Journal of Informatics and Web Engineering最新文献_第3页

A Campus-based Chatbot System using Natural Language Processing and Neural Network 使用自然语言处理和神经网络的校园聊天机器人系统

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.7

Tuan-Jun Goh, Lee-Ying Chong, Siew-Chin Chong, Pey-Yun Goh

{"title":"A Campus-based Chatbot System using Natural Language Processing and Neural Network","authors":"Tuan-Jun Goh, Lee-Ying Chong, Siew-Chin Chong, Pey-Yun Goh","doi":"10.33093/jiwe.2024.3.1.7","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.7","url":null,"abstract":"A chatbot is designed to simulate human conversation and provide instant responses to users. Chatbots have gained popularity in providing automated customer support and information retrieval among organisations. Besides, it also acts as a virtual assistant to communicate with users by delivering updated answers based on users' input. Most chatbots still use the traditional rule-based chatbot, which can only respond to pre-defined sentences, making the users unlikely to use the chatbot. This paper aims to design and build a campus chatbot for the Faculty of Information Science & Technology (FIST) of Multimedia University that facilitates the study life of FIST students. Before the FIST chatbot can be used, natural language processing techniques such as tokenisation, lemmatisation and bag of word model are used to generate the input that can be used to train the neural network model (multilayer perceptron model). It makes the FIST chatbot comprehends user intent by analysing their questions, enabling it to address a broader range of inquiries and cater to the student's need with accurate answers or information related to the Faculty of Information Science & Technology. Besides, we also developed the backend interface allowing the admin to add and edit the dataset in the proposed chatbot and enable it continuously responds to the student with the latest and updated information.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Campus-based Chatbot System using Natural Language Processing and Neural Network 使用自然语言处理和神经网络的校园聊天机器人系统

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.7

Tuan-Jun Goh, Lee-Ying Chong, Siew-Chin Chong, Pey-Yun Goh

{"title":"A Campus-based Chatbot System using Natural Language Processing and Neural Network","authors":"Tuan-Jun Goh, Lee-Ying Chong, Siew-Chin Chong, Pey-Yun Goh","doi":"10.33093/jiwe.2024.3.1.7","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.7","url":null,"abstract":"A chatbot is designed to simulate human conversation and provide instant responses to users. Chatbots have gained popularity in providing automated customer support and information retrieval among organisations. Besides, it also acts as a virtual assistant to communicate with users by delivering updated answers based on users' input. Most chatbots still use the traditional rule-based chatbot, which can only respond to pre-defined sentences, making the users unlikely to use the chatbot. This paper aims to design and build a campus chatbot for the Faculty of Information Science & Technology (FIST) of Multimedia University that facilitates the study life of FIST students. Before the FIST chatbot can be used, natural language processing techniques such as tokenisation, lemmatisation and bag of word model are used to generate the input that can be used to train the neural network model (multilayer perceptron model). It makes the FIST chatbot comprehends user intent by analysing their questions, enabling it to address a broader range of inquiries and cater to the student's need with accurate answers or information related to the Faculty of Information Science & Technology. Besides, we also developed the backend interface allowing the admin to add and edit the dataset in the proposed chatbot and enable it continuously responds to the student with the latest and updated information.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Lung Cancer Detection with Pre-Trained CNN Models 利用预先训练的 CNN 模型检测肺癌

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.3

Chee Chiet Chai, W. Khoh, Ying Han Pang, Hui-Yen Yap

{"title":"A Lung Cancer Detection with Pre-Trained CNN Models","authors":"Chee Chiet Chai, W. Khoh, Ying Han Pang, Hui-Yen Yap","doi":"10.33093/jiwe.2024.3.1.3","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.3","url":null,"abstract":"Lung cancer is a common cancer in Malaysia, affecting the majority of male citizens. The early detection of lung cancer will decrease its death rate. The only way to detect lung cancer is with a CT scan, and it also requires the doctor to check the scan to confirm the disease. In another way, the computer's support for the detection and diagnosis tool will assist doctors in determining lung cancer more accurately and efficiently. There are three main objectives for this research work. The first target is to study state-of-the-art research work to detect and recognize lung cancer from CT scan images. Then, the article will aim to adopt pre-trained convolutional neural network models in lung cancer detection. It also evaluates the performance of convolutional models on lung cancer imagery data. Then, the pre-trained models with a few added layers and modifications to parameters such as epochs, batch size, optimizer, etc. to conduct model training in this article. After that, Python Pylidc is used in image pre-processing to filter the dataset. Overall, pre-trained models such as ResNet-50, VGG-16, Xception, and MobileNet achieve above-state-of-the-art performance in classifying lung cancer from CT scan images in the range of 78% to 86% accuracy. The best detection accuracy result is the pre-trained VGG-16 model with the addition of some fully connected layers, 16 batch sizes, and the Adam optimizer, which achieved 86.71%.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"22 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment Analysis using Support Vector Machine and Random Forest 使用支持向量机和随机森林进行情感分析

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.5

Talha Ahmed Khan, Rehan Sadiq, Zeeshan Shahid, Muhammad Mansoor Alam, Mazliham Mohd Su'ud

{"title":"Sentiment Analysis using Support Vector Machine and Random Forest","authors":"Talha Ahmed Khan, Rehan Sadiq, Zeeshan Shahid, Muhammad Mansoor Alam, Mazliham Mohd Su'ud","doi":"10.33093/jiwe.2024.3.1.5","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.5","url":null,"abstract":"Sentiment analysis, is commonly known as opinion mining, is a vital field in natural language processing (NLP) that claims to find out the sentiment or emotion expressed in a given text. This research paper demonstrates an exhaustive survey of sentiment analysis, focusing on the application of machine learning techniques. Comprehensive parametric literature review has been completed to determine the sentiment analysis using SVM and Random Forest. Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. The findings of this research contribute to a deeper understanding of sentiment analysis and provide insights into the effectiveness of machine learning approaches in this domain. Based on the results obtained, two machine learning algorithms named as Random Forest and SVM were evaluated based on their accuracy in a classification task. The Random Forest algorithm achieved an accuracy of 0.78564, while SVM outperformed it slightly with an accuracy of 0.80394. Both Random Forest and SVM have demonstrated their strengths in achieving respectable accuracies in the given classification task. These results suggest that SVM, with its slightly higher accuracy of 0.80394, may be a more suitable choice when accuracy is the primary concern. However, the basic configuration need and characteristics of the problem at hand should be considered when choosing the better algorithm with better results.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"30 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vision-Based Gait Analysis for Neurodegenerative Disorders Detection 基于视觉的步态分析用于神经退行性疾病检测

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.9

Vincent Wei Sheng Tan, Wei Xiang Ooi, Yi Fan Chan, Connie Tee, Michael Kah Ong Goh

{"title":"Vision-Based Gait Analysis for Neurodegenerative Disorders Detection","authors":"Vincent Wei Sheng Tan, Wei Xiang Ooi, Yi Fan Chan, Connie Tee, Michael Kah Ong Goh","doi":"10.33093/jiwe.2024.3.1.9","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.9","url":null,"abstract":"Parkinson’s Disease (PD) is a debilitating neurodegenerative disorder that affects a significant portion of aging population. Early detection of PD symptoms is crucial to prevent the progression of the disease. Research has revealed that gait attributes can provide valuable insights into PD symptoms. The gait acquisition techniques used in current research can be broadly divided into two categories: vision-based and sensor-based. The markerless vision-based classification model has become a prominent research trend due to its simplicity, low cost and patient comfort. In this study, we propose a novel markerless vision-based approach to obtain gait features from participants' gait videos. A dataset containing gait videos from normal subjects and PD patients were collected, along with a control group of 25 healthy adults. The participants were requested to perform a Timed Up and Go (TUG) test, during which their walking sequences were recorded using two smartphones positioned at different angles, namely side and front. A multi-person pose estimator is used to estimate human skeletal joint points from the collected gait videos. Different gait features associated with PD including stride length, number of steps taken during turn, turning duration, speed and cadence are derived from these key point information to perform PD detection. Experimental results show that the proposed solution achieves an accuracy of 89.39%. The study's findings demonstrate the potential of markerless vision-based gait acquisition techniques for early detection of PD symptoms.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study 使用深度学习方法进行植物病害检测和分类：比较研究

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/2024.3.1.10

Pei-Wern Chin, Kok-Why Ng, Naveen Palanichamy

{"title":"Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study","authors":"Pei-Wern Chin, Kok-Why Ng, Naveen Palanichamy","doi":"10.33093/2024.3.1.10","DOIUrl":"https://doi.org/10.33093/2024.3.1.10","url":null,"abstract":"The presence issue of inaccurate plant disease detection persists under real field conditions and most deep learning (DL) techniques still struggle to achieve real-time performance. Hence, challenges in choosing a suitable deep-learning technique to tackle the problem should be addressed. Plant diseases have a detrimental effect on agricultural yield, hence early detection is crucial to prevent food insecurity. To identify and categorise the indications of plant diseases, numerous developed or modified DL architectures are utilised. This paper aims to observe the performance of the YOLOv8 model, which has better performance than its predecessors, on a small-scale plant disease dataset. This paper also aims to improve the accuracy and efficiency of plant disease detection and classification methods by proposing an optimised and lightweight YOLOv8 architecture model. It trains the YOLOv8 model on a public dataset and optimises the YOLOv8 algorithm with the integration of the GhostNet module into the backbone architecture to cut down the number of parameters for a faster computational algorithm. In addition, the architecture incorporates a Coordinate Attention (CA) mechanism module, which further enhances the accuracy of the proposed algorithm. Our results demonstrate that the combination of YOLOv8s with CA mechanism and transfer learning obtained the best result, yielding score of 72.2% which surpassed the studies that utilised the same dataset. Without transfer learning, our best result is demonstrated by YOLOv8s with GhostNet and CA mechanism yielding a score of 69.3%.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Machine Learning Methods for Calories Burn Prediction 用于预测卡路里消耗量的机器学习方法比较

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.12

Jing Sheng Alfred Tan, Zarina Che Embi, N. Hashim

引用次数: 0

Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance 用于具有高斯噪声方差的 CT 扫描图像的自适应高斯维纳滤波器

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.11

Kai Liang Lew, Chung Yang Kew, Kok-Swee Sim, Shing Chiang Tan

{"title":"Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance","authors":"Kai Liang Lew, Chung Yang Kew, Kok-Swee Sim, Shing Chiang Tan","doi":"10.33093/jiwe.2024.3.1.11","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.11","url":null,"abstract":"Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise variance without compromising the image details. The AGWF combines the Gaussian filter for initial noise reduction, followed by the implementation of Wiener filter, which can adaptively estimate noise variance and signal power in localized regions. This approach not only outperforms other existing techniques but also showcases a remarkable balance between noise reduction and image detail preservation. The experiment evaluates 300 images from the dataset and each image is corrupted with Gaussian noise variance to ensure a comprehensive evaluation of the AGWF’s performance. The evaluation indicated that AGWF can improve the Signal-to-Noise Ratio (SNR) value and reduce the Root Mean Square Error (RMSE) and Mean Square Error (MSE) value, showing a qualitative improvement in CT scan imagery. The proposed method holds promising potential for advancing medical imaging technology with the implementation of deep learning.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"428 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emojis And Miscommunication in Text-Based Interactions Among Nigerian Youths 表情符号与尼日利亚青少年文本互动中的误解

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.15

U. Udoudom, Godwin William, A. Igiri, Ememobong Okon, K. Aruku

{"title":"Emojis And Miscommunication in Text-Based Interactions Among Nigerian Youths","authors":"U. Udoudom, Godwin William, A. Igiri, Ememobong Okon, K. Aruku","doi":"10.33093/jiwe.2024.3.1.15","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.15","url":null,"abstract":"This paper explores the dynamic role of emojis in text-based communication among Nigerian youths and the potential implications for miscommunication. Emojis have become integral to contemporary digital conversations, offering users a visual means of expressing emotions, tone, and context within the constraints of text-based interactions. In the context of Nigeria, a country with a diverse linguistic landscape and a youthful population heavily engaged in online communication, understanding the impact of emojis on interpersonal exchanges becomes particularly pertinent. This paper examines the prevalence and patterns of emoji usage among Nigerian youths across various digital platforms. It investigates the cultural nuances and interpretations associated with emojis within the Nigerian context, considering factors such as regional differences, linguistic diversity, and socio-cultural influences. Furthermore, the study examines instances where emojis may contribute to miscommunication or misunderstanding, potentially exacerbating conflicts or hindering effective communication. Through a comprehensive review of existing literature, online discourse analysis, and case studies, this research aims to shed light on the ways in which emojis influence the interpretation of textual messages and the potential challenges they pose to clear and accurate communication. The study concludes that as digital communication continues to be a primary mode of interaction, it is essential for users to recognize the potential for misinterpretation, prompting the need for increased emoji literacy and awareness.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"37 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized Healthcare: A Comprehensive Approach for Symptom Diagnosis and Hospital Recommendations Using AI and Location Services 个性化医疗：利用人工智能和定位服务进行症状诊断和医院推荐的综合方法

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.8

Seng-Keong Tan, Siew-Chin Chong, Kuok-Kwee Wee, Lee-Ying Chong

引用次数: 0