Journal of Informatics and Web Engineering最新文献_第4页

An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution 深入分析基于云的可搜索对称加密解决方案的效率和漏洞

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.19

Prithvi Chaudhari, Ji-Jian Chin, Soeheila Moesfa Mohamad

{"title":"An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution","authors":"Prithvi Chaudhari, Ji-Jian Chin, Soeheila Moesfa Mohamad","doi":"10.33093/jiwe.2024.3.1.19","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.19","url":null,"abstract":"Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption Standard (AES) in Cypher Block Chaining (CBC) mode. A primary goal of the research is to conduct a thorough evaluation of the security and performance of the system. In order to assess search performance, a variety of database settings were extensively tested, and the system's security was tested by simulating intricate threat scenarios such as count attacks and leakage abuse. The efficiency of operation and cryptographic robustness of the SSE system are critically examined by these reviews.","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":"139837629","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

GoHoliday: Development of An Improvised Mobile Application for Boutique Hotels and Resorts GoHoliday：为精品酒店和度假村开发改进型移动应用程序

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.13

Iftiaj Alom, Ismail Ahmed Al-Qasem Al-Hadi, Neesha Jothi, Sook Fern Yeo

引用次数: 0

Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets 利用 LDA 模型实现术语标准化，使用英语和孟加拉语推文检测服务中断事件

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.1

Noraysha Yusuf, Maizatul Akmar Ismail, Tasnim M. A. Zayet, Kasturi Dewi Varathan, Rafidah MD Noor

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

Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification 混合乌鸦搜索和 RBFNN：医疗数据分类的新方法

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.17

Marai Ali, Faisal Khan, Muhammad Nouman Atta, Abdullah Khan, Asfandyar Khan

{"title":"Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification","authors":"Marai Ali, Faisal Khan, Muhammad Nouman Atta, Abdullah Khan, Asfandyar Khan","doi":"10.33093/jiwe.2024.3.1.17","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.17","url":null,"abstract":"The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"58 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777746","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

Optimizing Medical IoT Disaster Management with Data Compression 利用数据压缩优化医疗物联网灾难管理

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.4

Nunudzai Mrewa, Athirah Mohd Ramly, Angela Amphawan, T. Neo

{"title":"Optimizing Medical IoT Disaster Management with Data Compression","authors":"Nunudzai Mrewa, Athirah Mohd Ramly, Angela Amphawan, T. Neo","doi":"10.33093/jiwe.2024.3.1.4","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.4","url":null,"abstract":"In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce \"Beyond Orion,\" a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"59 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777812","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":"268 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139836542","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

Optimizing Medical IoT Disaster Management with Data Compression 利用数据压缩优化医疗物联网灾难管理

Journal of Informatics and Web Engineering Pub Date : 2024-02-14 DOI: 10.33093/jiwe.2024.3.1.4

Nunudzai Mrewa, Athirah Mohd Ramly, Angela Amphawan, T. Neo

{"title":"Optimizing Medical IoT Disaster Management with Data Compression","authors":"Nunudzai Mrewa, Athirah Mohd Ramly, Angela Amphawan, T. Neo","doi":"10.33093/jiwe.2024.3.1.4","DOIUrl":"https://doi.org/10.33093/jiwe.2024.3.1.4","url":null,"abstract":"In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce \"Beyond Orion,\" a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"180 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837416","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 Marker Free Visual-based Home Rehabilitation Framework 基于视觉的无标记家庭康复框架

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.9

Roy Kwang Yang Chang, Kok Swee Sim, Siong Hoe Lau

{"title":"A Marker Free Visual-based Home Rehabilitation Framework","authors":"Roy Kwang Yang Chang, Kok Swee Sim, Siong Hoe Lau","doi":"10.33093/jiwe.2023.2.2.9","DOIUrl":"https://doi.org/10.33093/jiwe.2023.2.2.9","url":null,"abstract":"Adhesive capsulitis or more commonly known as frozen shoulder, is a familiar occurrence for adults aged above 40 caused by the inflammation of the connective tissues surrounding the shoulder joint. There are different severity of adhesive capsulitis but patients afflicted with frozen shoulder typically will experience stiffness, severe pain, and reduced range of motion (ROM) for the shoulder. No matter the course of treatment being non-steroidal anti-inflammatory drugs (NSAIDs) or steroid injections, which can help reduce the inflammation and reduce pain, in order to restore ROM for the afflicted shoulder joint, rehabilitation exercises need to be performed. Even without the current climate where medical workers are severely overworked, physical therapists are in short order especially for developing countries like Malaysia. A remedy for this situation would be to deploy home rehabilitation instead. This would be a way for patients to get proper rehabilitation exercises in between visits to the clinic to meet the physical therapist. This can also reduce the frequency of in-clinic visits while still allowing the patient to progress in the rehabilitation of their afflicted shoulder joint. Though home rehabilitation seems like a clear solution, it does come with its own set of challenges. How open will the patients themselves be to utilizing a home rehabilitation system? In light of that, this paper proposes a home rehabilitation framework focusing on a marker free visual-based implementation using the Microsoft Kinect camera. The framework will measure the impact of variables such as capability, motivation and opportunity on the adoption rate of the home rehabilitation. Cronbach’s Alpha tests were conducted to ascertain the reliability of the variables used in the framework.","PeriodicalId":484462,"journal":{"name":"Journal of Informatics and Web Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134989651","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

Analysing Gamma Frequency Components in EEG Signals: A Comprehensive Extraction Approach 分析脑电信号中的伽马频率成分:一种综合提取方法

Journal of Informatics and Web Engineering Pub Date : 2023-09-13 DOI: 10.33093/jiwe.2023.2.2.11

Tanvir Hasib, Vijayakumar Vengadasalam

引用次数: 0