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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Laimeheriwa, Jonathan Iswanto, Irene Anindaputri Oktriani, Wiwi Hidayat, Muhammad Fadlan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

In today's digital world, where people consume information quickly through social media like Facebook, Instagram, and Twitter, there's a growing concern about fake news. It's crucial to know whether information and its sources are reliable, especially as people may disregard real news if it doesn't align with their beliefs. This survey aims to explore the landscape of fake news in the context of Indonesia, focusing on the development and implementation of hoax analyzers and fake news detection using deep learning techniques. Our review shows that deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models like IndoBERT have demonstrated effectiveness in identifying misinformation within Indonesian content, with promising accuracy and efficiency metrics. However, challenges persist, including the need for extensive data, computational resources, and tailored approaches to handle Indonesian language complexities. This survey highlights the importance of advancing these detection systems to help reduce the spread of misinformation and support informed decision-making in Indonesian society. © 2024 IEEE.

关键词： Tweets

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Predictive Autoscaling for Containerized Applications Using Machine Learning 1

Predictive Autoscaling for Containerized Applications Using ...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. School of Computer-Science and Engineering Sandip University Department of Computer Science & Applications Nashik India

ISBN: (纸本)9798350361780

As containerization continues to gain prominence in modern application deployment, the need for efficient autoscaling mechanisms becomes paramount to ensure optimal resource utilization and adherence to service level objectives. This research paper proposes a Predictive Auto-scaling framework leveraging machine-learning(ML) techniques to anticipate and proactively adjust the resources allotted to containerized applications. The aim is to enhance the scalability and responsiveness of applications running in dynamic and cloud-native environments. An auto-scaling technique with burst awareness for containerized applications is proposed in this research. This innovative method optimizes container utilization while meeting the Quality-of-Service requirement by taking into account a mix of both vertical and horizontal skills. The proposed proactive autoscaling strategy addresses the challenges posed by unpredictable workloads and varying application demands, fostering adaptability to dynamic environments. By highlighting the significance of machine learning in improving the predictability and effectiveness of resource scaling for containerized systems, this study adds to new work of research on auto-scaling methodologies. © 2024 IEEE.

关键词： Containers

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IoT Door Locking a Review 9

IoT Door Locking a Review

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Yulianto Rahayu, Anita School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Traditional mechanical locks need a key for locking or unlocking the door, with the fundamental drawback of the possibility of losing the key. Researchers have proposed replacing the mechanical key with electronic one, also equipped with a sensor, in order to improve the fully mechanical system of door locks. The Internet of Things (IoT) scheme was also implemented for better control and monitoring via the cloud network. IoT schemes usually involve several components such as sensors, microcontrollers, and network components. The sensor is used to read the state of the environment. It can also be used to capture the biometric characteristics of the users authorized to access the system. For some types of sensors, it is not possible to connect them directly to a computer to collect the result of the reading of the sensor data. Thus, microcontrollers are needed. However, there are still a few papers that provide a summary of what the main component is and what other authors have proposed in their research. The aim of this study is to briefly summarize which sensor types, microcontrollers and communication protocols have recently been used to improve IoT-based door locks, especially for indoor environments. Whether the use of a single sensor or multiple sensors is also discussed. The types of microcontrollers used in recent research are also discussed. © 2024 IEEE.

关键词： Locks (fasteners)

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Enhancing Clothing Size Prediction in E-Commerce: A Machine Learning Approach for Improved Customer Experience

Enhancing Clothing Size Prediction in E-Commerce: A Machine ...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer Engineering, ICSSEEC 2024

作者： Prakash, J. Nashita, V. Shivani Sri, S. Shri Shivathmika, V. Sivesh, K. Ashwin, V. PSG College of Technology Department Computer Science & Engineering Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378177

Clothing and apparel companies face high rates of returns due to customers ordering incorrect sizes. This can be frustrating for customers and is a waste of time and resources for the company, as they have to process the return and potentially restock the item. Uncertainty about fit also deters online purchases, impacting sales. Most currently available systems can predict the size based on all measurements provided by the customer, but that is not always feasible. This brings a need for the integration of Machine Learning to solve this problem of predicting sizes and provide better results with increased accuracy. This paper presents a thorough evaluation of various ML models for size prediction, namely Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, and Decision Tree models. The results indicate that our model showcases considerable promise for enhancing sizing accuracy, with the Random Forest and Decision Tree models achieving noteworthy accuracy scores of 0.99 in shirt size prediction. In the realm of pant size prediction, these models maintain their effectiveness, with an accuracy score of 0.89 and 0.82, respectively. This underscores the potential of our ML model to alleviate sizing woes in the clothing retail industry. © 2024 IEEE.

关键词： Garment industry

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Enhancing Hydroponic Farming Productivity Through IoT-Based Multi-Sensor Monitoring System 9

Enhancing Hydroponic Farming Productivity Through IoT-Based ...

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9th International Conference on Internet of Things, Big Data and Security, IoTBDS 2024

作者： Khadijah Febriana, R. Thakur, Rahul Roy, Sudip Department of Computer Science & Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9789897586996

Hydroponic farming has gained prominence in modern agriculture owing to its inherent advantages in resource efficiency and crop yield. This research explores the integration of Internet of Things (IoT) technologies to further optimize hydroponic systems by monitoring and controlling crucial solution and environmental parameters. A novel IoT-based hydroponic monitoring system has been developed, featuring a comprehensive array of sensors including solution’s temperature, acidity (pH), total dissolved solids (TDS) and electrical conductivity (EC), ambient temperature and humidity, and light intensity. This system leverages both WiFi and LoRaWAN technologies to enhance connectivity, ensuring reliable communication over extended ranges. This integration of communication protocols facilitates seamless data transmission and real-time control of hydroponic conditions. The proposed IoT-based system aims to provide growers with a comprehensive and user-friendly platform to monitor and adjust key parameters critical for plant growth, thereby maximizing the productivity and yield in hydroponic farming. The results of this study contribute valuable insights into the potential of IoT technologies to revolutionize precision agriculture and sustainable food production. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Hydroponics

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Topic Identification of science and Mathematics Literature Using Latent Dirichlet Allocation

Topic Identification of Science and Mathematics Literature U...

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2024 International Conference on Artificial Intelligence and Mechatronics System, AIMS 2024

作者： Ferdinand, Bryan Josafat Aviarta, Nabiilah Putri Jordan, Matthew Gabe Purwandari, Kartika School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350350524

Amidst the ever-expanding realm of scientific and mathematical literature, distilling valuable insights from a multitude of articles holds paramount importance. This study introduces a fresh perspective on identifying topics within science and mathematics articles, employing Latent Dirichlet Allocation (LDA) as its cornerstone. LDA, serving as a probabilistic generative model, is adeptly harnessed to unveil latent topics nestled within a corpus of articles, thereby illuminating intrinsic thematic architectures. Our LDA model, meticulously crafted, has the capability to discern five distinct categorizations of topics, offering a nuanced understanding of the diverse array of subjects encapsulated within the domain of science and mathematics. This approach not only facilitates the systematic organization of voluminous literature but also fosters a deeper comprehension of prevalent themes and emerging trends across these disciplines. Evaluation result, based on coherence scores value, in modelling topics related to science and mathematics, with a coherence score of 0,47508 as the highest using alpha is 0.01 and beta is 0.91. © 2024 IEEE.

关键词： Text processing

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MycoAR: Augmented Reality Mobile Application for Mycology Education 9

MycoAR: Augmented Reality Mobile Application for Mycology Ed...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Hutapea, Salomo Agung Adrianto Rehmina Tumilaar, Kristania Yohana Nugroho, Ady Maulana, Fairuz Iqbal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Augmented reality (AR) technology represents a powerful tool for enhancing learning experiences across a range of disciplines. Although AR has significant potential, it is necessary to examine its unique influence on teaching mycology, a complex discipline that might substantially benefit from interactive and immersive learning methods. This project introduces MycoAR, a cutting-edge augmented reality (AR) application developed to facilitate comprehension of fungal creatures and their environments. MycoAR is a Unity-based application that integrates digital content with the physical world, providing students with a comprehensive and dynamic educational experience. The testing process involved a total of eighteen participants and yielded three significant discoveries. (1) Augmented reality (AR) technology significantly enhances students' understanding and memory of mycology material;(2) The application improves the learning process by presenting information in a visually captivating format, surpassing conventional methods;(3) The capabilities of Unity enable the creation of an interactive and practical AR application. The findings indicate that MycoAR provides a distinct and effective technique for teaching mycology. This suggests that augmented reality (AR) technology can play a vital role in updating educational approaches and enhancing student achievements in intricate scientific disciplines. © 2024 IEEE.

关键词： Federated learning

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Multi-layer Perceptron Approach for Diabetes Risk Prediction using BRFSS Data 10

Multi-layer Perceptron Approach for Diabetes Risk Prediction...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Prasetyo, Simeon Yuda Izdihar, Zahra Nabila Bina Nusantara University School of Computer Science Computer Science Department Jakarta1480 Indonesia

ISBN: (纸本)9798350365283

The escalating prevalence of diabetes worldwide, affecting approximately 425 million individuals by 2020, underscores the urgent need for accurate predictive models to address this pressing public health challenge. In this study, we investigate the efficacy of Multi-layer Perceptron (MLP) models with varying units across two hidden layers - specifically, configurations with 5, 10, 15, and 20 neurons per layer. Leveraging the BRFSS dataset for diabetes risk prediction, our meticulous evaluation of these configurations unveils compelling insights. Notably, the MLP model attains its peak accuracy of approximately 84.91% with 5 neurons in Hidden Layer 1 and 10 neurons in Hidden Layer 2. These findings underscore its potential for robust predictive modeling. Furthermore, despite minor fluctuations, our analysis highlights the model's reliability and effectiveness in achieving high predictive accuracy. This research offers valuable contributions to the field of diabetes risk prediction, providing insights into the optimal configuration of MLP models for accurate and reliable predictions, thus aiding in the development of improved diagnostic tools and preventive strategies to mitigate the global burden of diabetes. © 2024 IEEE.

关键词： Prediction models

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Spoonie: Self-Stabilizing Feeding Spoon For Tremored Hands 3

Spoonie: Self-Stabilizing Feeding Spoon For Tremored Hands

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3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Das, Susmita Kar, Ankit Ghosh, Hritabrata Das, Priyanshu Mitra, Aniruddha Narula Institute of Technology Department of Electronics and Computer Science Kolkata India

ISBN: (纸本)9798350374643

Spoonie is a self-stabilizing feeding spoon designed for people who have Parkinson's disease, elderly persons, and babies who cannot hold their hands steadily during their meals. The spoon is so uniquely designed that the spoon head remains stable, thus automatically correcting for shakiness or other unwanted movements that would allow easier and classier feeding. It is powered by a microcontroller that controls servo motors in relation to real-time data from an accelerometer, ensuring the spoon head remains at a steady level, even if a user's hand is shaking or moving unpredictably. With this spoon, users are able to self-feed without any assistance, hence improving their quality of life. The self-stabilizing spoon represents a breakthrough in assistive technology that really helps people with motor control issues in solving practical dilemmas every day. Further refinements could include adjustable settings for different levels of stability so that the device is much more attuned to individual needs. © 2024 IEEE.

关键词： Assistive technology

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ISAC-Fi: Enabling Full-Fledged Monostatic Sensing over Wi-Fi Communication

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2024年 1卷 139-153页

作者： Chen, Zhe Hu, Chao Zheng, Tianyue Cao, Hangcheng Yang, Yanbing Chu, Yen Jiang, Hongbo Luo, Jun Fudan University Institute of Space Internet School of Computer Science Shanghai200437 China Sichuan University College of Computer Science Chengdu610017 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Hunan University College of Computer Science and Electronics Engineering Changsha410012 China University of Electronic Science and Technology of China Chengdu610054 China

Although Wi-Fi communications have been exploited for sensing purpose for over a decade, the bistatic or multistatic nature of Wi-Fi still poses multiple challenges, hampering real-life deployment of integrated sensing and communication (ISAC) within the Wi-Fi framework. In this article, we aim to redesign Wi-Fi so that monostatic sensing (mimicking radar) can be achieved over the multistatic communication infrastructure. Specifically, we propose, design, and implement ISAC-Fi as an ISAC-ready Wi-Fi prototype. We first present a novel self-interference cancellation scheme, in order to extract reflected (radio frequency) signals for sensing purpose in the face of transmissions. We then subtly revise the existing Wi-Fi framework so as to seamlessly operate monostatic sensing under the Wi-Fi communication standard. Finally, we offer two ISAC-Fi designs: 1) a Universal Software Radio Peripheral (USRP)-based design emulates a totally redesigned ISAC-Fi device and 2) another plug-and-play design allows for backward compatibility by attaching an extra module to an arbitrary Wi-Fi device. We perform extensive experiments to validate the efficacy of ISAC-Fi and also to demonstrate its superiority over existing Wi-Fi sensing proposals. © 2024 IEEE.

关键词： Wi-Fi

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