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machine learning-Based Automated Medical Diagnosis for Healthcare 6

Machine Learning-Based Automated Medical Diagnosis for Healt...

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Khatter, Harsh Yadav, Ankit Srivastava, Ayush Group of Institutions Delhi-NCR Department of Computer Science Kiet Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350346961

A sizable segment of the world's population lacks access to quality healthcare. the success of healthcare ultimately depends on the doctor's skill. In this study, we investigate if this knowledge may be represented as an information corpus, or as data that has been retrieved using data mining methods, particularly the machine learning & Deep learning Model, to make a diagnosis. When the medical diagnosis is made widely available, coverage increases, and life quality improves. In order to determine whether inferences about the causes of various diseases can be made from the data, this paper provides an overview of machine learning approaches used in the classification of various diseases. We outline a few of our findings from the trials we ran before offering some suggestions for the future. the difference between the current state of health and an acceptable or desirable health condition is the health problem. By lowering doctor visits, hospital stays, and diagnostic testing procedures, monitoring systems are designed to lower health care expenditures. Using the data mining modeling technique, the integration of clinical decision support with computer-based patient records could decrease medical errors, increase patient safety, stop unwelcome practice variance, and improve practice outcomes. Connecting patients and doctors through a user- friendly interface will make it easier for patients to use in emergency situations.. © 2023 IEEE.

关键词： Diagnosis

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A Review on machine learning Techniques in IoT-Based Smart Grid Applications 6th

A Review on Machine Learning Techniques in IoT-Based Smart...

引用

6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

作者： Goyal, Samarth Goyal, Ishita Ahmed, Tanveer School of CSET Bennett University Greater Noida India

ISBN: (纸本)9783031530814

the deployment of machine literacy (ML) in the electric structure has been innovated by the arrival of the coming-generation power networks known as the smart grid. A detailed analysis related to smart grids has been performed by this article. the Internet of things (IoT) offers access and is at the heart of these advanced grid architectures. the system’s inter connectivity and requirement for ongoing communication resulted in a vast data flow that necessitates technologies considerably superior to traditional methods for appropriate data analysis and interpretation. In the modern world, cyber security has become a crucial concern in smart grid sophisticated networks, with sensor nodes and the data constituting key targets of cyberattacks. the article critically examines such security challenges and their remedies. the reviews are summarised to provide a succinct image of the practice and prospective future disciplines for industry developments, including existing constraints and feasible remedies, as well as their applicability. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart power grids

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MLMI 2023 - Proceedings of the 6th international conference on machine learning and machine Intelligence

MLMI 2023 - Proceedings of the 6th International Conference ...

引用

6th international conference on machine learning and machine Intelligence, MLMI 2023

ISBN: (纸本)9798400709456

the proceedings contain 26 papers. the topics discussed include: distributed photovoltaic load forecasting model based on clustering characteristic;short-term load forecasting method of power distribution station area integrating multiple temporal characteristics;shape and style enhancement for domain generalization;research on diabetes prediction based on machine learning;a fairness parameter selection method for convex fairness-aware classification models: understanding fairness parameters and a novel approach to balance between model performance and fairness metric;predictive analysis of NBA game outcomes through machine learning;a feature saliency based hybrid neural network model for object recognition;fusion of image and point cloud for accurate obstacle detection in autonomous driving;and combining dynamic programming and GPU to accelerate k-distance discord detection in time series data.

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Classification of Nursing Care Activities Using Smartwatches

Classification of Nursing Care Activities Using Smartwatches

引用

6th international conference on Activity and Behavior Computing (ABC)

作者： Staab, Sergio Abt, Vincent Schmid, Henning Luderschmidt, Johannes Wetter, Mark Martin, Ludger RheinMain Univ Appl Sci Wiesbaden Germany Fraunhofer Inst Comp Graph Res Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350375510;9798350375503

Around 46.8 million people worldwide currently suffer from dementia. More than 7.7 million new cases occur every year. As the prevalence increases with age and our life expectancy trends more and more towards an older population, this can become a growing burden for care homes, the healthcare system and the public. Nursing homes are already suffering from a shortage of nursing staff. To make matters worse, nurses are overwhelmed by the increasing amount of care documentation that takes away their time to interact with and care for their patients. With this work, we therefore evaluate the performance of wearable sensors for automatic recognition and documentation of 10 common nursing tasks using machine learning. Our main goal is to reduce the documentation workload and allow the nursing staff to refocus on patient care by finding the most suitable, robust and accurate combination of sensors and machine learning algorithms for automatic documentation of nursing activities. We evaluated 12 machine learning algorithms using 3D motion data collected at the wrist. Our test data contains 10 activities and was generated in collaboration with two care communities using a Google Pixel Watch GQF4C. While there was no single best solution to classify all activity types, Fast Tree, Generalized Additive Models and Light Gradient Boosting machine showed superior results compared to all other classifiers. Additional improvements could be achieved by dividing the activities into 3 main groups with best per group classifier types and implementing a quick model calibration per user.

关键词： Human Motion Analysis machine learning

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FedCER - Emotion recognition Using 2D-CNN in Decentralized Federated learning Environment 6

FedCER - Emotion Recognition Using 2D-CNN in Decentralized F...

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Agrawal, Manan Anwar, Mohd Ayaan Jindal, Rajni Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350346961

Emotion recognition using physiological signals has received much attention in recent literature. However, current development relies on the use of centralized datasets for training prediction models. But this approach raises a significant risk of privacy violation, especially in cases where the researchers use medically sensitive data like EEG recordings. the following paper proposes a privacy-preserving emotion recognition framework using Federated learning. It is a decentralized method of training machine learning models. We validate our results by comparing them against a baseline model and discuss the privacy-performance trade-off in Federated learning. Our proposed model is a convolutional neural network that works upon EEG signal recordings directly and does not rely upon extracted features from the DEAP dataset recordings of each subject. Instead, we have kept the non-IID data in the dataset intact. the proposed architecture achieves 72.22 percent, 70.10 percent, and 66.99 percent accuracy scores for the Dominance, Arousal, and Valence labels on the public DEAP dataset.. © 2023 IEEE.

关键词： Emotion recognition

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Driver Drowsiness Detection Using machine learning 6

Driver Drowsiness Detection Using Machine Learning

引用

6th IEEE international conference on Advances in Science and Technology, ICAST 2023

作者： Ritesh, Aryan Jagatia, Neel Deshmukh, Pankaj K J Somaiya Institute of Technology Dept of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350359817

the 'Driver Drowsiness Detector' system aims to improve road safety by creating a real time system that can detect and alert drivers when they become drowsy or fatigued. Drowsy driving is a hazard that can result in accidents, injuries and even fatalities. this system tackles this issue by combining computer vision techniques, machine learning algorithms and sensor integration to accurately identify signs of driver *** detect drowsiness the system takes an approach using recognition eye movement analysis and vehicle control data. It utilizes a camera positioned inside the car to monitor the driver's face. By tracking features and movements associated with drowsiness such, as eye closure duration, head nods and changes, in facial expression. © 2023 IEEE.

关键词： Real time systems

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Modeling of Metaheuristics with Deep Transfer learning Based Visual data mining for the Healthcare Sector 6

Modeling of Metaheuristics with Deep Transfer Learning Based...

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6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Abed, Z. Shnawa, Ammar H. Hadi, M.R. Ibrahim, Karrar Adnan, Myasar Mundher Hameed, Waleed Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq Altoosi University College Najaf Iraq Al-Zahraa University for Women Karbala Iraq The Islamic University Najaf Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350303339

data mining is a process that interacts with a large dataset to determine complex interesting patterns from unknown structured data. Visual data mining (VDM) is considered a combination of two disciplines: data mining and visualization to explore useful and implicit knowledge from a huge dataset. Also, it is strongly associated with multimedia systems, high-performance computing, computer graphics, human-computer interaction, and pattern recognition. Recently, VDM approaches find useful in the healthcare sector to aid the decision-making process. In addition, the design of VDM approaches for healthcare applications needs special consideration to make sure security of data. Artificial intelligence (AI) methods play a vital role in achieving scalability and accurate analysis from real-time environments. therefore, this study develops a blockchain-assisted quantum bacterial colonial optimization with deep learning (BAQBCO-DL) algorithm for VDM in the healthcare environment. the proposed BAQBCO-DL model exploits BC technology for secured data communication in the healthcare sector. In addition, the BAQBCO-DL model designs a U-Net-based segmentation with an optimal densely connected network (DenseNet) model for feature extraction, and the hyperparameter tuning process is performed using the QBCO algorithm. Finally, a probabilistic neural network (PNN) classifier is utilized to define appropriate class labels. An extensive experimental analysis is conducted to report the superior performance of the BAQBCO-DL technique and the outcomes are measured under several aspects. the simulation outcomes underlined the supremacy of the BAQBCO-DL algorithm over recent methodologies. © 2023 IEEE.

关键词： data mining

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Efficient machine learning Approach for Diabetes Mellitus Disease Prediction 6

Efficient Machine Learning Approach for Diabetes Mellitus Di...

引用

6th international conference on pattern Analysis and Intelligent Systems, PAIS 2024

作者： Oualid, Mecili Hadj, Barkat Nouioua, Farid Attia, Abdelouahab Samir, Akhrouf University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj Algeria Aix-Marseille University Lis UMR-CNRS 7020 Marseille France University Mohamed El Bachir El Ibrahim of Bordj Bou Arreridj Lmse Laboratory Laboratory Materials And Electronic System Algeria Mohamed Boudiaf University M'sila Algeria

ISBN: (纸本)9798350350265

Diabetes mellitus, a widely recognized chronic disease often termed a silent killer, disrupts insulin production, leading to elevated blood sugar levels and complications affecting organs including the eyes, kidneys, and nerves. Despite significant research interest, diabetes classification-based machine learning methods suffer from low performance due to missing data and imbalanced class data. thus, this paper introduces a new method for Diabetes Mellitus classification for Imbalanced data with Missing values (DMIM). the proposed approach involves these key steps. Firstly, missing values are preprocessed using the Isolation Forest method that provides data normalization. then, an adaptive synthetic sampling technique, Smote T-link, is employed to counter the impact of class imbalance. Finally, we utilized a Random Forest (RF) classifier and conducted experiments on the Pima Indians diabetes dataset from the University of California. Our proposed approach yielded highly accurate predictions, as evidenced by impressive performance metrics such as an Area Under the Curve (AUC) of 97.2%, Specificity (SP) of 91.7%, and Recall (Rec) of 92.3%, Further comparison between other ML methods including k-nearest Neighbors, Decision Trees, Random Forest, AdaBoost, Naive Bayes, XGBoost, Extra Trees. the results demonstrate the effectiveness and superiority of our proposed method and improved diabetes classification performance compared to existing techniques. © 2024 IEEE.

关键词： Forecasting

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Seismic Magnitude Determination Based on Deep learning 6

Seismic Magnitude Determination Based on Deep Learning

引用

6th international conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Mao, Fei Huang, Yongming College of Software Southeast University Nanjing China College of Automation Southeast University Nanjing China

ISBN: (纸本)9798350353174

Earthquakes are a natural disaster that have serious impacts on human society. Accurately determining the magnitude of earthquakes is crucial for understanding their danger and taking appropriate measures. Traditional methods for seismic magnitude determination face challenges such as data complexity and computational complexity. With the development of deep learning technology, convolutional neural networks have been introduced as a powerful tool for pattern recognition in the field of seismology. this study aims to investigate the effectiveness of seismic magnitude determination methods based on convolutional neural networks. In this study, we designed a deep convolutional neural network model to automatically extract features from seismic waveform data and estimate magnitudes. We conducted experiments using a large-scale earthquake dataset from seismic monitoring networks for validation. the experimental results demonstrate that compared to traditional methods, seismic magnitude determination based on convolutional neural networks achieves significant improvements in accuracy and generalization ability. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved R3D for Human Action recognition Based on Transfer learning 6

Improved R3D for Human Action Recognition Based on Transfer ...

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6th international conference on Robotics and Computer Vision, ICRCV 2024

作者： Han, Xiaowei Guo, Qiuyang Lu, Yunjing Liu, Jiwu Fei, Chaolong Harbin University of Commerce Postdoctoral research workstation of Northeast Asia Service Outsourcing Research Center Station of Applied Economics Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbi China Harbin University of Commerce Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin China Guangdong Technology College Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Zhaoqing China

ISBN: (纸本)9798331527426

Human action recognition is an important research direction in the field of computer vision and machine learning, which is widely used in intelligent surveillance, human-computer interaction, sports analysis and other fields. Traditional action recognition methods usually rely on large-scale labeled datasets and complex feature extraction processes, which have problems such as high data acquisition cost and long model training time, as an effective solution, transfer learning can significantly improve the performance of the model and reduce the dependence on labeled data by transferring the features learned by the pre-trained model on large-scale datasets to the target task. In this paper, the R3D model is used as the backbone network, and the model is improved by strengthening the channel and temporal feature extraction capabilities. the model achieves good performance on Shopping Action_ v l by transfer learning based on HMDB51 and UCF101 datasets, and obtains 89.53% and 94.14% of the optimal Top-1 and Top-5 indicators after pre-training on UCF101 dataset. In summary, the C-TSAB ResNets model is proposed in this paper performs well in accuracy and robustness on human action recognition tasks through transfer learning, which verifies the actual effect of the innovation in the research work of this topic. © 2024 IEEE.

关键词： Convolutional neural networks

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