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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是761-770 订阅

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Exploring Nutraceuticals in Diabetes Mellitus Management through machine learning Techniques - A Review 3

Exploring Nutraceuticals in Diabetes Mellitus Management thr...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Manivannan, E. Richard, J. Dineshkumar, P. Munirathinam, T. Velusamy, A. Mohanraj, B. Department of Pharmacology Salem India Sona College of Technology Department of It Salem India Kpr Institute of Engineering and Technology Department of Cse Coimbatore India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528201

A chronic metabolic disorder called diabetes mellitus is characterized by elevated blood glucose levels. It results from deficiencies in insulin production, action, or both. Diabetes has traditionally been managed with insulin, oral hypoglycemic drugs, and lifestyle changes. However, growing interest in alternative medicine is often driven by conventional treatments' challenges and side effects. Nutraceuticals are becoming popular supplements for managing diabetic symptoms, as they are derived from food sources that provide health benefits beyond basic nutrition. This study explores the role of various nutraceuticals in preventing and managing diabetes mellitus, focusing on their mechanisms, effectiveness, and potential advantages. Additionally, the use of machine learning techniques to evaluate and predict the effectiveness of nutritional supplements in diabetes management is discussed. © 2024 IEEE.

关键词： Support vector machines Logistic regression Reviews Modulation Production Predictive models Diabetes Reliability Insulin Intelligent systems

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Breast cancer diagnosis using machine learning Techniques 3

Breast cancer diagnosis using machine learning Techniques

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3rd international conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Muhamediyeva, D.K. Shaazizova, M.E. Doshanova, M. Yu. Tashkent University of Information Technologies Named after Muhammad Al-Kharezmy Tashkent Uzbekistan Digital Technologies and Artificial Intelligence Development Research Institute Tashkent Uzbekistan

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and na39;ve Bayesian classifiers. The properties of cell nuclei taken f... 详细信息

ISBN: (纸本)9781510674486

The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and naïve Bayesian classifiers. The properties of cell nuclei taken from breast biopsies are included in the Breast Cancer Wisconsin dataset, which is used in this study. Three different machine learning algorithms-naive Bayesian classifier, SVM and decision tree-are used to develop predictive models aimed at classifying samples as malignant or benign tumours. The study involves training the models on evaluation data and then evaluating their performance on test data. In order to compare the effectiveness of each method, many metrics like accuracy, sensitivity, and specificity are used in the evaluation of the results. The outcomes demonstrate the efficacy of every technique examined in this research. This work can be used as a springboard for future research into refining and customizing these techniques to intricate clinical situations, in addition to offering a useful comparison of the efficacy of three distinct machine learning approaches for breast cancer diagnosis. The results may facilitate the application of machine learning techniques in clinical settings, hence facilitating early detection and bettering the prognosis of patients with breast cancer. © 2024 SPIE. All rights reserved.

关键词： Computer vision

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Research on Intelligent Recommendation Algorithm of Online learning Platform Based on Artificial Intelligence 3

Research on Intelligent Recommendation Algorithm of Online L...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Song, Kuangye Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350380989

As science and technology continue to advance, artificial intelligence technology is progressively being applied in diverse fields, among which the development of online learning platform has become a convenient way for people to obtain knowledge. With the theme of ' Research on Intelligent Recommendation Algorithm of online learning Platform based on Artificial Intelligence '', this research is devoted to explore how to improve the user experience of online learning platform through advanced artificial intelligence technology. With the popularity of online learning, the problems of information overload and difficulty in course selection are becoming more and more prominent. In order to solve this problem, we introduce intelligent recommendation algorithm, which aims to provide personalized and efficient learning resource recommendation for each learner through the powerful learning and analysis ability of artificial intelligence. The core idea of this research is to comprehensively analyze learners' historical learning behavior, interest preference and subject level through artificial intelligence technologies such as deep learning and data mining. Based on this information, we built an intelligent recommendation system that can accurately predict the needs of learners and recommend the courses, textbooks and learning resources that best fit the personalized learning trajectory. In our research, we employ advanced algorithms such as deep neural networks to model and train large amounts of learning data. Through these algorithms, we can dig out the potential learning patterns and rules, and tailor the most suitable learning path for each learner. The research results show that the intelligent recommendation algorithm of online learning platform based on artificial intelligence has achieved remarkable results in improving the recommendation accuracy. © 2024 IEEE.

关键词： artificial intelligence data mining deep learning intelligent recommendation

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Signal Detection and Demodulation Algorithm Based on Deep learning in Communication Network 3

Signal Detection and Demodulation Algorithm Based on Deep Le...

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3rd international conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Song, Meng Song, Xuan Qin, Kunwen Weifang Engineering Vocational College Qingzhou Shandong262500 China

ISBN: (纸本)9798350376173

With the continuous development of communication network, the demand for signal detection and demodulation algorithms is increasing. Traditional methods based on feature engineering and model often face performance bottlenecks in complex communication environments. In recent years, the rapid development of deep learning technology provides new possibilities for solving this problem. In this paper, a signal detection and demodulation algorithm based on deep learning is proposed. By constructing an end-to-end neural network model, the automatic identification and demodulation of communication signals are realized. In the experiment, this paper compares the proposed deep learning model with the traditional support vector machine (SVM), Gaussian mixture model (GMM) and maximum likelihood estimation (MLE) methods, and evaluates the performance under various modulation formats and signal-to-noise ratios. The experimental results show that the proposed deep learning model has obvious advantages in accuracy and generalization ability, and has higher performance and faster convergence speed than the traditional methods. In addition, through error analysis, this paper further discusses the performance of deep learning model in misjudgment, missed judgment and multiple judgment, and puts forward the possible improvement direction and application prospect. Therefore, this study provides a new idea and method for the research and application of signal detection and demodulation algorithm based on deep learning in communication networks. © 2024 IEEE.

关键词： Support vector machines

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Performance of machine learning Algorithms in Digital Finance Risk Prediction 3

Performance of Machine Learning Algorithms in Digital Financ...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Shao, Yuze University of California Irvine Irvine92612 United States

ISBN: (纸本)9798331519032

Increasing financial risks show that digital financial risk prediction is very important for financial stability and security. By applying the random forest (RF) method to build and evaluate the financial risk prediction model, this article made an empirical study on the application and effectiveness of machine learning algorithm in digital financial risk prediction. The first step involved data preprocessing of financial related information, such as data cleaning, feature selection and data set segmentation. Then, the random forest algorithm was expounded, including the construction of decision tree in random forest, the role of randomness in random forest and the analysis of feature validity. For the process of constructing risk prediction model, this article also expounded how to generate the model, specify the training process of the model and the optimization of model parameters, and explain the verification of the model. The empirical research results showed that RF model could effectively predict various types of financial risks and achieve high confidence and coverage. The final coverage rate was as high as 99% and the confidence rate was 98.3%. Through comparative experiments with support vector machines and gradient boosting machines, the RF model showed significant advantages in prediction accuracy and prediction time, with an accuracy of up to 98.8% and a prediction time of only 210 milliseconds. In addition, this article also discussed the potential value of the RF model in practical application scenarios such as credit scoring, fraud detection, market risk management, and regulatory compliance. © 2024 IEEE.

关键词： Decentralized finance

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Energy Efficient Distribution of Heterogeneous Workloads in Cloud data Center 3

Energy Efficient Distribution of Heterogeneous Workloads in ...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Sharma, Anu Priya Singh, Jaspreet Chandigarh University Department of CSE Gharuan India

ISBN: (纸本)9798350381931

Cloud computing has become a popular technology for on-demand services for end consumers. Geo-distributed data centers conduct most cloud computing jobs and may use a lot of energy. In this paper geo-distributed cloud data center energy-efficient workflow scheduling approaches are discussed. Scheduling jobs to virtual machines to prevent overloading servers is a key scheduling goal. To address the scheduling issue, load balancing is necessary. Effective load balancing reduces response time and maximizes resource use. In this paper, a technique for energy-efficient workload assignment to cloud data centers is proposed. The heterogeneous workloads from Google dataset are classified using machine learning techniques for energy-efficient assignment to appropriate data centers. Various clustering algorithms produced equal clusters. In our proposed approach, 2 clusters segregated data properly. The performances of various load balancers in varying scenarios are also compared in this research. © 2024 IEEE.

关键词： Energy efficiency

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A Classification Method for Unordered Steel Scrap Images Based on Hybrid Convolutional Networks 24

A Classification Method for Unordered Steel Scrap Images Bas...

引用

3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Yu, Yan School of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (纸本)9798400710834

With the development of deep learning and image recognition technologies, these methods have found widespread applications across various fields. However, the annotation of industrial application-specific data, such as steel scrap images, remains challenging due to the unordered nature of the images, which often requires domain experts to perform the annotation. To address this, we propose a feature learning method for unordered images based on hybrid convolutional networks. Utilizing a preprocessed steel scrap dataset, we employ a cascaded convolutional network to capture deep correlation features within such unordered images while enhancing their fine-grained structures. A self-supervised approach is used to train the encoder, which is subsequently fine-tuned using a minimal set of labeled samples to complete the classification and annotation of steel scrap. Experimental results on four different categories of steel scrap annotation demonstrate that the proposed method achieves accuracies of 60.47%, 61.24%, 23.45%, 62.46%, respectively, verifying the effectiveness of the method. © 2024 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Advancing Sustainability in Mushroom Cultivation by leveraging IoT and ML Techniques 3

Advancing Sustainability in Mushroom Cultivation by leveragi...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Aravindhaa, V. Sanjanaa, R.J. Mithul Raaj, A.T. Rani, C. Rajesh Kumar, M. Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Electrical Engineering Vellore India

ISBN: (纸本)9798350372120

Mushroom cultivation represents a crucial sector in sustainable agriculture, offering rich nutrients and economic benefits. However, traditional cultivation methods often lack efficiency and sustainability, compounded by the risk of cultivating inedible or poisonous mushrooms. ML algorithms, such as SMOTE and pipeline models are employed to classify mushrooms into edible or poisonous categories, mitigating the risk associated with cultivation. Agaricus bisporous, or white button mushroom is the most common mushroom grown in many parts of India. The proposed IoT system is designed for convenience and scalability, accommodating both small and large-scale mushroom cultivation operations. The work focuses on efficient and profitable management of mushroom cultivation, providing greater yield and profit. © 2024 IEEE.

关键词： machine learning

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RVTS: retrieval-based text-to-speech 3

RVTS: retrieval-based text-to-speech

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3rd international conference on Electronic Information engineering and data Processing, EIEDP 2024

作者： Yang, Jin Su, Xuesong Xu, Wenke School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9781510680531

The single-stage text-to-speech model has made significant advancements in recent years. A comparison between two prominent models, VITS and VITS2, reveals that training with the same individual's voice dataset led to the voiceprint model generated by VITS closely aligning with the original voiceprint;however, its segmentation and fluency were subpar. On the other hand, VITS2 showed the opposite characteristics. Through the implementation of Retrieval-based Speech (RVTS), involving the extraction of intonation and fragmented sentences from the VITS2 model followed by code utilization, we were able to achieve speech synthesis. Our experimental findings demonstrate that our approach surpasses both VITS and VITS2, leveraging the strengths of both models. © 2024 SPIE.

关键词： machine learning

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Fortifying Public Area Networks: Leveraging Ensemble learning to Combat VPN Malicious Transmissions 3

Fortifying Public Area Networks: Leveraging Ensemble Learnin...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Khan, Aadil Gupta, Deepali Dutta, Monica Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350354171

In this article, a novel cybersecurity framework is created by using a variety of machine learning algorithms. The objective of the work is to seek and stop malicious communications from entering network infrastructures, especially in public places (which are prone to digital attacks). Issues arise as virtual private networks (VPNs) are more and more used on public networks. The purpose of this research is to track vulnerabilities that may exist in the virtual private network setup of networks and to see which directions people tend to take. This enables the computer to quickly discern deviations from the norm so that false information can be found and remedied in time. The method improves digital security by addressing flaws in VPN, thereby reducing the risk of cyber-attacks, and stopping illegal abuse. The results of this experiment show that the algorithm proposed in the article can find and prevent VPN connections and other possible incursions of harmful information. That ensemble learning was both precise and accurate, makes the effectiveness of the algorithm more credible. This research also improves security measures that can be taken to protect public networks from cyber-attacks. © 2024 IEEE.

关键词： Federated learning

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