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检索条件"机构=Artificial Intelligence and Machine Learning Engineering"

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Rice Leaf Disease Diagnosis Using Dense EfficientNet Model 1

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Roopa Devi, E.M. Shanthakumari, R. Rajadevi, R. Anusuyaa Harini Lokesh Department of Information Technology Kongu Engineering College Perundurai India Department of Artificial Intelligence and Machine Learning Kongu Engineering College Perundurai India

ISBN: (数字)9783031648472

ISBN: (纸本)9783031648465

Global food security and agricultural sustainability are seriously threatened by the paddy leaf diseases that is rapidly spread. Four of the most common widespread disease that affect rice leaf are Hispa, brown spot, Leaf blast and as well as Healthy leaf are identified. Quickly and correctly identifying a specific disease in order to implement effective strategies for its control and treatment. After the dataset performed the necessary pre-processing, a variety of deep learning algorithms, comprising VGG19, Resnet50, and InceptionV3, were applied on it. Our deep learning model is based on the EfficientNetB3 architecture, which has demonstrated excellent performance in image analysis applications. The EfficientNetB3 model is trained on the collected dataset, employing transfer learning to leverage pre-trained weights and accelerate convergence. To enhance the discriminative power of the model, employ feature extraction techniques. These techniques extract relevant features from the images and provide valuable information for disease detection. The integration of deep learning and feature extraction techniques, specifically using EfficientNetB3 demonstrates the potential of modern technologies in the field of agriculture. The ultimate objective of this integrated technique is to enable agricultural precision by providing farmers with a technology that is capable of identifying and monitoring plant diseases precisely and efficiently. It aids to sustainable agricultural production by providing focused interventions and minimising resource use. In summary, the originality in this work is found in the careful coupling of cutting-edge deep learning architecture, transfer learning, and feature extraction approaches, showcasing the advances being made in disease identification for the improvement of agricultural sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Feature extraction

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MarineLens: Empowering Conservation through AI-Powered Fish Species Detection and Insights

MarineLens: Empowering Conservation through AI-Powered Fish ...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sorna Shanthi, D. Vijay, K. Bhuvaneswaran, B. Praveen, N. Priyan, B. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390049

MarineLens is a groundbreaking conservation tool that leverages the power of artificial intelligence to revolutionize fish species detection and insights in marine environments. Using advanced machine learning algorithms, MarineLens is able to accurately identify and track different fish species, providing researchers and conservationists with valuable data on population dynamics, behavior patterns, and habitat usage. By harnessing the capabilities of AI, this technology offers a cost-effective and efficient solution for monitoring marine life and assessing the impact of human activities on underwater ecosystems. MarineLens enhances the ability to make data-driven decisions and implement targeted conservation strategies to protect vulnerable species and preserve biodiversity. With its user-friendly interface and customizable features, MarineLens empowers users to generate actionable insights and visualize complex data in a comprehensible manner, facilitating collaboration and knowledge sharing within the conservation community. Enabling a proactive approach to marine conservation, MarineLens is a game-changer in safeguarding our oceans for future generations. © 2024 IEEE.

关键词： Scalability Oceans Marine vegetation Fish Biodiversity Environmental management Sustainable development Protection Marine animals Monitoring

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A Comprehensive IoT Security Framework Empowered by machine learning 3

A Comprehensive IoT Security Framework Empowered by Machine ...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Pavithra, H.C. Rajeshwari, J. Bnmit Department of Artificial Intelligence and Machine Learning Bangalore India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bangalore India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518592

In the past few years, green computing has been widely accepted and implemented, especially in areas like social computing, mobile computing, AI-driven agent systems, and the Internet of Things (IoT). This work draws inspiration from these factors and seeks to delve into the constantly changing landscape of vulnerabilities associated with IoT devices. The paper presents an in-depth survey that thoroughly covers multiple features of the IoT domain. This paper specifically focuses on categorizing the attacks that exploit different vulnerabilities in IoT ecosystems. We delve into a classification system that clarifies the complex landscape of these attacks. After that, we shift our focus to techniques for assessing IoT vulnerabilities, offering valuable perspectives on how to evaluate and mitigate the related risks. The paper concludes by highlighting the emerging challenges in the IoT domain and its long-term viability, offering valuable insights for individuals to explore research opportunities in this dynamic and promising area. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Energy Consumption Forecasting in Smart Grids Using Quantum Boltzmann machines 2

Advanced Energy Consumption Forecasting in Smart Grids Using...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Velayutham, P. Balaji, V.S. Krissh Shankaran, N. Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

Smart grid systems are seen as the next big thing to energy distribution and management with more efficiency, stability, and sustainability in power distribution and energy management systems. It is challenging to predict the amount of energy consumption in smart grid system because of its randomness resulting from various factors such as consumer's behavior, weather conditions. This is mainly because traditional forecasting techniques are usually inadequate in capturing these complexities and as a result resources are poorly allocated and organizational processes become inefficient. The work provides the energy consumption forecasting in smart grids through the application of Quantum Boltzmann machine(QBM) in the smart grid utilizing historical data. Some of the variables incorporated in the data are reaction time, power balance, and price elasticity coefficients customized through smart meters. Once raw data is preprocessed, QBMs are developed to establish an empirical probability model that will predict actual future energy utilization characteristics. The QBM is tested against the actual consumption data and focuses on the visualization of energy profile and identification of points like local extremes. © 2024 IEEE.

关键词： Smart power grids

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Predictive Modeling Techniques for Anticipating Vaccine Demand 1

Predictive Modeling Techniques for Anticipating Vaccine Dema...

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Vinitha, S. Prasanthi, T. Nithya, D. Jerusalem College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Jerusalem College of Engineering Department of Cyber Security Chennai India

ISBN: (纸本)9798350365337

Infant vaccine stock prediction is a critical aspect of health care logistics management aimed at ensuring the availability of essential vaccines to safeguard children against preventable diseases. With an emphasis on newborn requirements, this study suggests employing cutting-edge machine learning algorithms to forecast vaccine supply requirements. Traditional forecasting methods often struggle to adapt to the dynamic and complex nature of vaccine demand, leading to inefficient stock management and potential shortages. To address these challenges, this research explores the efficacy of advanced machine learning algorithms such as Gradient Boosting machines (GBM), Long Short-Term Memory (LSTM) networks, and Support Vector regression (SVR) in predicting infant vaccination stock needs. These algorithms provide the capacity to examine past immunization data, spot trends, and more reliably and accurately proj ect future demand. Through extensive experimentation and evaluation using real-world vaccination data sets, the proposed algorithms demonstrate promising results in accurately predicting stock requirements for infant vaccines. The results of this study help to improve the efficacy and efficiency of vaccination supply chains, guaranteeing infants across the world will have timely access to vaccinations that can save their lives. The present investigation employed machine learning models, namely the linear regression model, Random Forest model, GBM and SVR model, and deep learning models, such as ANN and LSTM, to forecast future trends in essential infant vaccines. The models' performances were compared using the evaluation metrics RMSE and R-Square, as well as by comparing their predictions. © 2024 IEEE.

关键词： Long short-term memory

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Enhancing Fault Detection and Diagnosis through an Integrated Chaos-RNN Model

Enhancing Fault Detection and Diagnosis through an Integrate...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Vijay, K. Vijay, Priya Sudharsan, R. Swathy, B. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350390049

One of the most challenging problems is to identify the anomaly or fault in machines and materials, though identifying the faults are a tedious task once the fault or anomaly is identified. Identifying the reason or the root cause for the fault or anomaly is the expected or actual solution for the problem. Smart manufacturing based on Digital Twin has provided solutions for several non-trivial problems such as detecting the time taken for product delivery, fault detection in materials, machines, delivery and many more real time problems. To detect the anomaly real-time and identify or reason out the root cause behind the fault a novel digital twin architecture comprising of several novel modules is proposed;improving the productivity of the outcome from manufacturing. A Digital Twin architecture for identifying the Root Cause of a Fault in a Smart Manufacturing environment based on Anomaly Detection and Time Series analysis is proposed. The architecture is expected to improve the overall production rate and reduce the down time in identifying the root cause of the fault occurred. An artificial Neural Network that works on the principle of Chaos Theory is proposed and experimented with single and multi-class anomaly. © 2024 IEEE.

关键词： Smart manufacturing

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A Novel AI-Assisted e-Consult Platform Integrating Deep learning for Enhanced Healthcare Access and Diagnostic Precision

A Novel AI-Assisted e-Consult Platform Integrating Deep Lear...

引用

2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Vijayasekaran, G. Rajasree, R.S. Alam, Parvez Suryavanshi, Abhishek Ranjanagi, Vinayak Mulla, Umar Farooq New Horizon College of Engineering Dept of Computer Science and Engineering Bengaluru India Learning New Horizon College of Engineering Dept of Artificial Intelligence and Machine Bengaluru India

ISBN: (纸本)9798331509828

This study introduces the 'AI-Assisted E-Consult' initiative. The rapid digitization of healthcare is reshaping how medical services are delivered. This paper outlines the design of an AI-Driven E-Consult platform, serving as a digital interface that links patients, healthcare provider, and medical facilities. Utilizing state-of-the-art technologies such as AI and machine learning, the platform streamlines predictive health insights and appointment scheduling, thereby improving access to quality medical care. The platform employs Natural Language Processing (NLP) via the NLTK library and uses a Recurrent Neural Network (RNN) architecture trained on medical conversation datasets to power an AI chatbot. This chatbot analyzes symptoms and predicts potential health conditions, offering users preliminary insights into their health status. Furthermore, the system incorporates Haversine distance calculations to measure the geographical proximity between patients and doctors, enhancing logistical planning for consultations. To evaluate the platform's accuracy and performance, metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) will be used, ensuring precise predictions and operational effectiveness. The AI- Powered E-Consult platform is designed to eliminate healthcare barriers and maximize resource efficiency. Offering scalable, adaptable solutions tailored to the needs of contemporary healthcare, this platform represents a significant advancement toward a more accessible, efficient, and time-saving healthcare model, particularly for underserved populations. © 2025 IEEE.

关键词： Recurrent neural networks

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Precision Land Use and Land Cover Mapping with a High-Accuracy Time Series LSTM Model

Precision Land Use and Land Cover Mapping with a High-Accura...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Aryan, Adla Rv, Prakash Aluvala, Srinivas Chowdary, Venigalla Shashank Ardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal506371 India Nandyal518501 India

ISBN: (纸本)9798331529833

This paper introduces a machine-learning-based LULC classifying model developed with a time series data set, trained on a diverse set of agriculture and non-agricultural classes. It used a strong generalization capability employing an LSTM architecture;that is, it achieved accuracy of 90.04% in training and also validation accuracy of 90.55%. The ultimate accuracy was obtained at the minimal loss of 0.1909 at the accuracy of 95.58%, thereby validating its usability for LULC pattern classification. This model has a wide-range of potential application in environmental monitoring and resource management. Future work involves introducing more variables into the dataset, the inclusion of other environmental factors, and fine-tuning of the model towards more general applications in geospatial applications. Thus, this work advances the current state of remote sensing and LULC classification to present an effective tool in sustainable land management. © 2025 IEEE.

关键词： Environmental monitoring

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Targeted Ransomware Attacks and Detection to Strengthen Cybersecurity Strategies 2

Targeted Ransomware Attacks and Detection to Strengthen Cybe...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Chinmaya, B.J. Kudtarkar, Sujay Arun Mohana Artificial Intelligence and Machine Learning Engineering RV College of Engineering® Bengaluru India RV College of Engineering® Bengaluru India

ISBN: (纸本)9798350340235

Ransomware attacks have emerged as the most trending malware in recent eras. Ransomware attacks hold data and devices hostage until a ransom is paid. The proposed methodology involves virtual or practical demonstration of ransomware attacks using tools like TeamViewer and 7z, aiming to raise awareness on cybercriminal tactics. The main objective is to educate and empower individuals and organizations to protect against such threats. HostedScan, with its robust features, efficiently detects and categorizes risks, providing actionable insights to prioritize security efforts. Detailed reports sent to registered emails include information on risk levels, vulnerabilities, IPs, ports, and hostnames, enabling proactive system and data protection. This work promotes cybersecurity awareness, fostering proactive defense and helping users to mitigate the risk of ransomware and other cyber threats. © 2023 IEEE.

关键词： Cybersecurity Hostname Malware Ransomware Targeted Ransomware TeamViewer Vulnerabilities

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Sentiment Analysis: A machine learning Perspective 1

Sentiment Analysis: A Machine Learning Perspective

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Varma, Nadimpalli Madana Kailash Mattaparty, Sri Harsh Ismail, Shifa Thaduri, Joel Deep Arora, Gagan Anand Kumar, B. Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501286 India Vardhaman College of Engineering Department of Information Technology Telangana Hyderabad501286 India Vardhaman College of Engineering Department of Artificial Intelligent & Machine Learning Telangana Hyderabad501286 India

ISBN: (纸本)9798350364590

An essential part of natural language processing, sentiment analysis makes it possible to automatically extract sentiment from textual input. In this work, we examine how well different machine learning algorithms perform when used to analyze the sentiment of real-time Amazon reviews in the automobile niche. Leveraging a dataset comprising reviews from Amazon users, we employ SVM, KNN, Logistic Regression, and Random Forest algorithms to categorize text sentiment as positive, negative, or neutral. This work evaluates the performance of each algorithm in sentiment analysis tasks using strict evaluation standards and testing. Our findings provide insights into the effectiveness of machine learning approaches for analyzing sentiment in real-time reviews, offering valuable implications for businesses and organizations aiming to leverage customer feedback for decision-making and user experience enhancement. © 2024 IEEE.

关键词： Random forests

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