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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Oktavia, Tanty Roekman, Faza Fakhriansyah Putera, Muhammad Ikhsan Hidayah, Muhammad Alif Rafi, Muhammad Hosoda, Takaaki Gaol, Ford Lumban BINUS Graduate Program Information System Management Bina Nusantara University Information System Management Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan BINUS Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Social media has now become a place for others to exchange information in the form of writing, photos, videos, and audio. Social media has become a place for humans to interact from all over the world. In the use of social media, attractive professional information is conceptualized as access to relevant information and opening up better career opportunities. User content about work and choosing personal information compilation strategies consistently predict information usefulness. This study discusses the effect of using a linked-in application on the success of its users in the professional world. It is hoped that it can provide insight into the use of this recruitment application, making it easier for users to find work. The research model used in this research is quantitative. The survey was conducted on connected users in Indonesia using an online questionnaire, and the data collected was analyzed using PLS-SEM. Of the 68 respondents who took the survey, it can be said that all research models have a positive relationship with the successful use of the linked-in platform. © 2022 IEEE.

关键词： Surveys

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An Overview of Video Tampering Detection Techniques: State-of-the-Art and Future Directions

An Overview of Video Tampering Detection Techniques: State-o...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Saroj Kumar Pandey Lalan Kumar Gaurav Kumar Ankit Kumar Kamred Udham Singh Teekam Singh Department of Computer Engineering & Applications GLA University Mathura UP India Department of Master of Computer Application GL Bajaj Institute of Technology and Management India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

In the realm of video processing and forensics, finding video tampering is a critical challenge. The purposeful change or manipulation of video material to conceal or falsely depict the original information is referred to as “video tampering.” A significant demand for efficient video tampering detection methods has emerged in recent years as a result of the rise in false news, cyberbullying, and other types of criminal behavior. Modern approaches for detecting video manipulation may be generally divided into two categories: passive methods and aggressive methods. Whereas active methods entail putting digital watermarks or other identifiers on the video clip to aid tampering detection, passive methods rely on evaluating the video content for inconsistencies or artifacts generated during tampering. Techniques like sensor noise analysis, splicing detection, and forgery detection based on compression artifacts are examples of passive approaches. To find any discrepancies produced by manipulation, sensor noise analysis examines the noise patterns in the video. Splicing detection entails identifying the borders between several video segments and inspecting the content of each segment for irregularities. Analyzing the compressed video stream for irregularities caused during tampering is required for detecting forgeries based on compression artifacts. Techniques like digital watermarking, digital signatures, and fingerprinting are examples of active procedures. To detect any manipulation efforts, digital watermarking entails putting a digital watermark into the video clip. To confirm the legitimacy of the video clip, a digital signature is applied to it. To do fingerprinting, special characteristics from the video footage are extracted and saved in a database. In addition to the aforementioned methods, machine learning-based systems have also demonstrated promising outcomes in the identification of video manipulation. Using these methods, any tampering attempts in fresh mov

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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

arXiv

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

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Nyctophy: Development of virtual reality and smartwatch integrated serious game for nyctophobia therapy

Software Impacts

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Software Impacts 2025年

作者： Dimas Ramdhan Elshad Ryan Ardiyanto Patrick Alexander Edyth Novian Putra David Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Game Application & Technology Program Computer Science Departement School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Nyctophy is a serious game combining virtual reality (VR) and smartwatch integration for nyctophobia (fear of darkness) therapy. The paper thoroughly explores its development framework, simulating dark environments with real-time heart rate monitoring and adaptive flashlight mechanics. Built in Unity Engine, Nyctophy supports VR (Meta Quest 2) and keyboard-mouse interfaces. Performance tests achieved 71.9 FPS (”good” quality) across four devices. Tests with 34 participants revealed longer VR completion times (8:12 min) versus keyboard-mouse (3:54), highlighting immersive impact. Nyctophy demonstrates potential as a safe, innovative tool for diagnosing and treating nyctophobia, leveraging serious games to enhance accessibility and therapeutic outcomes.

关键词： Virtual reality Nyctophobia Heart rate Smartwatch Serious game

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Enhanced Carbon Flux Forecasting via STL Decomposition and Hybrid ARIMA-ES-LSTM Model in Amazon Forest

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IEEE Access 2025年 13卷 84713-84726页

作者： Dias, Jean A. C. Guimarães, Pedro H. Do V. Pereira, Williane G. S. Tamasauskas, Leonardo De O. Gomes, Marivan S. Correa, Alan B. S. Figueiredo, Karla Costa, Gilson Costa, Gabriel Brito Costa, Fernando A. R. Seruffo, Marcos C. Da R. Operational Research Laboratory Belém66075-110 Brazil School of Technology Amazonas 69850-000 Brazil Belém66075-110 Brazil Institute of Mathematics and Statistics Department of Informatics and Computer Science Rio de Janeiro20550-013 Brazil Institute of Biological and Health Sciences II Department of Genetics Rio de Janeiro20550-013 Brazil Santarém68035-110 Brazil Federal University of Rio Grande do Norte Climate Sciences Post-Graduate Program Rio Grande do Norte Natal59078-970 Brazil Pará Santarém68035-110 Brazil Post-Graduate Program in Forest Science Technology and Innovation-PPGCTIF Pará Santarém68035-110 Brazil Pará Santarém68035-110 Brazil Pará Belém66075-110 Brazil Pará Castanhal68746-630 Brazil

This study presents a hybrid model, STL-ARIMA-ES-LSTM, developed to improve the accuracy of Gross Primary Productivity (GPP) forecasts in the Amazon region. The model integrates seasonal and trend decomposition using Loess (STL) with statistical methods (ARIMA and Exponential Smoothing-ES) and a machine learning technique (Long Short-Term Memory - LSTM). Applied to GPP data from the PE-QFR site, the hybrid model achieved significantly better error metrics, with RMSE of 1.69 gC/m2/day, MAE of 1.35 gC/m2/day, and MAPE of 0.20%, compared to the standalone LSTM (RMSE of 2.16 gC/m2/day, MAE of 1.78 gC/m2/day, and MAPE of 0.27%). Furthermore, the hybrid model showed stronger agreement with the observed data, with correlation coefficient r =0.62 and R2 =0.39 , whereas the LSTM alone yielded r =0.26 and R2 = –0.002. The STL decomposition allowed effective separation of trend, seasonality, and residual components, enabling tailored modeling of each, which contributed to the improved predictive performance. These results demonstrate the advantage of hybrid approaches in capturing the nonlinear and seasonal patterns of GPP, supporting enhanced environmental monitoring and more informed climate change mitigation strategies in the Amazon. © 2013 IEEE.

关键词： Carbon sequestration

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YOLO: Redefining Real-Time Object Detection Accuracy and Efficiency

YOLO: Redefining Real-Time Object Detection Accuracy and Eff...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Shivam Gopikishan Verma Sudhanshu Maurya Himanshu Pant Ajay Kumar Yadav Sneha Thomas Varghese Rachit Garg Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India School of Computing Graphic Era Hill University Uttarakhand India Department of Computer Application United Institute of Management Prayagraj Uttar Pradesh India School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Image classification, also known as object detection, is an advanced form of computer vision in which a network can predict objects in an image and point them out as bounding boxes. Classifying and recognizing objects in an image is just one aspect of object detection because it also involves localizing these objects and attracting bounding boxes around them. Since object detection involves image and video analysis, it has been of critical interest to researchers over the past few years. The previous object recognition method relies on manually created features and traditional and trainable algorithms. By using the YOLO (You Only Look Once) process, this research aims to perform real-time object detection. Regarding speed and performance, the YOLO algorithm is much more efficient than the existing model. Unlike existing algorithms that do not scan the entire image in single forward propagation, YOLO uses a neural network to predict binding boxes for the whole image. In comparison with existing algorithms, YOLO performs faster.

关键词： YOLO Training Accuracy Neural networks Streaming media Prediction algorithms Real-time systems Object recognition Standards Testing

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Sample-Driven Adaptive Feature Selection with Weighted Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Yang, Xiaoling Chen, Hongmei Li, Tianrui Wan, Jihong Yao, Yiyu School of Computing and Artificial Intelligence Southwest Jiaotong University China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada

The noise in data may debase the performance of the classification model. An intuitive assumption suggests that data samples with a higher local density tend to have a greater probability of being correctly classified. Moreover, there are differences in the importance of features, which should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method to mitigate the impact of noise and obtain the importance of features. Firstly, we propose a Weighted Fuzzy Rough Sets model (WFRS) by using the more reliable samples with high local density. Inspired by the large margin theory, we further obtain a specific fuzzy relation with the goal of maximizing the within-class fuzzy relation while minimizing the between-class fuzzy relation. The approximation margin is derived by applying the WFRS model within the original feature space. Feature weights are further obtained by utilizing the information derived from the approximation margin. On this basis, the weighted feature space is obtained that compresses the features with the lower weight and stretches the features with higher weight. An evaluation function of features is constructed by capturing the uncertainty of fuzzy positive and non-negative regions in the weighted feature space. A fuzzy rough feature selection algorithm (FSW) is designed by capturing the local structure of data. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of FSW, and its superiority compared to alternative methods. © 2024, The Authors. All rights reserved.

关键词： Rough set theory

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E-Learning Satisfaction: Analysis of the Support Factors

E-Learning Satisfaction: Analysis of the Support Factors

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International Conference on Information technology, computer and Electrical Engineering (ICITACEE)

作者： Boby Siswanto Doni Purnama Alamsyah Doni Morika Norfaridatul Akmaliah Othman Billiam Christofer Wijaya Putri Giyan Adinda Computer Science Study Program School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Interior Design Study Program School of Design Bina Nusantara University Jakarta Indonesia Department of Management Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia

E-learning satisfaction is an important factor in assessing the effectiveness and efficiency of distance learning. The use of Big Data technology can have a significant impact on e-learning satisfaction. The purpose of this research is to examine the implementation of big data in e-learning and the factors that can increase student e-learning satisfaction. This study uses a quantitative survey method with a questionnaire as a data collection tool. The research respondents were randomly selected e-learning students from several educational institutions that use Big Data technology in their e-learning platforms. There are 663 data collected through online questionnaires, then the data is processed through SmartPLS to test the research hypothesis. The results of the study show that the use of Big Data technology in an e-learning environment has a positive impact on student satisfaction. Several factors determine e-learning satisfaction including usability requirements, quality level, learning competence, and material relatedness. Relatedness to e-learning is more important in increasing student motivation in learning, as can be seen from the correlation value which is more dominant in forming student satisfaction. The e-learning platform must have navigation that is easy for users to understand and use. Learning content must be well-designed and follow established learning standards. The use of Big Data technology in e-learning can have a significant impact on student satisfaction and the quality of learning. Future research can develop a more personal and adaptive e-learning platform, using Big Data technology as the basis for its development.

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Evaluation of Contingency-Based Learning Strategies for Precision Medicine

Evaluation of Contingency-Based Learning Strategies for Prec...

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International Conference on Computing and Networking technology (ICCNT)

作者： Harshita Kaushik Naveen Kumar Rajendran Ashwini Kumar Department of Computer Science& Application Vivekananda Global University Jaipur India Department of Aerospace Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Contingency-based learning strategies primarily based on synthetic intelligence (AI) are being hired within the area of precision remedy to enable greater powerful selection-making when treating patients. The overall performance of such learning strategies should be evaluated to make sure they are presenting accurate and dependable outcomes. This paper aims to assess contingency-based learning strategies for precision medication to help clinicians and researchers appropriately assess the efficacy of these learning techniques and decide the first-rate method for given software., a scientific evaluation of the current literature was undertaken to identify existing assessment strategies and analyze their strengths and weaknesses. The assessment criteria used in this analysis are accuracy, interpretability, complexity, scalability, and transferability, which had been selected as they affect the performance of AI-primarily based approaches in real-international settings. Furthermore, a discussion of current equipment and frameworks that could be used to assess AI-based techniques in the area of precision remedy was performed. From this assessment, it has been discovered that most of the present assessment techniques lack the necessary accuracy and transferability essential to appropriately check the efficacy of AI-based strategies for precision medicinal drugs. But, sure, evaluation metrics, together with explain ability, may be employed to degree the overall performance of AI-primarily based techniques in order to decide the nice approach for a given application.

关键词： Drugs Measurement Privacy Accuracy Scalability Precision medicine Contingency management Software Safety Medical diagnostic imaging

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An Asymptotically Consistent Shell Model for Compressible Biological Structures with Finite-Strain Deformation

SSRN

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SSRN 2024年

作者： Yu, Xiang Chen, Xiaoyi Department of Mathematics School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science Department of Applied Mathematics Beijing Normal University-Hong Kong Baptist University BNU-HKBU United International College Zhuhai519087 China

We derive an asymptotically consistent morphoelastic shell model to describe the finite deformations of biological tissues using an energy approach. Biological materials may exhibit remarkable compressibility when under large deformations, and we take this factor into account for accurate predictions of their morphoelastic changes. The morphoelastic shell model combines the growth model of Rodriguez et al. and a novel shell model developed by us. We start from the three-dimensional (3D) morphoelastic model and construct the optimal shell energy based on a series expansion around the middle surface. A two-step variational method is applied that retains the leading-order expansion coefficient while eliminating the higher-order ones. The main outcome is a two-dimensional (2D) shell energy depending on the stretching and bending strains of the middle surface. The derived morphoelastic shell model is asymptotically consistent with three-dimensional morphoelasticity and can recover various shell models in literature. Several examples are shown for the verification and illustration. © 2024, The Authors. All rights reserved.

关键词： Shells (structures)

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