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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

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Prediction of Alzheimer's disease - A Machine learning Perspective with Ensemble learning 6

Prediction of Alzheimer's disease - A Machine Learning Persp...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arya, Akhilesh Deep Singh Verma, Sourabh Chakarabarti, Prasun Bishnoi, Rimpy Manipal University Jaipur Computer and Communication Engineering Jaipur Jaipur India ITM SLS Baroda University Computer Science and Engineering Gujarat Vadodara India Techno India NJR Institute of Technology Computer Science and Engineering Udaipur India

ISBN: (纸本)9798350304480

According to the World Alzheimer's Report 2022, the current number of people affected by dementia stands at over 55 million. Projections indicate that this figure will escalate to 78 million by the year 2030. Alzheimer's is primarily characterized by dementia, a neurodegenerative condition that progressively damages brain cells. Alarmingly, 75% of Alzheimer's cases go undiagnosed due to a lack of awareness. Machine learning models have shown significant value in the early prediction of Alzheimer's disease. These models enable physicians to start medication at an early stage, which can help delay the onset of symptoms. This paper focuses on the application of five highly effective models for Alzheimer's prediction. The Open Access Series of Imaging Studies (OASIS) dataset is utilized, and an auto classifier is implemented using IBM SPSS Modeler. Eight machine learning models, including C5.0, Neural Network, and Logistic Regression, are employed. Among these, the top five influential models Support Vector Machine (SVM), C5.0 decision tree, Neural Network, eXtreme Gradient Boosting (XGBoost), and Chi-square Automatic Interaction Detection (CHAID) decision tree are compared based on their performance metrics. After evaluating the results achieved by the classifier, it is observed that ensemble learning based XGBoost, outperforms other models, it achieves an impressive accuracy of 96.75% in the prediction of Alzheimer's. In ensemble learning decision trees have demonstrated exceptional performance as weak classifiers in classification-based problems. This system proves to be highly effective, easily implementable, and capable of accurately predicting Alzheimer's using clinical data of patients. Findings of this paper hold great significance for enhancing early detection in Alzheimer's disease. © 2023 IEEE.

关键词： Alzheimer's Ensemble learning Machine learning

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Smart Diagnostic System for Early Detection and Prediction of Polycystic Ovary Syndrome

Smart Diagnostic System for Early Detection and Prediction o...

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2023 international conference on computer Communication and Informatics, ICCCI 2023

作者： Harish, K.P. Dhivyanchali, M.N. Devi, K.Nirmala Krishnamoorthy, N. Dhana Sree, R. Dharanidharan, R. Kongu Engineering College Department of Computer Science & Engineering Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tamilnadu Tiruchengode637 205 India Vellore Institute of Technology Vellore Campus School of Information Technology and Engineering Department of Software Systems and Engineering Tamilnadu 632014 India

ISBN: (纸本)9798350348217

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset. © 2023 IEEE.

关键词： Adaptive boosting

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Infant Birth Weight Estimation Using Machine learning 7

Infant Birth Weight Estimation Using Machine Learning

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7th international conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Sushiridha, A. Abinayaa, A. Koppala, Manasa Kaladevi, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397284

Recent studies show that at least 7 out of 100 babies born in 2015 were under the recommended birth weight of 2.5 kg according to UNICEF. There are various factors that influence low birth weight including baby's sex, age of mother and father, living conditions, smoking and drinking habits, weight gained during pregnancy, etc. According to doctors, the decline in the average birth weight is a warning sign of the dangers of undernourishment, juvenile illnesses, and even mortality. The idea of the project is to help mothers estimate the baby's birth weight using various machine learning algorithms and determine which algorithm is best suited for this research study. The proposed model can recommend appropriate meal programs to simultaneously improve the health of the mother and infant based on the estimation that the algorithm provides so this study will help to monitor the low birth weight of infants and also the mother's health. © 2023 IEEE.

关键词： Machine learning

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Beamforming Design for Multi-Cell SWIPT-Enabled Wireless Networks 6

Beamforming Design for Multi-Cell SWIPT-Enabled Wireless Net...

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6th IEEE international conference on computer and Communication engineering Technology, CCET 2023

作者： Liu, JainShing Chang, Wan-Ling Providence University Computer Science and Information Engineering Taichung Taiwan Chaoyang University of Technology Insurance Taichung Taiwan

ISBN: (纸本)9798350340686

In simultaneous wireless information and power transfer (SWIPT) wireless networks, beamforming and power split techniques are essential for optimizing the tradeoff between energy harvesting and information decoding. In the context of multi-cell multi-input single-output (MISO) networks, where the challenge of longer distance wireless charging is added to conventional information transmission, we propose a joint optimization approach for beamforming and power splitting, while satisfying both signal-to-interference-and-noise (SINR) and energy harvesting (EH) requirements. Specifically, we propose two distinct programming models for minimizing the total transmit power, and analyze their performance trends under different SINR and EH requirements. Then, a simulation study is conducted to verify the proposed models and provide insights into the design of efficient SWIPT schemes. Finally, through the simulation study, these models can be shown to have the performance benefits that can approach optimal results with fewer iterations compared to conventional optimization tools. © 2023 IEEE.

关键词： Beamforming

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A Fast Response Fault Detection Technique for HVAC Transmission Line Using Machine learning 10

A Fast Response Fault Detection Technique for HVAC Transmiss...

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10th IEEE international conference on Power Systems, ICPS 2023

作者： Muntasir, Fahim Al Wazed Wasi, Wasif Nafis, Mahdee Hossain, M. Mofazzal Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering and Technology Dept. of Electrical and Electronic Engineering Rajshahi Bangladesh Rajshahi University of Engineering and Technology Dept. of Electronics and Telecommunication Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350318739

High voltage alternating current (HVAC) transmission lines are the only way to transmit electrical energy from the power plant to the utilities. There are several types of transmission line faults that can interrupt the power supply, sometimes even resulting in national blackout. To ensure a highly reliable power supply system, detecting the faults in the quickest of time is crucial. This research used machine learning and neural network algorithm to find five different types of faults using data from a real power transmission line in Bangladesh. Apart from the accuracy of the methods, the focus was given on the response time in identifying the faults. The two boosting methods CatBoost and XGBoost stood out as the most precise among all the algorithms, achieving excellent accuracy and detection time in between 20 ms to 30 ms. The response time of all the methods was found to be in millisecond range, making them perfect for not only long term faults, but also to be implied in transient fault analysis. © 2023 IEEE.

关键词： Machine learning

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SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification 26

SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybr...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Ashraf, S.M. Nabil Mamun, Md. Adyelullahil Abdullah, Hasnat Md. Alam, Md. Golam Rabiul BRAC University Department of Computer Science and Engineering 66 Mohakhali Dhaka1212 Bangladesh

ISBN: (纸本)9798350359015

Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early detection and effective treatment. To address this challenge, we employed deep learning techniques to identify patterns in chest X-rays that correspond to different diseases. We conducted experiments on the "ChestX-ray14"dataset using various pre-trained CNNs, transformers, hybrid(CNN+Transformer) models, and classical models. The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%. By combining the predictions of all trained models using a weighted average ensemble where the weight of each model was determined using differential evolution, we further improved the AUROC to 85.4%, outperforming other state-of-the-art methods in this field. Our findings demonstrate the potential of deep learning techniques, particularly ensemble deep learning, for improving the accuracy of automatic diagnosis of thoracic diseases from chest X-rays. © 2023 IEEE.

关键词： Chest X-ray CNN Ensemble learning Medical Imaging Vision Transformer

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Improving Intrusion Detection Systems for IoT Devices using Automated Feature Generation based on ToN_IoT dataset 8

Improving Intrusion Detection Systems for IoT Devices using ...

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8th international conference on computer Science and engineering, UBMK 2023

作者： Eren, Kazim Kivanc Kucuk, Kerem Kocaeli University Department of Computer Engineering Kocaeli Turkey Kocaeli University Department of Software Engineering Kocaeli Turkey

ISBN: (纸本)9798350340815

The Internet of Things (IoT) has witnessed exponential growth in recent years, leading to a diverse and interconnected ecosystem of devices. However, this rapid expansion has also made IoT vulnerable to various security threats and attacks. The interconnected nature of IoT devices and their extensive integration into everyday life make them enticing targets for malicious actors. Consequently, the development and deployment of effective intrusion detection systems for IoT environments have become crucial. In the literature, it has been observed that feature engineering, feature extraction, and other preprocessing steps are problematic. The general trend has been to develop intrusion detection systems using complex models such as deep learning concepts, while reducing the effort spent on feature engineering. In this study, the importance of feature engineering is addressed, and it is demonstrated that effective results can be achieved with simple models when proper preprocessing and feature generation steps are applied. An intrusion detection system for IoT devices has been implemented in the ToN_IoT dataset by employing appropriate preprocessing steps and, additionally, utilizing mechanisms for automatic feature generation. In the experiments conducted on the ToN-IoT dataset, we propose a simple model that gives comparable results with the state-of-the-art deep learning models. This model utilizes a basic random forest algorithm and benefits f rom a different t raining scheme that take the benefits of grouping, stratification, re sampling, and automated feature generation strategies. We achieved 99.99% ROC-AUC values for both train and independent test sets. The proposed method shows mostly better performances for specifity, precision, recall, and F1-score than deep learning based models. © 2023 IEEE.

关键词： Internet of things

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Improving Diabetic Retinopathy Detection Using Patchwise CNN with biGRU Model 8

Improving Diabetic Retinopathy Detection Using Patchwise CNN...

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8th international conference on computer Science and engineering, UBMK 2023

作者： Darici, Muazzez Buket Yigit, Gulsum Kadir Has University Department of Electrical-Electronics Engineering Istanbul Turkey Kadir Has University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350340815

This study addresses Diabetic Retinopathy (DR), a diabetes complication that can lead to vision loss if not promptly diagnosed and treated. Recent advances in deep learning have shown promising results in detecting DR from retinal images. The study introduces a novel patch-based CNN-biGRU model for DR detection. The proposed model extracts patches from retinal images employing a sliding window strategy and then uses a Convolutional Neural Network (CNN) architecture to extract features from each patch. The features extracted from each patch are then concatenated, and a 4-layer bidirectional Gated Recurrent Unit (biGRU) is applied to predict the whole image. We assessed the proposed model on a publicly available dataset named APTOS 2019 Blindness Detection and achieved an accuracy of 73.5%, outperforming existing state-of-the-art approaches. The given patch-based CNN model can improve the accuracy of DR detection and aims to assist ophthalmologists in making more accurate diagnoses. © 2023 IEEE.

关键词： Convolutional neural networks

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Machine learning-Driven Educational Ethics Considerations: Striking A Balance Between Privacy And Personalization 10

Machine Learning-Driven Educational Ethics Considerations: S...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Siddiqua, Ayasha Sabeer, Shaista Rao, R. Suvarna Ahuja, Suniana Aggarwal, Shikha Lourens, Melanie Jazan University Dept. of Information Technology and Security Saudi Arabia Jazan University Computer Science Saudi Arabia Institute of Aeronautical Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Management Institute of Technology & Science U.P. Ghaziabad India Durban University of Technology Faculty of Management Sciences South Africa

ISBN: (纸本)9798350382471

This empirical study investigates the complex trade-off between personalization and privacy in the context of education powered by machine learning. Machine learning is emerging as a transformative force in the rapidly changing field of education, offering personalized as well as adaptive learning experiences. The incorporation of algorithms that can analyze large datasets together with produce insights presents a crucial contradiction that calls for a careful analysis of ethical issues. The study explores the revolutionary possibilities of machine learning, highlighting the manner in which it can be used to customize learning environments to meet the needs of each student. It does, nonetheless, also address privacy issues resulting from large-scale data collection, archiving, and possible abuse. Using a mixed-methods approach, the analysis combines stakeholder insights and quantitative data on customized interventions. Safeguarding privacy, being transparent, as well as obtaining informed consent are essential elements in the moral application of machine learning in education. Legal alongside regulatory measures are examined, with a focus on the necessity of flexible frameworks to meet changing technological challenges. The human element offers important practical insights, as demonstrated by the viewpoints of teachers and students. In the end, this paper emphasizes the importance that it is to work together, conduct continuous ethical analysis, and be dedicated to striking a balance between innovations, privacy, alongside personalization in the constantly changing field of education. © 2023 IEEE.

关键词： Adaptive learning Ethical Framework Machine learning in Education Personalization Privacy Concerns

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Scene Classification of Remote Sensing Images Based on Improved HrNet 3

Scene Classification of Remote Sensing Images Based on Impro...

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3rd international conference on Electronic Information engineering and computer Science, EIECS 2023

作者： Jin, Qianqian Luo, Jian Li, Yang Hou, Xiangjun China West Normal University School of Electronic Information Engineering Nanchong China China West Normal University School of Computer Science Nanchong China

ISBN: (纸本)9798350316674

Since remote sensing images are acquired by long-distance aerial photography, they contain complex and variable feature targets, and there will be occlusion, proximity, and scale inequality between different targets, so it is very important to choose suitable feature information to express the image when performing scene classification of remote sensing images. Therefore, we improved HrNet and proposed the ASFI-HrNet model. We added Acmix to enable the model to adaptively capture information from different regions;added IEBN to enable the model to reduce the effect of noise;added four horizontal convolutional branches to integrate information from different scales to a greater extent;and added SA-based feature fusion module to further enhance feature extraction. Through a series of comparison experiments and ablation experiments, it is found that the accuracy of the ASFI-HrNet model on the RSSCN dataset is 96.43%, which is 3.4% higher than that of the HrNet model, while ASFI-HrNet obtains higher accuracy and various evaluation metrics of image classification than the other classification models in the experiments. © 2023 IEEE.

关键词： Acmix HrNet IEBN Remote sensing images SA Scene Classification

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