Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.0140312
Mohammed Saeed, Asmala Ahmad, O. Mohd
org
org
{"title":"Optimal Land-cover Classification Feature Selection in Arid Areas based on Sentinel-2 Imagery and Spectral Indices","authors":"Mohammed Saeed, Asmala Ahmad, O. Mohd","doi":"10.14569/ijacsa.2023.0140312","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140312","url":null,"abstract":"org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"186 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80627839","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140439
Satyabrata Nath, P. Das, P. Debnath
Achieving the desired level of satisfaction for a decision-maker in any decision-making scenario is considered a challenging endeavor because minor modifications in the process might lead to incorrect findings and inaccurate decisions. In order to maximize the decision-maker’s satisfaction, this paper proposes a Single-valued Neutrosophic Geometric Programming model based on pentagonal fuzzy numbers. The decision-maker is typically assumed to be certain of the parameters, but in reality, this is not the case, hence the parameters are presented as neutrosophic fuzzy values. The decision-maker, with this strategy, is able to achieve varying levels of satisfaction and dissatisfaction for each constraint and even complete satisfaction for certain constraints. Here the decision maker aims to achieve the maximum level of satisfaction while maintaining the level of hesitation and minimizing dissatisfaction in order to retain an optimum solution. Furthermore, transforming the objective function into a constraint adds one more layer to the Ndimensional multi-parametrizes and . The advantages of this multi-parametrized proposed method over the existing ones are proven using numerical examples. Keywords—Decision making; pentagonal neutrosophic numbers; single-valued neutrosophic geometric programming; multi-parametric programming
{"title":"A Single-valued Pentagonal Neutrosophic Geometric Programming Approach to Optimize Decision Maker’s Satisfaction Level","authors":"Satyabrata Nath, P. Das, P. Debnath","doi":"10.14569/ijacsa.2023.0140439","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140439","url":null,"abstract":"Achieving the desired level of satisfaction for a decision-maker in any decision-making scenario is considered a challenging endeavor because minor modifications in the process might lead to incorrect findings and inaccurate decisions. In order to maximize the decision-maker’s satisfaction, this paper proposes a Single-valued Neutrosophic Geometric Programming model based on pentagonal fuzzy numbers. The decision-maker is typically assumed to be certain of the parameters, but in reality, this is not the case, hence the parameters are presented as neutrosophic fuzzy values. The decision-maker, with this strategy, is able to achieve varying levels of satisfaction and dissatisfaction for each constraint and even complete satisfaction for certain constraints. Here the decision maker aims to achieve the maximum level of satisfaction while maintaining the level of hesitation and minimizing dissatisfaction in order to retain an optimum solution. Furthermore, transforming the objective function into a constraint adds one more layer to the Ndimensional multi-parametrizes and . The advantages of this multi-parametrized proposed method over the existing ones are proven using numerical examples. Keywords—Decision making; pentagonal neutrosophic numbers; single-valued neutrosophic geometric programming; multi-parametric programming","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"75 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80664918","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140592
Jayanti Dansana, M. R. Kabat, P. Pattnaik
— With the rise in technology, a huge volume of data is being processed using data mining, especially in the healthcare sector. Usually, medical data consist of a lot of personal data, and third parties utilize it for the data mining process. Perturbation in health care data highly aids in preventing intruders from utilizing the patient’s privacy. One of the challenges in data perturbation is managing data utility and privacy protection. Medical data mining has certain special properties compared with other data mining fields. Hence, in this work, the machine learning (ML) based perturbation approach is introduced to provide more privacy to healthcare data. Here, clustering and IGDP-3DR processes are applied to improve healthcare privacy preservation. Initially, the dataset is pre-processed using data normalization. Then, the dimensionality is reduced by SVD with PCA (singular value decomposition with Principal component analysis). Then, the clustering process is performed by IFCM (Improved Fuzzy C means). The high-dimensional data are divided into several segments by IFCM, and every partition is set as a cluster. Then, improved Geometric Data Perturbation (IGDP) is used to perturb the clustered data. IGDP is a combination of GDP with 3D rotation (3DR). Finally, the perturbed data are classified using a machine learning (ML) classifier - kernel Support Vector Machine- Horse Herd Optimization (KSVM-HHO) to classify the perturbed data and ensure better accuracy. The overall evaluation of the proposed KSVM-HHO is carried out in the Python platform. The performance of the IGDP-KSVM-HHO is compared over the two benchmark datasets, Wisconsin prognostic breast cancer (WBC) and Pima Indians Diabetes (PID) dataset. For the WBC dataset, the proposed method obtains an overall accuracy of 98.08% perturbed data, and for the PID dataset, the proposed method obtains an overall accuracy of 98.04%.
-随着技术的发展，正在使用数据挖掘处理大量数据，特别是在医疗保健领域。通常，医疗数据由大量个人数据组成，第三方利用这些数据进行数据挖掘。在医疗保健数据的扰动高度有助于防止入侵者利用病人的隐私。数据扰动的挑战之一是管理数据效用和隐私保护。与其他数据挖掘领域相比，医疗数据挖掘具有一定的特殊性。因此，在这项工作中，引入了基于机器学习(ML)的扰动方法来为医疗保健数据提供更多隐私。在这里，应用聚类和IGDP-3DR流程来改进医疗保健隐私保护。首先，使用数据规范化对数据集进行预处理。然后，利用主成分分析的奇异值分解(singular value decomposition with Principal component analysis)进行SVD降维。然后，通过IFCM(改进模糊C均值)进行聚类处理。IFCM将高维数据分成若干段，并将每个分区设置为一个聚类。然后，采用改进的几何数据摄动(IGDP)对聚类数据进行摄动。IGDP是GDP与3D旋转(3DR)的组合。最后，使用机器学习(ML)分类器-核支持向量机-马群优化(KSVM-HHO)对扰动数据进行分类，以确保更好的精度。提出的KSVM-HHO的总体评估是在Python平台上进行的。IGDP-KSVM-HHO的性能在两个基准数据集上进行了比较，威斯康星州预后乳腺癌(WBC)和皮马印第安人糖尿病(PID)数据集。对于WBC数据集，所提方法得到的扰动数据总体准确率为98.08%，对于PID数据集，所提方法得到的扰动数据总体准确率为98.04%。
{"title":"Improved 3D Rotation-based Geometric Data Perturbation Based on Medical Data Preservation in Big Data","authors":"Jayanti Dansana, M. R. Kabat, P. Pattnaik","doi":"10.14569/ijacsa.2023.0140592","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140592","url":null,"abstract":"— With the rise in technology, a huge volume of data is being processed using data mining, especially in the healthcare sector. Usually, medical data consist of a lot of personal data, and third parties utilize it for the data mining process. Perturbation in health care data highly aids in preventing intruders from utilizing the patient’s privacy. One of the challenges in data perturbation is managing data utility and privacy protection. Medical data mining has certain special properties compared with other data mining fields. Hence, in this work, the machine learning (ML) based perturbation approach is introduced to provide more privacy to healthcare data. Here, clustering and IGDP-3DR processes are applied to improve healthcare privacy preservation. Initially, the dataset is pre-processed using data normalization. Then, the dimensionality is reduced by SVD with PCA (singular value decomposition with Principal component analysis). Then, the clustering process is performed by IFCM (Improved Fuzzy C means). The high-dimensional data are divided into several segments by IFCM, and every partition is set as a cluster. Then, improved Geometric Data Perturbation (IGDP) is used to perturb the clustered data. IGDP is a combination of GDP with 3D rotation (3DR). Finally, the perturbed data are classified using a machine learning (ML) classifier - kernel Support Vector Machine- Horse Herd Optimization (KSVM-HHO) to classify the perturbed data and ensure better accuracy. The overall evaluation of the proposed KSVM-HHO is carried out in the Python platform. The performance of the IGDP-KSVM-HHO is compared over the two benchmark datasets, Wisconsin prognostic breast cancer (WBC) and Pima Indians Diabetes (PID) dataset. For the WBC dataset, the proposed method obtains an overall accuracy of 98.08% perturbed data, and for the PID dataset, the proposed method obtains an overall accuracy of 98.04%.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"70 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80672950","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.01406118
Tien Vu-Van, Huy Tran, Thanh-Van Le, Hoang-Anh Pham, Nguyen Huynh-Tuong
This paper proposes a testcase recommendation system (TRS) to assist beginner-level learners in introductory programming courses with completing assignments on a learning management system (LMS). These learners often struggle to generate complex testcases and handle numerous code errors, leading to disengaging their attention from the study. The proposed TRS addresses this problem by applying the recommendation system using singular value decomposition (SVD) and the zone of proximal development (ZPD) to provide a small and appropriate set of testcases based on the learner’s ability. We implement this TRS to the university-level Fundamental Programming courses for evaluation. The data analysis has demonstrated that TRS significantly increases student interactions with the system. Keywords—Testcases recommendation system (TRS); learning management system (LMS); zone of proximal development (ZPD); singular value decomposition (SVD)
{"title":"An Adaptive Testcase Recommendation System to Engage Students in Learning: A Practice Study in Fundamental Programming Courses","authors":"Tien Vu-Van, Huy Tran, Thanh-Van Le, Hoang-Anh Pham, Nguyen Huynh-Tuong","doi":"10.14569/ijacsa.2023.01406118","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01406118","url":null,"abstract":"This paper proposes a testcase recommendation system (TRS) to assist beginner-level learners in introductory programming courses with completing assignments on a learning management system (LMS). These learners often struggle to generate complex testcases and handle numerous code errors, leading to disengaging their attention from the study. The proposed TRS addresses this problem by applying the recommendation system using singular value decomposition (SVD) and the zone of proximal development (ZPD) to provide a small and appropriate set of testcases based on the learner’s ability. We implement this TRS to the university-level Fundamental Programming courses for evaluation. The data analysis has demonstrated that TRS significantly increases student interactions with the system. Keywords—Testcases recommendation system (TRS); learning management system (LMS); zone of proximal development (ZPD); singular value decomposition (SVD)","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81032414","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140755
B. Omarov, Nazgul Abdinurova, Zhamshidbek Abdulkhamidov
—In the rapidly evolving landscape of cyber threats, the efficacy of traditional rule-based network intrusion detection systems has become increasingly questionable. This paper introduces a novel framework for identifying network intrusions, leveraging the power of advanced machine learning techniques. The proposed methodology steps away from the rigidity of conventional systems, bringing a flexible, adaptive, and intuitive approach to the forefront of network security. This study employs a diverse blend of machine learning models including but not limited to, Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. This research explores an innovative feature extraction and selection technique that enables the model to focus on high-priority potential threats, minimizing noise and improving detection accuracy. The framework's performance has been rigorously evaluated through a series of experiments on benchmark datasets. The results consistently surpass traditional methods, demonstrating a remarkable increase in detection rates and a significant reduction in false positives. Further, the machine learning-based model demonstrated its ability to adapt to new threat landscapes, indicating its suitability in real-world scenarios. By marrying the agility of machine learning with the concreteness of network intrusion detection, this research opens up new avenues for dynamic and resilient cybersecurity. The framework offers an innovative solution that can identify, learn, and adapt to evolving network intrusions, shaping the future of cyber defense strategies.
{"title":"A Novel Framework for Detecting Network Intrusions Based on Machine Learning Methods","authors":"B. Omarov, Nazgul Abdinurova, Zhamshidbek Abdulkhamidov","doi":"10.14569/ijacsa.2023.0140755","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140755","url":null,"abstract":"—In the rapidly evolving landscape of cyber threats, the efficacy of traditional rule-based network intrusion detection systems has become increasingly questionable. This paper introduces a novel framework for identifying network intrusions, leveraging the power of advanced machine learning techniques. The proposed methodology steps away from the rigidity of conventional systems, bringing a flexible, adaptive, and intuitive approach to the forefront of network security. This study employs a diverse blend of machine learning models including but not limited to, Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. This research explores an innovative feature extraction and selection technique that enables the model to focus on high-priority potential threats, minimizing noise and improving detection accuracy. The framework's performance has been rigorously evaluated through a series of experiments on benchmark datasets. The results consistently surpass traditional methods, demonstrating a remarkable increase in detection rates and a significant reduction in false positives. Further, the machine learning-based model demonstrated its ability to adapt to new threat landscapes, indicating its suitability in real-world scenarios. By marrying the agility of machine learning with the concreteness of network intrusion detection, this research opens up new avenues for dynamic and resilient cybersecurity. The framework offers an innovative solution that can identify, learn, and adapt to evolving network intrusions, shaping the future of cyber defense strategies.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"4 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84894582","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140425
Sundas Khan, Samra Urooj Khan, Onyeka J. Nwobodo, K. Cyran
— To meet the increasing security requirement of authorized users of flight simulators, personal identification is becoming more and more important. Iris recognition stands out as one of the most accurate biometric methods in use today. Iris recognition is done through different edge detection methods. Therefore, it is important to have an understanding of the different edge detection methods that are in use these days. Specifically, the biomedical research shows that irises are as different as fingerprints or the other patterns of the recognition. Furthermore, because the iris is a visible organism, its exterior look can be examined remotely using a machine vision system. The main part of this paper delves into concerns concerning the selection of the best results giving method of the recognition. In this paper, three edge detection methods, namely Canny, Sobel and Prewitt, are applied to the image of eye (iris) and their comparative analysis is discussed. These methods are applied using the Software MATLAB. The datasets used for this purpose are CASIA and MMU. The results indicate that the performance of Canny edge detection method is best as compared to Sobel and Prewitt. Image quality is a key requirement in image-based object recognition. This paper provides the quality evaluation of the images using different metrics like PSNR, SNR, MSE and SSIM. However, SSIM is considered best image quality metric as compared to PSNR, SNR and MSE.
{"title":"Iris Recognition Through Edge Detection Methods: Application in Flight Simulator User Identification","authors":"Sundas Khan, Samra Urooj Khan, Onyeka J. Nwobodo, K. Cyran","doi":"10.14569/ijacsa.2023.0140425","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140425","url":null,"abstract":"— To meet the increasing security requirement of authorized users of flight simulators, personal identification is becoming more and more important. Iris recognition stands out as one of the most accurate biometric methods in use today. Iris recognition is done through different edge detection methods. Therefore, it is important to have an understanding of the different edge detection methods that are in use these days. Specifically, the biomedical research shows that irises are as different as fingerprints or the other patterns of the recognition. Furthermore, because the iris is a visible organism, its exterior look can be examined remotely using a machine vision system. The main part of this paper delves into concerns concerning the selection of the best results giving method of the recognition. In this paper, three edge detection methods, namely Canny, Sobel and Prewitt, are applied to the image of eye (iris) and their comparative analysis is discussed. These methods are applied using the Software MATLAB. The datasets used for this purpose are CASIA and MMU. The results indicate that the performance of Canny edge detection method is best as compared to Sobel and Prewitt. Image quality is a key requirement in image-based object recognition. This paper provides the quality evaluation of the images using different metrics like PSNR, SNR, MSE and SSIM. However, SSIM is considered best image quality metric as compared to PSNR, SNR and MSE.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"54 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83613365","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140533
N. Cahyani, D. Pratama, N. H. A. Rahman
org
org
{"title":"Proactive Acquisition using Bot on Discord","authors":"N. Cahyani, D. Pratama, N. H. A. Rahman","doi":"10.14569/ijacsa.2023.0140533","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140533","url":null,"abstract":"org","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"43 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81828055","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.01407114
Xiaoyan Huo, Xue-ming Wang
—Fog computing can be considered a decentralized computing approach that essentially extends the capabilities offered by cloud computing to the periphery of the network. In addition, due to its proximity to the user, fog computing proves to be highly efficient in minimizing the volume of data that needs to be transmitted, reducing overall network traffic, and shortening the distance that data must travel. But this technology, like other new technologies, has challenges, and scheduling and optimal allocation of resources is one of the most important of these challenges. Accordingly, this research aims to propose an optimal solution for efficient scheduling within the fog computing environment through the application of the advanced cat swarm optimization algorithm. In this solution, the two main behaviors of cats are implemented in the form of seek and tracking states. Accordingly, processing nodes are periodically examined and categorized based on the number of available resources; servers with highly available resources are prioritized in the scheduling process for efficient scheduling. Subsequently, the congested servers, which may be experiencing various issues, are migrated to alternative servers with ample resources using the virtual machine live migration technique. Ultimately, the effectiveness of the proposed solution is assessed using the iFogSim simulator, demonstrating notable reductions in execution time and energy consumption. So, the proposed solution has led to a 20% reduction in execution time while also improving energy efficiency by more than 15% on average. This optimization represents a trade-off between improving performance and reducing resource consumption.
{"title":"Optimal Scheduling using Advanced Cat Swarm Optimization Algorithm to Improve Performance in Fog Computing","authors":"Xiaoyan Huo, Xue-ming Wang","doi":"10.14569/ijacsa.2023.01407114","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01407114","url":null,"abstract":"—Fog computing can be considered a decentralized computing approach that essentially extends the capabilities offered by cloud computing to the periphery of the network. In addition, due to its proximity to the user, fog computing proves to be highly efficient in minimizing the volume of data that needs to be transmitted, reducing overall network traffic, and shortening the distance that data must travel. But this technology, like other new technologies, has challenges, and scheduling and optimal allocation of resources is one of the most important of these challenges. Accordingly, this research aims to propose an optimal solution for efficient scheduling within the fog computing environment through the application of the advanced cat swarm optimization algorithm. In this solution, the two main behaviors of cats are implemented in the form of seek and tracking states. Accordingly, processing nodes are periodically examined and categorized based on the number of available resources; servers with highly available resources are prioritized in the scheduling process for efficient scheduling. Subsequently, the congested servers, which may be experiencing various issues, are migrated to alternative servers with ample resources using the virtual machine live migration technique. Ultimately, the effectiveness of the proposed solution is assessed using the iFogSim simulator, demonstrating notable reductions in execution time and energy consumption. So, the proposed solution has led to a 20% reduction in execution time while also improving energy efficiency by more than 15% on average. This optimization represents a trade-off between improving performance and reducing resource consumption.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"17 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85316991","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140205
E. Lattanzi, Paolo Capellacci, Valerio Freschi
—Vibration monitoring of civil infrastructures is a fundamental task to assess their structural health, which can be nowadays carried on at reduced costs thanks to new sensing devices and embedded hardware platforms. In this work, we present a system for monitoring vibrations in buildings based on a novel, cheap, Hall-effect vibration sensor that is interfaced with a commercially available embedded hardware platform, in order to support communication toward cloud based services by means of IoT communication protocols. Two deep learning neural networks have been implemented and tested to demonstrate the capability of performing nontrivial prediction tasks directly on board of the embedded platform, an important feature to conceive dynamical policies for deciding whether to perform a recognition task on the final (resource constrained) device, or delegate it to the cloud according to specific energy, latency, accuracy requirements. Experimental evaluation on two use cases, namely the detection of a seismic event and the count of steps made by people transiting in a public building highlight the potential of the adopted solution; for instance, recognition of walking-induced vibrations can be achieved with an accuracy of 96% in real-time within time windows of 500ms. Overall, the results of the empirical investigation show the flexibility of the proposed solution as a promising alternative for the design of vibration monitoring systems in built environments.
{"title":"A Machine Learning Enabled Hall-Effect IoT-System for Monitoring Building Vibrations","authors":"E. Lattanzi, Paolo Capellacci, Valerio Freschi","doi":"10.14569/ijacsa.2023.0140205","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140205","url":null,"abstract":"—Vibration monitoring of civil infrastructures is a fundamental task to assess their structural health, which can be nowadays carried on at reduced costs thanks to new sensing devices and embedded hardware platforms. In this work, we present a system for monitoring vibrations in buildings based on a novel, cheap, Hall-effect vibration sensor that is interfaced with a commercially available embedded hardware platform, in order to support communication toward cloud based services by means of IoT communication protocols. Two deep learning neural networks have been implemented and tested to demonstrate the capability of performing nontrivial prediction tasks directly on board of the embedded platform, an important feature to conceive dynamical policies for deciding whether to perform a recognition task on the final (resource constrained) device, or delegate it to the cloud according to specific energy, latency, accuracy requirements. Experimental evaluation on two use cases, namely the detection of a seismic event and the count of steps made by people transiting in a public building highlight the potential of the adopted solution; for instance, recognition of walking-induced vibrations can be achieved with an accuracy of 96% in real-time within time windows of 500ms. Overall, the results of the empirical investigation show the flexibility of the proposed solution as a promising alternative for the design of vibration monitoring systems in built environments.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"12 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85378288","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140725
Maher Abuhamdeh, O. Qtaish, Hasan Kanaker, Ahmad Alshanty, Nidal Yousef, A. Alali
—This study investigates the current state of mobile shopping in Jordan and the integration of big data and AI technologies in this context. A mixed-methods approach, combining qualitative and quantitative data collection techniques, utilized to gather comprehensive insights. The survey questionnaire distributed to 105 individuals engaged in mobile shopping in Jordan. The findings highlight the popularity of mobile shopping and the preference for mobile apps as the primary platform. Personalized product recommendations emerged as a crucial factor in enhancing the mobile shopping experience. Privacy concerns regarding data sharing were present among respondents. Trust in AI-powered virtual assistants varied, indicating the potential for leveraging AI technologies. Respondents recognized the potential of big data and AI in improving the mobile shopping experience. The study concludes that businesses can enhance mobile shopping by utilizing AI-powered virtual assistants and prioritizing data security. The findings contribute to understanding mobile shopping dynamics and provide guidance for businesses and policymakers in optimizing mobile shopping experiences and driving economic growth in Jordan's digital economy. Future research and implementation efforts are encouraged to harness the potential of big data and AI in the mobile shopping landscape.
{"title":"Leveraging Big Data and AI in Mobile Shopping: A Study in the Context of Jordan","authors":"Maher Abuhamdeh, O. Qtaish, Hasan Kanaker, Ahmad Alshanty, Nidal Yousef, A. Alali","doi":"10.14569/ijacsa.2023.0140725","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140725","url":null,"abstract":"—This study investigates the current state of mobile shopping in Jordan and the integration of big data and AI technologies in this context. A mixed-methods approach, combining qualitative and quantitative data collection techniques, utilized to gather comprehensive insights. The survey questionnaire distributed to 105 individuals engaged in mobile shopping in Jordan. The findings highlight the popularity of mobile shopping and the preference for mobile apps as the primary platform. Personalized product recommendations emerged as a crucial factor in enhancing the mobile shopping experience. Privacy concerns regarding data sharing were present among respondents. Trust in AI-powered virtual assistants varied, indicating the potential for leveraging AI technologies. Respondents recognized the potential of big data and AI in improving the mobile shopping experience. The study concludes that businesses can enhance mobile shopping by utilizing AI-powered virtual assistants and prioritizing data security. The findings contribute to understanding mobile shopping dynamics and provide guidance for businesses and policymakers in optimizing mobile shopping experiences and driving economic growth in Jordan's digital economy. Future research and implementation efforts are encouraged to harness the potential of big data and AI in the mobile shopping landscape.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":"47 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83134844","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}
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