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Examining the impact of attitude, availability, price, and quality of service on broadband Internet customer satisfaction

Examining the impact of attitude, availability, price, and q...

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international conference on Computing, Engineering, and design (ICCED)

作者： Ikrar Adinata Arin Dina Fitria Murad Harco Leslie Hendric Spits Warnars Meyliana Information System Department School of Information Systems Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Indonesia Jakarta

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.

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A Trust Aware Secure Ant Colony Optimization Based Routing Algorithm for Internet of things

A Trust Aware Secure Ant Colony Optimization Based Routing A...

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international conference on computer and Communication Engineering, ICCCE

作者： Afsah Sharmin F. Anwar S. M. A. Motakabber Aisha-Hassan A. Hashim Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

With the 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.

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User Experience Evaluation of the D'Fastint Application using the User Experience Questionnaire

User Experience Evaluation of The D'Fastint Application usin...

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HCI and UX conference in Indonesia (CHIuXiD), international

作者： Daffane Aurelia Maharani Mirza Ramadhani Mutiara Puspitasari Wulandari Rania Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

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 with the 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.

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Features Reduction with PCA Technique for Malware Detection Using Machine Learning

Features Reduction with PCA Technique for Malware Detection ...

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international conference on Computing, Engineering, and design (ICCED)

作者： Teddy Mantoro Agung Ginanjar Media Anugerah Ayu Umar Aditiawarman Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia

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 with the average accuracy was 0.991688.

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Exploring Artificial Neural Networks in Virtual Reality: A Unity and Leap Motion-Based Visualization of ONNX Models

Exploring Artificial Neural Networks in Virtual Reality: A U...

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Virtual Reality (ICVR), IEEE international conference on

作者： Manzood Naqvi P. P. Sujith Sohan Naidu Kevin thomas U. Ananthanagu Computer Science Engineering PES University Bangalore India

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.

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Sentiment Analysis With N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language

Sentiment Analysis With N-Gram Preprocessing for Online-Shop...

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international conference on Computing, Engineering, and design (ICCED)

作者： Jelita Asian Oktavianus Tegar N.K Putra Media Anugerah Ayu Teddy Mantoro Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia

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 $\mathbf{97.26\%}$.

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Browser Preference Prediction for computer Users using Machine Learning

Browser Preference Prediction for Computer Users using Machi...

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international conference on Computing, Engineering, and design (ICCED)

作者： Agus Pratondo Nanang Ismail Astri Novianty Dept. of Multimedia Engineering School of Applied Sciences Telkom University Bandung Indonesia Dept. of Electrical Engineering Faculty of Engineering UIN Sunan Gunung Djati Bandung Indonesia Dept. of Computer Engineering School of Electrical Engineering Telkom University Bandung Indonesia

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)

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Optically Transparent Ultra-High Frequency (UHF) RFID Tag Antennas

Optically Transparent Ultra-High Frequency (UHF) RFID Tag An...

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international Multidisciplinary conference on computer and Energy Science (SpliTech)

作者： Aakash Bansal William G. Whittow Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University UK

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) with the 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.

关键词： Microwave antennas Optical design Optical device fabrication Indium tin oxide UHF antennas Conductivity Conductors

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design Space Exploration of CNN Accelerators based on GSA Algorithm

Design Space Exploration of CNN Accelerators based on GSA Al...

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IEEE international conference on Signal and Image Processing (ICSIP)

作者： Zheren Xie Kui Dai Zhilin Wu Jinyue Wang Xin Lu Shuanglong Liu Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control Key Laboratory of Physics and Devices in Post-Moore Era College of Hunan Province Hunan Normal University Changsha China

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, with the 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.

关键词： Image processing Software algorithms Simulated annealing Genetics Rendering (computer graphics) Software Space exploration

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Monkeypox Image Recognition: Leveraging ResNet-MobileNet Fusion for Enhanced Performance

Monkeypox Image Recognition: Leveraging ResNet-MobileNet Fus...

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Information Science, computer Technology and Transportation (ISCTT), international conference on

作者： Yi-Zhi Liu Tsung-Ming Lo Wan-Ying Zhang Hong-Tao Zhang Zi-Jing Lin Xin-Yan Liu Yu Chen Nanfang College-Guangzhou School of Electrical and Computer Engineering Guangzhou China Dongguan University of Technology Guangdong-Taiwan College of Industrial Science & Technology Dongguan China Department of Artificial Intelligence and Data Science Guangzhou Xinhua University Dongguan China

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.

关键词： Image recognition Accuracy Corporate acquisitions Telemedicine Transportation computer architecture Feature extraction Skin Vaccines Viruses (medical)

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