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检索条件"主题词=Machine Learning Algorithms"

共 26009 条记录，以下是2041-2050 订阅

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Using machine learning to Predict Effective Compression algorithms for Heterogeneous Datasets

Using Machine Learning to Predict Effective Compression Algo...

引用

Data Compression Conference (DCC)

作者： Brandon Alexander Burtchell Martin Burtscher Department of Computer Science Texas State University San Marcos TX USA

ISBN: (数字)9798350385878

ISBN: (纸本)9798350385885

Heterogeneous datasets are prevalent in big-data domains. However, compressing such datasets with a single algorithm results in suboptimal compression ratios. This paper investigates how machine-learning techniques can help by predicting an effective compression algorithm for each file in a heterogeneous dataset. In particular, we show how to train a very simple model using nothing but the compression ratios of a few algorithms as features. We named this technique "MLcomp". Despite its simplicity, it is very effective as our evaluation on nearly 9,000 files from a heterogeneous dataset and a library of over 100,000 compression algorithms demonstrates. Using MLcomp to pick one lossless algorithm from this library for each file yields an average compression ratio that is 97.8% of the best possible.

关键词： machine learning algorithms Data compression machine learning Prediction algorithms Libraries Compression algorithms

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Credit Card Fraud Detection Using machine learning & Deep learning algorithms

Credit Card Fraud Detection Using Machine Learning & Deep Le...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Tamilkodi R Pravallika Satti Maduri N Ratalu S Saritha K SanthaRaju N Dept of CSE (AIML&CS) Godavari Global University Rajahmundry Andhra Pradesh CSE Godavari Global University Rajahmundry Andhra Pradesh

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Credit card Abuse has increased lately, coinciding with a rise in credit card use. Both card holders & fiscal institutions suffer significant fiscal losses as a result about credit card theft. star ideal about this exploration study is towards relating these frauds. material literature presents a wide range about machine literacy- grounded credit card recognition algorithms, similar as XG Boost, Decision Tree, Random Forest, Support Vector machine, Logistic Regression, & Extreme learning Method. ultramodern deep literacy algorithms are still needed towards lower fraud losses due towards their low delicacy. exercising most recent developments in deep literacy algorithms has been primary ideal in order towards achieve this. towards achieve effective results, a relative analysis about machine literacy & deep literacy ways was conducted. Better results are set up when exploration is reviewed. These include an delicacy about 99.9, perfection, a f1- score about 85.71, & AUC angles with optimum values about 98, 93, & 88, independently. proposed model outperforms state- of- the- art deep literacy & machine literacy ways considering credit card identification tasks. towards lower false negative rate, we also experimented with data balancing & deep literacy styles. ways handed are salutary considering relating credit card fraud in factual deals.

关键词： Deep learning Support vector machines machine learning algorithms Signal processing algorithms Stars Signal processing Credit cards Data models Fraud Classification algorithms

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Assessing the Efficacy and Influence of learning Objectives via Natural Language Processing algorithms and machine learning Techniques

Assessing the Efficacy and Influence of Learning Objectives ...

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Soni Maitrik Chandrakant Department of Computer Science Sabarmati University Ahmedabad Gujarat India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This paper presents a new approach exercising artificial intelligence(AI) to prognosticate hand performance dynamics throughout work hours, easing informed opinions regarding payment adaptations. using advanced AI algorithms, the proposed system integrates colorful factors similar as literal performance data, real-time criteria, and contextual variables to induce accurate performance vaticinations. By assaying patterns and trends in hand geste and productivity, the model offers precious perceptivity into implicit oscillations in performance situations during the course of the workday. also, grounded on these vaticinations, the system suggests applicable payment adaptations acclimatized to individual performance circles, thereby optimizing compensation structures for enhanced hand provocation and organizational productivity. Through empirical confirmation and case studies, the efficacity and trustability of the proposed system are demonstrated, pressing its eventuality to revise performance operation practices and foster a further indifferent and satisfying work terrain.

关键词： Productivity machine learning algorithms Pressing machine learning Predictive models Prediction algorithms Market research

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Non-Invasive Blood Glucose Estimation Using Photo Plethysmography and Extreme learning machine algorithms

Non-Invasive Blood Glucose Estimation Using Photo Plethysmog...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Mudra Narasimharao Biswaranjan Swain Praveen Priyaranjan Nayak Satyanarayan Bhuyan Department of Electronics and Communication Engineering Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The current research describes a non-invasive blood sugar level estimation device that uses Photo Plethysmography (PPG). Prior research has demonstrated that using a light-sensitive sensor improves blood sugar level prediction. The PPG signal of the individuals is captured using a constructed light sensor-based data-gathering device. The primary advancement of the results paper is the examination of many PPG signal characteristics through the efficient estimate of BGL values using the Only One Pulsed Assessment approach. Regarding the ADC conversion, the sampling rate of the frequency was set at 1 kHz with an accuracy of twelve bits during the dataset collection process. This PPG dataset segment contains 219 sample points. Features such as time and frequency domain characteristics are used in BGL value estimation. Using the previously suggested feature sets, a machine learning network is developed, and a BGL value estimate is carried out. The PPG medical data are also obtained, and both of them are trained to predict patient diabetes or nondiabetes using ELM algorithms. The method that produces the best results is taken into consideration. The top classifier was the ELM, which had a training time of 9 seconds and an efficiency of 99.6%. Based on the research, the Extreme learning machine (ELM) achieved more accurately in the machine learning methods.

关键词： Accuracy machine learning algorithms Extreme learning machines Estimation Prediction algorithms Glucose Biosensors Biomedical monitoring Blood Plethysmography

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Crop Prediction Using machine learning and IoT: A Comparative Analysis of algorithms

Crop Prediction Using Machine Learning and IoT: A Comparativ...

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Recent Progresses in Science, Engineering and Technology (ICRPSET), International Conference on

作者： Aishwarya Roy Afif Hossain Irfan Mukut Protim Memo Labib Bin Shahed Rashedul Arefin Ifty Department of Electrical and Electronics Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798331509477

ISBN: (纸本)9798331509484

Agriculture is a cornerstone of Bangladesh's econ-omy, yet it faces significant challenges due to weather fluctuations, including changes in temperature, humidity, and precipitation. These environmental variables critically affect farming activities, demanding constant adaptation. To mitigate these challenges, we propose an loT-based model designed to enhance agricultural data collection and provide valuable insights to farmers. Our model integrates various sensors, such as the ESP8266-0l mi-crocontroller, NPK sensor, DHT22 humidity sensor, DSl8B20 temperature sensor, pH sensor, and water level sensor, all housed in a protective enclosure and powered by a DC source. This system collects real-time data on critical environmental parameters, forming the foundation of predictive models. We employ multiple machine learning algorithms, including Logistic Regression, Naive Bayes, Support Vector machine, K-Nearest Neighbours, Decision Tree, Random Forest, Bagging, AdaBoost, Gradient Boosting, and Extra Trees. Using a dataset containing parameters like nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall, we split the data into training and testing sets. Models are evaluated based on accuracy, precision, recall, Fl score, and undergo l0-fold cross-validation to assess performance comprehensively. The integration of loT-derived data with machine learning models helps identify the most suitable crops based on soil and climate conditions. By offering real-time, actionable insights, our system enables farmers to make better crop selection decisions. This approach aims to enhance agricultural productivity in Bangladesh by optimizing weather-dependent farming strategies and ensuring more informed agri-cultural practices.

关键词： Temperature sensors Adaptation models machine learning algorithms Crops Humidity Real-time systems Data models Sensors Random forests Farming

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Experimental study and comparison of medical methodology and machine learning models to enhance algorithms for morphological classification of clinical and hematologic syndromes

Experimental study and comparison of medical methodology and...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Indira Uvaliyeva David Borozenets School of Digital Technology and Artificial Intelligence D. Serikbayev East Kazakhstan Technical University Ust-Kamenogorsk Kazakhstan

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The dynamic evolution of modern medicine highlights the increasing significance of automating diagnostic procedures. This progression is not solely a matter of convenience but a vital step towards fully leveraging technological advancements, with the goal of elevating both research and clinical outcomes to unprecedented levels. Among the notable advancements in this arena, the emergence of diagnostic systems founded on morphological classification algorithms holds particular prominence. This study compares the performance of different methods, including both traditional medical approaches and modern machine learning techniques. Clinical and hematologic syndromes refer to a broad range of medical conditions characterized by specific sets of signs, symptoms, and laboratory findings. Experimental study and comparison of medical methodology and machine learning models allowed to determine the most effective approaches to morphological classification of clinical-hematologic syndromes, which may lead to improved diagnosis and treatment of patients.

关键词： Human computer interaction Adaptation models machine learning algorithms machine learning Forestry Classification algorithms Medical diagnostic imaging

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Optimization of Management Decision Based on machine learning: Application of Intelligent algorithms in Enterprise Management Systems

Optimization of Management Decision Based on Machine Learnin...

引用

Computers, Information Processing and Advanced Education (CIPAE), 2020 International Conference on

作者： Jian Cao Yuxi Zhang Faculty of Business City University of Macau Macau China School of Business Shanghai Normal University Tianhua College Shanghai China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The optimization of an organization's management decisions through the inclusion of machine learning remains a factor that represents significant advancements in the element of enterprise management. These algorithms are designed to focus on the leveraging of intelligent algorithms intended at enhancing the decision-making process. An initial data collection process that involves the collection and the pre-processing of real-time and historical data from different enterprise systems plays a critical role in ensuring the system operates on quality data. Further, a feature selection model is used in the system in identifying varied variables that influence managerial decisions, a process that relies on techniques that include principal component and correlation analysis. A training and validation process equally help in the design of the system as it splits data into validation and training sets through a cross-validation approach that prevents the overfitting while ensuring robust system performance. Lastly, the deployment and optimization of the system helps in refining the model and systems parameters through the use of Bayesian optimization, grid search, and the integration of optimized models into real-time support in the enterprise management system. According to the findings of the study, a continuous improvement of the system is achieved through the monitoring of the model's performance and its periodical retention of new data in a bid to maintain the systems accuracy. The algorithms model facilitates the making of informed and data-driven managerial decisions, thus enhancing the effectiveness and efficiency of enterprise operations.

关键词： Training machine learning algorithms System performance Refining Decision making machine learning Feature extraction Real-time systems Optimization Monitoring

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Evaluating the Effectiveness of Random Forest versus Decision Tree algorithms in Predictive Analytics for Enhancing Sustainable Development Strategies in Real Estate, Infrastructure, and Construction: A machine learning Perspective

Evaluating the Effectiveness of Random Forest versus Decisio...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Deepali Mayur Gala Bhakti Pawar Gayathri Band Arvind Dalal Arnab Chakraborty Utpala Das Bhaskar V. Patil Bharati Vidyapeeth (Deemed to be University) Institute of Management Kolhapur India School of Leadership and Management Manav Rachna Educational Institutions MRIIRS Faridabad Haryana India School of Management Ramdeobaba University Nagpur Manav Rachna Educational Institutions MRIIRS Faridabad Haryana India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

This study explores the application of machine learning algorithms, specifically decision tree and random forest models, to predict house prices within the Indian real estate market. Utilizing a comprehensive dataset obtained from Kaggle, this research encompasses extensive data preprocessing, feature engineering, and exploratory data analysis to identify key predictors of house prices. The comparative analysis aims to assess the accuracy, efficiency, and applicability of both models in the context of predictive analytics for sustainable development goals (SDGs) in real estate. The findings reveal that the random forest model outperforms the decision tree model, demonstrating superior predictive accuracy with a lower Root Mean Squared Error (RMSE). Key predictors identified include living area, number of bathrooms, and the condition of the property, with the random forest model providing deeper insights into feature interactions and their collective impact on house prices. This research highlights the potential of advanced machine learning techniques in enhancing real estate market analysis and strategic decision-making. The superior performance of the random forest model underscores its value in predictive analytics, offering stakeholders in the real estate sector a robust tool for achieving greater accuracy in property valuation and market analysis. Future research directions include the integration of additional data sources and the exploration of other machine learning algorithms to further refine predictive models and extend their applicability to sustainable development within the real estate, infrastructure, and construction industries.

关键词： Analytical models Accuracy machine learning algorithms Soft sensors Predictive models Decision trees Stakeholders Sustainable development Predictive analytics Random forests

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Non Invasive Early Pancreatic Cancer Prediction with Gradient Boosting algorithms machine learning Models with Clinical dataset collected from Urinary Biomarkers

Non Invasive Early Pancreatic Cancer Prediction with Gradien...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： B Vasudevan R SenthilKumaran K Immanuel M V Karthikeyan Dept. of ECE SRM Institute of Science and Technology Kattangullathur Chennai India Dept. of ECE St.Joseph’s Institute of Technology Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Pancreatic cancer has a poor prognosis, but despite continuous challenges in medical research, efforts are still underway to identify strategies to diagnose the illness early and boost survival rates. This attempt has the potential to assist a greater population affected by this terrible illness. Using the revolutionary potential of machine learning (ML) algorithms, the healthcare industry has led the way in creating unique techniques to recognizing and categorizing pancreatic cancer risk. This study looks at the various ways used by researchers to diagnose pancreatic cancer using machine learning models. Through our research, we shed light on the sector’s ongoing difficulties while also recognizing significant advancements in this field. Our work relies on a comprehensive analysis of several boost algorithms and tactical strategies.

关键词： machine learning algorithms Biological system modeling Surveillance Sociology Pancreatic cancer Biomarkers Prediction algorithms

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Predicting Bank Loan Eligibility with machine learning : A Comparative Study of Advanced algorithms

Predicting Bank Loan Eligibility with Machine Learning : A C...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Sure Lakshmi Vyshnavi D. S. V. Pavan T. Teja Sadineni Sri Pujitha Syed Shareefunnisa Dept. of CSE VFSTR Deemed to be University Vadlamudi Andhra Pradesh India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

This paper presents a unique way for determining client loan eligibility utilizing powerful machine learning algorithms and data from several institutions. It focuses on the crucial phases of data purification, attribute selection, and comparative study of several machine learning approaches such as Gradient Boosting, CatBoost, XGBoost, AdaBoosting, and LightGBM. The study emphasizes the value of ensemble models in enhancing forecast accuracy and lowering default risk. Using cutting-edge machine learning techniques, this study offers banks and financial institutions with a powerful tool for improving loan eligibility checks. The suggested technique is shown to be reliable and effective through empirical validation and practical implementations.

关键词： machine learning algorithms Accuracy Purification Computational modeling Predictive models Prediction algorithms Boosting Reliability

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