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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Research on short term stock selection strategy based on machine learning

Research on short term stock selection strategy based on mac...

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Yanzheng Ren School of Mechatronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665417914

Based on theoretical analysis, this paper constructs a short-term stock selection strategy based on machine learning. The sample set is constructed based on the closing price trend of individual stocks in the last 20 trading days, and the machine learning algorithms GBDT and GBRank are used for training, and machine learning is used to automatically perform pattern recognition on the processing capabilities of high-dimensional nonlinear data. When forecasting, the former will rank stocks with higher rising probability in the next 3 trading days, and the latter will rank stocks with greater gains in the next 3 trading days. The stock selection strategy swaps positions every 3 trading days, and each time the top 10 stocks given by the equal-weight buying algorithm are used to construct an investment portfolio. The experimental results show that the short-term quantitative stock selection strategy based on the GBDT algorithm can outperform the market combination, namely the Shanghai and Shenzhen 300 Index, and has certain practicability.

关键词： Analytical models machine learning algorithms Computational modeling machine learning Mathematical models data models pattern recognition

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862953

The proceedings contain 28 papers. The special focus in this conference is on Advancements in Smart Computing and Information Security. The topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using machine learning Techniques;scrutinize Search Engine Optimization Strategies with Artificial Intelligence to Rank a Website;a Study on machine learning Algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game Theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on machine learning Algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention mining for Mental Health Diagnosis Using Logistic Regression;machine learning for Accessible and Precise Assessment in Smart Monitoring Systems;an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on data mining and machine learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive learning Techniques;asthma Prediction Using Fuzzification and machine learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of machine learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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Transfer learning: Survey and classification 3rd

Transfer learning: Survey and classification

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3rd international conference on Smart Innovations in Communications and Computational Sciences, ICSICCS 2020

作者： Agarwal, Nidhi Sondhi, Akanksha Chopra, Khyati Singh, Ghanapriya Department of Electronics and Communication Engineering Shri Ramswaroop Memorial University Barabanki India Department of Electrical and Electronics Engineering G D Goenka University Gurugram India Department of Electronics Engineering National Institute of Technology Uttarakhand SrinagarUttarakhand India

ISBN: (纸本)9789811553448

A key notion in numerous data mining and machine learning (ML) algorithms says that the training data and testing data are essentially in the similar feature space and also have the alike probability distribution function (PDF). Though, in several real-life applications, this theory might not retain true. There are issues where training data is costly or tough to gather. Thus, there is a necessity to build high-performance classifiers, trained using more commonly found data from distinct domains. This methodology is stated as transfer learning (TL). TL is usually beneficial when enough data is not available in the target domain but the large dataset is available in source domain. This survey paper explains transfer learning along with its categorization and provides examples and perspective related to transfer learning. Negative transfer learning is also discussed in detail along with its effects on the accomplishment of learning in target domain. © Springer Nature Singapore Pte Ltd 2021.

关键词： Surveys

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031863042

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Length of Stay Prediction for Diabetes Mellitus Patients on Indonesia Health Insurance data using Logistic Regression and Process mining

Length of Stay Prediction for Diabetes Mellitus Patients on ...

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Intelligent Cybernetics Technology & Applications (ICICyTA), international conference on

作者： Fitri Eka Cahyanti Angelina Prima Kurniati Amirah Alharbi Adiwijaya School of Computing Telkom University Bandung Indonesia Computer Science Department Umm Al-Qura University Makkah Kingdom of Saudi Arabia

Diabetes Mellitus has a substantial impact on Indonesians, affecting 88.51% of those aged 44-94, as revealed by the Indonesia Health Insurance program. The urgency of this health concern is underscored by the international Diabetes Federations alarming statistic: one person succumbs to Diabetes Mellitus every five seconds, demanding immediate attention to diagnosis and risk reduction. This study focuses on predicting the Length of Stay (LOS) for Diabetes Mellitus patients, utilizing Logistic Regression and various machine learning models, particularly Random Forest. Concurrently, Process mining, encompassing Process Discovery and Conformance Checking, elevates model quality. Key factors influencing patient LOS, including the healthcare facilitys location, patient age, and arrival time, are unveiled. Logistic Regression exhibits commendable accuracy at 0.76 with rapid prediction times of 0.00082 seconds, while Random Forest excels with an impressive accuracy of 0.88 and a prediction time of 0.07 seconds. Specific medical procedures, such as those involving 'Diabetes and mild Nutritional/Metabolic Disorders,' reveal prolonged sojourn times, averaging 41.2 days. Best conformance-checking results emerge with a dependency threshold of 0.6 and a loop-two threshold of 32, yielding a Precision of 0.97, Trace Fitness of 0.66, and Generalization of 0.60. The finding of this study underscores the pivotal role of model selection and class distribution in LOS prediction. Despite Random Forest superior performance, Logistic Regression demonstrates notable accuracy and efficiency. Analysis of sojourn times underscores the importance of efficient healthcare services, particularly in cases involving 'Diabetes and Mild Nutritional/Metabolic Disorders.' Adjusting threshold values significantly impacts model accuracy, with lower threshold values potentially leading to overfitting.

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HerbSimNet: Deep learning -Based Classification of Indian Medicinal Plants with High Inter-Class Similarities

HerbSimNet: Deep Learning -Based Classification of Indian Me...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Rani, N. Shobha Bhavya, K.R. Pushpa, B.R. Devadas, Ragavendra M. University Bengaluru India University Bengaluru India Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Medicinal plant species recognition is important across diverse sectors such as Ayurveda, agriculture, environment conservation and botanical research. Specific groups of plants in Indian medicinal plant ecosystem exhibit significant inter-class similarities due to varying abundance and ecological factors. To address the challenges involved in the process of classifying these species in this work a deep learning model Herb-SimNet is proposed. The Herb-SimNet analyzes similarity of plant species over other plant species using vision based deep learning and machine learning techniques. The proposed model works based on the combination of wavelet features and convolutional features extracted using three sequential convolution layers to extract the prominent features that distinguish variations among the inter class similarity plant species. To perform experiments, a dataset is created by capturing medicinal plant leaf images using box model in plain background and uniform lighting. A smart phone captured twelve Indian medicinal plant species comprising of about 1400+ samples that belongs different plant species but similar morphological structure is collected. Baseline experiments are carried out between Herb-SimNet and other state-of-the-art deep learning models for classification based on the proposed dataset. The outcomes demonstrate that Herb_SimNet provides clear interpretation one plant variety with others and achieves superior accuracy in prediction than that of state-of-the-art approaches. Furthermore, the model demonstrates better generalization towards the other inter-class similarity groups considered for testing. In conclusion, the proposed dataset and Herb-SimNet plays a a crucial role in advancement of research concerning Indian medicinal plant species classification resulting into enhancement of AI-based technology for biodiversity conservation and ethnobotanical studies. © 2025 The Author(s).

关键词： Vegetation

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031863011

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862892

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862984

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An Innovative Approach to Predict early Stage Depression using Mobile Network Usage

An Innovative Approach to Predict early Stage Depression usi...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Yogendra Swaroop Dwivedi Ganesh Semalty Amit Moondra Research and Development Ericsson Global India Limited Gurgaon India

ISBN: (纸本)9781665438124

This article introduces an early-stage depression identification technique without a physical presence in the medical unit. Currently used methods do not provide any early-stage depression detection techniques and require continuous investigation to diagnose depression. In general, depression detection is based on changes in human behavior. Changes in human behavior are also reflected in his/her mobile usage pattern. Mobile usage can correlate with human behavior and predict early-stage depression based on artificial intelligence (AI). The mobile usage data collected (e.g. location, activity, phone usage) can be used for effective depression screening. The proposed approach suggests using the user’s mobile usage pattern and applying machine learning algorithms to identify the change in mobile usage patterns over time. The machine learning algorithm like Density-Based Spatial Clustering of Applications with Noise (DBSCAN) can be effectively used to identify the change in pattern. This change in mobile usage pattern is then used to detect if the user is slowly moving into depression and warn the individual or concerned authority to handle the case.

关键词： machine learning algorithms machine learning Depression Feature extraction data models Object recognition data mining

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