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检索条件"主题词=Machine Learning Algorithms"

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Power Estimation of Digital VLSI Circuits Using machine learning

Power Estimation of Digital VLSI Circuits Using Machine Lear...

引用

International Conference on VLSI Systems, Architecture, Technology and Applications (VLSI-SATA)

作者： Manish I. Patel Srushti Patel Electronics and Communication Engineering Department Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9798350362268

ISBN: (纸本)9798350362275

For any VLSI design, accurate power estimation is significant to make it optimized. Conventional approaches for power estimation are based on some analytical models and involve simulation, which is typically computationally expensive and time-consuming depending on the level of abstraction where it is performed. As an alternative machine learning-based approaches have gained popularity due to their ability to predict power consumption with higher accuracy and speed. In this paper, we presented a comparative analysis of four best performing machine learning-based approaches for power estimation of digital circuits. The dataset of various digital circuits was prepared based on the features collected from the synthesis reports obtained by Quartus tool. The Root Mean Squared Error (RMSE) and R-squared values are used to compare the performance of the models. In our results the best values of R-squared and RMSE obtained are 0.99 and 0.29581 respectively.

关键词： Power demand Accuracy machine learning algorithms Circuits Estimation machine learning Very large scale integration

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Anomaly Detection and Deep Vein Thrombosis Prediction using machine learning and Secure Data Storage using Blockchain

Anomaly Detection and Deep Vein Thrombosis Prediction using ...

引用

Smart Electronics and Communication (ICOSEC), International Conference on

作者： S Harini A Afrose Aasmi B. Uma Maheswari Department of Computer Science and Engineering St. Joseph’s College Of Engineering OMR Chennai

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

An AI-powered companion system is proposed to enhance astronaut health monitoring and mitigate risks, particularly Deep Vein Thrombosis (DVT). By analyzing health data collected via IoT wearables, the system establishes personalized baselines and employs machine learning algorithms to detect anomalies indicative of potential health issues. Predictive models, trained on historical astronaut health data, identify patterns associated with DVT risk, enabling early intervention. Securely transmitted health data and risk assessments support remote medical consultations and proactive care. This system has the potential to significantly reduce DVT incidence, improve astronaut well-being, and enhance mission success by enabling timely medical interventions and optimizing resource allocation.

关键词： machine learning algorithms Space missions Smart contracts Collaboration Blockchains Resource management Wearable devices Thrombosis Monitoring Anomaly detection

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Predicting Recurrence in Differentiated Thyroid Cancer: A machine learning Approach

Predicting Recurrence in Differentiated Thyroid Cancer: A Ma...

引用

Advances in Data Engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： Bharath K A. Sai Sabitha Dept of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

A predictive model refers to a mathematical or computational model that is designed to make predictions or forecasts based on input data. These models are used in various fields such as finance, healthcare, marketing, and many others to analyze historical data and make predictions about future events or trends. This study focuses on developing a predictive model for well-differentiated thyroid cancer recurrence using machine learning algorithms. Leveraging a dataset with clinicopathologic features spanning a 15-year period, the study aims to contribute to the advancement of predictive modeling in thyroid cancer prognosis. The Various algorithms, including logistic regression, decision tree, random forest, support vector machine, k-nearest neighbors, and XGBoost, are evaluated for their performance in binary classification. The highest-performing model is then utilized to predict thyroid cancer recurrence in new data. Integrating this with frontend tools has resulted in an intuitive interface that streamlines the process of uploading datasets, executing the predictive model, and presenting the results. This integration has made the prediction process more accessible and user-friendly, ultimately benefiting clinicians and researchers in the field of thyroid cancer research.

关键词： Support vector machines machine learning algorithms Thyroid cancer Computational modeling Predictive models Prediction algorithms Data models

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Bit-Heart: Wearable Device for Healthcare Diagnosis Employing IOT and machine learning

Bit-Heart: Wearable Device for Healthcare Diagnosis Employin...

引用

Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Pratyush Jagdish Birole Garima Jain Sanat Jain Vedant Kanoje Subhranil Bhattacharya Harsh Kanoje School of Computer Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computer Science and Engineering VIT Bhopal University Sehore India School of Electrical and Electronics Engineering VIT Bhopal University Sehore India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

In this rapidly changing world, timely and cost-effective medical diagnosis is of utmost importance. This article introduces BitHeart, an IoT-based wearable device that tracks health events over time by combining wearable technology with machine learning models. This study proposes two models: first Heart Health Model which is employing IoT sensors such as heart rate monitors, accelerometers, etc. and the K Nearest Neighbor (KNN) method to predict the probability of heart disease based on physical measurements, include heartbeat rate, step count, and calories burned. Another is the body fitness model driven by a classifier called Gradient Boosting Classifier (LightGBM) is used in the evaluation of the patient by examining parameters such as step count, number of thoughts, weight and sleep duration. The hybrid technology processes the data to eliminate uncertainty and increase accuracy. The web-based interface allows users to view health data in real time and gain insights. Experimental studies have shown that the heart health model has 77% accuracy, while the physical health model can classify functional levels with high confidence. These results demonstrate BitHeart’s potential to improve personalized healthcare by providing users with early disease detection and recommendations.

关键词： Accuracy machine learning algorithms Uncertainty Transforms Predictive models Nearest neighbor methods Wearable devices Biomedical monitoring Diseases Testing

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Optimization Method for Communication Security in Millimeter Wave and Fiber Hybrid Networks Based on machine learning

Optimization Method for Communication Security in Millimeter...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Qiang Chen Suriyani Binti Ariffin Gang Peng Sanxin Wang School of artificial Intelligence DongGuan City University Dongguang GuangDong China College of Computing Informatics and Mathematics Universiti Teknologi MARA Alam Selangor Malaysia

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

With 5G and 6G's rise, millimeter wave and fiber hybrid networks are gaining popularity for their high data rates and low latency. Yet, they confront significant security issues during transmission. This paper presents a machine learning-based communication security optimization method for these networks. It involves creating a channel model and applying ML algorithms to enhance channel prediction and optimization, thereby boosting transmission security and efficiency. Experiments demonstrate the method's effectiveness in lowering bit error rates, increasing signal-to-noise ratios, and bolstering the system's anti-interference capabilities.

关键词： machine learning algorithms Millimeter wave technology Bit error rate Optimization methods Optical fiber networks Prediction algorithms Security Channel models Millimeter wave communication Signal to noise ratio

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machine learning-Based Fault Detection in VLSI Circuits

Machine Learning-Based Fault Detection in VLSI Circuits

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Chandrama Saha Bushra Siddiqui Harsh Bhoi Sudhanya P. Department of Electronics and Communication Engineering SRM Insttitute of Science and Technology Chennai India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This paper addresses the growing complexity of electronic devices and the need for efficient fault detection in VLSI circuits. Traditional fault detection methods are often inaccurate and time-consuming, motivating the use of artificial intelligence techniques. The paper proposes a deep learning-based model for detecting stuck-at faults in digital VLSI circuits. Our model leverages an Autoencoder algorithm to compress features and mitigate the search space explosion problem, achieving a range of accuracies of 75.3% to 99.9% for various ISCAS'85 benchmark circuits. The model incorporates ATPG tools such as the ATALANTA tool for generating test patterns and corresponding fault-free responses using the FAN algorithm. These test patterns are preprocessed to be used as a dataset where the Autoencoder algorithm classifies faulty and non-faulty responses based on their reconstruction error. These approaches demonstrate the potential of deep learning in improving the efficiency and accuracy of fault detection in digital VLSI circuits.

关键词： Accuracy machine learning algorithms Fault detection Circuits Autoencoders Very large scale integration Benchmark testing Electrical fault detection Classification algorithms Integrated circuit modeling

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Algorithmic Innovations in machine learning for Drug Discovery and Pharmaceutical Innovations

Algorithmic Innovations in Machine Learning for Drug Discove...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： M. Sangeetha F. Mary Harin Fernandez A. Ganesh Ramachandran S. K. Saravanan M. Bhanumathi Department of Computer Engineering University of Technology and Applied Sciences Nizwa Sultanate of Oman Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Science and Engineering BigData and Network Security Saveetha School of Engineering SIMATS Chennai Tamil Nadu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Algorithmic advancements in machine learning have transformed drug discovery and pharmaceutical innovation. This study examines how machine learning might speed medication development and pharmaceutical innovation. Data from genomes, chemistry, and clinical trials is collected to start the research. Careful data cleansing, normalization, and feature engineering prepare the data for model building. Random Forest, SVM, NN, Decision Tree, and Logistic Regression are tested for prediction. ROC AUC results show that Random Forest is the most accurate model at 86%. Ensemble approaches have drug discovery potential. Beyond accuracy, models are assessed for precision, false positive, and false negative rates. Drug discovery relies on precision to measure positive predictions. The Random Forest model makes the most accurate positive predictions at 0.87. However, false positives and negatives also matter in drug discovery. The Decision Tree model contains the falsest positives (35), underlining the need to reduce them to save resources. The Neural Network approach reduces false negatives to 12, ensuring good medication candidates are not overlooked. The results show that different machine learning models have trade-offs, underscoring the necessity for a personalized strategy depending on objectives and restrictions. Algorithmic breakthroughs in pharmaceutical innovation reduce medication development time by 40% and cost by 25%, according to the study.

关键词： Support vector machines Drugs Technological innovation Logistic regression machine learning algorithms Genomics Prediction algorithms

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Automation of PGAA Spectra Analysis with Deep learning

Automation of PGAA Spectra Analysis with Deep Learning

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Daniel Boschmann Christian Stieghorst David Knezevic Loubna Kadri Oliver Niggemann Institute of Automation Technology Helmut Schmidt University Hamburg Germany Research Neutron Source FRM II Technical University of Munich Munich Germany Engineering Materials Science Centre (GEMS) at MLZ Helmholtz-Zentrum Hereon GmbH Munich Germany

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Analyzing Prompt Gamma Activation Analysis (PG AA) spectra poses significant challenges, particularly in accurately identifying and quantifying the elements present. Traditional expert analysis, while effective, is time-consuming. This paper addresses the need for an efficient, automated solution to enhance the analysis process. The research investigates the use of machine learning (ML) and deep learning (DL) algorithms in the automated analysis of PGAA spectra. We aim to establish a new metric for comparing automated analysis with expert analysis, providing a baseline using Linear Regression, Random Forest, 1D Convolutional Neural Network, Feed Forward Neural Network, and Autoencoder algorithms. Established metrics like Mean Square Error (MSE) and Mean Absolute Error (MAE) are utilized to compare the performance of these automated approaches against traditional expert analysis. Using actual spectra from various research projects and semi-real augmented data, the study demonstrates that the Feed Forward Neural Network (FFNN) and Autoencoder algorithms can effectively predict the magnitude of the present elements. These findings suggest especially D L algorithms could signifi-cantly assist researchers and industry personnel by providing a rough overview of the material and saving valuable time. However, the automated approach requires further refinement, particularly in handling noisy data, predicting additional crucial information, and integrating more prior knowledge into the anal-ysis. This research offers valuable insights into the application of ML algorithms in spectral analysis and lays a foundation for further advancements in the field.

关键词： Deep learning machine learning algorithms Neural networks Mean square error methods Prediction algorithms Feeds Personnel Noise measurement Spectral analysis Random forests

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Collaborative Prediction of Baseline Functions of Wind, Coal and Water Based on Stacked Generalized Integrated learning for a Coal-fired Plant

Collaborative Prediction of Baseline Functions of Wind, Coal...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zhihao Zhang Lin Wang Kai Deng Bingyuan Liu Yao Kuigao Yang Xianglei Jun Li Lin Gao Ming Liu Xi’an Thermal Power Institute. Co. Ltd Xi'an China Shannxi Yanchang Petroleum Fuxian Power Generation Co. Ltd. Yan'an China School of Energy and Power Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

Under the background of China's construction of a new type of power system, thermal power is faced with the new situation of frequent peak and frequency adjustments, which puts forward higher requirements for the flexibility. Wind, coal and water are three key variables in the control of coal-fired units. With time going on, they mismatch with each other for the shift of combustion characteristics of the boiler and the deviation between the coal type in use and the designed coal type; and then economy and flexibilities decrease. To solve the problem, a method based on machine learning to predict the wind-coal-water coordination is proposed. Firstly, the R-Value algorithm and local outlier algorithm are used to eliminate the anomalies; secondly, the Least Squares algorithm, the Support Vector machine algorithm and the Polynomial Fitting algorithm are used as the basis function to fit the wind-coal-water function under different load conditions, respectively; finally, the stacking generalization ensemble learning model is used as the second-level integration algorithm to make the final prediction. The application of the modified wind-coal-water matching function in a 1000MW ultra-supercritical unit shows that the wind-coal-water matching function is more coordinated, critical parameters’ tracking performance is improved, and the unit operates more smoothly and economically.

关键词： Support vector machines machine learning algorithms Stacking Collaboration Coal Predictive models Wind power generation Prediction algorithms Combustion Wind forecasting

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Assessing the Applicability of Adversarial machine learning Approaches for Cybersecurity

Assessing the Applicability of Adversarial Machine Learning ...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Prashamsh Takkalapally Nandan Sharma Arjun Jaggi Karim Hudani Ketan Gupta Yuvaraj Natarajan Department of Engineering Computing and Mathematical Sciences Lewis University USA BC Public Service Victora British Columbia Canada Sr. Director of Sales Engineering HCLTech San Diego California USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer Science Engineering Sri Shakthi institute of engineering and technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Adverse machine learning (AML) is a rising area of study that uses system-mastering algorithms to perceive malicious hobbies or to discover malicious adversaries in cybersecurity settings. This research domain combines device-gaining knowledge with game principles to assess and expect the behavior of adversaries. This paper discusses the capacity of using AML tactics for cybersecurity and provides an overview of existing research study findings and safety literature to help the dialogue. Specifically, the paper investigates the security challenges posed by using AML techniques, together with the technical regulations and pointers, to ensure their relaxed and effective deployment. Moreover, the paper offers recommendations and future instructions for furthering studies on AML in cybersecurity. Typical, the evaluation highlights the significance of assessing the applicability of AML techniques for security applications, in addition to its capability to enhance the effectiveness of security systems.

关键词： learning systems machine learning algorithms Games Automated machine learning Feature extraction Regulation Safety Information and communication technology Computer security Sports

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