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检索条件"任意字段=International Conference on Advances in Machine Learning and Systems Engineering"

共 69114 条记录，以下是41-50 订阅

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A Review on Enhancing Efficiency of the Numerical Weather Prediction Model through machine learning Methods 15

A Review on Enhancing Efficiency of the Numerical Weather Pr...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kowshiga, C. Maiyarasu, S. Kiruthika, S. Kanna, Lingesh K. Kumar, Ganesh K. Velalar College of Engineering and Technology Department of IT Erode India

ISBN: (纸本)9798331300579

The machine learning techniques into configuration validation and prediction systems, aiming to enhance performance and decision-making processes across various industries. The identified problems include the scarcity of solar radiation and wind speed data in certain locations, complexities in integrating real-time observational data into numerical models, and the susceptibility of complex machine learning models like LSTM. A machine learning-based optimization method is proposed, focusing on improving numerical weather prediction. Data on hardware and software parameters are collected to train a model for optimal parameter combinations in various environments. The Random Forest model aims to increase weather prediction accuracy, with hyperparameter fine-tuning to mitigate overfitting. The algorithm's used are data handling, support vector machines (SVMs), K-Nearest Neighbors (KNN) classifier, and Random Forest classifier, aiming to gather relevant data, visualize patterns, and implement classification algorithms for improved system performance. It addresses data scarcity, enhances prediction accuracy, and mitigates overfitting. Integrating machine learning into validation and prediction systems offers efficient systems and informed decision-making capabilities, facilitating proactive maintenance and improving system reliability. © Grenze Scientific Society, 2024.

关键词： Weather forecasting

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Early Breast Cancer Prediction Using machine-learning Algorithms

Early Breast Cancer Prediction Using Machine-Learning Algori...

引用

2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Abirami, S. Sri Vignesh, R.B. Srihariprasath, V. Mathialahan, U. Yaswanth, T.S. Computer Science And Engineering Sri Krishna College Of Engineering And Technology India

ISBN: (纸本)9798350364828

Breast cancer poses a significant global health challenge, affecting millions of women each year and leading to substantial cancer-related deaths. Early detection is crucial, particularly in regions with limited resources and inefficient healthcare systems where late-stage diagnoses are common. To tackle this issue, we propose a novel approach that employs machine-learning algorithms for early breast cancer prediction. Our model integrates a diverse ensemble of machine-learning algorithms, including K-Nearest Neighbor (KNN), Naive Bayes, Support Vector machines (SVM), and Decision Tree Classifier. This ensemble harnesses the collective predictive power of these algorithms to distinguish between benign and malignant tumor types, enabling early diagnosis and prompt intervention. Furthermore, our approach enhances predictive capabilities by incorporating Logistic Regression, Random Forest, and additional KNN algorithms. Each algorithm contributes unique strengths to the ensemble: SVM excels in identifying complex data relationships, Logistic Regression offers interpretability, Random Forest handles large datasets and captures intricate patterns, while KNN adapts well to irregular decision boundaries. By leveraging the strengths of these diverse algorithms, our proposed system provides a more comprehensive and accurate breast cancer prediction model. Through ensemble learning, we mitigate individual algorithm weaknesses and improve predictive performance, thereby advancing the effectiveness of early breast cancer detection and ultimately improving patient outcomes. Keywords: machine learning, Breast Cancer Prediction, Ensemble learning, K-Nearest Neighbor (KNN), Naive Bayes, Support Vector machines (SVM), Decision Tree Classifier, Logistic Regression, Random Forest, Early Detection, Healthcare systems © 2024 IEEE.

关键词： learning systems

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Forecasting of Crime Hotspots and Analysis using machine learning

Forecasting of Crime Hotspots and Analysis using Machine Lea...

引用

2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Maheswari, G. Felixa, S. Aj, Vidhya Sambath, M. Remya, K. Hindustan Institute Of Technology And Science Department Of Information Technology Chennai India

ISBN: (纸本)9798350364828

One of the most important and concerning aspects of our society is crime. Countless crimes are committed daily, and as a result, the lives of all individuals are becoming increasingly unpleasant. Thus, stopping the crime from happening is difficult. In order to prevent the worldwide community from falling victim to the certain actions of criminals or other disruptive elements, the rate of crime should be reduced through the application of machine learning techniques. As a result, crime prediction techniques are essential for foretell future crimes and decreasing overall crime rates. Understanding the crime pattern is a major step to analyzing and planning a response to any kind of criminal activity. Identifying the type of crime along with the time and place where it has occurred, is the main goal of this system. This work implies the use of machine learning methods, such as decision tree classifier and linear regression, to categorize among different criminal patterns. The accuracy attained was relatively high when compared to previously planned work. © 2024 IEEE.

关键词： Crime

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Recognition of Human Anomalies Using Convolution Neural Network

Recognition of Human Anomalies Using Convolution Neural Netw...

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2024 international conference on advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Parella, Teja Ponnam, Balakrishna Santhiya, P. Praveen, A. Sujihelen, L. Sathyabama Institute Of Science And Technology Dept. Of Computer Science And Engineering Tamilnadu Chennai600119 India

ISBN: (纸本)9798350364828

By instantly identifying suspicious occurrences, people, and things, smart CCTV systems greatly improve monitoring capacities. In this research, we investigate how to enhance CCTV video stream anomaly detection using state-of-the-art machine-vision and machine-learning techniques, namely UNET and ALEXNET. In contrast to more conventional methods that depend on supervised learning, this research makes use of uncontrolled and semi-supervised learning techniques to tackle the variety and complexity of anomalies. Using algorithms like UNET and ALEXNET, the system can learn to spot outliers on its own, with little to no help from humans. Because it can tell the difference between normal and abnormal situations, this method improves monitoring efficiency and decreases false alarms. In addition, the system maintains original quality for unusual occurrences while keeping typical video at a lower level, optimizing storage economy. By combining UNET and ALEXNET algorithms, smart CCTV systems may detect suspicious events before they happen, which increases security and allows for faster reactions to any dangers. © 2024 IEEE.

关键词： Anomaly detection

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Data-Driven Decisions: A Review of machine learning Models, Soil Sensors and Crop Recommendation systems 15

Data-Driven Decisions: A Review of Machine Learning Models, ...

引用

15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nagaraju, Gujjeti Rishika, Nayakoti Pranav, Paruchuri Mukesh, Pothala Anthyush, Talluri Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Telanagana Hyderabad500090 India

ISBN: (纸本)9798331300579

The integration of advanced technologies in revolutionary agriculture transformed decision-making processes through the implementation of data-driven techniques. This study presents a thorough examination of the connection of machine learning models, soil sensors, and crop recommendation systems, with the purpose of demonstrating their combined impact on agricultural practices. The research delves into the crucial role of machine learning algorithms in agricultural data collecting, highlighting their ability to analyze complex datasets and derive helpful insights for informed decision-making. Furthermore, this paper explores the ever-evolving environment of soil-sensing technologies, emphasizing their significance to precision agriculture by providing real-time, high-resolution data on soil conditions. In addition, crop recommendation integration into systems is investigated, with a focus on its effectiveness in improving crop choices based on data-driven insights acquired from historical and real-time data. The combination of these technical aspects has been identified as a turning point in the development of sustainable agriculture. Embracing these advances in technology not only makes agriculture more efficient, but also accounts for a more resilient and environmentally conscientious future, demonstrating the transformative potential of data-driven techniques in influencing the trajectory of global food supply. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Forecasting Power Prices for Cloud Computing using an Enhanced machine learning 15

Forecasting Power Prices for Cloud Computing using an Enhanc...

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15th international conference on advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nithisha, C. Tejaswini, Chillara Pavana Samyuktha, Busireddy Harika, Banda Chatrapathi, Gantala Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

The use of several machine learning regression models for the prediction of power costs is explored in this work. The following models are taken into consideration: Gradient Boosting, Adaboosting, Lgbmregressor, Cataboost regressor, Random Forest Regressor, XGBoost Regressor, Support Vector Regressor, and Stacking Regressor. Historical power pricing data with variables like time, demand, and weather make up the training and assessment dataset. A portion of the dataset is used to train each model, which is then adjusted to maximize prediction performance. To evaluate each model's precision, resilience, and computational effectiveness, a comparative study is done. Insights into the applicability of various regression models for the forecast of power prices are provided by the study's findings, which may help decision-makers optimize the planning and management of energy resources. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Reinforcement learning Models for Autonomous Decision Making in Sensor systems

Reinforcement Learning Models for Autonomous Decision Making...

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2024 international conference on advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Ramu, K. Suman, Sanjay Kumar Rajeswari, U. Sumana, S. Poddar, Hitha Arulananth, T.S. Vmrf Deemed University Paiyanoor Chennai India St. Martin's Engineering College Dept. of Ece Secunderabad India Kalasalingam Academy of Research and Education Department of Business Administration Virudhnagar India Dayananda Sagar Collge of Engineering Dept. of Eee Bengaluru India Easwari Engineering College Dept. of Cse Chennai India Mlr Institute of Technology Dept. of Ece Hyderabad India

ISBN: (纸本)9798350388800

This Smart homes and self-driving vehicles are using sensor networks increasingly in the IoT era. However, handling these systems' massive data sets and letting them make informed judgments is difficult. Reinforcement learning a sort of machine learning may work. This research investigates how sensor systems can make autonomous judgments using reinforcement learning models. It discusses the basic ideas, challenges, and opportunities of this method in numerous fields. The paper also discusses how reinforcement learning makes electronics smarter and more sensitive by learning from and adapting to their environment. The recommended technique combines Deep Q-Networks, Proximal Policy Optimization, and Actor-Critic reinforcement learning. These strategies improve monitoring system decision-making while remaining safe, efficient, and adaptive. The experiments reveal that these strategies increase sensor system decision- making speed and accuracy and learning efficiency. Reinforcement learning is effective in many scenarios, but it excels at safety-sensitive jobs like self-driving automobiles. Despite the potential, resources and safety and dependability remain issues. This article aims to explain reinforcement learning in sensor systems, including its real-world applications, moral difficulties, and future prospects. © 2024 IEEE.

关键词： Adversarial machine learning

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An Extensive Analysis of Neuromorphic Computing

An Extensive Analysis of Neuromorphic Computing

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2024 international conference on advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Vajpayee, Amit Kaur, Palak Preet Sharma, Ankit Varshney, Santosh Parul Institute of Engineering and Technology Department of Computer Science and Engineering Vadodara India Apex Institute of Technology Chandigarh University Department of Bigdata Analytics Mohali India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making. © 2024 IEEE.

关键词： Adversarial machine learning

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RASSE 2024 - 2024 IEEE international conference on Recent advances in systems Science and engineering, Proceedings

RASSE 2024 - 2024 IEEE International Conference on Recent Ad...

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4th IEEE international conference on Recent advances in systems Science and engineering, RASSE 2024

ISBN: (纸本)9798331528263

The proceedings contain 23 papers. The topics discussed include: dynamic modeling and control of hybrid electric vessels with a super-capacitor and a lithium battery;optimal home energy management system for a residential grid-connected microgrid using improved slime mold algorithm;acoustic fault detection in brushless direct current motors using machine learning for predictive maintenance;advanced obstacle avoidance in walking aids: overcoming the challenges of low-lying obstacles with deep learning;analysis of renewable energy potential combinations in microgrid systems;optimal route planning and energy consumption analysis of electric logistics vehicles;and refinement of generation schedule and under-frequency load shedding protection scheme for Nangan-Beigan islanded power grid in Taiwan.

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A survey on feature selection algorithms for machine learning

A survey on feature selection algorithms for machine learnin...

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international conference on Innovation and Technological advances for Sustainability, ITAS 2023

作者： Almesleh, Ziad Chkirbene, Zina Hamila, Ridha College of Engineering Qatar University Qatar

ISBN: (纸本)9781032803722

Wireless communication is one of the most substantial types of communication nowadays, leading to the IoT revolution and increasing demand for IoT networks. On the other hand, this demand also leads to an increase in the number of resource constrained devices, which creates a new problem related to vulnerabilities to attacks. The handling of attacks on IoT devices must be closely monitored due to resource limitations for these devices, which makes research in this field important. Many intrusion detection systems have been improved by using machine learning models that classify data as normal or abnormal and use evaluation parameters such as accuracy, f1-score, and precision to assess their performance. One of the most important steps in building a good intrusion detection system is feature selection, which can help improve the reasonable classification of data. This paper discusses the latest research on using machine learning techniques for intrusion detection systems. It examines various prominent algorithms and provides further analysis on their appropriateness. CICIDS2017 dataset is used to evaluate the robustness and determinism of intrusion detection system models. © 2025 The Author(s).

关键词： Network intrusion

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