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检索条件"主题词=Machine Learning Algorithms"

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Parasitic Capacitance Prediction for Standard Cells Using machine learning and K-Means Clustering Algorithm

Parasitic Capacitance Prediction for Standard Cells Using Ma...

引用

International Conference on Electronics, Information and Communications (ICEIC)

作者： Jaejun Lee SoYoung Kim Department of Semiconductor and Display Engineering Sungkyunkwan University Suwon South Korea Dram Design Team Samsung Electronics Suwon South Korea Department of Semiconductor Systems Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

With the continuous scaling of process nodes in the semiconductor industry, the importance of accurately predicting parasitic capacitance based on layout characteristics has increased significantly. This is especially important because post-layout parasitic capacitance can have a significant impact on power consumption and overall circuit performance. However, conventional method for parasitic capacitance extraction is time-consuming as it must be iterated from layout to post-layout extraction and simulation. This is one of the critical factors of delay in the chip design flow. In this study, we propose a method to predict parasitic capacitance for standard cells using an artificial neural network (ANN) model. The standard cells for training the ANN model were generated using a 3 nm process and buried power rail (BPR) was applied for backside power delivery network (BSPDN). The ANN model using netlist parameters can predict parasitic capacitance with over 98% accuracy compared to StarRC. Then, K-means clustering is applied to minimize data sampling. The K-means clustering algorithm was employed to reduce the dataset size by 68.25% for model training. The ANN model trained on the sampled data showed 98% accuracy. The ANN model using training data sampled by K-means clustering is an efficient alternative for accurate and fast parasitic capacitance extraction.

关键词： Training Accuracy machine learning algorithms Layout Clustering algorithms Artificial neural networks Predictive models Prediction algorithms Standards Parasitic capacitance

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Portable IoT-Based Health Monitoring and Prediction System with machine learning

Portable IoT-Based Health Monitoring and Prediction System w...

引用

AI-Driven Smart Healthcare for Society 5.0

作者： Arpan Biswas Gargi Chakraborty Sunipa Roy Department of Electronics & Communication Engineering Future Institute of Engg & Management Kolkata India Department of Electronics & Communication Engineering Guru Nanak Institute of Technology Kolkata India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

Regular monitoring of body parameters is crucial for maintaining good health. This paper proposes an affordable and compact device designed for routine health checkups. Utilizing two ESP32 microcontrollers and various sensors— SpO2, temperature, and ECG—it measures blood oxygen levels, body temperature, and heart rate. A machine learning algorithm enables the device to autonomously conduct preliminary health assessments. This system is particularly beneficial for individuals in remote areas, those in accident-prone locations, security personnel in challenging terrains, and anyone facing sudden emergencies. Health-related information can be transmitted to medical practitioners via IoT cloud technology, ensuring timely and appropriate medical treatment.

关键词： Temperature measurement Temperature sensors Heart rate machine learning algorithms Microcontrollers Medical treatment machine learning Security Personnel Monitoring

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An Approach for Aspect Based Sentiment Analysis Using Multivariate Feature Extraction and machine learning

An Approach for Aspect Based Sentiment Analysis Using Multiv...

引用

AIML-Applications for Engineering & Technology (ICAET), International Conference on

作者： Priyanka Pravin Patil Sunil Gupta School of Computer & System Sciences JNU Jaipur India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Aspect-based sentiment analysis (ABSA) is a nuanced field within natural language processing that focuses on extracting sentiment information at a granular level, targeting specific aspects of entities within a text. This paper presents an innovative approach to ABSA by leveraging multivariate feature extraction techniques combined with advanced machine learning algorithms. Our method begins with a comprehensive pre-processing phase that includes tokenization, part-of-speech tagging, and dependency parsing to structure the textual data. We then employ multivariate feature extraction techniques, such as term frequency-inverse document frequency (TF-IDF), word embeddings, and syntactic dependency features, to capture the contextual and semantic richness of the text. These features are fed into machine learning models, including support vector machines (SVM), random forests, and deep learning architectures like recurrent neural networks (RNN) etc. We evaluate the performance of these models using a well-defined benchmark dataset, highlighting the effectiveness of combining multivariate features with machine learning in improving the accuracy of aspect-based sentiment classification. Our experimental results demonstrate that the proposed approach significantly outperforms traditional sentiment analysis methods, providing more precise and insightful sentiment evaluations at the aspect level. This advancement holds promising applications in fields such as customer feedback analysis, social media monitoring, and market research, where understanding specific sentiment nuances is crucial.

关键词： Support vector machines Sentiment analysis Recurrent neural networks machine learning algorithms Social networking (online) Semantics Syntactics Feature extraction Market research Monitoring

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Sleep Disorder Detection Using Deep learning

Sleep Disorder Detection Using Deep Learning

引用

International Conference on Computer, Electrical & Communication Engineering (ICCECE)

作者： K. Rakesh Kumar Reddy K. Abinaya G.L.V. Satya Sai Ram BE CSE IoT Sathyabama Institute of Science and Technology Semmancheri Chennai Tamil Nadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Semmancheri Chennai Tamil Nadu India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Since sleep disorders can have a major impact on general well-being, classifying them is crucial to enhancing human health. Experts have historically categorized the stages of sleep, but this is a challenging and error-prone process. By more efficiently assessing, tracking, and diagnosing sleep disturbances, accurate machine learning algorithms (MLAs) can be beneficial. This study uses the publicly accessible Sleep Health and Lifestyle Dataset to evaluate deep learning algorithms and traditional MLAs for the categorization of sleep disorders. Thirteen characteristics pertaining to sleep and everyday activities are included in the 400row dataset. A genetic algorithm was employed to adjust the parameters of the machine learning models in order to maximize their performance. The Artificial Neural Network (ANN) method was evaluated in the study. Significant performance differences were found in the results, with the ANN obtaining the best classification accuracy of 92.92%. In addition, it outperformed the other algorithms tested with high precision (92.01%), recall (93.80%), and F1-score (91.93%).

关键词： Deep learning Accuracy machine learning algorithms Artificial neural networks Manuals Sleep apnea Real-time systems Reliability Monitoring Medical diagnostic imaging

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Comparative Study of Deep and machine learning Models in Knee Osteoarthritis Detection

Comparative Study of Deep and Machine Learning Models in Kne...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： Vaidhi Kapoor Makul Mahajan Salil Batra School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The knee osteoarthritis (KOA) is a common type of arthritis characterised by recent cartilaginous joint destruction and alteration of bone, ligaments and synovial fluid. This paper presents a brief knowledge about the knee OA, including its cause and effect, assessment, signs, prevalence, and the methods of managing it together with Different algorithms like Decision Tree, Random Forest, Artificial Neural Network (ANN) Naive Bayes and CNN. The dataset was collected from GitHub which was from OAI dataset which consists of csv datasets. The accuracy of the algorithms were 85%, 89%, 88%, 95% and 85%. Out of five algorithms, the best algorithm were Naïve Bayes and Random Forest algorithm.

关键词： Support vector machines machine learning algorithms Magnetic resonance imaging Artificial neural networks Classification algorithms Bayes methods Osteoarthritis Ligaments Random forests Software development management

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Comparative Analysis of machine learning Models for Breast Cancer Diagnosis

Comparative Analysis of Machine Learning Models for Breast C...

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Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Aasiya Fatima Mohd Yusuf Ansari Azra Malik Department of Electrical Engineering Jamia Millia Islamia New Delhi India DESIDOC DRDO HR Department Govt of India New Delhi India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Breast cancer is the most prevalent, and deadly cancer impacting women globally. Therefore, diagnosing it as soon as possible is crucial to improving patient health outcomes. Conventional diagnostic techniques mostly rely on the knowledge of radiologists and pathologists, which can make it difficult to identify cancer in their early stages and cause subtle symptoms to go unnoticed. By evaluating genetic, molecular, and clinical data to forecast how particular tumors could react to different treatments, artificial intelligence (AI) provides a promising answer and makes treatment methods more efficient. This study investigates the classification of breast cancers as benign or malignant using comparative analysis of machine learning (ML) based algorithms. Although the accuracy of all the classifiers was excellent, the ANN performed better with highest accuracy. The study highlights the capability of machine learning in enhancing the diagnosis of breast cancer and stresses the significance of carefully choosing classifiers based on the particular requirements of medical diagnostics.

关键词： Industries Accuracy machine learning algorithms machine learning Genetics Breast cancer Medical diagnosis Stress Medical diagnostic imaging Tumors

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Latest Advanced Artificial Intelligence Methods for machine learning Model-Based Predictive Parkinson's Disease Identification

Latest Advanced Artificial Intelligence Methods for Machine ...

引用

Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Shankari Seethalakshmi Mohanakrishnan Mastro services Richmond USA

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

For these treatment and test data to have any significance or importance in the context of Parkinson's disease, the measures of the extent of the disease are hence quantitative, consistent, and repeatable. For the last half-century, a subjective emotional measure has governed human understanding of the phenotype of the disease during routine clinical visits. The work aimed to develop and validate artificial intelligence models for the identification of Parkinson's disease. The paper uses modern machine learning approaches to design an identification model for Parkinson's Disease tested on its subjects. Researchers employed an integrated dataset that combined clinical, genetic, and neuroimaging data for the diagnosis and control of Parkinson's disease. The performance on a predictive model implemented for better diagnostic accuracy and efficacy is used. The supervised learning algorithms Random Forest, Deep Neural Networks, and Support Vector machines/SVM are exercised for the analysis and pattern recognition with PD. In this way, the possibility to point out several of the most important diagnostic discordant for both motor and non-motor symptoms, genetic markers, and neuroimaging results was established, which is critical for differentiating the complex clinical phenotypes of Parkinson's disease. An AI model, which was trained on a subset of this data, was validated using a different test set so as to verify the predictive capability of the model. Preliminary findings have shown that, on account of the high sensitivity, specificity, and extent of model appropriateness, the diagnostic model can be more effective than that of the conventional diagnostic methods.

关键词： Neuroimaging Accuracy Phenotypes machine learning algorithms Parkinson's disease Predictive models Feature extraction Data models Vectors Random forests

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Future Horizons: Key Trends Shaping the Evolution of machine learning

Future Horizons: Key Trends Shaping the Evolution of Machine...

引用

International Conference on Trends in Electronics and Informatics (ICEI)

作者： D. Prabhu Tejasri M V S L Durga Devi G B. Saratha Anish T P Siva Subramanian R Dept of CSE Vel tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai R.M.D Engineering College Velammal Institute of Technology R.M.K College of Engg & Tech

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Artificial intelligence - machine learning (ML) is an emergent area of AI that aims to enhance the capability of a system to make predictions or decisions based on the data fed to it without the system being specifically programmed for such functions. Its importance stems from its capacity to analyze and abstract information, predict, and even make decisions in various contexts, transforming sectors such as healthcare, finance, and technology. With the increasingly large volume of data being created, the role played by ML in processing this data to discover meaningful patterns becomes more prominent. ML algorithms can be broadly categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning. Linear regression and support vector machines, for instance, work from labeled training sets in order to make predictions and classifications. Such algorithms are used in many applications, like face recognition, filtering spam emails, and even diagnosing diseases. On the other hand, unsupervised learning operators, including K-means clustering and principal component analysis, work with data sets lacking labels to discover latent structures and patterns common in market segmentation and anomaly detection. Reinforcement learning can be viewed as enabling an agent to take a set of actions in an environment to maximize the total reward, as in Q-learning. This approach is especially useful in applications such as robotics, games, and the use of autonomous systems. In summary, machine learning algorithms are a key differentiator in improving productivity and fostering innovation, thereby facilitating advancements across various industries while creating future possibilities in artificial intelligence.

关键词： Ethics machine learning algorithms Biological system modeling Decision making Finance Medical services Market research Prediction algorithms Classification algorithms Unsupervised learning

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Strategic Predictions and Explanations By machine learning : The Prediction Model of Credit Default Swaps for the Telecommunication Service Sector

Strategic Predictions and Explanations By Machine Learning :...

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International Conference on Information Networking

作者： Caesar Wu Jian Li Jingjing Xu Pascal Bouvry SnT /Faculty of Science Technology and Medicine (FSMT) University of Luxembourg Luxembourg Institute for Advance Economic Research Dongbei University of Finance and Economics & University of Luxembourg Faculty of Science Technology and Medicine(FSTM) University of Luxembourg Luxembourg Faculty of Science Technology and Medicine(FSTM) University of Luxembourg

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Many machine learning (ML) models can make predictions regarding credit default swaps (CDS) for the telecommunication (telco) service sector. However, some ML algorithms can only offer a black-box model. It is crucial to explain the prediction result for strategic decisions. We compare various the state of arts, including deep learning (transformers), gradient boost machine (GBM), and Xgboost, plus different explainable tools: Variable Importance (VI) Partial Dependent Plots (PDP), Local Individual Conditional Expectation (LIME), Interpretable Model-agnostic Explanations (ICE), and Shapley values for the prediction model. Moreover, we also conducted a hyperparameter search of the prediction model by leveraging high-performance computing (HPC). Our experiment results show that the Xgboost provides the best solution with fewer constraints. We aim to find an optimal solution for strategic CDS investment decisions.

关键词： Measurement Deep learning machine learning algorithms Computational modeling High performance computing Predictive models Transformers

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Revolutionizing Plant Disease Detection: A Comprehensive Review of machine learning Techniques and Applications

Revolutionizing Plant Disease Detection: A Comprehensive Rev...

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Advances in Computing Research on Science Engineering and Technology (ACROSET), International Conference on

作者： Sanket Gupta Suresh Jain Kailash Chandra Bandhu Department of Computer Science and Engineering Medi-Caps University Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Being one of the greatest sectors in the world and a source of food, agricultural products, raw materials, and employment for billions of people, agriculture has a tremendous effect on the Indian economy as well as on the global economy. But crop diseases that can happen at any time at any stage of crop farming have a big influence on agriculture, lowering crop yields and lowering the quality of the goods that are collected. Plant diseases occasionally even result in the extinction of entire plant species. In the field of managing agricultural and plant diseases, artificial intelligence (AI) is a powerful tool that supports farmers and researchers in identifying, diagnosing, preventing, and treating illnesses more successfully. This review will evaluate several previously published studies on the detection of plant diseases using deep learning and other machine learning methods, which will provide an advantage to upcoming researchers. A variety of performance measurements were also reviewed to evaluate different datasets on different plant diseases, architectures, and methodologies. Further, we will review well-known ML and DL approaches, including support vector machines (SVM), convolutional neural networks (CNN), artificial neural networks (ANN), k-mean clustering, Stochastic Gradient Descent (SGD), etc., to see how they perform differently. At last, we will conclude which is the better option for disease detection between DL and ML and which algorithm is more accurate in predicting the disease so that farmers can make appropriate solutions to protect them. We will also discuss the future scope of these modern technologies in the fields of agriculture and detecting disease in its primary stages.

关键词： Support vector machines Deep learning Measurement Plant diseases Accuracy machine learning algorithms Reviews Prediction algorithms Raw materials Convolutional neural networks

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