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检索条件"任意字段=2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2023"

共 1092 条记录，以下是51-60 订阅

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Madanian, Samaneh Dai, Sherry Natarajan, Priyadarshini Rasoulipanah, Hamidreza Auckland University of Technology Dept. of Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350341072

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large. © 2023 ieee.

关键词： Risk assessment

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An Efficient Deep Learning Approach to Detect Neurodegenerative Diseases Using Retinal Images

An Efficient Deep Learning Approach to Detect Neurodegenerat...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Irfanuddin, Chowdhury Mohammad Shafin, Wasique Islam Ahmed, Koushik Khan, Md. Hasib Ashraful Alam, Md. Rahman, Rafeed Dipto, Shakib Mahmud Brac University Department of Computer Science and Engineering Dhaka Bangladesh Research Lab Dept. of Cse Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Neurodegenerative disorders like Multiple Sclerosis, Parkinson's disease, and Alzheimer's disease typically get diagnosed through brain MRI, CT scans, genetic testing, and various laboratory screening tests that are often tedious, time-consuming, and beyond the reach of the financial capability of many people especially, those living in the developing world. Moreover, they often pose potential hazards for patients with certain pre-existing conditions. To remedy this, we propose an efficient deep-learning approach that enables the detection of such neurodegenerative diseases through retinal images rather than brain images. Our proposed system can proactively detect such disorders simply through retinal scans, which are fast and cost-effective compared to traditional diagnostic approaches, which require expensive and sophisticated equipment. We used a dataset containing retinal cross-sectional images of 21 Multiple Sclerosis patients and 14 healthy individuals for our research, and our model achieved 98.85% accuracy while classifying healthy and diseased individuals from retinal scans. © 2023 ieee.

关键词： Neurodegenerative diseases

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Explainable Artificial Intelligence for Maize Disease Diagnostics

Explainable Artificial Intelligence for Maize Disease Diagno...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Banadda, Mubaraka Aloysius, Ntanda Kimuli Nakazzi, Solome Ernest, Othieno Benedict Owekitiibwa, Mukisa Samuel Marvin, Ggaliwango Makerere University Department of Computer Science Kampala Uganda Makerere University Department of Computer Kampala Uganda

ISBN: (纸本)9798350341072

This study focuses on the use of machine learning techniques, specifically Explainable AI and Deep Learning, to diagnose two prevalent diseases in maize crops: maize streak and maize leaf blight. These diseases have been major obstacles for farmers in effectively managing and identifying infections, leading to decreased harvest quantity and quality. By developing and training a customized model using supervised machine learning, combined with Transfer Learning using pretrained models like VGG19, MobileNet, we aim to improve maize disease diagnostics. The accuracy of the generated models was assessed, and the best performing model is selected for further analysis utilizing Explainable AI methods. The final step involves deploying the model on a mobile application, with the web application serving as a platform for visualizing the stored data collected through the mobile app in an external database. The ultimate objective of this research is to equip farmers with the necessary tool to enhance agricultural productivity by enabling precise and efficient diagnosis of maize diseases. © 2023 ieee.

关键词： Diagnosis

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Stuttering Prediction With Machine Learning

Stuttering Prediction With Machine Learning

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Scott, Thomas Seliya, Naeem University of Wisconsin - Eau Claire Dept. of Computer Science Eau ClaireWI United States

ISBN: (纸本)9798350341072

Addressing stuttering is an important problem. It affects the quality of an affected person's life. If machine learning algorithms can be designed to detect stuttering in audio files containing speech, there is great potential for helping people with stuttering communicate better. Recently, there have been some labeled (stuttering types) speech datasets made available to the public. The Sep-28k and FluencyBank datasets collectively account for 32,000 three-second audio clips containing labels on whether stuttering is present or not. This paper models this data using a variety of machine learning models, including Decision Trees, Random Forest, Gradient Boosting, XGBoost, and CatBoost. In addition, different empirical case studies are conducted: predicting an individual stuttering type;predicting different types of stuttering with one model and predicting the presence or absence of any stuttering speech at all. The features in the model are a combination of audio features extracted using the Librosa Python package, and a new custom-derived audio feature. Model accuracies computed on the test data are in the 80s, which is competitive to some more advanced models. © 2023 ieee.

关键词： Decision trees

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Breaking the Cyberbullying Cycle With Machine Learning

Breaking the Cyberbullying Cycle With Machine Learning

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Chan, Johnny Kishore, Shohil Yang, Xin Information Systems and Operations Management University of Auckland Auckland New Zealand

ISBN: (纸本)9798350341072

The proliferation of digital communication platforms has provided adolescents with unparalleled connectivity, yet it has concurrently amplified the menace of cyberbullying. This paper introduces 'Serenity,' a chat application prototype underpinned by advanced machine learning (ML) and natural language processing (NLP) techniques, designed to proactively identify and counteract cyberbullying incidents in real-time. Unlike traditional lexiconbased approaches, Serenity employs dynamic ML models, ensuring adaptability to the evolving linguistic patterns. The application not only detects potential cyberbullying but also incorporates mechanisms to deter such behaviors, thereby safeguarding the mental well-being of its young users. By integrating parental oversight without infringing on adolescents' privacy, Serenity strikes a balance between safety and autonomy. This research underscores the imperative of embedding sophisticated technological interventions in digital platforms catering to vulnerable populations and advocates for the broader adoption of such mechanisms in mainstream social media platforms. The design principles presented herein hold implications for platform developers, policymakers, and educators striving to consider in co-creating a safer digital ecosystem for adolescents. © 2023 ieee.

关键词： Human computer interaction

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Intelligent Control System for Efficient Energy Management in the Oil and Gas Industry

Intelligent Control System for Efficient Energy Management i...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Tasmurzayev, Nurdaulet Amangeldy, Bibars Nurakhov, Yedil Shinassylov, Shona Bekele, Samson Dawit Al-Farabi Kazakh National University Department of Artifical Intelligence and Big Data Almaty Kazakhstan Astana It University Department of Information Technology Astana Kazakhstan Al-Farabi Kazakh National University Department of Computer Science Almaty Kazakhstan

ISBN: (纸本)9798350341072

This article focuses on developing a smart Heating, Ventilation, and Air Conditioning (HVAC) control system tailored for the oil, gas, and energy industry. We discuss methods for developing an automated sensor monitoring system and regulating sensor parameters for air conditioning and temperature control. The proposed HVAC intelligent system receives real-time data from temperature sensors, leak sensors, air conditioners, and switches. The article presents the system's architecture, its operational principles, structural diagrams of the control unit, as well as the monitoring and control system. The central part of the real-time monitoring and control system was implemented using the SCADA program for regulating and monitoring sensor parameters. This work underscores the potential of intelligent HVAC systems in promoting energy conservation, enhancing operational safety, and catering to the specific needs of the oil and gas sectors. © 2023 ieee.

关键词： Energy efficiency

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Understanding Facial Expression of Children with Autism Using Learning Theory

Understanding Facial Expression of Children with Autism Usin...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Zaman, Anushka Khan, Evea Zerin Rabiul Alam, Md. Golam Chakrabarty, Amitabha Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

It is challenging for autistic individuals to gather socially and empathically, as we understand, so this subject matter differs from the identification of a common human facial expression. In our research, we have taken pictures of children and teenagers from school of special children based in Bangladesh, aged between 5 to 20 with Autism Spectrum Disorder (ASD) and identified their emotions from their images. Our objective is to find a technique that will allow their emotions to be accurately perceived and thus, make it easy for them including other individuals around them to interact socially without any barriers. In our paper, we implemented VGG16 and six Machine Learning Algorithms along with one Feature Extraction technique to detect Facial Expression of children with autism. The proposed system has showcased an accuracy of 75% for VGG16, 68% for Random Forest Classifier, 61% for Random Forest Classifier along with PCA, 61% for Support Vector Machine (SVM) and 67% for SVM with PCA, 56% for Logistic Regression and 53% for Logistic Regression along with PCA, 52% for Linear Discriminant Analysis (LDA) and 54% for LDA with PCA, 46% for Decision Tree and 50% for Decision Tree along with PCA and 46% for Gaussian Naïve Bayes and 38% for Gaussian Naïve Bayes along with PCA. © 2023 ieee.

关键词： Support vector machines

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Comparative Study of Object Detection Models for Safety in Autonomous Vehicles, Homes, and Roads Using IoT Devices

Comparative Study of Object Detection Models for Safety in A...

引用

2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Hasan, Mehedi Alavee, Kazi Ahnaf Bin Bashar, Syed Ziaul Rahman, Md. Tahmid Hossain, Muhammad Iqbal Rabiul Alam, Md. Golam Brac University Department of Computer Science and Engineering 66 Mohakhali Dhaka1212 Bangladesh

ISBN: (纸本)9798350341072

Object detection plays a pivotal role in enhancing security, surveillance, and automation. It enables timely threat identification, streamlines traffic management, and facilitates efficient resource allocation. By automating object recognition, it creates value through improved safety, productivity, and resource optimization in various domains, from smart cities to industrial settings. This paper explores modern object detection methods to develop a Real-Time Responsive CCTV Camera Model. As we transition to a 5G-connected world, smart devices are poised to manage our daily security. We focus on transforming conventional CCTV cameras into responsive, internet-connected security guards. By evaluating methods like HOG, Viola-Jones, R-CNN, SSD, and YOLO, we aim to select the most efficient algorithm. We found Yolov8 perform best among all the models based on accuracy 99.8% and FPS 40. Our research strives to create cost-effective, intelligent security systems, paving the way for automated alerts and a digitally secured future. © 2023 ieee.

关键词： Security systems

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Gender Classification in Bangla Language Using Deep Learning-Based Voice Analysis

Gender Classification in Bangla Language Using Deep Learning...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Labib, Md. Zarif Monsur, Sayema Binte Shuvo, Abtahi Maskawath Azrine, Tasmia Hakim, Talukder Juhaer Ali, Syed Muaz Alam, Md. Ashraful Brac University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Gender classification based on voice analysis is one of the essential tasks in speech and audio processing, with various applications such as speech recognition systems, voice assistants, call center analytics, Etc. For speech synthesis, human-computer interaction, and speaker identification - gender classification plays a vital role. Although extensive research on this topic has been done in various languages, studies can hardly be found regarding gender classification in the Bangla language. Our research aims to recognize gender in the Bangla language using deep learning approaches and voice analysis. The proposed strategy in this study consists of three stages: i) Pre-processing of the data;ii) Feature extraction utilizing the Short-Time Fourier Transforms (STFT) and Mel-Frequency Cepstral Coefficients (MFCC);iii) Classification using Convolutional Neural Network (CNN) models such as ResNet50, EfficientNetB0, InceptionV3, and DenseNet-121. Notably, 12 distinct feature combinations are used for model training and testing, using both the MFCC and STFT features singly or in combination. After thorough training and testing, InceptionV3 and EfficientNetB0 CNN models with MFCC features as input resulted in the highest accuracy of 92%, which demonstrates the system's excellent accuracy rate and its potential for use in practical settings. © 2023 ieee.

关键词： Neural network models

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Combining First-Order and Texture Features with Convolutional Neural Network for Classification of Residential Area Density in Aerial Images

Combining First-Order and Texture Features with Convolutiona...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Remigio, Adrian S. Hong, Jonel Aborot, Jeffrey DOST-ASTI Computer Software Division Quezon city Philippines

ISBN: (纸本)9798350341072

With the increasing availability of aerial images, computational methods have been used to efficiently extract information and apply analyses for various applications. The goal of this study is to improve residential area density classification in aerial images by combining first-order and texture features with deep features from convolutional neural network (CNN). Gray level co-occurence matrix (GLCM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), gray level dependence matrix (GLDM), and neighbouring gray tone difference matrix (NGTDM) were calculated from the images as texture features. Classical machine learning (CML) and multilayer perceptron (MLP) models based on handcrafted features alone were also evaluated in terms of residential area density classification. Our results from NPWU-RESISC45 and AID dataset experiments have showed that our proposed combined deep and handcrafted features outperformed popular and state-of-the-art CNN models that are based on deep features alone. Thus, FO and texture features provide additional and non-overlapping information that can support deep features for a more accurate residential area density prediction. © 2023 ieee.

关键词： Convolutional neural networks

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