International Journal of Computing and Digital Systems最新文献_第2页

Improvement in Depth–of–Return-Loss and Augmentation of Gain-bandwidth with Defected Ground Structure For Low Cost Single Element mm–Wave Antenna 利用缺陷地层结构改善低成本单元素毫米波天线的损耗深度并提高增益带宽

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160108

Simerpreet Singh, Gaurav Sethi, Jaspal Singh Khinda

引用次数: 0

Exploring the Landscape of Health Information Systems in the Philippines: A Methodical Analysis of Features and Challenges 探索菲律宾卫生信息系统的前景：对特点和挑战的方法分析

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160118

Mia Amor C. Tinam-isan, January F. Naga

{"title":"Exploring the Landscape of Health Information Systems in the Philippines: A Methodical Analysis of Features and Challenges","authors":"Mia Amor C. Tinam-isan, January F. Naga","doi":"10.12785/ijcds/160118","DOIUrl":"https://doi.org/10.12785/ijcds/160118","url":null,"abstract":": A thorough analysis was conducted to evaluate Health Information Systems (HIS) in the Philippines utilizing the PRISMA approach. An initial pool of 313 potential articles, with 285 articles being excluded based on the exclusion criteria, resulting in a focused analysis of 28 articles. This analysis classifies the many HIS features while highlighting each one’s distinct value inside the Philippine healthcare system. These features encompass scheduling and communications, record-keeping and prescription, knowledge and information management, and marketplace and payment systems. Common features to most HIS are the profiling of patient, notification system, membership verification, laboratory result generation, and electronic appointment and scheduling. Parallel to this, the study examined the many di ffi culties encountered in the adoption and application of HIS in the Philippines, tackling issues like a lack of human resources, infrastructure-related challenges, and the impact of regional strategies and policies. Additionally, financial issues were also found to be a major challenge hampering the successful development and maintenance of HIS within the hospital system. This methodical investigation, Philippine-specific, provides insights into the dynamic environment of HIS, providing a basis for wise choice-making and strategic planning adapted to the distinct healthcare context of the Philippines.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"80 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714993","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

Deduplication using Modified Dynamic File Chunking for Big Data Mining 利用修改后的动态文件分块技术进行重复数据删除以实现大数据挖掘

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160105

Saja Taha Ahmed

引用次数: 0

DIC2FBA: Distributed Incremental Clustering with Closeness Factor Based Algorithm for Analysis of Smart Meter Data DIC2FBA：基于邻近因子的分布式增量聚类算法，用于分析智能电表数据

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160103

A. Chaudhari, Preeti Mulay, Ayushi Agarwal, Krithika Iyer, Saloni Sarbhai

{"title":"DIC2FBA: Distributed Incremental Clustering with Closeness Factor Based Algorithm for Analysis of Smart Meter Data","authors":"A. Chaudhari, Preeti Mulay, Ayushi Agarwal, Krithika Iyer, Saloni Sarbhai","doi":"10.12785/ijcds/160103","DOIUrl":"https://doi.org/10.12785/ijcds/160103","url":null,"abstract":": Due to increased civilization, smart cities, and the advent of technology, lots of buildings including commercials, residential, and other types are populating in numbers in the recent past. The electricity consumption is also a ff ecting due to increased occupancy in these buildings. The analysis of the electricity consumption patterns will be helpful for consumers and electricity generation units to know about consumption and future requirements of electricity. As per the literature, the Incremental clustering algorithm is the best choice to handle ever-increasing data. In this research work, in the first phase, the electricity consumption data was extracted from smart meter images, and then in the second phase, the data was taken from extracted .csv files merging data from various sources together. This research proposes Distributed Incremental Clustering with Closeness Factor Based Algorithm (DIC2FBA) to update load patterns without overall daily load curve clustering. The proposed DIC2FBA has used Amazon Web Service(AWS) and Microsoft Azure HDInsight service. The AWS EC2 instance, along with the AWS S3 bucket and HdInsight, operates by clustering data from numerous sites using an iterative and incremental approach. The DIC2FBA first extracts load patterns from new data and then intergrades the existing load patterns with the new ones. Further, we have compared the findings achieved using the DIC2FBA with IK means and NFICA based on time, features, silhouette score, and the Davis Bouldin index, which indicate that our method can provide an e ffi cient response for electricity consumption patterns analysis to end consumers via smart meters.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706098","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

Design of an Intelligent Tutor System for the Personalization of Learning Activities Using Case-Based Reasoning and Multi-Agent System 利用基于案例的推理和多代理系统设计个性化学习活动的智能辅导系统

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160136

Lamya Anoir, Ikram Chelliq, Maha Khaldi, Mohamed Khaldi

引用次数: 0

A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare 物联网和机器学习算法在电子医疗中的应用系统综述

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160122

Deepika Tenepalli, Navamani T M

{"title":"A Systematic Review on IoT and Machine Learning Algorithms in E-Healthcare","authors":"Deepika Tenepalli, Navamani T M","doi":"10.12785/ijcds/160122","DOIUrl":"https://doi.org/10.12785/ijcds/160122","url":null,"abstract":": In recent years, the Internet of Things (IoT) has been adopted in many applications since its usage is essential to daily life. Also, it is a developing technology in the healthcare system to provide e ff ective emergency services to patients. In the current scenario, medical cases and diseases among people are growing enormously. Thus, it is becoming challenging to accommodate and provide healthcare services for more incoming patients in clinics and hospitals with limited space and medical resources. Hence, the integration of IoT and assistive technologies came into the healthcare sector for providing e ffi cient healthcare services wirelessly as well as for continuous monitoring of the patients. With the help of IoT and Machine Learning technologies, healthcare providers can keep a closer eye on their patients and maintain more proactive lines of communication with them. Data collected from IoT devices can be fed to Machine Learning technologies for predicting and diagnosing diseases. Due to the severity of diseases, lack of early disease prediction methods, lack of resources, and a smaller number of specialized doctors, most of the population is dying. Hence, to address these issues in the healthcare domain, more research works are proposed based on Machine Learning and IoT-based healthcare systems. This work reviews the research works related to IoT-based healthcare systems and machine learning comprehensively.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"90 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711869","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: 4

Sentiment Analysis from Texts Written in Standard Arabic and Moroccan Dialect Based on Deep Learning Approaches 基于深度学习方法的标准阿拉伯语和摩洛哥方言文本情感分析

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160135

Abdellah Ait elouli, El mehdi Cherrat, Hassan Ouahi, Abdellatif Bekkar

引用次数: 0

Enhancing Image Clarity with the Combined Use of REDNet and Attention Channel Module 结合使用 REDNet 和注意力通道模块提高图像清晰度

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160117

Rico Halim, Gede Putra Kusuma

{"title":"Enhancing Image Clarity with the Combined Use of REDNet and Attention Channel Module","authors":"Rico Halim, Gede Putra Kusuma","doi":"10.12785/ijcds/160117","DOIUrl":"https://doi.org/10.12785/ijcds/160117","url":null,"abstract":": The primary aim of our study is to improve the e ffi cacy of image denoising, specifically in situations when there is a limited availability of data, such as the BSD68 dataset. Insu ffi cient data presents a challenge in achieving optimal outcomes due to the complexity involved in constructing models. In order to tackle this di ffi culty, we provide a method that incorporates Channel Attention, Batch Normalization, and Dropout approaches into the current REDNet framework. Our investigation indicates enhancements in performance parameters, such as PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index), across various levels of noise. With a noise level of 15, we obtained a Peak Signal-to-Noise Ratio (PSNR) of 34.9858 dB and a Structural Similarity Index (SSIM) of 0.9371. At a noise level of 25, our tests yielded a PSNR of 31.7886 decibels and an SSIM of 0.8876. In addition, at a noise level of 50, we achieved a Peak Signal-to-Noise Ratio (PSNR) of 27.9063 decibels and a Structural Similarity Index (SSIM) of 0.7754. The incorporation of Channel Attention, Batch Normalization, and Dropout has been demonstrated to be a crucial element in enhancing the e ffi cacy of image denoising. The Channel Attention approach enables the model to choose and concentrate on crucial information inside the image, while Batch Normalization and Dropout techniques provide stability and mitigate overfitting issues throughout the training process. Our research highlights the e ff ectiveness of these three strategies and emphasizes their integration as a novel way to address the constraints presented by the scarcity of data in image denoising jobs. This emphasizes the significant potential in creating dependable and e ff ective image denoising methods when dealing with circumstances when there is a limited dataset.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"59 52","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689481","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

Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning 通过 SVM 核微调改进数字市场中的情感分析

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160113

Abdul Fadlil Abdul Fadlil, Imam Riadi, Fiki Andrianto

{"title":"Improving Sentiment Analysis in Digital Marketplaces through SVM Kernel Fine-Tuning","authors":"Abdul Fadlil Abdul Fadlil, Imam Riadi, Fiki Andrianto","doi":"10.12785/ijcds/160113","DOIUrl":"https://doi.org/10.12785/ijcds/160113","url":null,"abstract":": The rapid growth of the online market, particularly in the digital realm, has spurred the need for in-depth studies regarding marketing strategies through public opinion, especially on platforms like Twitter. The sentiments expressed in customer tweets hold significant insights into their satisfaction or dissatisfaction levels with a service. Therefore, the use of ML algorithms in sentiment analysis is imperative to detect whether such comments lean towards positivity or negativity regarding a service. This research focuses on sentiment analysis towards three major e-commerce platforms in Indonesia: Tokopedia, Shopee, and Lazada, through the utilization of Twitter. The classification process involves various stages, including preprocessing, feature extraction and selection, data splitting for classification, and evaluation. The selection of both linear and non-linear SVM models as the focus of this research is based on their ability to handle large and complex datasets. The linear kernel is chosen for its proficiency in cases with a linear relationship between features and class labels, while the non-linear SVM provides flexibility in dealing with complex and non-linear relationships. Based on the evaluation results of the SVM model on the dataset, it is found that the polynomial kernel provides the highest accuracy value of 93%, with a training data share of 85%. This model features strong prediction capabilities with a precision of 93% for negative and 93% for positive labels. Although the linear kernel and other kernels showed solid performance, the polynomial kernel provided the most optimal results in the context of online marketplace sentiment analysis using data from Twitter","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141694091","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

Lib-Bot: A Smart Librarian-Chatbot Assistant Lib-Bot：智能图书管理员聊天机器人助手

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160101

Tong-Jun Ng, Kok-Why Ng, S. Haw

{"title":"Lib-Bot: A Smart Librarian-Chatbot Assistant","authors":"Tong-Jun Ng, Kok-Why Ng, S. Haw","doi":"10.12785/ijcds/160101","DOIUrl":"https://doi.org/10.12785/ijcds/160101","url":null,"abstract":": Library is a knowledge warehouse and various long past references can be found in it. Students, professors, kids, and adults are regularly encouraged to visit the library as it provides a conducive environment for building the habit of reading books and improving individual critical-thinking skills. As technology is getting more and more advanced nowadays, some common problems faced by the librarians can be replaced by machines. For instance, the librarians may not be available all the time at the counter; reduction of physical contact due to Covid19 infection et cetera, machines can take over the librarians’ roles to handle the tasks. In this paper, an Artificial Intelligence (AI) chatbot is proposed and implemented on mobile application to answer library-related questions. Bidirectional Encoder Representations from Transformers (BERT) algorithm is employed to classify the intent of the user’s messages. Besides, many existing chatbot applications support only the text input. This paper proposes a speech-to-text recognition feature to enable both text and voice input. If there are any queries that cannot be solved by the chatbot system, it will store the queries in the database and the library admins can filter the queries and upload new training data for the AI model to cover a wider range of questions.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"57 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689459","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