In the process of pile foundation design and construction, pile foundation will produce different degrees of damage in order to protect the pile foundation from damage during the construction process. In this paper, three failure methods of pile foundation are analyzed by static simulation, namely the total deformation of the pile foundation, the maximum principal stress and the bending deformation of the pile body caused by excessive equivalent force. For the pile foundation, when the pressure value is between 2Mpa-3Mpa, the main stress, total deformation, and equivalent force of the pile foundation grow slowly, but when the pressure value exceeds 3Mpa, the deformation effect of the pile foundation increases significantly, and the distribution of the pile foundation is reasonably arranged in the later construction process to ensure that the pressure value of the upper part of the pile foundation is maintained at 2Mpa-3Mpa, so as to greatly reduce the damage of the pile foundation, of course, you can also use concrete materials with higher strength grades to reduce the deformation effect of the pile foundation and protect the pile foundation from being damaged.
{"title":"Destruction and Protection Based on ANSYS Pile Foundations","authors":"Xingsheng Jin, Xuanpeng Cao, Xingtao Jin, Dong Zhang","doi":"10.32996/jcsts.2024.6.1.2","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.2","url":null,"abstract":"In the process of pile foundation design and construction, pile foundation will produce different degrees of damage in order to protect the pile foundation from damage during the construction process. In this paper, three failure methods of pile foundation are analyzed by static simulation, namely the total deformation of the pile foundation, the maximum principal stress and the bending deformation of the pile body caused by excessive equivalent force. For the pile foundation, when the pressure value is between 2Mpa-3Mpa, the main stress, total deformation, and equivalent force of the pile foundation grow slowly, but when the pressure value exceeds 3Mpa, the deformation effect of the pile foundation increases significantly, and the distribution of the pile foundation is reasonably arranged in the later construction process to ensure that the pressure value of the upper part of the pile foundation is maintained at 2Mpa-3Mpa, so as to greatly reduce the damage of the pile foundation, of course, you can also use concrete materials with higher strength grades to reduce the deformation effect of the pile foundation and protect the pile foundation from being damaged.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139390416","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 : 2024-01-01DOI: 10.32996/jcsts.2024.6.1.1
Md. Zahid, Md. Zahid Hassan
Cognitive Wireless Mesh Network (CWMN) is an opportunistic network in which radio channels can be assigned according to their availability to establish connections among nodes. After establishing a radio connection among nodes, it is necessary to find an optimal route from the source node to the destination node in the network. If there remain more channels among nodes, the minimum weighted channel should be taken into account to establish expected routes. The graph theoretic approach fails to model the multi-channel cognitive radio networks due to abrupt failure in finding new successful routes as it can’t figure multi-channel networks. In this paper, a multi-edged graph model is being proposed to overcome the problems of cognitive radio networks, such as flooding problems, channel accessing problems etc. A new channel accessing algorithm has been introduced, and optimal routes have been selected using a heuristic algorithm named RBFS. Simulation results are compared with DJKSTRA based routing protocols.
{"title":"Advanced Recursive Best-First Search (RBFS) based Routing Protocol for Multi-hop and Multi-Channel Cognitive Wireless Mesh Networks","authors":"Md. Zahid, Md. Zahid Hassan","doi":"10.32996/jcsts.2024.6.1.1","DOIUrl":"https://doi.org/10.32996/jcsts.2024.6.1.1","url":null,"abstract":"Cognitive Wireless Mesh Network (CWMN) is an opportunistic network in which radio channels can be assigned according to their availability to establish connections among nodes. After establishing a radio connection among nodes, it is necessary to find an optimal route from the source node to the destination node in the network. If there remain more channels among nodes, the minimum weighted channel should be taken into account to establish expected routes. The graph theoretic approach fails to model the multi-channel cognitive radio networks due to abrupt failure in finding new successful routes as it can’t figure multi-channel networks. In this paper, a multi-edged graph model is being proposed to overcome the problems of cognitive radio networks, such as flooding problems, channel accessing problems etc. A new channel accessing algorithm has been introduced, and optimal routes have been selected using a heuristic algorithm named RBFS. Simulation results are compared with DJKSTRA based routing protocols.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128462","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 : 2023-12-28DOI: 10.32996/jcsts.2023.5.4.21
Tuti Anjarsari, Farida Ardiani
The beauty business is experiencing rapid growth along with the changing times, where almost all activities now adopt digital technology. This transformation has had a significant impact on the beauty business world, especially in salons like Elsa Eyelash Salon. Although some salons have switched to online booking, there are still some that use a manual system. To overcome this challenge, this research develops an Android-based reservation system application with Rest API. The development method applied is the waterfall method, with an emphasis on requirements analysis, design, implementation, and testing. The implementation results show an intuitive user interface, making it easier for customers to make reservations online. Functional tests were conducted using the black box testing method, which successfully identified potential bugs before the application was widely used. The hope is that this application can improve the quality of service in beauty salons and provide a better customer experience. Thus, this application is expected to be an effective solution to support the development of the beauty industry in the future.
随着时代的变迁，美容行业正在经历快速发展，现在几乎所有的活动都采用了数字技术。这种转变对美容行业，尤其是像 Elsa Eyelash Salon 这样的美容院产生了重大影响。虽然有些美容院已经改用在线预订，但仍有一些美容院使用人工系统。为了克服这一挑战，本研究利用 Rest API 开发了一个基于 Android 的预订系统应用程序。应用的开发方法是瀑布法，重点是需求分析、设计、实施和测试。实施结果显示，用户界面直观，使客户更容易进行在线预订。功能测试采用了黑盒测试法，成功地在应用程序广泛使用前找出了潜在的错误。我们希望该应用程序能够提高美容院的服务质量，为顾客提供更好的体验。因此，该应用程序有望成为支持未来美容业发展的有效解决方案。
{"title":"Application of Rest Api Technology in Android-Based Beauty Salon Service Reservation System","authors":"Tuti Anjarsari, Farida Ardiani","doi":"10.32996/jcsts.2023.5.4.21","DOIUrl":"https://doi.org/10.32996/jcsts.2023.5.4.21","url":null,"abstract":"The beauty business is experiencing rapid growth along with the changing times, where almost all activities now adopt digital technology. This transformation has had a significant impact on the beauty business world, especially in salons like Elsa Eyelash Salon. Although some salons have switched to online booking, there are still some that use a manual system. To overcome this challenge, this research develops an Android-based reservation system application with Rest API. The development method applied is the waterfall method, with an emphasis on requirements analysis, design, implementation, and testing. The implementation results show an intuitive user interface, making it easier for customers to make reservations online. Functional tests were conducted using the black box testing method, which successfully identified potential bugs before the application was widely used. The hope is that this application can improve the quality of service in beauty salons and provide a better customer experience. Thus, this application is expected to be an effective solution to support the development of the beauty industry in the future.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139151099","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 : 2023-12-22DOI: 10.32996/jcsts.2023.5.4.20
Maliha Tayaba, Eftekhar Hossain Ayon, Md Tuhin Mia, Malay Sarkar, Rejon Kumar Ray, Md. Salim Chowdhury, Md. Al-Imran, Nur Nobe, Bishnu Padh Ghosh, MD Tanvir Islam, Aisharyja Roy Puja
The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.
{"title":"Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining","authors":"Maliha Tayaba, Eftekhar Hossain Ayon, Md Tuhin Mia, Malay Sarkar, Rejon Kumar Ray, Md. Salim Chowdhury, Md. Al-Imran, Nur Nobe, Bishnu Padh Ghosh, MD Tanvir Islam, Aisharyja Roy Puja","doi":"10.32996/jcsts.2023.5.4.20","DOIUrl":"https://doi.org/10.32996/jcsts.2023.5.4.20","url":null,"abstract":"The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139163279","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}
The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has rapidly spread across the globe, leading to a significant number of illnesses and fatalities. Effective containment of the virus relies on the timely and accurate identification of infected individuals. While methods like RT-PCR assays are considered the gold standard for COVID-19 diagnosis due to their accuracy, they can be limited in their use due to cost and availability issues, particularly in resource-constrained regions. To address this challenge, our study presents a set of deep learning techniques for predicting COVID-19 detection using chest X-ray images. Chest X-ray imaging has emerged as a valuable and cost-effective diagnostic tool for managing COVID-19 because it is non-invasive and widely accessible. However, interpreting chest X-rays for COVID-19 detection can be complex, as the radiographic features of COVID-19 pneumonia can be subtle and may overlap with those of other respiratory illnesses. In this research, we evaluated the performance of various deep learning models, including VGG16, VGG19, DenseNet121, and Resnet50, to determine their ability to differentiate between cases of coronavirus pneumonia and non-COVID-19 pneumonia. Our dataset comprised 4,649 chest X-ray images, with 1,123 of them depicting COVID-19 cases and 3,526 representing pneumonia cases. We used performance metrics and confusion matrices to assess the models' performance. Our study's results showed that DenseNet121 outperformed the other models, achieving an impressive accuracy rate of 99.44%.
由 SARS-CoV-2 病毒引起的 COVID-19 大流行已在全球迅速蔓延，导致大量人员患病和死亡。病毒的有效遏制有赖于及时准确地识别受感染的个体。虽然 RT-PCR 检测等方法因其准确性而被视为 COVID-19 诊断的黄金标准，但由于成本和可用性问题，它们的使用可能会受到限制，尤其是在资源有限的地区。为了应对这一挑战，我们的研究提出了一套利用胸部 X 光图像预测 COVID-19 检测的深度学习技术。胸部 X 光成像因其非侵入性和广泛可及性，已成为管理 COVID-19 的一种有价值且具有成本效益的诊断工具。然而，由于 COVID-19 肺炎的影像学特征可能很微妙，而且可能与其他呼吸道疾病的特征重叠，因此解读胸部 X 光图像以检测 COVID-19 可能很复杂。在这项研究中，我们评估了各种深度学习模型的性能，包括 VGG16、VGG19、DenseNet121 和 Resnet50，以确定它们区分冠状病毒肺炎和非 COVID-19 肺炎病例的能力。我们的数据集包括 4,649 张胸部 X 光图像，其中 1,123 张描绘了 COVID-19 病例，3,526 张代表肺炎病例。我们采用性能指标和混淆矩阵来评估模型的性能。研究结果表明，DenseNet 121 的表现优于其他模型，准确率高达 99.44%。
{"title":"Deep Learning-Based COVID-19 Detection from Chest X-ray Images: A Comparative Study","authors":"Duc M. Cao, Md. Shahedul Amin, Md Tanvir Islam, Sabbir Ahmad, Md Sabbirul Haque, Md Abu Sayed, Md Minhazur Rahman, Tahera Koli","doi":"10.32996/jcsts.2023.5.4.13","DOIUrl":"https://doi.org/10.32996/jcsts.2023.5.4.13","url":null,"abstract":"The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has rapidly spread across the globe, leading to a significant number of illnesses and fatalities. Effective containment of the virus relies on the timely and accurate identification of infected individuals. While methods like RT-PCR assays are considered the gold standard for COVID-19 diagnosis due to their accuracy, they can be limited in their use due to cost and availability issues, particularly in resource-constrained regions. To address this challenge, our study presents a set of deep learning techniques for predicting COVID-19 detection using chest X-ray images. Chest X-ray imaging has emerged as a valuable and cost-effective diagnostic tool for managing COVID-19 because it is non-invasive and widely accessible. However, interpreting chest X-rays for COVID-19 detection can be complex, as the radiographic features of COVID-19 pneumonia can be subtle and may overlap with those of other respiratory illnesses. In this research, we evaluated the performance of various deep learning models, including VGG16, VGG19, DenseNet121, and Resnet50, to determine their ability to differentiate between cases of coronavirus pneumonia and non-COVID-19 pneumonia. Our dataset comprised 4,649 chest X-ray images, with 1,123 of them depicting COVID-19 cases and 3,526 representing pneumonia cases. We used performance metrics and confusion matrices to assess the models' performance. Our study's results showed that DenseNet121 outperformed the other models, achieving an impressive accuracy rate of 99.44%.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139221322","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 : 2023-11-24DOI: 10.32996/jcsts.2023.5.4.12
Arkan A. Ghaib, Yahya Eneid Abdulridha Alsalhi, Israa M. Hayder, Hussain A. Younis, Abdullah A. Nahi
High utility pattern mining is an analytical approach used to identify sets of items that exceed a specific threshold of utility values. Unlike traditional frequency-based analysis, this method considers user-specific constraints like the number of units and benefits. In recent years, the importance of making informed decisions based on utility patterns has grown significantly. While several utility-based frequent pattern extraction techniques have been proposed, they often face limitations in handling large datasets. To address this challenge, we propose an optimized method called improving the efficiency of Distributed Utility itemsets mining in relation to big data (IDUIM). This technique improves upon the Distributed Utility item sets Mining (DUIM) algorithm by incorporating various refinements. IDUIM effectively mines item sets of big datasets and provides useful insights as the basis for information management and nearly real-time decision-making systems. According to experimental investigation, the method is being compared to IDUIM and other state algorithms like DUIM, PHUI-Miner, and EFIM-Par. The results demonstrate the IDUIM algorithm is more efficient and performs better than different cutting-edge algorithms.
{"title":"Improving the Efficiency of Distributed Utility Item Sets Mining in Relation to Big Data","authors":"Arkan A. Ghaib, Yahya Eneid Abdulridha Alsalhi, Israa M. Hayder, Hussain A. Younis, Abdullah A. Nahi","doi":"10.32996/jcsts.2023.5.4.12","DOIUrl":"https://doi.org/10.32996/jcsts.2023.5.4.12","url":null,"abstract":"High utility pattern mining is an analytical approach used to identify sets of items that exceed a specific threshold of utility values. Unlike traditional frequency-based analysis, this method considers user-specific constraints like the number of units and benefits. In recent years, the importance of making informed decisions based on utility patterns has grown significantly. While several utility-based frequent pattern extraction techniques have been proposed, they often face limitations in handling large datasets. To address this challenge, we propose an optimized method called improving the efficiency of Distributed Utility itemsets mining in relation to big data (IDUIM). This technique improves upon the Distributed Utility item sets Mining (DUIM) algorithm by incorporating various refinements. IDUIM effectively mines item sets of big datasets and provides useful insights as the basis for information management and nearly real-time decision-making systems. According to experimental investigation, the method is being compared to IDUIM and other state algorithms like DUIM, PHUI-Miner, and EFIM-Par. The results demonstrate the IDUIM algorithm is more efficient and performs better than different cutting-edge algorithms.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139239605","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 : 2023-11-18DOI: 10.32996/jcsts.2023.5.4.11
Mohamed Mokrani, Messaoud Bensabti
In this paper, we did programming regarding the Block diagonalization technology in the 5G standalone SA network, in this program, we have created a 5G site with 16 antennas(minimum of Massive MIMO) and 4 active users equipped of 4 antennas, this system is called Multi Users Massive MIMO system, the link that was chosen is the downlink,we have calculated the maximum throughput in the 5G downlink where we have obained a value of 1673864 b/ms, this value is divided by the number of Massive MIMO layers which worth 16 to get a transport block size of 104616 b/ms (no Cyclic redundancy check CRC). The Block Error rate BLER is null (no detection of errors in reception) because we are in the case of no crc and no channel coding (uncoded transmission), the signal of each user among 4 to be transmitted consists of 4 vectors, each vector has a length of 52308 that corresponds to the number of symbols which are the outputs of Quadrature Phase Shift Keying QPSK Mapping Operation. The received signal at each user equipment UE has a form which can be represented by the multiplication of preconding matrix of this UE with the channel matrix between this UE and the 5G site plus the noise received at the antennas of this UE. the results show that the product of channel gain between UE and the 5G site(known in emission) with the precoding matrix of the other UE gives a matrix which composes of imaginary elements each of which has a real part and imaginary part which both tend to zero(the inter users interferences IUI is canceled). The results show also that when the Signal to Noise Ratio SNR increases(several transmissions) the Bit Error Rate decreases.
{"title":"Block Diagonalization in the 5G SA Network","authors":"Mohamed Mokrani, Messaoud Bensabti","doi":"10.32996/jcsts.2023.5.4.11","DOIUrl":"https://doi.org/10.32996/jcsts.2023.5.4.11","url":null,"abstract":"In this paper, we did programming regarding the Block diagonalization technology in the 5G standalone SA network, in this program, we have created a 5G site with 16 antennas(minimum of Massive MIMO) and 4 active users equipped of 4 antennas, this system is called Multi Users Massive MIMO system, the link that was chosen is the downlink,we have calculated the maximum throughput in the 5G downlink where we have obained a value of 1673864 b/ms, this value is divided by the number of Massive MIMO layers which worth 16 to get a transport block size of 104616 b/ms (no Cyclic redundancy check CRC). The Block Error rate BLER is null (no detection of errors in reception) because we are in the case of no crc and no channel coding (uncoded transmission), the signal of each user among 4 to be transmitted consists of 4 vectors, each vector has a length of 52308 that corresponds to the number of symbols which are the outputs of Quadrature Phase Shift Keying QPSK Mapping Operation. The received signal at each user equipment UE has a form which can be represented by the multiplication of preconding matrix of this UE with the channel matrix between this UE and the 5G site plus the noise received at the antennas of this UE. the results show that the product of channel gain between UE and the 5G site(known in emission) with the precoding matrix of the other UE gives a matrix which composes of imaginary elements each of which has a real part and imaginary part which both tend to zero(the inter users interferences IUI is canceled). The results show also that when the Signal to Noise Ratio SNR increases(several transmissions) the Bit Error Rate decreases.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139261805","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 : 2023-11-17DOI: 10.32996/jcsts.2023.5.4.10
Yuguo Gao
With the Internet entering the inventory stage, subscription services have become a major trend in the industry. As a technology company driven by artificial intelligence and with beauty as core, Meitu has launched VIP subscription services in several image processing applications. By December 2022, the number of VIP members grew to about 5.6 million, becoming a new engine for the company to open up more business space. At present, there is few research in academia on the VIP subscription intention of image processing APP. Combining the characteristics and usage experience of image processing APP, this thesis constructed the research model by introducing the concept of user’s mental model in the technology acceptance model. Using the structural equation modeling method, the hypothetical model and the relationship between critical variables was validated. With SPSS28.0 and AMOS24.0 software, the confirmatory factor analysis, exploratory factor analysis and structural equation modeling was conducted. The results indicate that both quality of system interface and quality of subscription service positively influence user’s mental model; mind model of users influences purchase intention through the direct path. At the same time, it also influences purchase intention through perceived ease of use and perceived usefulness, and the chain mediating path between them. Based on the findings, this thesis claims that Meitu should increase the investment in scientific research; it should not only focus on the optimization of system interface design, pay attention to the professionalism and personalized upgrade of subscription services, but also dig deeper into users’ needs and occupy their minds. At the same time, Meitu App should promote the subscription model with precise positioning and tiered payment, so as to increase users’ intention of subscription.
{"title":"Empirical Study on the Relationship between Users’ Mental Model and Purchase Intention of VIP Subscription: Evidence from Image Processing App in China","authors":"Yuguo Gao","doi":"10.32996/jcsts.2023.5.4.10","DOIUrl":"https://doi.org/10.32996/jcsts.2023.5.4.10","url":null,"abstract":"With the Internet entering the inventory stage, subscription services have become a major trend in the industry. As a technology company driven by artificial intelligence and with beauty as core, Meitu has launched VIP subscription services in several image processing applications. By December 2022, the number of VIP members grew to about 5.6 million, becoming a new engine for the company to open up more business space. At present, there is few research in academia on the VIP subscription intention of image processing APP. Combining the characteristics and usage experience of image processing APP, this thesis constructed the research model by introducing the concept of user’s mental model in the technology acceptance model. Using the structural equation modeling method, the hypothetical model and the relationship between critical variables was validated. With SPSS28.0 and AMOS24.0 software, the confirmatory factor analysis, exploratory factor analysis and structural equation modeling was conducted. The results indicate that both quality of system interface and quality of subscription service positively influence user’s mental model; mind model of users influences purchase intention through the direct path. At the same time, it also influences purchase intention through perceived ease of use and perceived usefulness, and the chain mediating path between them. Based on the findings, this thesis claims that Meitu should increase the investment in scientific research; it should not only focus on the optimization of system interface design, pay attention to the professionalism and personalized upgrade of subscription services, but also dig deeper into users’ needs and occupy their minds. At the same time, Meitu App should promote the subscription model with precise positioning and tiered payment, so as to increase users’ intention of subscription.","PeriodicalId":509154,"journal":{"name":"Journal of Computer Science and Technology Studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139264631","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 : 2023-11-16DOI: 10.32996/jcsts.2023.5.4.8
Jonayet Miah, Md Sabbirul Haque, Duc M. Cao, Md Abu Sayed
This study investigates the utilization of Graph Neural Networks (GNNs) within the realm of traffic forecasting, a critical aspect of intelligent transportation systems. The accuracy of traffic predictions is pivotal for various applications, including trip planning, road traffic control, and vehicle routing. The research comprehensively explores three notable GNN architectures—Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs)—specifically in the context of traffic prediction. Each architecture's methodology is meticulously examined, encompassing layer configurations, activation functions, and hyperparameters. With the primary aim of minimizing prediction errors, the study identifies GGNNs as the most effective choice among the three models. The outcomes, presented in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), reveal intriguing insights. While GCNs exhibit an RMSE of 9.25 and an MAE of 8.2, GraphSAGE demonstrates improved performance with an RMSE of 8.5 and an MAE of 7.6. Gated Graph Neural Networks (GGNNs) emerge as the leading model, showcasing the lowest RMSE of 9.2 and an impressive MAE of 7.0. However, the study acknowledges the dynamic nature of these results, emphasizing their dependency on factors such as the dataset, graph structure, feature engineering, and hyperparameter tuning.
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