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Development of educational game as a media to raise public awareness of illegal animal trafficking issue in Indonesia

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Procedia computer science 2024年 245卷 337-345页

作者： Jonathan William Khalid Ahmadi Muhamad Fajar Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Abstract The amount of illegal wildlife trade cases increases every year in Indonesia. This causes some unique Indonesian wildlife to become endangered or even extinct. Therefore, the purpose of this research is to raise public awareness to illegal wildlife trade issue by using a game as its media to reduce the number of cases of illegal wildlife trade in Indonesia. The game made in this research will be developed using the GDLC development method and 2021.3.11f1 version of Unity as its game engine. To evaluate the game that has been developed in this research, a comparation to other related games is conducted and resulted in the game that has been developed in this research is significantly better than other related games in Challenge, Learnability and Concentration aspects (p < 0.001).

关键词： Animal Game Wildlife Education Awareness

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Comparative usability analysis across different driving control scenarios in a driving simulation game

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Procedia computer science 2024年 245卷 330-336页

作者： David Juan Davis Leon Lewis Christian Oliver Muhamad Fajar Galih Dea Pratama Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

This study aims to address the gap in the evaluation of driving simulation games by further assessing and developing ProjectStir (a Driving Simulation Game) through its adaptation to two different driving control devices: a steering wheel and an arcade stick. Despite the growing popularity of simulation games, limited research has been conducted on how different control devices impact user experience and usability. Usability was evaluated through variables including Easiness of Game Control, Learnability of Gameplay, Remembrance, Challenge, Efficiency, Tension, Familiarity, and Satisfaction. Involving 30 respondents from the computer science program at BINUS University, the results revealed no significant differences in usability between the two scenarios (p<0.05), as determined by the Wilcoxon signed-rank test and paired t-test. These findings suggest that ProjectStir offers consistent usability, regardless of whether a steering wheel or an arcade stick is used for gameplay, thus providing valuable insights for future development and refinement of driving simulation games.

关键词： Game Based Leaning Serious Game Usability User Experience

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The Adoption of Artificial Intelligence technology in Parking Security System 7th

The Adoption of Artificial Intelligence Technology in Parkin...

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Alhafizh, Muhammad Rifqi Adriawan, Albertus Baskara Yunandito Fernaldy, Darryl Gaol, Ford Lumban Matsuo, Tokuro Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819916238

Artificial intelligence (AI) refers to human-like intelligence exhibited by computers, robots, or other machines. In popular use, artificial intelligence refers to the ability of a computer or machine to mimic the ability of the human mind to learn from examples and experiences, recognize objects, understand and respond to language, make decisions, solve problems, and combine these and other abilities to perform functions that humans might perform. Artificial intelligence requires experience and data to smarten up the technology. The most important things in making artificial intelligence are learning, reasoning, and self-correction. At the learning stage, AI gives machines the ability to learn tasks without requiring a defined programming language. Then, the reasoning stage is the stage where the reason AI is applied in a technology. The self-correction stage is the stage where the AI is refining itself from and learning from experience in order to minimize errors or problems that exist. Then, this matter is concerned with the parking lot to be discussed. Parking can be interpreted as public facilities available in agencies or offices that serve to store vehicles. Vehicles entering the parking area become tens or even thousands, because it requires a parking system and management area. Such arrangements are capable of parking procedures and even other support systems such as adequate parking facilities and infrastructure, and another function is to create and develop parking systems in general to provide safety and comfort. The methodology obtained from this problem is checking the research model to check and find out the comparison of vehicle user’s Parking System Security between cars or motorcycles, as well as the usual parking lots visited. Then, we created a questionnaire. This study used questionnaires with the aim to get respondents’ results about the Parking Security System. Our sampling method is based on respondents aged 17–20 years. Questionnaires are su

关键词： Security systems

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Investing in Products with the Greatest Demand on Online Stores During the Pandemic 7th

Investing in Products with the Greatest Demand on Online Sto...

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Hassya, Titan Hanif, Muhammad Fauzi Alvian Gaol, Ford Lumban Matsuo, Tokuro Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819916238

From the year 2019, every aspect of our lives has been negatively impacted as a result of the spread of a deadly disease known as COVID-19, particularly the health and business domains. The primary goal of this study is to develop a solution, or at the very least an overall analysis, to the readers on how to overcome the economic crisis during pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Forgive and forget: Self-stabilizing swarms in spite of Byzantine robots

Forgive and forget: Self-stabilizing swarms in spite of Byza...

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作者： Ashkenazi, Yotam Dolev, Shlomi Kamei, Sayaka Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Graduate School of Science and Technology Nara Institute of Science and Technology Nara Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Tokyo Japan

In this article, we consider the case in which a swarm of robots collaborates in a mission, where a few of the robots behave maliciously. These malicious Byzantine robots may be temporally or constantly controlled by an adversary. The scope is synchronized full information robot operations, where a robot that does not follow the program/policy of the swarm is immediately identified and can be remembered as Byzantine. As robots may be suspected of being Byzantine due to benign temporal malfunctions, it is imperative to forgive and forget, otherwise, a robot cannot assume collaborative actions with any other robot in the swarm. Still, remembering for a while may facilitate a policy of surrounding, isolating and freezing the movement of the misbehaving robots, by several robots, allowing the rest to perform the swarm task with no intervention. We demonstrate the need to periodically forgive and forget to realize swarm several tasks including patrolling/cleaning in the presence of possible Byzantine robots. The policy for achieving the task consists of blocking the movement of the Byzantine robot(s) by some of the robots, while the rest patrol/clean the plane. © 2020 John Wiley & Sons, Ltd.

关键词： Robots

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Synthesizing Speech from ECoG with a Combination of Transformer-Based Encoder and Neural Vocoder 48

Synthesizing Speech from ECoG with a Combination of Transfor...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Shigemi, Kai Komeiji, Shuji Mitsuhashi, Takumi Iimura, Yasushi Suzuki, Hiroharu Sugano, Hidenori Shinoda, Koichi Yatabe, Kohei Tanaka, Toshihisa Tokyo University of Agriculture and Technology Graduate School of Engineering Japan Juntendo University School of Medicine Department of Neurosurgery Japan Tokyo Institute of Technology Department of Computer Science Japan

ISBN: (纸本)9781728163277

This paper reports on a novel invasive brain-computer interface (BCI) paradigm that has successfully reconstructed spoken sentences from invasive electrocorticogram (ECoG) signals using deep-neural-network-based encoders and a pre-trained neural vocoder. We recorded ECoG signals while 13 participants were speaking short sentences. Our BCI could map the ECoG recording to the log-mel spectrograms of the spoken sentences using a bidirectional long short-term memory (BLSTM) or a Transformer. The estimated log-mel spectrograms were used in Parallel WaveGAN to synthesize speech waveforms. An evaluation of the model performance revealed that the Transformer model significantly outperformed (Wilcoxon signed-rank test, p © 2023 IEEE.

关键词： Speech synthesis

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Trace Determination of Cardiac Troponin I Using a Label-Free Fiber Optical Localized Surface Plasmon Resonance Biosensors

Trace Determination of Cardiac Troponin I Using a Label-Free...

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2024 Laser Applications to Chemical, Security and Environmental Analysis, LACSEA 2024 - Part of Optica Sensing Congress

作者： Chiang, Chang-Yue Chen, Chien-Hsing Wang, Chien-Tsung Wu, Chin-Wei Chiang, Hsing-Yu Graduate School of Engineering Science and Technology Interdisciplinary Program of Engineering National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Biomechatronics Engineering National Pingtung University of Science and Technology Pingtung91201 Taiwan Department of Chemical & Materials Engineering National Yunlin University of Science and Technology Yunlin64002 Taiwan

ISBN: (纸本)9781957171371

This work proposes a carboxyl-graphene-oxide-based fiber optic localized surface plasmon resonance biosensor which employed anti-Cardiac Troponin I (anti-cTnI) as the recognition element to detect the cTnI-protein in 10 min with a limit of detection (LOD) of 5.8 pM, which meets the acceptable LOD for clinical testing of 0.04 ng/mL. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Surface plasmon resonance

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HCNNet: hybrid convolution neural network for automatic identification of ischaemia in diabetic foot ulcer wounds

HCNNet: hybrid convolution neural network for automatic iden...

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作者： Das, Sujit Kumar Namasudra, Suyel Sangaiah, Arun Kumar Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Computer Science and Engineering National Institute of Technology Agartala Tripura India International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Electrical and Science Engineering Lebanese American University Byblos Lebanon

The diabetic foot ulcer (DFU) is a significant medical complication for diabetic patients, which often leads to lower limb amputation. The manual identification of ischaemia in DFU is laborious, time-consuming, and costly. This study aims to develop an efficient deep learning (DL) method for the early identification of ischaemia in DFU. Therefore, a novel Convolutional Neural Network (CNN) architecture (HCNNet) is proposed by integrating multiple hybridised blocks from inception, residual, and dense modules along with appropriately placed Squeeze-and-Excitation (SE) block and intermediate transition layers. The proposed HCNNet is trained several times using various optimizer and learning rate settings to optimise its performance. It achieves promising Area Under the ROC Curve (AUC) scores of 0.999 for ischaemia identification. The experimental results show that the proposed HCNNet outperforms existing State-Of-The-Art (SOTA) methods. © 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Medical imaging

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Improved YOLOv5 based on CBAM and BiFPN for Rice Pest and Disease Detection

Improved YOLOv5 based on CBAM and BiFPN for Rice Pest and Di...

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2023 International Conference on computer, Internet of Things and Smart City, CIOTSC 2023

作者： Zhao, Yanqin Cui, Yichao Wang, Ziyi Heilongjiang University of Science and Technology Computer and Information Engineering Harbin150022 China Heilongjiang University of Science and Technology Graduate School Harbin150022 China Jiangxi Institute of Fashion Technology Fashion Design School 330201 China

ISBN: (纸本)9798350306569

Addressing the issue where traditional YOLOv5 algorithm struggles in accurately detecting pests and diseases of small to medium scale with dense leaf distribution in rice, this study introduces an enhanced YOLOv5-CW algorithm model, derived from the original YOLOv5, specifically for rice pest and disease detection. Firstly, the feature fusion is improved by introducing the improved three-layer BiFPN (Bidirectional Feature Pyramid Network) weighted bidirectional feature pyramid network BiFPN-W into the original YOLOv5 algorithm to replace the original Feature Pyramid Network (FPN) structure and Path Aggregation Network (PAN) structure in the Neck part, which can fuse more features without increasing the cost. Second, the introduction of Convolutional Block Attention Module (CBAM) attention mechanism will significantly improve the detection of small targets for the identification of small-scale pests and dis-eases in rice plants. Finally, experiments are conducted on the dataset. The experimental results showed that compared with the detection of rice pests using the traditional YOLOv5 algorithm, the precision (P) and recall (R) rates of the improved YOLOv5-CW algorithm model detection increased from 86.05% and 77.46% to 90.65% and 85.63%, respectively, and the mean Average Precision (mAP) also increased from 83.52% to 87.40%, while the detection speed reached 22.38ms, which is faster than the detection speed of the original YOLOv5 algorithm. Meanwhile, compared with target detection algorithms such as SSD, Faster R-CNN, and YOLOv4, this improved algorithm has certain superiority and is an ideal model in rice pests and diseases. © 2023 IEEE.

关键词： Attentional Mechanisms BiFPN CBAM component Rice pest and disease detection YOLOv5

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