After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. the pandemic has changed how people work, study, communicate, shop, and carry out man...
After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. the pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. this quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. the implications of this research are significant for service providers. they provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions.
Withthe rapid spread of Internet of things (IoT) systems enhancing the development of IoT applications, the issue of designing a secure routing algorithm for IoT, including reasonable trust management, has attracted ...
Withthe rapid spread of Internet of things (IoT) systems enhancing the development of IoT applications, the issue of designing a secure routing algorithm for IoT, including reasonable trust management, has attracted more and more research attention. the unique characteristics of IoT networks make them vulnerable to attacks due to the resource-constrained nature of IoT and the complex distribution of the network. Moreover, the sensors deployed in these kinds of networks are also energy-constrained. It is a challenging task to implement security in IoT networks when the design consideration comprises light weighted security mechanisms and routing protocols due to the insight that security is pricy regarding memory, computational power, and CPU cycles. the adoption of bio-inspired approaches contributes to discovering the optimal path for IoT routing by modeling the cognitive behavior of insect colonies to attain security cost-effectively. For wireless sensor network (WSN) integrated dynamic IoT networks, this paper presents a trust-aware secure Ant colony optimization (ACO)-based routing algorithm to provide security while searching for an energy-efficient optimal routing path. Implemented in MATLAB, the assessment results of the proposed routing algorithm are benchmarked to demonstrate that it has minimized the average energy consumption by nearly 50% even as the number of nodes has increased compared to the existing standard and secure routing protocols.
Interior architecture, a dynamic field meeting evolving community needs, plays a pivotal role in enhancing built environments. D9;Fastint, an interior design services provider, developed a mobile app for client int...
Interior architecture, a dynamic field meeting evolving community needs, plays a pivotal role in enhancing built environments. D'Fastint, an interior design services provider, developed a mobile app for client interactions. Before its full release, a comprehensive User Experience (UX) evaluation via the User Experience Questionnaire (UEQ) was conducted. the primary goal was to assess user satisfaction, pinpoint areas lacking in meeting expectations, and plan improvements. the evaluation revealed that the Novelty dimension scored the lowest average (1.234), while the Attractiveness dimension scored the highest (1.677). Overall, users expressed satisfaction, happiness, and motivation withthe D'Fastint app. this research provides a novel perspective on interior architecture services and mobile app utilization, addressing unique user needs and preferences. In the context of related works, our study enriches the discourse on UX evaluations and their implications for mobile apps in interior architecture services. It emphasizes the significance of preemptive evaluation and continuous improvement for exceptional user experiences.
Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today,...
Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. this study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. there were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. the n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. the results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator withthe average accuracy was 0.991688.
there is a rapidly increasing need for learning resources in the field of Artificial Intelligence aimed at newcomers. To this end, we have attempted to make an intuitive visualization of Artificial Neural Networks in ...
there is a rapidly increasing need for learning resources in the field of Artificial Intelligence aimed at newcomers. To this end, we have attempted to make an intuitive visualization of Artificial Neural Networks in Virtual Reality, while trying to make it as general and extensible as possible to allow for future work to build upon it to create a series of highly intuitive and interactive learning resources. Our implementation results in a framework built using careful design considerations that we think results in a solid building block for work that aims to interactively educate individuals in fields such as Artificial Intelligence and other such algorithms.
Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. this condition motivates the need for analyzing how the users/customers’ opinions on using such platform. Sentim...
Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. this condition motivates the need for analyzing how the users/customers’ opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users’ opinions and attitudes toward specific topics, is a good tool for the required analysis. this study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. the dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, $\mathrm{N}=2$ for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. this work applied the Naive Bayes Classifier and k-Nearest Neighbor with $K=11$ as the machine learning algorithms. the best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms $(N=2)$ with Split Validation (8:2), which is $\mathbf{97.26\%}$.
Using machine learning techniques to predict people’ preferred web browsers has become a powerful way to improve user experience and personalize web browsing. In a time where social media interactions are a part of e...
Using machine learning techniques to predict people’ preferred web browsers has become a powerful way to improve user experience and personalize web browsing. In a time where social media interactions are a part of everyday life, it is critical to recognize and accommodate personal preferences. this study presents the Random Forest algorithm as the selected approach and investigates the role of prediction in web browser preference. the system provides a tailored and efficient surfing experience by utilizing user information to anticipate browser preferences. the experimental findings highlight the model’ s predictive power with a noteworthy accuracy rate of 96.2 percent. this high accuracy suggests that the model is able to recognize and accommodate the individual preferences of users. the predictive model has a great deal of promise for practical uses with its level of accuracy, making it an invaluable resource for marketers, user experience designers, and web developers. By employing the insights gained from this study, practitioners can better tailor their services to individual users, contributing to an improved browsing experience in everyday life. (Abstract)
the paper presents an optically transparent ultra-high frequency (UHF) radio frequency identification (RFID) tag antenna design operating at 850 – 1000 MHz frequency band to cover global market. the antenna is design...
详细信息
ISBN:
(数字)9789532901351
ISBN:
(纸本)9798350390797
the paper presents an optically transparent ultra-high frequency (UHF) radio frequency identification (RFID) tag antenna design operating at 850 – 1000 MHz frequency band to cover global market. the antenna is designed on a thin PET substrate using two versions of an optically transparent conductor, Indium Tin Oxide (ITO) withthe conductivities of 4.8×105 S/m and 2×105 S/m. the antenna is simulated in CST Microwave Studio and the optically transparent antenna is compared with its perfect electric conductor (PEC) counterpart.
Convolutional Neural Networks (CNNs) exhibit exceptional performance within the image processing domain. the acceleration of convolutions for CNNs has consistently represented a focal point within machine learning har...
详细信息
ISBN:
(数字)9798350350920
ISBN:
(纸本)9798350350937
Convolutional Neural Networks (CNNs) exhibit exceptional performance within the image processing domain. the acceleration of convolutions for CNNs has consistently represented a focal point within machine learning hardware accelerators. However, withthe continuous development of CNNs, the design costs and project workloads of hardware accelerators have significantly increased. To enhance accelerator performance while reducing time-related expenses, it is necessary to determine a series of optimal design parameters during the early stages of accelerator design. To achieve this objective, the concept of design space exploration (DSE) for CNN accelerators is proposed. However, as neural networks become increasingly complex, the demands for DSE methods have also grown, rendering the existing methods unsuitable for meeting the real-time requirements of accelerators, and unable to discover the optimal design. In this paper, we introduce a DSE framework based on the Genetic Simulated Annealing (GSA) algorithm. the proposed framework autonomously generates the hardware design parameters such as parallelism degrees based on the resource constraint and CNN model. Our method is evaluated with two typical CNN accelerators. Experimental results show that our method largely improves the DSE efficiency, reducing the exploration time by up to 73.7× when compared to existing DSE methods.
In recent years, the monkeypox virus has become a global health concern. Monkeypox is highly transmissible and usually results in severe skin damage and systemic complications. therefore, accurate and rapid detection ...
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ISBN:
(数字)9798350388435
ISBN:
(纸本)9798350388442
In recent years, the monkeypox virus has become a global health concern. Monkeypox is highly transmissible and usually results in severe skin damage and systemic complications. therefore, accurate and rapid detection of monkeypox skin symptoms is crucial for timely diagnosis and treatment, as well as for informing outbreak control strategies and vaccine development efforts. In this paper, we propose an efficient method for monkeypox image recognition using deep learning techniques that integrate ResNet and MobileNet models. By integrating the residual learning architecture of ResN et and the deep detached convolutional design of MobileNet, our method achieves an ultra-high classification accuracy of 99.3%, along with a mean absolute error (MAE) of 0.0119 and a root mean square error (RMSE) of 0.1558.
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