International Journal of Computing and Digital Systems最新文献_第4页

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

YOLOv8 and Faster R-CNN Performance Evaluation with Super-resolution in License Plate Recognition 车牌识别中的 YOLOv8 和带有超分辨率的更快 R-CNN 性能评估

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160129

Diva Angelika Mulia, Sarah Safitri, I. Gede Putra Kusuma Negara

引用次数: 2

Neutrosophic Clustering: A Solution for Handling Indeterminacy in Medical Image Analysis 中性聚类：医学图像分析中处理不确定性的解决方案

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160111

Sitikantha Mallik, Suneeta Mohanty, Bhabani Shankar Mishra

{"title":"Neutrosophic Clustering: A Solution for Handling Indeterminacy in Medical Image Analysis","authors":"Sitikantha Mallik, Suneeta Mohanty, Bhabani Shankar Mishra","doi":"10.12785/ijcds/160111","DOIUrl":"https://doi.org/10.12785/ijcds/160111","url":null,"abstract":": The need for additional innovation in the healthcare industry has become more apparent as the world begins to recover from the ravages of the pandemic. While computational intelligence has quietly become integrated into more and more fields, its applications were not something the average person discussed until recently. Computational Intelligence is becoming more and more applicable in several sectors around the world like health, industrial, business and commercial sectors. A.I.’s ability to provide faster and improved functionality is what healthcare workers at healthcare centers believe will be a significant implication in the strife towards improving healthcare delivery and patient care. One of the major applications of A.I. in healthcare is pattern mapping of medical images which mainly involves image processing. It seeks to extract significant things from the image through clustering. Therefore, choosing a suitable clustering method for a specific data set is a crucial step in the process of image segmentation. Numerous modifications to the clustering algorithm, such as the fuzzy k-mean algorithm, have been presented up to this point. All of the data mining techniques currently in use are capable of handling the uncertainty brought on by numerical deviations or unpredictable phenomena in the natural world. But, present data mining challenges in the real world may include indeterminacy components. In this article, we propose a new clustering approach for the segmentation of dental X-ray images that is based on neutrosophic logic. The authentic dental patients’ dataset from KIDS(Kalinga Institute of Dental Science) Hospital is used to validate the proposed approach. The experimental findings demonstrated the proposed method’s superiority in terms of clustering quality over the existing ones.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141714053","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 using Negation Scope Detection and Negation Handling 利用否定范围检测和否定处理改进情感分析

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160119

Kartika Makkar, Pardeep Kumar, Monika Poriye, Shalini Aggarwal

{"title":"Improving Sentiment Analysis using Negation Scope Detection and Negation Handling","authors":"Kartika Makkar, Pardeep Kumar, Monika Poriye, Shalini Aggarwal","doi":"10.12785/ijcds/160119","DOIUrl":"https://doi.org/10.12785/ijcds/160119","url":null,"abstract":": Negation is one of the challenges in sentiment analysis. Negation has an immense influence on how accurately text data can be classified. To find accurate sentiments of users this research identifies that the impact of negations in a sentence needs to be properly handled. Traditional approaches are unable to properly determine the scope of negations. In the proposed approach Machine learning (ML) is used to find the scope of negations. Moreover, the removal of negative stopwords during pre-processing leads to the flipped polarity of sentences. To resolve these challenges this research proposes a method for negation scope detection and handling in sentiment analysis. First, negation cue (negative words) and non cue words are determined, these negation cue and non cue words in addition to lexical and syntactic features determine the negation scope (part of sentence a ff ected by cue) using the Machine Learning (ML) approach i.e. Conditional Random Fields (CRF). Subsequently, in negation handling the sentiment intensity of each token in a sentence is established, and a ff ected tokens are processed to determine the final polarity. It is revealed that sentiment analysis with negation handling and calculated polarity gives 3.61%, 2.64%, 2.7%, and 1.42% increase in accuracy for Logistic regression, Support Vector Machine, Decision Tree (DT), and Naive Bayes (NB) consecutively for Amazon food products dataset. Consecutively, 9.4%, 3%, and 2% improvement for Logistic Regression (LR)","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"11 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141713849","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 Unique Approach for Ex-filtering Sensitive Data Through an Audio Covert Channel in Android Application 在 Android 应用程序中通过音频隐蔽信道过滤敏感数据的独特方法

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160112

Abhinav K. Shah, D. M. M. Rathod, Bharat Buddhadev, Jeet Rami

{"title":"A Unique Approach for Ex-filtering Sensitive Data Through an Audio Covert Channel in Android Application","authors":"Abhinav K. Shah, D. M. M. Rathod, Bharat Buddhadev, Jeet Rami","doi":"10.12785/ijcds/160112","DOIUrl":"https://doi.org/10.12785/ijcds/160112","url":null,"abstract":": The rapid global evolution of technology has led to a significant shift in the field of cybersecurity. The advent of technologies such as Artificial Intelligence, Machine Learning, Blockchain, and Data Science has significantly increased the complexity and demand for research in the field of cybersecurity. Numerous authors and academics consistently engage in endeavors within this field to enhance existing implementations. Due to this process of evolution, several entities have experienced growth, leading to the transformation of numerous small-scale organizations into large-scale counterparts. The advent of the internet has resulted in significant growth for companies, albeit with divergent outcomes. Researchers are utilizing the internet to advance technological developments, while concurrently, certain individuals persistently seek means to undermine this technology. In conjunction with the proliferation of the internet, mobile phones have acquired a heightened level of sophistication, particularly with the advent of the Android operating system, leading to a shift in the manner in which individuals utilize these devices. As the Android operating system continues to advance, individuals are increasingly embracing and incorporating its features into their daily lives. Nevertheless, there are those that persistently endeavor to discover methods for illicitly extracting information from Android smartphones via a covert communication channel. This work introduces a novel methodology for transmitting confidential data, such as contact information and text messages, utilizing a concealed audio channel. The researchers conducted trials utilizing the most recent Android smartphones and arrived at the conclusion that it is feasible to transmit critical information to Android using this methodology. The experiment was conducted by the authors using various detection techniques, and it was determined that none of these tools are capable of detecting a hidden channel of this nature.","PeriodicalId":37180,"journal":{"name":"International Journal of Computing and Digital Systems","volume":"99 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141695873","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 Time-Delay Convolutional Neural Networks(TDCNN) Model for Feature Extraction for Side-Channel Attacks 设计用于侧信道攻击特征提取的时延卷积神经网络（TDCNN）模型

International Journal of Computing and Digital Systems Pub Date : 2024-07-01 DOI: 10.12785/ijcds/160127

Amjed Abbas Ahmed, Mohammad Kamrul Hasan, Shahrul Azman Mohd Noah, Azana Hafizah Aman

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