitra Mahato, Ganesh Kumar Kumar Chakraborty, Swarnendu National Institute of Technology Department of Computer Science & Engineering Arunachal Pradesh India

ISBN: (纸本)9798350338607

Edge intelligence has paved the way for a stronger and more secure system by combining security and artificial intelligence. A lot of research have been undertaken, revealing remarkable development and a variety of viewpoints on this novel phenomenon. We suggested a way enabling edge intelligence to move via homomorphic encryption-enabled gadgets and automobiles. This encrypted data will be delivered to an edge node for the building of a machine learning model in order to gather any potential information hidden in the data as well as any potential obstacles that may need to be addressed before any event happens. Several machine learning models, such as KNN, K-means, SVM, and others, are used to gain the best possible data analysis. Once secure, decisions will be taken, and the outcome will be visible. All machine learning training will be done in encrypted data, which will be encrypted to ensure that no one's privacy is abused. This may be set up for a variety of machine learning modules. © 2023 IEEE.

关键词： Deep learning

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An Extensive Review of intelligent Deep learning Based Anomaly Detection and Classification of 3D MRI Brain Imaging data 3

An Extensive Review of Intelligent Deep Learning Based Anoma...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Hima Bindu, J. Uma Dev, M. Vignan's Foundation for Science Dept. of Computer Science and Engineering Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350396157

MRI - Magnetic Resonance Imaging is one of the frequently employed imaging modalities for brain anomaly detection. MRI produces a massive quantity of data, which includes a consecutive set of scans taken at several time instants. Since the existence of brain anomalies that exists examined on every MR sequence, manual brain anomaly detection necessitates anatomical knowledge, costly, laborious, and inaccurate due to human error. Automatic brain anomaly segmentation from 3D Magnetic Resonance Image (MRI) is essential to perform proper diagnosis, monitoring, and treatment planning of the disease. However, owing to the structural difficulties such as hazy boundaries with uneven shapes, precise 3D brain tumor demarcation is interesting. With the recent developments of Deep learning (DL) models, this research study reviews the recent brain anomaly detection and classification models based on DL approach with 3D MRI data. The existing techniques related to brain tumor segmentation, classification, and validations are reviewed. Every reviewed technique is investigated based on the aim, underlying technique used, dataset, and evaluation parameters. Besides, a comparison table is provided by summarizing the reviewed techniques under several aspects. Finally, a quick analysis of the evaluated techniques' results is conducted to gauge their effectiveness. © 2024 IEEE.

关键词： Brain

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