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检索条件"主题词=Machine Learning Algorithms"

共 25877 条记录，以下是461-470 订阅

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Employing machine learning algorithms for the Prediction of Alzhiemer’s Disease

Employing Machine Learning Algorithms for the Prediction of ...

引用

Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Raja Rajeswari Sethuraman Harshini S Dhaarshieth K R Subhashini M Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Swamy Vivekanandha Medical College Hospital and Research Institute Tiruchengode Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Alzheimer's disease is a neurological condition that progressively worsens over time and impairs thinking, memory, social skills, and behavior. Its onset is most commonly seen in the age groups above 65. The World Health Organization's survey indicates that dementia, including AD, is the sixth largest cause of death globally. For older people, this is the most frequent cause of dementia. The word dementia is used to refer to a wide range of brain disorders that are characterized by behavioural abnormalities, loss of memory and reasoning skills, and difficulty carrying out everyday tasks. In order to lower the death rate, early screening for this condition is essential. machine learning techniques are quite useful in the identification of the relation between risk factors of Alzheimer’s disease and its incidence. This study examines the relationship between several characteristics that lower the risk of Alzheimer's disease using a benchmarking dataset of 2149 Americans. Early detection of AD and associated symptoms is more successful when classifiers such as Random Forest, Support Vector machine, K-Nearest Neighbours, XGBoost, CatBoost, and Logistic Regression are used. Classifier performance and AD predictions are evaluated using metrics like as F1-score, Precision, Accuracy, and Recall. According to this suggested study, the Random Forest and CatBoost algorithm predicts the test data with 95.5% accuracy. This score is better than the other modern and latest algorithms.

关键词： Support vector machines Surveys Logistic regression machine learning algorithms Accuracy Refining Prediction algorithms Classification algorithms Alzheimer's disease Random forests

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Perceptual learning Technology Based on Computer Vision and machine learning algorithms

Perceptual Learning Technology Based on Computer Vision and ...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision Image segmentation machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

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Depression Detection Based on Eeg Signals Using machine learning algorithms

Depression Detection Based on Eeg Signals Using Machine Lear...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Fatima M. Alneqrat Ali Hussein Maamar Asmaa Nasr Mohammed Hajer A. Aghnaya College of Electronic Technology - Bani Walid

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

Depression, a psychological disorder with detrimental effects on an individual's thoughts, actions, and mood, afflicts millions of people. As it has evolved into a serious illness, there is a pressing need for accurate diagnostic methods. This research employs features extracted from EEG signals, utilizing the Discrete Wavelet Transform (DWT) algorithm to diagnose depression. Two machine-learning algorithms, Support Vector machines (SVM) and K-nearest Neighbors (KNN), were employed for classification. The study, conducted using a dataset from Hospital University Sains Malaysia (HUSM), demonstrates the efficacy of this approach. Results indicate that the KNN algorithm achieved a remarkable test accuracy of 99.51 %, while SVM reached 91.18%, both utilizing features derived from the DWT algorithm. These models successfully differentiate between depressed patients and healthy controls with high accuracy rates. In summary, this research highlights the potential of EEG signals as a biomarker for identifying depression, offering significant advancements in diagnosis and treatment approaches for this condition.

关键词： Support vector machines Accuracy machine learning algorithms Signal processing algorithms Nearest neighbor methods Depression Feature extraction

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Exploration of the Application of Big Data and machine learning algorithms in Electric Shock Prevention Warning

Exploration of the Application of Big Data and Machine Learn...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Kan Zhang Yong Li Yichen Cui Ke Zheng Peiyi Zhang Qian Yang Marketing Service Center of State Grid Henan Electric Power Company Zhengzhou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In view of the high incidence of electric shock accidents and the imperfect early warning system, big data analysis and machine learning algorithms are applied to precisely predict potential electric shock risks and give early warnings, thereby reducing the occurrence of electric shock accidents. First, various environmental, equipment, and personnel behavior data related to electric shock are collected and integrated, including indicators such as temperature and humidity, current fluctuations, and equipment usage frequency. Then, data preprocessing technology is used to clean the noise data to ensure data quality. Next, the support vector machine (SVM) machine learning algorithm is used to build a prediction model and train it through historical electric shock cases. Finally, the electric shock risk is predicted by inputting the model into the real-time monitoring data, and automatic warning is realized by combining the warning threshold. Studies have shown that the electric shock prevention warning system based on big data and machine learning has significantly improved in accuracy and response speed. Compared with traditional methods, the model can respond within 30 seconds after the electric shock event occurs, and the recall rate is close to 92%. The application of big data and machine learning algorithms in electric shock prevention warning has high practical value and potential. It not only improves the accuracy and real-time nature of warning, but also provides data support for preventive measures and reduces the risk of electric shock accidents.

关键词： Support vector machines machine learning algorithms Accuracy Electric shock Prevention and mitigation Big Data Predictive models Real-time systems Personnel Accidents

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Comparative Analysis of machine learning algorithms for Classifying Bearing Failures Using Time and Frequency Domain Features

Comparative Analysis of Machine Learning Algorithms for Clas...

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Intelligent Systems and machine learning Conference (ISML)

作者： Pallavi Meshram Shilpa Sondkar Department of Instrumentation Engineering Vishwakarma Institute of Technology Pune Maharashtra India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Amongst the most crucial components of rotating machinery, bearings failure rate is high and it can directly impact the efficiency of the complete piece of machinery. As a result, diagnosing bearing faults is crucial because it can help ensure running stability and upkeep, boosting production efficiency and economic gains. The objective of the study is to evaluate various machine learning algorithms to find out the best method for categorizing bearing failure into three types: (a) Normal bearing, (b) Inner race fault (c) Outer race fault. The raw vibration signal is processed to obtain the temporal and frequency domain information. The classification is obtained using the MFPT dataset of rolling bearings. To evaluate the classification accuracy receiver operating characteristics (ROC) is employed as a performance metric. The classification models are compared: K-nearest neighbor, Support Vector machine, Neural Network, and Ensemble. The Ensemble subspace KNN and Cubic SVM algorithms are proven to be the most effective among other algorithms. The cubic SVM has shown better accuracy with shorter training times. The dataset of normal bearing and the faults (inner race and outer race) were compared for the time domain and frequency domain analysis. According to the study, it is found that power spectrum-frequency domain parameters and time domain parameter kurtosis show major variations amongst the normal and the faulty bearings.

关键词： Support vector machines Training machine learning algorithms Accuracy Frequency-domain analysis Neural networks Rolling bearings Nearest neighbor methods Classification algorithms Time-domain analysis

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Predictive Modeling of Cryptocurrency Prices Using machine learning algorithms

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Rishita Chakma Tikle Barua Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling.

关键词： Deep learning machine learning algorithms Accuracy Statistical analysis Computational modeling Linear regression Predictive models Prediction algorithms Long short term memory Investment

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Studying Dengue spread using Climatic conditions and machine learning algorithms

Studying Dengue spread using Climatic conditions and Machine...

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Technological Innovations and Advance Computing (TIACOMP), International Conference on

作者： Manas Bisht Gautam Tripathi Manisha Aeri Kumar Shantanu Akash Singh Shiv Ashish Dhondiyal Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed To be University Dehradun India

ISBN: (数字)9798350392111

ISBN: (纸本)9798350392128

Dengue is one of the serious mosquito-borne disease, which causes flu-like symptoms like high fever, sever headache, pain in joint, muscles, eyes etc. It is suggested to control mosquito populations in an area to avoid the disease. Our research aims on identifying climatic conditions which influence mosquito population to grow rapidly in an area, and find a machine learning algorithm that best predicts dengue risk in an area based on these climatic conditions. The dataset used was sourced from ***, in which after data cleaning we implemented algorithms like K-Nearest Neighbor, Logistic regression, Gaussian Naive Bayes and Random Forest on the cleaned dataset. Each algorithm was evaluated and compared on the basis of accuracy and confusion matrix out of which K-Nearest neighbor gave best results with 99.00% accuracy while offering superior performance in confusion matrix respectively, also Temperature, Humidity and Wind displayed best relation with spread of dengue diseases among other climatic conditions.

关键词： machine learning algorithms Accuracy Computational modeling Nearest neighbor methods Predictive models Prediction algorithms Boosting Bayes methods Viruses (medical) Diseases

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Automated School Lunch Menu Planning with machine learning algorithms

Automated School Lunch Menu Planning with Machine Learning A...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Hao-Yu Lin Jeen-Shing Wang Ya-Ting C. Yang Dept. of Electrical Engineering National Cheng Kung University Tainan Taiwan Institute of Education National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

This study introduces an automated menu planning system for schools that leverages machine learning to optimize and streamline menu creation, focusing on nutritional guidelines, cost-effectiveness, and variety. The system comprises two main algorithms: the menu combination algorithm, which employs content-based filtering to select diverse recipes by analyzing and quantifying recipe features like color, flavor, and ingredients; and the menu optimization algorithm, which adjusts ingredient quantities to adhere to budget and nutritional standards. To evaluate the system's effectiveness, satisfaction surveys were conducted among school lunch menu planners, using a seven-point Likert scale to measure feedback on menu variety and cost-effectiveness. Results showed that the system aligns well with nutritional and budgetary requirements, with minimal errors in nutritional content and ingredient costs, and substantially reduces menu planning time.

关键词： Surveys machine learning algorithms Costs Process control machine learning Planning Informatics Standards Optimization Guidelines

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Prediction of Land Use and Land Cover Using Google Earth Engine (GEE) and machine learning algorithms: A Review

Prediction of Land Use and Land Cover Using Google Earth Eng...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Promtucs Garnagee Jackson Paurav Goel Sartajvir Singh University Institute of Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Reliability is a critical requirement for classifiers that generate maps of land use and land cover (LULC), and these classifiers are highly sought after. Particularly, machine learning classifiers yield accurate classification outcomes even with complicated, high-dimensional data. Numerous parameters, including training sample size, quality, thematic correctness, classifier selection, research region size, and others, influence the classified maps’ accuracy. Understanding these aspects will help in attaining the highest possible classification accuracies for a particular requirement. Large-scale satellite picture and feature classification jobs become computationally demanding, giving rise to Big Data issues. Lately, cloud-based platforms that are free to use, like Google Earth Engine (GEE), have made environments for parallel processing available to handle picture categorization tasks. Both land use and land cover (LULC) changes are crucial for disciplines like monitoring natural disasters and assessing the effects of environmental change. Assessing the state and progress of relevant machine learning is the aim of this study to LULC by analyzing several publications published in the last ten years. Trend analysis of the quantity and yearly citations of published works, identification of top institutions, nations/regions, and publication sources, investigation of scientific partnerships between major institutions and nations/regions, and analysis of important research topics and their trends of development are the main areas of focus. Additionally, the paper clarifies how ML is used in LULC, offering insights that may lead future research paths to hasten advancements in this field. This review assesses various Geospatial machine learning algorithms. Among the algorithms are Naïve Bayes (NB), Random Forest (RF), Support Vector machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Gradient Boosting (GTB) as well as Extreme Gradien

关键词： Support vector machines Earth machine learning algorithms Accuracy Reviews Land surface Artificial neural networks Boosting Random forests Regression tree analysis

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Prediction of Medicinal Leaves Using machine learning algorithms

Prediction of Medicinal Leaves Using Machine Learning Algori...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： T. Swathi S. Sudha Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Medicinal plants are used for centuries by various cultures worldwide to treat a wide range of ailments and conditions. As there are vast numbers of medicinal plant species it will be challenging for a layman to identify them accurately. In such a situation, the experts' knowledge is sought many times. Unfortunately, lack of expertise, commercial motive and reduced availability of medicinal plants sometimes result in maltreatment due to improper plant identification. The advent of machine learning techniques and their uniqueness motivated the proposal of models that help to classify and predict medicinal plants based on their leaf images. A dataset of 30 different types of medicinal plant leaves sourced from Kaggle is used for this research. In this research a total of 19 features including the texture and shape feature of the leaves are considered extracted through image processing. Six machine learning models such as Extreme Gradient Boosting, Decision Tree, Support Vector machine, Random Forest, K-Nearest Neighbour, and Naïve Bayes and three ensemble models namely XGRF, XGDT and XGNB are proposed and developed using Python. XGRF achieves an AUC of 93.21% for train and test splits 80%:20% and cross-validation is employed to ensure the robustness of the model. Of the 19 features, through feature importance 8 features are observed to dominate in leaf identification. With these 8 features, XGRF is found to achieve an AUC of 93.19%. The result indicates the feasibility of the model to automatically classify medicinal leaves.

关键词： Medicinal plants Support vector machines Technological innovation machine learning algorithms Shape Computational modeling Predictive models Feature extraction Robustness Biomedical imaging

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