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Scheduling Production in the Flexible Packaging Industry Using Mathematical Models and Genetic Algorithms

Scheduling Production in the Flexible Packaging Industry Usi...

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Informatics Engineering, science & technology (INCITEST), International Conference on

作者： Masmur Tarigan Ford Lumban Gaol Tuga Mauritsius Widodo Budiharto Computer Science Department / Computer Science Graduate Program Esa Unggul University/Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Department of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Minimizing time wastage is a crucial objective for all production operations. Reliable manufacturing scheduling The objective is to mitigate time-based production waste caused by excessive processing, waiting, and transportation. Industry 4.0 promotes technological innovation across all industrial sectors to increase efficiency and effectiveness while preserving high competitiveness. The implementation of artificial intelligence in Industry 4.0 is expected to result in a reduction in time wastage. Non-dominance sequencing genetic algorithms (NSGA), genetic algorithms (GA), and evolutionary algorithms (EA) are the three most common scheduling approaches, according to literature-indexed research summaries from the past five years. The examination of production scheduling in the flexible packaging business, which involves the utilization of different machines and processes and is produced on demand, had not been conducted before in this study. A mathematical scheduling model will be developed in this research to ascertain the shortest production time span in the flexible packaging industry. As a form of technological innovation, this mathematical model will be utilized in experiments employing a genetic algorithm approach. For the genetic algorithm to generate a minimum makespan in the flexible packaging industry's production scheduling process.

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Exploring Auditory Hand Guidance for Eyes-free 3D Path Tracing

Exploring Auditory Hand Guidance for Eyes-free 3D Path Traci...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Abe, Yuki Hara, Kotaro Sakamoto, Daisuke Ono, Tetsuo Human-Computer Interaction Lab. Hokkaido University Hokkaido Sapporo Japan School of Computing and Information Systems Singapore Management University Singapore Singapore Division of Computer Science and Information Technology Hokkaido University Hokkaido Sapporo Japan Faculty of Engineering Kyoto Tachibana University Kyoto Japan

ISBN: (纸本)9798400713958

Guiding a user’s hand along a 3D path can help individuals avoid obstacles and manipulate everyday items with eyes-free. While prior work focused on haptic approaches using robots, auditory approaches for 3D path guidance remain underexplored. We prototyped and assessed two auditory hand guidance techniques for 3D path tracing: (1) VERBAL repeating spoken directional prompts;(2) Follow-Your-Finger (FYF) using the user’s index finger in moving hand as an embodied reference and sonifying whether the user should follow the index finger direction. In a controlled pilot study with 12 sighted participants under an eyes-free condition, VERBAL yielded about 1.5 times faster performance than FYF, while FYF achieved an error rate of less than half VERBAL’s. We thus recommend using VERBAL for wider paths with fewer obstacles and FYF for narrower paths with higher collision risks. We also discuss the research direction for applying these findings to real-world tasks. © 2025 Copyright held by the owner/author(s).

关键词： Three dimensional computer graphics

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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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2024 International Conference on information technology and Computing, ICITCOM 2024

作者： Alita, Debby Ahmad, Imam Jafar Rumandan, Rhaishudin Erkamim, Moh Widyasmoro Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Institut Agama Islam Negeri Ambon Islamic Education Management Study Program Ambon Indonesia Universitas Tunas Pembangunan Smart City Information Systems Study Program Surakarta Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data. © 2024 IEEE.

关键词： Classification Data Mining Decision Tree Stunting

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Effects of the Attunement of an Attack Target by Whispering in AIWolf

Effects of the Attunement of an Attack Target by Whispering ...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Tomohiro Murakami Nozomi Nakao Hikaru Miyake Hayate Murata Itsuki Amano Taisei Kojima Keisuke Ando Nobuhiro Ito Graduate School of Business Administration and Computer Science Aichi Institute of Technology Toyota Japan Department of Information Science Aichi Institute of Technology Toyota Japan Faculty of Information Science Aichi Institute of Technology Toyota Japan

Recently, research on AIWolf, which is artificial intelligence that plays the Werewolf game, has attracted much attention. Werewolves in the Werewolf game should cooperate using “whispers;” which are conversations among werewolves only. However, whispers have not been used effectively in the AIWolf Competition and little research has been conducted on this topic. Therefore, the purpose of this study is to clarify the effectiveness of strategies that use whispers in AIWolf. A wide variety of strategies use whispers, but we focus on unifying attack targets, which was shown to improve the winning rate in previous studies. In the strategy of unifying attack targets, the strategy of the guiding agents is important. Therefore, we used 13 AIWolf Competition finalists with a wide variety of strategies and added statements of whom to attack in whispers to each finalist as a guide for whom to attack. Because there are three werewolf competitions in the AIWolf Competition setting, we created two agents to be attuned to the attack target of one of the guiding agents. In an experiment, we used these agents in the same setting as in the AIWolf Competition, and analyzed the differences between when the attack targets were unified and when they were not. The results showed that when the werewolves were a combination of two attuning agents and one guiding agent, the werewolves’ win rate increased by an average of 2.3 percentage points. We also found that attuning to the agent with a high winning rate or high success rate in attacks improved the winning rate. These results indicate that attuning only the attack targets of agents with high winning rates and high success rates in attacks led to higher winning rates.

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Leveraging EEG Signals and Machine Learning for Schizophrenia Classification

Leveraging EEG Signals and Machine Learning for Schizophreni...

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Computing and Informatics (ICCI), International Conference on

作者： Abdelrahman Elfarsy Sara El-Metwally Medical Informatics Program Faculty of Computers and Information Mansoura University Mansoura Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt

Schizophrenia is a neurological disorder known for its potential to disrupt brain function and cause erratic behavior. Timely diagnosis and intervention are crucial for improving patient outcomes. This paper conducts a comprehensive comparative study of three machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) to classify EEG signals associated with schizophrenia. The dataset utilized in this study comprises EEG data obtained from 28 individuals, data preprocessing techniques, including artifact removal, filtering, and normalization, were applied to enhance data quality. A set of informative statistical features was extracted from the EEG signals to capture relevant information. The three machine learning models are trained and evaluated using various performance metrics, including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Random Forest achieved the highest accuracy (96 % ), while SVM demonstrated strong precision and recall (95 % ). These findings highlight the potential of machine learning in aiding early schizophrenia diagnosis through EEG signal analysis.

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An Empirical Study of MPQUIC Schedulers in Mobile Wireless Networks 22

An Empirical Study of MPQUIC Schedulers in Mobile Wireless N...

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11th International Symposium on information and Communication technology, SoICT 2022

作者： Vu, Minh Hai T. T. Nguyen, Giang Tran, Hai Dang Nguyen, Thanh Trung Do, Phan Thuan Nguyen, Phi Le Nguyen, Kien School of Information and Communication Technology Hanoi University of Science and Technology Viet Nam Faculty of Computer Science Phenikaa University Viet Nam Institute for Advanced Academic Research Chiba University Japan

ISBN: (纸本)9781450397254

Multipath QUIC (MPQUIC), an emerging multipath transport protocol (MTP) that inherits the advantages of the canonical multipath TCP (MPTCP) and the widespread QUIC, potentially plays a vital role in 5G and beyond. MPQUIC can exploit multiple networks (e.g., Wi-Fi, LTE, 5G) on a mobile device to boost the quality of services while efficiently utilizing network resources. In MPQUIC, the scheduler, which is in charge of concurrently scheduling data transmission in several paths, largely impacts the protocols' performance, especially in dynamic environments. In fact, in the literature, a considerable number of MTP schedulers for MPQUIC have been proposed. Unfortunately, their performances have been primarily evaluated in static networks without (or simply) considering mobile ones. Hence, this work attempts to investigate the performance of MPQUIC schedulers in the mobile context, aiming to fill the literature gap. Specifically, we implement and assess the performance of five MPQUIC schedulers in various mobility patterns using the Mininet-WiFi emulator. More importantly, we introduce q-ReLeS, an extension of an MPTCP scheduler called ReLeS for MPQUIC. The experimental results show that q-ReLeS reduces the download time from to compared to the others. Besides, the empirical investigation demonstrates that mobility and velocity patterns substantially impact the performance of MPQUIC schedulers. © 2022 ACM.

关键词： Wi-Fi

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Exploiting Mode-Switching between Aerial-RIS and Active Radio in UAV-Assisted Wireless Networks 25

Exploiting Mode-Switching between Aerial-RIS and Active Radi...

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25th IEEE Wireless Communications and Networking Conference, WCNC 2024

作者： Zhao, Songhan Long, Yusi Gu, Bo Luong, Nguyen Cong Lyu, Bin Gong, Shimin School Of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus China Guangdong Provincial Key Laboratory Of Fire Science And Intelligent Emergency Technology China Phenikaa University Faculty Of Computer Science Viet Nam School Of Communications And Information Engineering Nanjing University Of Posts And Telecommunications China

ISBN: (纸本)9798350303582

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode. © 2024 IEEE.

关键词： Antennas

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ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with Uncertainty Data

ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with ...

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International Conference on Software Engineering and computer Systems (ICSECS)

作者： Muhammad Shukri Che Lah Nureize Arbaiy Pei-Chun Lin Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Malaysia Department of Information Engineering and Computer Science Feng Chia University Taiwan

Public health is seriously threatened by air pollution. Systems for early warning are crucial for preventing its negative impacts on humans. But predicting air quality is difficult because it needs precise data from time series gathered at air monitoring sites. Due to things like measurement mistakes, this data may be ambiguous. Using fuzzy symmetry triangular fuzzy numbers, we suggest a strategy in this study for preparing data that contains uncertain information. Linear programming is utilized to obtain the midpoint value for defuzzification. The fuzzy pre-processed data is then used to build a predictive model using ARIMA. Our findings imply that the linear program can greatly lower prediction errors, resulting in more precise predictions.

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A Proposed Approach for Risk Planning and Management 25

A Proposed Approach for Risk Planning and Management

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25th International Arab Conference on information technology, ACIT 2024

作者： Al Refai, Mohammed N. Al-Smadi, Yazan Al-Momani, Adai Jamhawi, Zeyad M. Otoom, Ahmed Ali Atoum, Issa Yaseen, Ahid Ababneh, Jafar Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Applied Science Private University Department of Computer Science Amman Jordan Applied Science Private University Department of Cyber Security Amman Jordan Philadelphia University Faculty of Information Technology Software Engineering Department Jordan Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan. © 2024 IEEE.

关键词： Project management

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Fuzzy Risk Model for Research and Development Department in Clinical Laboratory

Fuzzy Risk Model for Research and Development Department in ...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Nilo Legowo Wahyu Sarjdono Eddy Susanto Ditdit Nugeraha Utama Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

information security risk is of utmost importance and a crucial concern, particularly within a clinical laboratory responsible for managing sensitive public health information. Various endeavors have been undertaken by institutions to tackle this pressing challenge effectively. This research seeks to develop a computer-based decision model for assessing information security risks. The model is scientifically constructed using the fuzzy logic method as its core approach and designed through an object-oriented approach. Impressively, the model successfully simulates 31 risk scenarios with an accuracy rate of 93.55%.

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