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检索条件"主题词=Machine Learning Algorithms"

共 26140 条记录，以下是2051-2060 订阅

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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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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...

引用

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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Using Nature-Inspired Optimization algorithms and machine learning to Optimize Hadoop Resource Scheduling

Using Nature-Inspired Optimization Algorithms and Machine Le...

引用

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

作者： Satish Muppidi Balajee Maram Aravapalli Rama Satish B. Manivannan Departnent of Computer Science and Engineering GMR Institute of Technology Rajam India School of Computer Science and Artificial Intelligence SR University Warangal Telangana School of Computer Science & Engineering VIT-AP University Andhra Pradesh India Department of CSE Vivekanandha College of Engineering for Women Tiruchengode

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

This paper investigates the ability of utilizing nature-inspired optimization techniques alongside machine learning (ML) to enhance resource adjustment in Hadoop clusters. The use of ML models with adaptive optimization methodology is aimed at minimizing the time incurred to complete a task in addition to enhancing the efficiency of resource utilization. It reviews the existing Hadoop scheduling approaches and focusing on machine learning and bio-inspired techniques. For the categorization of workloads and prediction of resource needs for a specific future time, the suggested approach includes using ML algorithms like supervised/unsupervised or reinforcement ***, new efficient work schedules and resources distribution will be used due to the nature-inspired optimization algorithms, for example, the GA, PSO. This research framework for scheduling in Hadoop will be created that will incorporate elements of artificial intelligence by using machine learning to provide expectations along with bio-inspired techniques. The tests will indicate how much resources will be utilised, how long certain jobs take, how well and scalable or the system is using actual or simulated data sets. Comprehensive comparative assessment with the traditional and improved approaches will reveal the efficiency of the proposed methodology. The results part consist of optimised approach for resource distribution, efficient flexibility in handling the workloads, and increased Hadoop specialised cluster reinforcement for scalability and heterogeneously challenging data processing activities.

关键词： Schedules machine learning algorithms Heuristic algorithms Scalability Clustering algorithms machine learning Big Data Prediction algorithms Resource management Optimization

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Quantum-Inspired Optimization algorithms for Scalable machine learning in Edge Computing

Quantum-Inspired Optimization Algorithms for Scalable Machin...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Rohit Goyal Krishan Kumar Vivek Sharma Rudramani Bhutia Arpit Jain Munish Kumar School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Department of ECE Bhagat Phool Singh Mahila Vishwavidyalaya Sonipat Haryana India Department of Computer Science and Engineering School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vadeshawaram A.P. India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

The convergence of machine learning and edge computing has led to the development of scalable solutions that bring computation closer to the data source. However, optimizing machine learning models efficiently for edge devices poses challenges due to limited resources such as power, memory, and computational capability. Quantum-inspired optimization algorithms (QIOAs) offer a promising alternative to traditional optimization techniques, providing superior performance in constrained environments. This paper explores the application of QIOAs for scalable machine learning models in edge computing. We propose a novel hybrid quantum-inspired approach, benchmark it against classical algorithms, and demonstrate its efficacy in various edge computing environments. Experimental results show improved accuracy, reduced latency, and enhanced resource utilization.

关键词： Performance evaluation machine learning algorithms Accuracy Computational modeling Memory management machine learning Real-time systems Computational efficiency Optimization Edge computing

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“Comparison of TCP Congestion Control algorithms: Harnessing the power of Traditional Hybrid and machine learning Fusion.”

“Comparison of TCP Congestion Control Algorithms: Harnessin...

引用

Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： Asha M. Raikar Manjaiah D. H. R. Elumalai Department of Computer Science Mangalore University Karnataka India Department of Electronics and Communication CMR Institute of Technology Bengaluru India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Modern evolution of communication technologies have resulted in complexity of network communications. In addition increasing diversity of preferred applications with large number of end users have further exaggerated this complexity. As a result the traditional rule-based Transmission Control Protocol (TCP) congestion control approach have now become absolute to efficiently handle this new type of intricacy. Presently machine learning approach finds a major role in solving TCP congestion issue. Both the traditional TCP congestion control and congestion control by machine learning approach have their own merits and demerits. The traditional TCP have less overhead, faster convergence, and show fairness and hence are practical but not adaptable and flexible to the dynamic environment of the networks[1]. On the other hand the machine learning algorithms are good at adaptability and flexibility but are not practical. This paper presents overview on hybrid modes of TCP congestion control namely (i) Combining more than one traditional rule-based approach, (ii) Combining Traditional rule-based and machine learning-based approach and (iii) The combination of more than one machine learning based approach, in order to overcome the present intricacy in communication networks. The main focus of this overview is to highlight that hybrid mode of TCP with machine learning can be an efficient solution for the present TCP congestion issues.

关键词： TCP machine learning algorithms Protocols Heuristic algorithms Diversity reception machine learning Data transfer

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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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Analysis of machine learning and Deep learning algorithms for Plant Disease Detection and Classification

Analysis of Machine Learning and Deep Learning Algorithms fo...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Saif O. Husain Sripelli Jagadish Vinaye Armoogum Dharmapuri Siri R. Palanivel Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India School of Innovative Technologies and Engineering University of Technology Mauritius Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Agriculture is the fundamental pillar of civilization and raising the livestock, and economic prosperity of the country. Detection of plant diseases is a most important aspect of agriculture, aimed at identifying and diagnosing diseases that affect plants. Early detection of plant disease is key to reducing yield loss and ensuring quality of crops. Traditional detection methods done by researchers, experts, and farmers are subjective matter, time-consuming, and sometimes the results may not be accurate. In this paper, recent technological approaches like computer vision, machine learning (ML), and Deep learning (DL) have been used for detection and classification of diseases. Moreover, techniques like image processing, and spectral imaging efficiently identify the symptoms of disease from leaf samples as faster compared to previous detecting methods. This real-time monitoring and detection system are big support to the farmers to identify diseases and prevent loss in agriculture production. The outcome illustrates that ML and DL models in this paper are well suited for detection and classification of plant disease.

关键词： Deep learning Plant diseases machine learning algorithms Plants (biology) Imaging Production Agriculture Real-time systems Livestock Information technology

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A Comparative Analysis of Traditional machine learning, Deep learning and Boosting algorithms on Phishing URL Detection

A Comparative Analysis of Traditional Machine Learning, Deep...

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Computer Vision and machine Intelligence (CVMI), International Conference on

作者： Arvind Ashok Dhiksha Rathis Richa Raghavendra V Umadevi Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Phishing attacks pose a significant threat to user’s data security by using counterfeit Uniform Resource Locators (URLs) that are hard to distinguish from legitimate ones. machine learning can help identify phishing URLs by analyzing their features. This research work compares phishing detection mechanisms using machine learning, Deep learning, and Boosting techniques, evaluating their accuracy across five datasets. The datasets are: Dataset-1 (Kaggle, 2 class), Dataset-2 (UCI, 2 class), Dataset-3 (Mendeley, 2 class), Dataset-4 (UNB, 5 class) and Dataset-5 (Kaggle, 4 class). The analysis shows that boosting techniques outperform traditional machine learning and Deep learning, achieving detection accuracies of $\mathbf{9 7 \%, 9 6 \%, 9 8 \%, 9 8 \%}$ and 92% across the five datasets respectively.

关键词： Deep learning Uniform resource locators Computer vision machine learning algorithms Accuracy Phishing Data security Boosting machine intelligence

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Diabetes Prediction Using machine learning: A Comparative Analyses of Classification algorithms

Diabetes Prediction Using Machine Learning: A Comparative An...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Arjun Singh Sandhu Gaurav Sharma Pawan Kumar Mishra Department of School of Computing Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science and Engineering Graphic Era Deemed to be University

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Diabetes is a chronic metabolic condition identified by increased levels of sugar in the blood. It is also commonly referred to as diabetes mellitus. Either inadequate insulin production or inadequate insulin use by the organism cause this condition. Early identification and prediction of the onset of diabetes can lead to timely interventions and improved patient outcomes. In an effort to increase the precision and effectiveness of machine learning predictive models, this research article provides a thorough analysis of diabetes prediction utilizing these techniques. The study utilizes a diverse dataset comprising of 768 observations, lifestyle factors, and clinical indicators obtained from a cohort of individuals with and without diabetes. Feature selection methods are employed to identify the most relevant variables, ensuring a robust and parsimonious model. Various machine learning algorithms are implemented and compared to determine the optimal predictive model.

关键词： machine learning algorithms machine learning Production Predictive models Signal processing Feature extraction Organisms Diabetes Insulin Sugar

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Achieving Provably Effective Quantum algorithms for Extensive machine learning Models

Achieving Provably Effective Quantum Algorithms for Extensiv...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International Conference on

作者： N. Satheesh Rashmi Dixit S. Venkataramana S. Anthoniraj Bala Gurivi Reddy Vemi Reddy Jitendra Singh Department of Artificial Intelligence Jain (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science and Engineering Walchand Institute ofTechnology Solapur Maharashtra India Department of Information Technology S.R.K.R.Engineering College Bhimavaram AndhraPradesh India School of Computer Science and Engineering Jain (Deemed-to-be University) Ramanagara India Department of Physics New Middle East International School Riyadh Saudi Arabia Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Ghaziabad Uttar Pradesh India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

Massive machine learning models are cutting edge artificial intelligence technologies, but they have significant computational costs, power requirements, and tuning time constraints. We demonstrate in this work that verifiable well-organized solutions for nonspecific (stochastic) ramp succession procedures may be possible with fault-tolerant quantum computing. We demonstrate that comparable procedures are effective for (stochastic) ramp succession, the main machine learning procedure, based on previous efficient quantum algorithms for dissipative differential equations. We yardstick pictures of large machine learning models with between 7 million and 103 million restrictions in real-world scenarios. It has been observed that in the scarce keeping fit setting, a substantial enrichment can occur during the initial learning phase following model pruning. This finding provides impetus for a sparse parameter download and re-upload strategy. It is evident from our work that most large-scale, state-of-the-art machine-learning problems may benefit from the use of fault-tolerant quantum algorithms.

关键词： Fault tolerance machine learning algorithms Quantum algorithm Computational modeling Fault tolerant systems Stochastic processes machine learning Mathematical models Time factors Tuning

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