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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4173 条记录，以下是451-460 订阅

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Fourth international conference on Telecommunications, Optics, and computer Science, TOCS 2023

Fourth International Conference on Telecommunications, Optic...

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4th international conference on Telecommunications, Optics, and computer Science, TOCS 2023

ISBN: (纸本)9781510679832

the proceedings contain 36 papers. the topics discussed include: design and analysis of properties of the photonic crystal fibers with chalcogenide glass hollow core;design and calibration of a coherent wind measurement system with paraxial structure;malware feature selection and adversarial sample generation method based on reinforcement learning;design and construction of computer laboratory security monitoring system based on STM32 microcontroller;Sybil attacks detection for dynamic environment in federated learning;a fast hardware implementation method of interrupt handling based on armv8-m architecture;managing energy-efficient virtual machines with QoS-awareness in cloud computing;and multi-modal similarity fusion for user behavior sequence modeling.

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Prediction of Diabetic Retinopathy Based on Risk Factors Using machine learning Algorithms 4

Prediction of Diabetic Retinopathy Based on Risk Factors Usi...

引用

4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Azamen, Nor Tasha Nadira Nor Ali, Azliza Mohd Aziz, Nor Azimah Abd College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam Malaysia Universiti Teknologi Mara Faculty of Medicine Department of Ophthalmology Selangor Sg Buloh Malaysia

ISBN: (纸本)9798350318432

Diabetic Retinopathy (DR) is an eye condition triggered by Diabetes Mellitus (DM). It could be detrimental to diabetic patients since it may lead to permanent blindness if they do not receive early treatment. However, the risk of getting DR among DM patients can be preliminarily identified using patients' risk factors, including age, gender, body mass index (BMI), systemic comorbidities and DM-related complications. this paper presents the prediction of diabetic retinopathy using machine learning algorithms based on risk factors. Data collection was obtained from the Department of Ophthalmology, Faculty of Medicine, UiTM. the data is then processed and trained using machine learning algorithms such as logistic regression, support vector machine, and k-nearest neighbour. these machine-learning algorithms are executed with different parameters and yield different sensitivity, specificity, and accuracy values based on the training and testing data. the logistic regression model with a random state value of 11 and training and testing sizes in the ratio of 90:10 has achieved 83.78% accuracy, the highest accuracy of other models. then, we develop a web-based application that will later assist healthcare organisations in predicting DR among patients and enhancing the DR screening workflow without executing the eye screening procedure. this system implementation aims to assist medical staff, doctors, physicians, and at-risk patients. © 2023 IEEE.

关键词： Forecasting

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

4th International Conference on Frontiers in Computing and S...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

ISBN: (纸本)9789819726134

the proceedings contain 48 papers. the special focus in this conference is on Frontiers in Computing and Systems. the topics include: machine learning-Based Early Epilepsy Diagnosis with Secure EEG Data Sharing Using Blockchain;application of Different Decision Tree Classifier for Diabetes Prediction: A machine learning Approach;exploring Electric Vehicle Adoption Research through Bibliometric Analysis and Visualization;optimizing Solar Power Distribution in Microgrids to Reduce Energy Waste;GPA: Uni-directional GRU-Based Traffic Prediction Model for Minimizing Air Pollution;A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm;Unveiling the Art of Music Generation with LSTM;few-Shot learning with Fine-Tuned Language Model for Suicidal Text Detection;a Comparative Analysis of Feature Selection Approaches for Sensor-Based Human Activity Recognition;GuideBP: Guided Backpropagation in Multi-output Neural Networks by Channeling Gradients through Weaker Logits;a Study on Users Sentiment from Twitter Data and Stock Market During Russia-Ukraine War;illegitimate Comment Filtration Method for Social Media applications Using Logistic Regression;Unsupervised MTS Anomaly Detection with Variational Autoencoders;SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems;predicting Suicidal Behavior Among Indian Adults Using Childhood Trauma, Mental Health Questionnaires and machine learning Cascade Ensembles;analyzing Students’ Emotion and Activities in the Classroom: A Rural Education Perspective;MARS: Manual and Automatic Robotic Sanitization on Social Milieu;cervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification;enhancing Graph-Based Representation learning with Adversarial Policy Gradient: A Hyperparameter Analysis;Exploring Label-Specific Feature Weights for Multi-label Feature Selection Using FWMABAC-MFS;CoViT-Net: A Pre-trained Hybrid Vision Transformer for COVID-

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Improving IoT Security through machine learning 4

Improving IoT Security Through Machine Learning

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Raju Kumar, Rajendra Department of Computer Science New Delhi India

ISBN: (纸本)9798350306118

the swift proliferation of the Internet of things (IoT) has escalated security apprehensions, prompting extensive research into viable solutions using established techniques and algorithms. Nevertheless, the intricacies arising from IoT limitations, such as constrained power backup and storage capacity, impede the implementation of intricate security algorithms. Addressing these challenges, Computational Intelligence emerges as a panacea. By meticulously analyzing data patterns, these techniques assess gathered information to make well-informed decisions, thereby reinforcing the security of IoT devices. Computational Intelligence encompasses several key pillars, including Artificial Neural Networks (ANN), Fuzzy Set theory, Evolutionary Computing, and Swarm Intelligence. Within this realm, machine learning assumes a pivotal role. this investigation primarily focuses on the utilization of. Naive Bayes, Decision Trees, Random Forest and k-Nearest Neighbors algorithms to detect potential attacks within the MQTT dataset-A well-established benchmark for IoT traffic experimentation. Notably, the simulation results underscore remarkable achievements, as all algorithms attain an impressive accuracy rate of 99%, concurrently yielding a corresponding fl-score of 99%. these findings underscore the potential of Computational Intelligence in revolutionizing IoT security, paving the way for robust protection in the ever-expanding IoT landscape. © 2023 IEEE.

关键词： Internet of things

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Detecting Cardiovascular Disease From PPG Signals using machine learning 4

Detecting Cardiovascular Disease From PPG Signals using Mach...

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4th IEEE international Multidisciplinary conference on Engineering Technology, IMCET 2023

作者： Sadek, Tamara Ahmad, Julia Khoury, Fadi Badawe, Heba Khraiche, Massoud American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon American University of Beirut Biomedical Engineering Department Beirut Lebanon

ISBN: (纸本)9798350313826

Cardiovascular diseases (CVD) are the leading cause of death worldwide. Early detection of CVD allows for the use of effective and inexpensive interventions. CVD prediction using machine learning has achieved high accuracy using multiple data types such as Photoplethysmography (PPG), Electrocardiogram (ECG), and Blood Pressure (BP). However, these biological signals cannot all be reliably collected in real-time from a single wearable device and necessitate patients to be equipped with several devices. In this work, we trained a machine learning model to predict CVD using only PPG data collected from wearable devices, achieving an accuracy of 88% along with an F1-score of 90%. the information that this system can provide the patients with regarding their health status would enable the early detection and management of CVD, eventually decreasing the risk of morbidity. © 2023 IEEE.

关键词： Photoplethysmography

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5th international conference on machine learning for Cyber Security, ML4CS 2023

5th International Conference on Machine Learning for Cyber S...

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5th international conference on machine learning for Cyber Security, ML4CS 2023

ISBN: (纸本)9789819724574

the proceedings contain 11 papers. the special focus in this conference is on machine learning for Cyber Security. the topics include: PassFile: Graphical Password Authentication Based on File Browsing Records;on the Role of Similarity in Detecting Masquerading Files;A Password-Based Mutual Authenticated Key Exchange Scheme by Blockchain for WBAN;traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration;a Client-Side Watermarking with Private-Class in Federated learning;research on Evasion and Detection of Malicious JavaScript Code;Tackling Non-IID for Federated learning with Components Alignment;security on Top of Security: Detecting Malicious Firewall Policy Changes via K-Means Clustering;Penetrating machine learning Servers via Exploiting BMC Vulnerability.

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A Deep machine learning Approach for Intrusion Detection in IoT 4

A Deep Machine Learning Approach for Intrusion Detection in ...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Wadate, Akash J. Deshpande, S.P. DCPE HVPM Department of Computer Science Maharashtra Amravati India

ISBN: (纸本)9798350306118

It has proven to be extremely beneficial in various initiatives, including medical automation, defense systems, and even power grids. Essentially, the security surrounding fundamental processing and communications infrastructures is more important compared to the protection of loT networks. However, due to their relatively limited assets and computing capacity, loT devices are vulnerable to a range of attacks. therefore, defending loT networks from attackers is critical, and this can be achieved through the creation and deployment of effective safety regulations, such as intrusion detection technologies that incorporate specific controllers. In this work, we describe a unique traffic flow classification method for loT networks that employs deep machine learning to identify intrusions. We use a recently released loT database to develop general characteristics using domain values at the protocol packet level. Addressing binary or multi-class categorization, which includes loss of assistance, extended loss of assistance, surveillance, and data theft operations over loT devices, we create a feed-forward neural network framework. Findings from the examination based on the proposed strategy using the prepared information demonstrate a high level of precision in classification. © 2023 IEEE.

关键词： Intrusion detection

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proceedings - 2023 IEEE international conference on Quantum Computing and Engineering, QCE 2023

Proceedings - 2023 IEEE International Conference on Quantum ...

引用

4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

ISBN: (纸本)9798350343236

the proceedings contain 302 papers. the topics discussed include: an empirical comparison of optimizers for quantum machine learning with SPSA-based gradients;towards redefining the reproducibility in quantum computing: a data analysis approach on NISQ devices;crosstalk-based parameterized quantum circuit approximation;a study on concentric feature embeddings in variational multiclass classification for small-scale quantum computers;QubiC 2.0: a flexible advanced full stack quantum bit control system;harnessing the power of long-range entanglement for Clifford circuit synthesis;investigating students’ strengths and difficulties in quantum computing;design of quantum machine learning course for a computer science program;quantum computing, math, and physics (QCaMP): introducing quantum computing in high schools;and leveraging dual enrollment programs to expand secondary education in quantum computation.

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Research on Quality Estimation of Chinese-English Translation Based on machine learning 6

Research on Quality Estimation of Chinese-English Translatio...

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6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Han, Yonggong Zhao, Chuangang Beijing Forestry University School of Information Science and Technology China

ISBN: (纸本)9781510668546

With the deepening of international communication, the demand for translation software is also increasing. Traditional intelligent translation equipment still suffers from poor translation quality and incoherent words. To solve relevant problems. this paper, by using machine learning method, adds contextual word vectors into the original translation process, extracts and analyzes contextual words in input speech, automatically correlates relevant words, and conducts quality estimation of translation. It is helpful to improve the automatic calibration of Chinese-English translation and improve the accuracy of intelligent speech translation. © 2023 SPIE.

关键词： machine learning

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Comparison of machine learning Models for Chronic Kidney Disease Prediction using UCI dataset 4

Comparison of Machine Learning Models for Chronic Kidney Dis...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Sonwalkar, Mrunali S. Tamane, Sharvari C. Mgm University Dept. of Computer Science Charapati Sambhaji Nagar India Jnec and University Dept. of Information Technology Charapati Sambhaji Nagar India Charapati Sambhaji Nagar India

ISBN: (纸本)9798350349900

Chronic Kidney Disease (CKD) prediction remains a formidable obstacle in the field of medicine. CKD is a potentially fatal condition that may continue for the rest of a person's life and is commonly associated with kidney cancer or impaired kidney function. the development of CKD to its terminal stage can be prevented or reduced by means of timely identification and suitable treatment. the present study investigates the potential of different machine learning (ML) predictive models to aid in the early detection of CKD. Utilizing publicly accessible data to predict the occurrence of CKD is the main goal of this study. In order to accomplish this, a generic model was constructed through the implementation of a series of data pre-processing steps. these steps encompass tasks such as missing data handling and encoding, in addition to the application of the Chi-Squared (Chi-2) algorithm for feature selection. ML techniques including Support Vector machine (SVM), Decision Tree (DT), and Convolutional Neural Network (CNN) were employed to develop predictive models. Among these, the CNN model emerged as the most promising, achieving the highest number of correct predictions with 119 cases, while making only one incorrect prediction. © 2024 IEEE.

关键词： Prediction models

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