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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

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Generation and Adversarial Testing for Assessing Autonomous Vehicles in Complex Environments 3

Generation and Adversarial Testing for Assessing Autonomous ...

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

作者： Lohan, Vishal Kumar Agnihotri, Shantanu Upes School of Computer Science Dehradun India

ISBN: (纸本)9798331528201

Autonomous cars are the new future of the world so they must be reliable and trusted before handing them over to the general public. In order to ensure their reliability, it is important to realize that when naturalistic data is used for training, the idea of safety does not change. This is because, in natural settings, the likelihood of an automobile being involved in an accident is relatively low, making training for such situations challenging. This work proposes an efficient technique by which an adversarial environment is created to train the autonomous vehicle for difficult scenarios. This technique facilitates the evaluation of autonomous vehicles in a multi-agent setting, where adversarial car policies are taught and compared. The experiments are carried out in a simulated urban driving environment that relies on the difficulty of scenarios and provides a high level of accuracy. The findings and training are used to enhance the effectiveness of deep reinforcement learning-based autonomous driving rules. © 2024 IEEE.

关键词： Adversarial machine learning

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Performance optimization of learning Trees modules in the ECL repository 3

Performance optimization of Learning Trees modules in the EC...

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

作者： Shashank, B. Sudershan, K.S. Shobha, G. Manonmani, S. Dev, Roger Rv College of Engineering Dept. of Computer Science Bangalore India LexisNexis Risk Solutions United States

ISBN: (纸本)9798331528201

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC Systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC Systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling learning Tree algorithms of machine learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For Real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest. © 2024 IEEE.

关键词： Parallel processing systems

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Net Load Prediction Using machine learning Methods: A Case Study of Residential Households 3

Net Load Prediction Using Machine Learning Methods: A Case S...

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3rd international conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Herodotou, Panagiotis Tziolis, Georgios Makrides, George Georghiou, George E. PV Technology Laboratory Department of Electrical and Computer Engineering Cyprus for Intelligent Nicosia Cyprus

ISBN: (纸本)9798350375923

As residential solar photovoltaic (PV) systems are increasingly integrated into distribution networks globally, there is a growing necessity to shift from traditional load forecasting to net load forecasting (i.e., predicting the combination of PV and load collectively). Understanding and predicting the daily net energy of a typical household is crucial for designing and sizing appropriate renewable energy systems and storage solutions. This paper compares the performance of different machine learning methods in predicting the net load patterns of a typical household. The database contained PV production and energy load records from 68 actual households in Cyprus, over a period of one year. The results showed that the eXtreme Gradient Boosting (XGBoost) model outperformed all other models yielding a normalized root mean square error of 5.52%. Despite the inherent unpredictability of net load patterns, the findings indicate that the proposed XGBoost model can achieve high prediction accuracies for household electricity net load profiles. © 2024 IEEE.

关键词： Prediction models

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Stock Price Prediction using machine learning 3

Stock Price Prediction using Machine Learning

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Vardhan, Marepalli Vishnu Jaffino, G. Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350372120

Within the domain of budgetary markets, stock price forecasting has continuously been a challenging but noteworthy undertaking for both pros and financial specialists. The energetic nature of stock markets, which are impacted by a wide range of variables counting financial specialist estimation, geopolitical occasions, and financial figures, makes anticipating more challenging. Through the use of the AutoTS library, a cutting-edge mechanized time arrangement estimating innovation, we aim to improve and speed up the method of stock cost estimation. In contrast to customary machine learning strategies, AutoTS chooses models and adapts to hyperparameters naturally, altering the always moving money related markets. Our investigation, which made use of Yahoo Finance's chronicled stock showcase information, involved meticulous preprocessing of the information to ensure its quality and worldly consistency. The benefits of AutoTS, its adaptability in taking care of complicated designs, and a nitty gritty appraisal of the model's rightness by broad testing on various time periods are secured in detail within the parts that take after. This comprehensive analysis aims to provide insight into the reliability and validity of the AutoTS-based stock cost forecasting system. © 2024 IEEE.

关键词： Reliability analysis

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3rd international conference on Innovation in engineering, ICIE 2024

3rd International Conference on Innovation in Engineering, I...

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3rd international conference on Innovation in engineering, ICIE 2024

ISBN: (纸本)9783031626838

The proceedings contain 108 papers. The special focus in this conference is on Innovation in engineering. The topics include: Transmission Power Based Congestion Control Using Q-learning Algorithm in Vehicular Ad Hoc Networks (VANET);collaborative Robot Laboratory Setup for Repeatable Force and Speed Experiments;towards Global Sustainability: Exploratory Analysis Through Unsupervised machine learning Techniques;structural Kinematic Analysis of a Wheelchair Mechanism Designed for Disabled Persons;application of Graph Theory in Designing the Communication System of a Robotic Production Cell;machine learning Algorithms in Scheduling Problems: An Overview and Future Paths;on the Relation Between the Smith Predictor and Algebraic Control Approach for Time Delay Systems: A Case Study;image as a Way of Processing Multidimensional Production data for Product Quality Prediction Using Deep learning;OPC-UA in Digital Twins—A Performance Comparative Analysis;design Improvements for an Inspection Rover;decoding the Effect of Descriptive Statistics on Informed Decision-Making;Simulation Control of Three-Tank-System Using 2DOF Controller;development of a Serious Game to Support Alzheimer’s Patients: First Insights;e-voting Platform Based on Blockchain;the Transformation of Maintenance with the Arise of Industry 4.0;real-World Comparison of Predictive Controllers Based on Internal and External Description of the Controlled System;the Use of Virtual Reality in Lower-Limb Robotic Rehabilitation;the Contribution of Photovoltaic Systems to Sustainable Agriculture—An Analysis of Agrivoltaic Systems;integration of Augmented Reality and IoT Elements in the Maintenance 4.0;A Fuzzy Logic-Based Risk Assessment Tool for IT Startups;wireless Localization System Applied to a Kitting Pick-to-light System;Posture Correction Device Based on IMU Sensors and CNN.

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Remote Sensing and IoT based Global Crop Disease Detection and Treatment System using machine learning 3

Remote Sensing and IoT based Global Crop Disease Detection a...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Venkatesh, Ummaka Rao, Ummaka Subba Sugitha, N. Saveetha Engineering College India

ISBN: (纸本)9798331509675

A global crop growth monitoring system based on remote sensing technologies, Arduino-based hardware and machine learning for accurate plant disease diagnosis and management has been proposed. It consists of a set of sensors such as soil moisture and humidity connected to an Arduino platform, which monitors environmental factors critical for the sustenance of crops. The data is sent in real time to a web page through an Internet of Things (IoT) network that allows the monitoring or even decision making from remote. In this method, a MATLAB-based machine learning algorithm is implemented on the system which predicts diseases in crops like Pepperbell (Bacterial Spot), Potato (Early Blight) and Grapevine (Esca or Black Measles) with very high accuracy. The system is designed to automatically identify and treat crops affected by disease conditions, using pump motors to dispense specific medicines when the appropriate condition is identified in the plant. By avoiding chemicals for disease management, it maximizes the efficiency of disease management with a focus on sustainability in agriculture and minimizing the usage of chemical inputs. In this manner, the adaptation and expansion of crop monitoring strategies in different environments by utilizing remote sensing, machine learning, and IoT are facilitated with the help of this system which ultimately supports sustainable food security on a global scale while also optimizing crop yield. © 2025 IEEE.

关键词： Soil moisture

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data Mining Technology in the international Student Evaluation Using Artificial Neural Network with Latent Dirichlet Allocation 3

Data Mining Technology in the International Student Evaluati...

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

作者： Dong, Hao Yan, Tingxing Xi'an Shiyou University Xi'an China

ISBN: (纸本)9798350318609

Recently, Educational data Mining (EDM) has increased remarkable consideration. The significant aim of educational institutions is to offer efficient education for students to improve academic performance. When data mining approaches are used in the context of education, they can discover hidden knowledge and patterns that can aid in decision-making processes focused at improving an educational system. Forecasting the student's achievements is a significant problem for higher education administrators. To address this challenge, this research proposes the machine learning (ML) method of Artificial Neural Network (ANN) with the Latent Dirichlet Allocation (LDA) approach for the prediction of the performance of the international student's academics. The presented approach considered the last semester for the international student's performance as well as their personal features were examined. The pre-processing techniques like cleaning, integration, transformation, feature scaling as well as reduction were utilized for the prevention of anomalies as well as high dimensionality reduction in the collected data and to acquire the most correlated features. The experimental results shows that the proposed ANN-LDA approach achieves the accuracy of 95.7% when compared to the previous works like K-Nearest Neighbor (KNN) and Support Vector machine (SVM). © 2024 IEEE.

关键词： data mining

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Triple-Stream Temporal Model (TSTM): Enhancing Solar Energy Forecasting Using Hybrid Deep learning Methods 3

Triple-Stream Temporal Model (TSTM): Enhancing Solar Energy ...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Pranav, G. Karuppiah, Natarajan Mounica, Patil Kriahnamoorthy, Murugaperumal Kumar, B. Praveen Vardhaman College of Engineering Dept of EEE India

ISBN: (纸本)9798350354171

Solar energy forecasting is a very significant part of optimizing the solar power management and integrating it into the energy grid. The accurate power output of the solar energy helps balance supply and demand, ensuring stability on the grid while enhancing efficiency in the use of energy. Current research in the prediction of solar energy output using deep learning models builds on the ability of said models to handle complicated temporal patterns and dependencies in solar power data. Models, including but not limited to the ones based on Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), and Gated Recurrent Units (GRU), have turned out to be very effective in making predictions over such data with help from historical knowledge in order to provide accurate results. © 2024 IEEE.

关键词： Efficiency Long Short-Term Memory Photovoltaic Solar Energy Forecasting Solar Power

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A Real-World dataset "IDSIoT2024" for machine learning/Deep learning based Cyber Attack Detection System for IoT Architecture 3

A Real-World Dataset "IDSIoT2024" for Machine Learning/Deep ...

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

作者： Koppula, Manasa Leo Joseph, L.M.I. Dept. of ECE SR University Warangal India

ISBN: (纸本)9798331527549

The Internet of Things (IoT) is an emerging technology evaluating its inception in all domains like industries, home automation, healthcare, agriculture, etc. The major challenge in IoT is securing the IoT devices and information associated with the IoT devices from hackers or unauthorized persons. These cyber security attacks can be detected early for timely interventions and reduce major risks. machine learning (ML)/Deep learning (DL) has become an influential technique for automatic Attack or Intrusion Detection Systems (IDS), putting forward numerous advantages over traditional techniques. A well-structured, real-time IoT security dataset must be used to develop IDS using ML/DL approaches. The article proposes a Real-World dataset IDSIoT2024 consisting of 16,230,955 records to help researchers advance solutions for tackling cyber security in IoT networks. The records presented in the dataset were captured using the Wireshark tool in a real-world IoT architecture. The data collection has taken two months from the middle of June 2023 to August 2023. The proposed dataset can be employed in the field of Artificial Intelligence (AI) to develop ML/DL approaches and detect cyber-attacks in IoT networks. A large amount of data needs to be trained to an ML algorithm to get precise detection of attacks. Therefore, the proposed dataset with a vast number of records with different attacks can accommodate the researchers for the development of Intrusion Detection Systems. © 2025 IEEE.

关键词： Cyber attacks

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Advancements in Disease Diagnosis and Prediction Leveraging Insights Extraction and Computational learning Techniques 3

Advancements in Disease Diagnosis and Prediction Leveraging ...

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3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Konda, Niveditha Vidya, Koprada Pydi Durga Lakshmi Sri Chavalam, Varshith Vadde, Karthikeya Rao, K B Venkata Brahma Koneru Lakshmaiah Education Foundation Department Of Computer Science and Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350372120

Technology has revolutionized the health domain largely. Accurate and on-time analysis of any health-related problem It is crucial for the prevention and treatment of the ailment. The traditional way of diagnosis may in the event of a severe illness, it may not prove to be satisfactory. This project aims to design a diagnostic framework for different diseases based on their symptoms. This system utilizes data mining methodologies including classification techniquesin order to achieve such a mode and also ML algorithms for prediction of any diseases can help in a more accurate diagnosis than the conventional method. We have introduced a system for disease prediction that employs several machine learning algorithms. The dataset contains information on symptoms, age, blood group, and gender of individuals, which is used for processing by the diagnosis system. The output of the system is the disease. Here, the SympoDx algorithm, which incorporates Random Forest, Decision Tree, KNN, and Support Vector Classifier, is employed for classification tasks and computes the probabilities of diseases. Following the results, patients can contact the doctor accordingly for further treatment. © 2024 IEEE.

关键词： Forecasting

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