Pub Date : 2024-04-03DOI: 10.37934/araset.42.2.112121
Sanhan Muhammad, Salih Khasraw, Nor Haniza, Sarmin, N. I. Alimon, Nabilah Najmuddin, Ismail
Sombor index is a newly developed degree-based topological index which involves the degree of the vertex in a simple connected graph. The Sombor index is known as the square root of the sum of the squared degrees of two adjacent vertices in a graph. Meanwhile, the noncommuting graph associated to a group is a graph where its vertices are the non-central elements of the group and two vertices are adjacent if and only if they do not commute. In this study, a new notion called the Sombor polynomial is introduced. Then, the general formula of the Sombor index and the Sombor polynomial of the noncommuting graph associated to some finite groups are determined by using their definitions and some preliminaries. The groups involved in this research are the dihedral groups, the quasidihedral groups, and the generalized quaternion groups. The results found can help the chemists and biologists to predict the chemical and physical properties of the molecules without involving any laboratory work.
{"title":"Sombor Index and Sombor Polynomial of the Noncommuting Graph Associated to Some Finite Groups","authors":"Sanhan Muhammad, Salih Khasraw, Nor Haniza, Sarmin, N. I. Alimon, Nabilah Najmuddin, Ismail","doi":"10.37934/araset.42.2.112121","DOIUrl":"https://doi.org/10.37934/araset.42.2.112121","url":null,"abstract":"Sombor index is a newly developed degree-based topological index which involves the degree of the vertex in a simple connected graph. The Sombor index is known as the square root of the sum of the squared degrees of two adjacent vertices in a graph. Meanwhile, the noncommuting graph associated to a group is a graph where its vertices are the non-central elements of the group and two vertices are adjacent if and only if they do not commute. In this study, a new notion called the Sombor polynomial is introduced. Then, the general formula of the Sombor index and the Sombor polynomial of the noncommuting graph associated to some finite groups are determined by using their definitions and some preliminaries. The groups involved in this research are the dihedral groups, the quasidihedral groups, and the generalized quaternion groups. The results found can help the chemists and biologists to predict the chemical and physical properties of the molecules without involving any laboratory work.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"118 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748624","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}
Pub Date : 2024-04-03DOI: 10.37934/araset.42.2.234249
Davin Arkan Admoko, Bambang Darmawan, A. Ana, Vina Dwiyant i
Emerging technology presents itself as a futuristic solution since its early development stage in the industry. Concurrently, the industry has proposed framework implementations as well as efforts to integrate emerging technologies in the supply chain, particularly in logistics. This study aimed to unveil the applicability of either supply chain or logistic functions in the present industry. This study used Publish and Perish to mine academic document data based on the keyword ‘Logistic’ and ‘Emerging technology’ in the past five years. Furthermore, the retrieved data were compiled and processed as a bibliographical map to visualize relevant clusters as the bottom-line discussion for this study. Five clusters had different items/keywords associated with them, excluding clusters three and four which were discussed in tandem. Cluster one revealed that AI and blockchain could support manufacturers for a circular economy business model through reverse logistics operations in the pandemic. Cluster two was a bigger picture discussing enhancement efficiency and risk reduction in the supply chain using AI, blockchain, and IoT. Clusters three and four had overlapping keywords specifying the discussion of blockchain implementation for the Agri-industry in China. Finally, cluster five reaffirmed the conceptualism of emerging technology integration for transportation from other clusters. Despite a unanimous agreement on the potential use of emerging technologies, challenges were also found, such as complex implementation, uncertain investment, and technology immaturity accompany. Thus, as the implication of this research, it reveals the capabilities and issues of the implementation of emerging technologies within multiple aspects of logistics and supply chain.
新兴技术在行业的早期发展阶段就将自己视为未来的解决方案。与此同时，业界也提出了框架实施方案，并努力将新兴技术融入供应链，尤其是物流领域。本研究旨在揭示供应链或物流功能在当前行业中的适用性。本研究使用 "出版与消亡"（Publish and Perish）方法，以 "物流 "和 "新兴技术 "为关键词，挖掘过去五年的学术文献数据。此外，还对检索到的数据进行了编译和处理，将其制作成书目地图，以可视化的方式呈现出相关集群，作为本研究的底线讨论。五个群组有不同的相关项目/关键词，但不包括同时讨论的群组三和群组四。第一组显示，人工智能和区块链可通过大流行病中的逆向物流操作，为制造商的循环经济商业模式提供支持。第二组从更大的角度讨论了利用人工智能、区块链和物联网提高供应链效率和降低风险的问题。第三组和第四组有重叠的关键词，具体讨论了区块链在中国农业产业中的应用。最后，第五专题组重申了其他专题组关于新兴技术与交通融合的概念。尽管大家一致认同新兴技术的潜在用途，但也发现了一些挑战，如实施复杂、投资不确定、技术不成熟等。因此，本研究的意义在于揭示了新兴技术在物流和供应链多个方面的实施能力和问题。
{"title":"A Cluster-Based Bibliometric Analysis of the Emerging Technological Landscape in Logistics using Vosviewer","authors":"Davin Arkan Admoko, Bambang Darmawan, A. Ana, Vina Dwiyant i","doi":"10.37934/araset.42.2.234249","DOIUrl":"https://doi.org/10.37934/araset.42.2.234249","url":null,"abstract":"Emerging technology presents itself as a futuristic solution since its early development stage in the industry. Concurrently, the industry has proposed framework implementations as well as efforts to integrate emerging technologies in the supply chain, particularly in logistics. This study aimed to unveil the applicability of either supply chain or logistic functions in the present industry. This study used Publish and Perish to mine academic document data based on the keyword ‘Logistic’ and ‘Emerging technology’ in the past five years. Furthermore, the retrieved data were compiled and processed as a bibliographical map to visualize relevant clusters as the bottom-line discussion for this study. Five clusters had different items/keywords associated with them, excluding clusters three and four which were discussed in tandem. Cluster one revealed that AI and blockchain could support manufacturers for a circular economy business model through reverse logistics operations in the pandemic. Cluster two was a bigger picture discussing enhancement efficiency and risk reduction in the supply chain using AI, blockchain, and IoT. Clusters three and four had overlapping keywords specifying the discussion of blockchain implementation for the Agri-industry in China. Finally, cluster five reaffirmed the conceptualism of emerging technology integration for transportation from other clusters. Despite a unanimous agreement on the potential use of emerging technologies, challenges were also found, such as complex implementation, uncertain investment, and technology immaturity accompany. Thus, as the implication of this research, it reveals the capabilities and issues of the implementation of emerging technologies within multiple aspects of logistics and supply chain.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"758 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749316","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}
Pub Date : 2024-04-03DOI: 10.37934/araset.42.2.197208
M. Khairul, Aiman Daud, Ili Najaa, Aimi Mohd Nordin, Tuan Noor, Hasanah Tuan Ismail, Effendy Adam, N. Zulkarnain, Muhammad Rusydi, Muhammad Razif, Tariq Rehman
Overconsumption of food can result in environmental pollution, making it a particularly concerning issue in modern civilization. In Malaysia, food waste is generated at a rate of 16,688 tonnes per day. Despite its biodegradation properties and strong composting potential, about 80% of food waste is still disposed of in landfills. Air, soil and water pollution are risks often associated with food waste disposal. Since two-thirds of total waste is avoidable, preventing the rise of household food waste should be a top priority, among which is through composting. This project aims to build a smart composter that can chop food waste and monitor the mixing of food waste to become mature compost. A DC motor controlled by the Arduino Mega microcontroller was used to spin the chopper blades to shred the food into smaller sizes. Temperature, moisture and pH sensors were used to measure the essential parameters to ensure that the food waste mix can become mature compost. The Liquid-crystal display was used to display the parameter value in real time to facilitate the monitoring process. A fan will be activated if the temperature reaches 60 oC to reduce the heat, followed by a solenoid valve to increase the moisture level by supplying water to the compost when the compost is dry. The sensors were also compared with commonly used measuring devices to assess the effectiveness of the sensors used. From the results, all the sensors used were reliable as displayed by a high percentage of accuracy with an average error percentage per sensor of 3.45% for temperature, 2.62% for moisture and 3.52% for pH. Several improvements can be made in the future to achieve smaller amounts of chopped food waste in lesser time, which can be done by reducing the distance between the chopper blade and the container, besides adding more blades.
{"title":"Development of Smart Chopper Composting Monitoring System","authors":"M. Khairul, Aiman Daud, Ili Najaa, Aimi Mohd Nordin, Tuan Noor, Hasanah Tuan Ismail, Effendy Adam, N. Zulkarnain, Muhammad Rusydi, Muhammad Razif, Tariq Rehman","doi":"10.37934/araset.42.2.197208","DOIUrl":"https://doi.org/10.37934/araset.42.2.197208","url":null,"abstract":"Overconsumption of food can result in environmental pollution, making it a particularly concerning issue in modern civilization. In Malaysia, food waste is generated at a rate of 16,688 tonnes per day. Despite its biodegradation properties and strong composting potential, about 80% of food waste is still disposed of in landfills. Air, soil and water pollution are risks often associated with food waste disposal. Since two-thirds of total waste is avoidable, preventing the rise of household food waste should be a top priority, among which is through composting. This project aims to build a smart composter that can chop food waste and monitor the mixing of food waste to become mature compost. A DC motor controlled by the Arduino Mega microcontroller was used to spin the chopper blades to shred the food into smaller sizes. Temperature, moisture and pH sensors were used to measure the essential parameters to ensure that the food waste mix can become mature compost. The Liquid-crystal display was used to display the parameter value in real time to facilitate the monitoring process. A fan will be activated if the temperature reaches 60 oC to reduce the heat, followed by a solenoid valve to increase the moisture level by supplying water to the compost when the compost is dry. The sensors were also compared with commonly used measuring devices to assess the effectiveness of the sensors used. From the results, all the sensors used were reliable as displayed by a high percentage of accuracy with an average error percentage per sensor of 3.45% for temperature, 2.62% for moisture and 3.52% for pH. Several improvements can be made in the future to achieve smaller amounts of chopped food waste in lesser time, which can be done by reducing the distance between the chopper blade and the container, besides adding more blades.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"518 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140749948","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}
Pub Date : 2024-04-03DOI: 10.37934/araset.42.2.188196
H. Sadiah, Lia Dahlia Iryani, Tjut Awaliyah Zuraiyah, Yuli Wahyuni, C. Zaddana
Users sometimes write queries that are inaccurate or typos in the product search contained in the Koro Pedang Educational Tourism e-commerce, so the system is not find product search results because the query entered in the system is incorrect. This can frustrate users because they cannot find the product they are looking for, so the users leave the website. According to these problems, it is necessary to suggest a query on the product search function. This is expected to assist users in finding the product they are looking for if there is an error in typing the query. This research purposes were to implement the Levenshtein Distance Algorithm for product search query suggestions on Koro Pedang Educational Tourism e-commerce. The stages of this research, namely the development of the search module, implementation of the Levenshtein Distance Algorithm and testing. The implementation of the Levenshtein Distance Algorithm in the search function for Koro Pedang Educational Tourism e-commerce products, a Suggestion Query is generated for Query typos in the search function with an accuracy value of 90%, Precision 95% and Recall 90.9%. This shows that the performance of the algorithm that has been applied to the search function for query suggestion is very good. The application of the Levenshtein Distance Algorithm gives a positive value to the usability of searching for e-commerce products for Koro Pedang Educational Tourism.
{"title":"Implementation of Levenshtein Distance Algorithm for Product Search Query Suggestions on Koro Pedang Edutourism E-Commerce","authors":"H. Sadiah, Lia Dahlia Iryani, Tjut Awaliyah Zuraiyah, Yuli Wahyuni, C. Zaddana","doi":"10.37934/araset.42.2.188196","DOIUrl":"https://doi.org/10.37934/araset.42.2.188196","url":null,"abstract":"Users sometimes write queries that are inaccurate or typos in the product search contained in the Koro Pedang Educational Tourism e-commerce, so the system is not find product search results because the query entered in the system is incorrect. This can frustrate users because they cannot find the product they are looking for, so the users leave the website. According to these problems, it is necessary to suggest a query on the product search function. This is expected to assist users in finding the product they are looking for if there is an error in typing the query. This research purposes were to implement the Levenshtein Distance Algorithm for product search query suggestions on Koro Pedang Educational Tourism e-commerce. The stages of this research, namely the development of the search module, implementation of the Levenshtein Distance Algorithm and testing. The implementation of the Levenshtein Distance Algorithm in the search function for Koro Pedang Educational Tourism e-commerce products, a Suggestion Query is generated for Query typos in the search function with an accuracy value of 90%, Precision 95% and Recall 90.9%. This shows that the performance of the algorithm that has been applied to the search function for query suggestion is very good. The application of the Levenshtein Distance Algorithm gives a positive value to the usability of searching for e-commerce products for Koro Pedang Educational Tourism.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"248 S1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140746398","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}
Pub Date : 2024-02-13DOI: 10.37934/araset.39.2.153165
Tiew Yuan You, Mohd Ibrahim Shapiai, Fong Jia Xian, Nur Amirah Abd Hamid, RA Ghani, Noor Akhmad Setiawan
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that will cause the memory loss of patient and will progressively lead to loss of bodily function that will eventually lead to death. Therefore, diagnosing AD accurately is critical to provide the patients with suitable treatment to delay the progression of AD as well to facilitate the treatment interventions. Recent studies are more dependent on the Deep Learning Semantic Segmentation method to perform the Alzheimer's Disease diagnosis. However, semantic segmentation will segment every single pixel in the images which will affect the precision of the small targets like hippocampal region in MRI images, even though the overall loss is low enough. Therefore, a Deep Learning Instance Segmentation is introduced into the Alzheimer’s disease diagnosis field without using any pre-processing method. In this research, the Mask R-CNN will be used to localize the hippocampal region to do the segmentation, and then classified it as AD or NC. The dataset UTM_ADNI_RAW will be used in this study. The proposed method applied on UTM_ADNI_RAW shows the high accuracy of 92.67%. These results show that the proposed method to segment the hippocampal region without requiring pre-processing techniques has a good accuracy in classifying AD and NC subjects. In conclusion, the proposed Mask R-CNN generated a good result on segmenting the hippocampal region without requiring any pre-processing techniques.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，会导致患者记忆力减退，并逐渐丧失身体功能，最终导致死亡。因此，准确诊断 AD 对于为患者提供合适的治疗方法以延缓 AD 的进展以及促进治疗干预至关重要。最近的研究更多地依赖深度学习语义分割方法来进行阿尔茨海默病诊断。然而，语义分割会对图像中的每个像素进行分割，这将影响核磁共振图像中海马区等小目标的精确度，即使整体损失足够低。因此，在不使用任何预处理方法的情况下，将深度学习实例分割引入阿尔茨海默病诊断领域。本研究将使用 Mask R-CNN 对海马区进行定位分割，然后将其分为 AD 或 NC。本研究将使用数据集 UTM_ADNI_RAW。所提出的方法在UTM_ADNI_RAW上的应用显示了高达92.67%的准确率。这些结果表明，所提出的无需预处理技术的海马区分割方法在对 AD 和 NC 受试者进行分类时具有良好的准确性。总之，所提出的 Mask R-CNN 无需任何预处理技术就能产生良好的海马区分割结果。
{"title":"Bypassing Pre-processing Method in Alzheimer’s Disease Diagnosing using Deep Learning Instance Segmentation","authors":"Tiew Yuan You, Mohd Ibrahim Shapiai, Fong Jia Xian, Nur Amirah Abd Hamid, RA Ghani, Noor Akhmad Setiawan","doi":"10.37934/araset.39.2.153165","DOIUrl":"https://doi.org/10.37934/araset.39.2.153165","url":null,"abstract":"Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that will cause the memory loss of patient and will progressively lead to loss of bodily function that will eventually lead to death. Therefore, diagnosing AD accurately is critical to provide the patients with suitable treatment to delay the progression of AD as well to facilitate the treatment interventions. Recent studies are more dependent on the Deep Learning Semantic Segmentation method to perform the Alzheimer's Disease diagnosis. However, semantic segmentation will segment every single pixel in the images which will affect the precision of the small targets like hippocampal region in MRI images, even though the overall loss is low enough. Therefore, a Deep Learning Instance Segmentation is introduced into the Alzheimer’s disease diagnosis field without using any pre-processing method. In this research, the Mask R-CNN will be used to localize the hippocampal region to do the segmentation, and then classified it as AD or NC. The dataset UTM_ADNI_RAW will be used in this study. The proposed method applied on UTM_ADNI_RAW shows the high accuracy of 92.67%. These results show that the proposed method to segment the hippocampal region without requiring pre-processing techniques has a good accuracy in classifying AD and NC subjects. In conclusion, the proposed Mask R-CNN generated a good result on segmenting the hippocampal region without requiring any pre-processing techniques.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"97 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839483","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}
Biomedical data or information must be transmitted securely via the internet for smart healthcare. The Electrocardiogram (ECG) signal is amongst the most essential clinical signals which must be delivered to hospital facilities. Prime focus of this research is on the encryption of ECG for secure transmission. Chaos theory is used for the development of deterministic nonlinear systems, that can be used to create random numbers for the Chaotic Logistic Map (CLM) based encryption. This study describes a cryptographic algorithm for encrypting ECG signals that uses a mix of the CLM and fingerprint data. The common factor between the patient section and monitoring section is the operation on sample data points of ECG. The choice of proper encryption and decryption theme can save more amount of time and is invulnerable both to noise-based attacks and hacking instances. The proposed framework is implemented on Dropbox based cloud storage and access is possible from any given locations. Simulation tests are used to assess the system performance in terms of Structural Similarity Index Matrix (SSIM), Histogram, Spectral Distortion (SD), Correlation and Log-Likelihood Ratio (LLR). The incorporation of complex layers of CLM encryption increases security.
{"title":"Cloud Security System for ECG Transmission and Monitoring Based on Chaotic Logistic Maps","authors":"Rajasree Gopalakrishnan, Retnaswami Mathusoothana Satheesh Kumar","doi":"10.37934/araset.39.2.118","DOIUrl":"https://doi.org/10.37934/araset.39.2.118","url":null,"abstract":"Biomedical data or information must be transmitted securely via the internet for smart healthcare. The Electrocardiogram (ECG) signal is amongst the most essential clinical signals which must be delivered to hospital facilities. Prime focus of this research is on the encryption of ECG for secure transmission. Chaos theory is used for the development of deterministic nonlinear systems, that can be used to create random numbers for the Chaotic Logistic Map (CLM) based encryption. This study describes a cryptographic algorithm for encrypting ECG signals that uses a mix of the CLM and fingerprint data. The common factor between the patient section and monitoring section is the operation on sample data points of ECG. The choice of proper encryption and decryption theme can save more amount of time and is invulnerable both to noise-based attacks and hacking instances. The proposed framework is implemented on Dropbox based cloud storage and access is possible from any given locations. Simulation tests are used to assess the system performance in terms of Structural Similarity Index Matrix (SSIM), Histogram, Spectral Distortion (SD), Correlation and Log-Likelihood Ratio (LLR). The incorporation of complex layers of CLM encryption increases security.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840742","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}
Pub Date : 2024-02-13DOI: 10.37934/araset.39.2.141152
Azlan Mohmad, Mohd Hatta Mohammed Ariff, Mohd Ibrahim Shapiai, Mohd Solehin Shamsudin, Norulhakima Zakaria, Mohammad Adnan Sujan, Rasli Ghani, Ifran Bahiuddin
Establishing an effective HI model is challenging because it involves balancing cost, risk, and performance. The currently developed Reduced Features Model (RFM) for the transformer Health Index (HI) prediction may lead to late prediction. The RFM utilised non-routine input features to achieve a high-accuracy model where data availability is the primary concern. Hence, the common goal of Transformer Asset Management (TAM) in achieving acceptable availability and reliability of the transformer may not be achieved. In this paper, the primary objective is to investigate the performance of the HI model by considering routine test features as a baseline for developing the Early Detection Model (EDM). The development of EDM is significant, as the model shall provide a sustainable solution to the utility and plant owners in establishing their TAM strategies. Hence, this paper's case studies include performance investigation using routine, non-routine, and derived features from the routine test. Support Vector Machine (SVM) was used for the prediction modelling, and the model's performance was validated based on a 5-fold cross-validation technique to avoid biases. As a result, it was found that the average accuracy performance of 88.4% was obtained by considering only routine test features during the model validation process. However, complementing the routine test with other features, which were non-routine and derived features, increased the average performance accuracy model to 95.3%. Hence, further development of EDM is feasible and crucial for sustainable TAM solutions.
建立一个有效的健康指数模型具有挑战性，因为它涉及到成本、风险和性能之间的平衡。目前开发的用于变压器健康指数（HI）预测的简化特征模型（RFM）可能会导致预测过迟。RFM 利用非例行输入特征来实现高精度模型，而数据可用性是首要考虑因素。因此，可能无法实现变压器资产管理 (TAM) 的共同目标，即实现可接受的变压器可用性和可靠性。本文的主要目的是研究 HI 模型的性能，将常规测试特征作为开发早期检测模型（EDM）的基线。EDM 的开发意义重大，因为该模型将为电力公司和电厂业主制定 TAM 战略提供可持续的解决方案。因此，本文的案例研究包括使用例行测试、非例行测试和从例行测试中得出的特征进行性能调查。预测建模使用了支持向量机（SVM），并根据 5 倍交叉验证技术对模型性能进行了验证，以避免偏差。结果发现，在模型验证过程中，只考虑常规测试特征的平均准确率为 88.4%。然而，在常规测试的基础上补充其他特征（非例行特征和衍生特征），模型的平均准确率提高到 95.3%。因此，进一步开发 EDM 是可行的，对于可持续的 TAM 解决方案至关重要。
{"title":"Investigation of the Influence of Non-Routine and Derived Features in the Development of Early Detection Model for Transformer Health Index Classification","authors":"Azlan Mohmad, Mohd Hatta Mohammed Ariff, Mohd Ibrahim Shapiai, Mohd Solehin Shamsudin, Norulhakima Zakaria, Mohammad Adnan Sujan, Rasli Ghani, Ifran Bahiuddin","doi":"10.37934/araset.39.2.141152","DOIUrl":"https://doi.org/10.37934/araset.39.2.141152","url":null,"abstract":"Establishing an effective HI model is challenging because it involves balancing cost, risk, and performance. The currently developed Reduced Features Model (RFM) for the transformer Health Index (HI) prediction may lead to late prediction. The RFM utilised non-routine input features to achieve a high-accuracy model where data availability is the primary concern. Hence, the common goal of Transformer Asset Management (TAM) in achieving acceptable availability and reliability of the transformer may not be achieved. In this paper, the primary objective is to investigate the performance of the HI model by considering routine test features as a baseline for developing the Early Detection Model (EDM). The development of EDM is significant, as the model shall provide a sustainable solution to the utility and plant owners in establishing their TAM strategies. Hence, this paper's case studies include performance investigation using routine, non-routine, and derived features from the routine test. Support Vector Machine (SVM) was used for the prediction modelling, and the model's performance was validated based on a 5-fold cross-validation technique to avoid biases. As a result, it was found that the average accuracy performance of 88.4% was obtained by considering only routine test features during the model validation process. However, complementing the routine test with other features, which were non-routine and derived features, increased the average performance accuracy model to 95.3%. Hence, further development of EDM is feasible and crucial for sustainable TAM solutions.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"60 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139841204","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}
Pub Date : 2024-02-13DOI: 10.37934/araset.39.2.231241
Farah Afiqah Affendi, Syahrul Nizam Junaini
This study aimed to evaluate the effectiveness of an interactive mobile Augmented Reality (AR) game to increase the knowledge about COVID-19 prevention primary school students. We tested the application for usability and effectiveness through pre- and post-tests, questionnaires, and interviews. 12 participants from four states in Malaysia took part in the study. Their average age is 9.5 years old. Results indicated a significant improvement in student performance from the pre-test to the post-test, with a mean score has increased from 3.67 to 8.25. The average System Usability Scale (SUS) score was 75%. These findings show the effectiveness of our mobile AR application as a tool to increase the knowledge about COVID-19 prevention among primary school. The findings of this study contribute to the body of research on the use of AR in COVID-19 prevention education among primary school students. This study provides both theoretical and practical implications for educators, researcher and policymakers seeking to use mobile AR to support the prevention education of any future pandemic or infectious disease.
本研究旨在评估互动式移动增强现实（AR）游戏在提高小学生对 COVID-19 预防知识的了解方面的效果。我们通过前后测试、问卷调查和访谈对应用程序的可用性和有效性进行了测试。来自马来西亚四个州的 12 名参与者参与了这项研究。他们的平均年龄为 9.5 岁。结果表明，从测试前到测试后，学生的成绩有了明显的提高，平均分从 3.67 分提高到了 8.25 分。系统可用性量表（SUS）的平均得分率为 75%。这些研究结果表明，我们的移动 AR 应用作为一种工具，在提高小学学生对 COVID-19 预防知识的了解方面非常有效。本研究的结果为在小学生中使用 AR 开展 COVID-19 预防教育的研究做出了贡献。这项研究为教育工作者、研究人员和政策制定者提供了理论和实践方面的启示，使他们能够利用移动 AR 支持未来任何流行病或传染病的预防教育。
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Pub Date : 2024-02-13DOI: 10.37934/araset.39.2.191203
Al-Ogaidi Ali Hameed Khalaf, Raihani Mohamed, Abdul Rafiez Abdul Raziff
In today's interconnected world, networks play a crucial role. Consequently, network security has become increasingly vital. To ensure network security, various methods are employed, including digital signatures, firewalls, and intrusion detection. Among these methods, intrusion detection systems have gained significant popularity due to their ability to identify new attacks. However, the accuracy of these systems still requires further improvement. One of the challenges is the potential bias introduced by using imbalance datasets that contains more information on normal activities than on attacks. To address it, SMOTE method was proposed and additionally, the study explores the use of Long Short-Term Memory (LSTM) for classification purposes. The experiments are conducted using two datasets: UNSW NB-15 and CICIDS 2017. The results obtained demonstrate that the proposed methods achieve an accuracy of 96% with the UNSW NB-15 dataset and 99% with the CICIDS 2017 dataset. These findings indicate an improvement of 3% and 1% respectively compared to existing literature.
{"title":"Detection Model for Ambiguous Intrusion using SMOTE and LSTM for Network Security","authors":"Al-Ogaidi Ali Hameed Khalaf, Raihani Mohamed, Abdul Rafiez Abdul Raziff","doi":"10.37934/araset.39.2.191203","DOIUrl":"https://doi.org/10.37934/araset.39.2.191203","url":null,"abstract":"In today's interconnected world, networks play a crucial role. Consequently, network security has become increasingly vital. To ensure network security, various methods are employed, including digital signatures, firewalls, and intrusion detection. Among these methods, intrusion detection systems have gained significant popularity due to their ability to identify new attacks. However, the accuracy of these systems still requires further improvement. One of the challenges is the potential bias introduced by using imbalance datasets that contains more information on normal activities than on attacks. To address it, SMOTE method was proposed and additionally, the study explores the use of Long Short-Term Memory (LSTM) for classification purposes. The experiments are conducted using two datasets: UNSW NB-15 and CICIDS 2017. The results obtained demonstrate that the proposed methods achieve an accuracy of 96% with the UNSW NB-15 dataset and 99% with the CICIDS 2017 dataset. These findings indicate an improvement of 3% and 1% respectively compared to existing literature.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780406","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}
Pub Date : 2024-02-13DOI: 10.37934/araset.39.2.231241
Farah Afiqah Affendi, Syahrul Nizam Junaini
This study aimed to evaluate the effectiveness of an interactive mobile Augmented Reality (AR) game to increase the knowledge about COVID-19 prevention primary school students. We tested the application for usability and effectiveness through pre- and post-tests, questionnaires, and interviews. 12 participants from four states in Malaysia took part in the study. Their average age is 9.5 years old. Results indicated a significant improvement in student performance from the pre-test to the post-test, with a mean score has increased from 3.67 to 8.25. The average System Usability Scale (SUS) score was 75%. These findings show the effectiveness of our mobile AR application as a tool to increase the knowledge about COVID-19 prevention among primary school. The findings of this study contribute to the body of research on the use of AR in COVID-19 prevention education among primary school students. This study provides both theoretical and practical implications for educators, researcher and policymakers seeking to use mobile AR to support the prevention education of any future pandemic or infectious disease.
本研究旨在评估互动式移动增强现实（AR）游戏在提高小学生对 COVID-19 预防知识的了解方面的效果。我们通过前后测试、问卷调查和访谈对应用程序的可用性和有效性进行了测试。来自马来西亚四个州的 12 名参与者参与了这项研究。他们的平均年龄为 9.5 岁。结果表明，从测试前到测试后，学生的成绩有了明显的提高，平均分从 3.67 分提高到了 8.25 分。系统可用性量表（SUS）的平均得分率为 75%。这些研究结果表明，我们的移动 AR 应用作为一种工具，在提高小学学生对 COVID-19 预防知识的了解方面非常有效。本研究的结果为在小学生中使用 AR 开展 COVID-19 预防教育的研究做出了贡献。这项研究为教育工作者、研究人员和政策制定者提供了理论和实践方面的启示，使他们能够利用移动 AR 支持未来任何流行病或传染病的预防教育。
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