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共 6842 条记录，以下是231-240 订阅

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Proceedings of the 3rd international conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

Proceedings of the 3rd International Conference on Intellige...

引用

3rd international conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

ISBN: (纸本)9798331529222

The proceedings contain 53 papers. The topics discussed include: exploring the capacity of a generative design software application based on AI applied to the design of sports foot prostheses;AI-enhanced IoT system for efficient traffic management: leveraging black data in smart cities;enhancing real-time clinical decision-making through AI-integrated FHIR solutions: a Medplum implementation;machine learning analysis of conductivity relative salinity seawater;optimization of machine learning models for diabetes prediction in Senegal;exploring the intersection of generative ai and cognitive science: insights and implications;and instant fingerprint recognition using optimized machine learning models by corona virus optimization algorithm.

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Agroinsights Chatbot: Ai-Driven Precision Farming For Optimal Yields, Crop Selection, And Disease-Free Harvests 3

Agroinsights Chatbot: Ai-Driven Precision Farming For Optima...

引用

3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Geethajali, T.M. Prithviraj, B. Prajwal, K.M. Prajwal Gowda, C.M. Priyanka Visvesvaraya Technological University Pes College of Engineering Mandya Dept. Information Science and Engineering Mandya India

ISBN: (纸本)9798350364569

The Agriculture AI-based chatbot presented offers a ground-breaking answer to farming challenges by fusing cutting-edge technologies. Through meticulous planning and execution, including data collection, preprocessing, and model selection, it solves significant challenges including agricultural yield projection, disease detection, and recommendations. The chatbot employs a range of machine learning and deep learning techniques to provide farmers with useful advice using an easy-to-use interface built with HTML, CSS, and Flask. A trained natural language processing (NLP) chatbot model increases user engagement and facilitates easier access to agricultural information. This comprehensive approach promotes the sustainability and prosperity of the global agriculture sector while boosting production and streamlining farming practices. © 2024 IEEE.

关键词： Agricultural Sustainability Artificial Intelligence (AI) in Agriculture Chatbot Technology Climate Sensitivity Crop Disease Identification Crop Health Monitoring Crop Yield Prediction data Preprocessing Techniques Decision Support Systems Environmental Impact Mitigation Fertilizer Recommendation machine learning Models Optimal Crop Selection Precision Agriculture Soil Analysis

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Sustainable Practices in Enhanced Diagnosis and Monitoring of Polycystic Ovary Syndrome (PCOS) using machine learning 3

Sustainable Practices in Enhanced Diagnosis and Monitoring o...

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3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Channabasamma Siri, Dharmapuri Harshitha, MacHa Babu, Kammagani Shobhan Mohammad, Aleem Alkhayyat, Ahmad Computer Science & Engineering Gokaraju Rangaraju Institute of Engineering and Technology Telangana India Gokaraju Rangaraju Institute of Engineering and Technology Telangana India Electronics & Communication Engineering K.G Reddy College of Engineering and Technology Telangana Hyderabad India Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9798350354379

Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting a significant percentage of women of reproductive age, leading to various metabolic, reproductive, and psychological issues. Despite its prevalence, the diagnosis and monitoring of PCOS remain challenging due to its heterogeneous nature and the multifactorial aspects of its manifestation. This study presents the development and implementation of advanced machine learning techniques aimed at enhancing the diagnosis and monitoring of PCOS, leveraging predictive analytics to improve medical outcomes. Introduced a comprehensive framework that integrates diverse data sources, including clinical, hormonal, and ultrasound data, along with patient lifestyle and genetic information. Advanced machine learning models including Random Forest, Decision Trees, and Logistic Regression were employed and compared to identify the most effective approach for PCOS diagnosis and prognosis. The models were trained, validated, and tested on a large dataset obtained from various healthcare facilities, ensuring a robust and generalizable system. The results demonstrate the potential of machine learning in revolutionizing the management of PCOS. The models achieved high accuracy, sensitivity, and specificity in diagnosing PCOS and predicting associated complications, such as infertility, metabolic syndrome, and cardiovascular risks. Furthermore, the system provides personalized treatment and lifestyle recommendations, contributing to a holistic approach to managing PCOS. © 2024 IEEE.

关键词： Decision Trees Logistic Regression Polycystic Ovary Syndrome Random Forest

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machine learning based Identification of Cardiac Function using PCG Signal 3

Machine Learning based Identification of Cardiac Function us...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Jahin, Md. Rifkat Tara, Kusum Islam, Md Hasibul Rajshahi University of Engineering & Technology Department of Electrical & Electronic Engineering Bangladesh

ISBN: (纸本)9798350388282

The study focuses on discerning normal and abnormal cardiac function via phonocardiogram (PCG) signals, employing various machine learning models including Support Vector machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). Extracting features such as mean, skewness, kurtosis, and symmetry index from PCG signals, Random Forest (RF) emerged as the preferred classification model, attributed to its faster convergence time compared to SVM. Precision, recall, and F1 score were utilized to evaluate classifier performance, demonstrating high accuracy and effectiveness, particularly with Random Forest. The methodology, combining machine learning models with PCG signal feature extraction, proves effective in identifying and classifying cardiac function. Notably, the low frequency to high frequency ratio stands at 4.6329 for normal and 0.3388 for abnormal cardiac functions. This approach holds significant potential as a diagnostic tool for detecting normal and abnormal cardiac functions, crucial for effective management of cardiovascular diseases. © 2024 IEEE.

关键词： Decision trees

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Improving Human Activity Recognition Through Multisensor data Fusion Techniques in Wireless Sensor Networks 3

Improving Human Activity Recognition Through Multisensor Dat...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Gargees, Rasha S. Cent Michigan Univ Dept Comp Sci Mt Pleasant MI 48859 USA

ISBN: (纸本)9798350372977;9798350372984

Wireless Sensor Networks (WSNs) have revolutionized data collection, especially in Human Activity Recognition (HAR). Multisensor datasets are crucial for a comprehensive understanding of human behavior, enabling more advanced classification techniques. This study explores the essential role of machine learning in categorizing activities, especially given the abundance of available multi-sensor data from WSN. The research utilizes information fusion as a pivotal mechanism to boost the accuracy of activity classifications. Employing Support Vector machine (SVM) and Decision Tree (DT) algorithms, the project utilizes advanced data fusion techniques, specifically Kalman Filter (KF) and Covariance Intersection (CI), to optimize information extraction from the provided data. The study encompasses six experiments, including applying SVM and DT on raw data, SVM and DT on data fused by CI, and SVM and DT on data fused by KF. The results of these experiments reveal a significant improvement in the accuracy of SVM and DT classification when incorporating CI and KF. This emphasizes the effectiveness of information fusion techniques in refining the outcomes of human activity recognition systems, showcasing their vital role in enhancing the reliability and precision of activity classifications. This research not only contributes to the field of HAR but also establishes a foundation for further advancements in real-world applications where precise activity classification holds utmost importance.

关键词： Wireless sensor network(WSN) human activity recognition (HAR) multi-sensor datasets data fusion machine learning classification Covariance Intersection (CI) Kalman Filter (KF)

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Enhancing Cybersecurity in Smart Grid Systems through Advanced Log File Analysis with machine and Deep learning Techniques 3

Enhancing Cybersecurity in Smart Grid Systems through Advanc...

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3rd international conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2024

作者： Natarajan, Arul Kumar Galety, Mohammad Gouse Noekhah, Shirin Soundararajan, Rajasoundaran Xurshed, Sobirov Xasan, Zikiryoyev Samarkand International University of Technology Uzbekistan

ISBN: (纸本)9798331532925

This research investigates the application of machine and deep learning techniques for enhancing cybersecurity in smart grid systems through log file analysis within digital forensics and incident response frameworks. Employing the NUSW-NB15_GT dataset, the study evaluates several machine learning models, including Random Forest, Gradient Boosting, and XGBoost, against deep learning architectures such as CNNs, LSTMs, and Transformers. The findings illustrate that machine learning models significantly outperform deep learning models, with XGBoost leading at an accuracy of 0.8933. The LSTM model shows moderate success with an accuracy of 0.8349. In contrast, the Transformer model lags with an accuracy of 0.5146, indicating its challenges with the structured, high-dimensional data typical of this dataset. This disparity highlights the superior capability of traditional machine learning methods in managing complex data interactions effectively, suggesting their preferential utility for smart grid cybersecurity. The results of this investigation establish a critical foundation for future advancements in protecting smart energy infrastructures and highlight the necessity for continued research into deep learning's potential in broader, more intricate datasets. © 2024 IEEE

关键词： Cybersecurity

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Privacy-Preserving machine learning for E-Health Applications: A Survey 3

Privacy-Preserving Machine Learning for E-Health Application...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Romeo, Jared Abbass, Mahmoud Sherif, Ahmed Mamun, Mohammad M. R. Khan Elsersy, Mohamed Khalil, Kasem Univ Southern Mississippi Sch Comp Sci & Comp Engn Hattiesburg MS 39406 USA Tennessee Technol Univ Elect & Comp Engn Dept Cookeville TN 38505 USA Higher Coll Technol Comp & Informat Syst Dept Abu Dhabi U Arab Emirates Univ Mississippi Elect & Comp Engn Dept University MS 38677 USA

ISBN: (纸本)9798350372977;9798350372984

As more information about people's health is gathered and analyzed, privacy concerns have become increasingly essential. This paper summarizes how machine learning (ML) methods are presently used to safeguard privacy in e-health apps. Furthermore, we discuss how privacy-preserving approaches such as differential privacy, secure multi-party computing, and homomorphic cryptography may be utilized to protect privacy in ML. The study also conducts an overview of the literature on how these methods can be used in various e-health scenarios, such as predictive modeling, disease detection, and clinical decision support. Based on the privacy-preserving technique, we classified current privacy-preserving ML work into four categories (i.e., data encryption, data anonymization, model encryption, and model obfuscation). Finally, the article discusses the challenges of applying privacy-preserving machine learning (PPML) in the e-health domain and what types of research could be conducted as open future problems in this field.

关键词： Privacy-Preserving machine learning E-Health and Intelligent Healthcare

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Estimating the level of urbanization based on machine learning 3

Estimating the level of urbanization based on machine learni...

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3rd international conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

作者： Muxamediyeva, Dildora K. Ismailov, Ilxom T. qizi Ermamatova, Shoxsanam E. Tashkent University of Information Technologies Tashkent Uzbekistan Tashkent State University of Economics Tashkent Uzbekistan

ISBN: (纸本)9781510681590

In this study, the problem of forecasting and analyzing the urbanization process using machine learning methods was solved using the example of Samarkand region. The official data of the State Statistics Committee of the Republic of Uzbekistan was used, and 25 important features affecting the level of urbanization were identified. Subsequently, various machine learning models were built, and their effectiveness was compared, with the artificial neural network model showing the highest result. With the help of this model, the levels of urbanization in Samarkand region for 2023-2025 were predicted. The obtained results are of practical importance for managing and regulating urbanization processes. © 2024 SPIE.

关键词： machine learning

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Marginal Contrastive Loss: A Step Forward for Forward-Forward 13

Marginal Contrastive Loss: A Step Forward for Forward-Forwar...

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13th Iranian/3rd international machine Vision and Image Processing conference (MVIP)

作者： Aghagolzadeh, Hossein Ezoji, Mehdi Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol Iran

ISBN: (纸本)9798350350494;9798350350500

The Forward-Forward algorithm was developed to increase the resemblance of artificial neural network training processes to those occurring in the brain, in contrast to the backpropagation algorithm, which has been shown to have less similarity to brain processes. While Forward-Forward is a fascinating and novel idea, it significantly differs in performance from backpropagation. Forward-Forward strives to achieve this similarity by updating each layer independently of the others, through the introduction of a loss function that facilitates the separability of data from different classes. This inherent nature of creating discrimination between data of different classes inspired us to take advantages of contrastive learning to improve Forward-Forward performance. We modified the contrastive loss to be used in Forward-Forward, and our experimental results show that the proposed method improves the model accuracy and increases the convergence speed by more than 20 times.

关键词： Forward-Forward Backpropagation Contrastive learning Brain-inspired Computing Neural Network Training

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Multimodal aneurysm rupture prediction analysis 3

Multimodal aneurysm rupture prediction analysis

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3rd international conference on machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Wang, Simin Song, Miao School of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781510681880

Cerebral aneurysms are highly dangerous diseases, with their rupture leading to severe disabilities, thus necessitating accurate prediction of rupture risk. In this study, we constructed a custom dataset containing 178 aneurysms and extracted four types of features for fusion, including deep learning, clinical, morphological, and hemodynamic features. We then built five distinct machine learning models based on these selected features to analyze and predict aneurysm rupture. Specifically, the Random Forest model demonstrated the best performance in estimating the likelihood of aneurysm rupture, achieving a ROC value of 0.885. Our findings highlight that leveraging multimodal data enhances the accuracy of predicting aneurysm rupture risk, which could improve early detection and inform better clinical decision-making. © 2024 SPIE.

关键词： Adversarial machine learning

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