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11th Electrical Power, Electronics, Communications, Control, and informatics Seminar, EECCIS 2022

作者： Sulistiani, Ita Widodo Nugraheni, Murien State University of Jakarta Dept. of Informatics and Computer Engineering Education Jakarta Indonesia

ISBN: (数字)9781665406482

ISBN: (纸本)9781665406482

In making financial decisions, many critical factors need to be considered, including the bankruptcy potential of the company. As a result, several bankruptcy prediction models, mainly using machine learning approaches, have been created. Based on relevant research, Support Vector Machine (SVM) and Artificial Neural Network (ANN) often provide the best performance in predicting companies' bankruptcy. On the other hand, both algorithms still have drawbacks, such as SVM is quite sensitive to feature selection, and ANN is prone to overfitting. In addition, many studies ignored the imbalance problems in the bankruptcy dataset, causing the prediction models to be prone to bias. Therefore, this research aimed to compare the performance of the SVM and ANN models in predicting companies' bankruptcy. To overcome the problem of overfitting and feature selection, we utilized the Principle Component Analysis (PCA) dimensionality reduction technique. In addition, for handling the imbalanced dataset problem, we used the Borderline Synthetic Minority Oversampling Technique (BSM). The data used in this study were gained from the annual financial statements of bankrupt and non-bankrupt public companies in Indonesia during the 2010 to 2019 years period. Based on the research, the ANN-BSM-PCA combination model with {PC =16} provided the best accuracy, precision, and recall scores of 0.96, 0.934, and 0.988, respectively. Therefore, the ANN-BSM-PCA with {PC =16} was the best model in the case of predicting companies' bankruptcy. © 2022 IEEE.

关键词： Support vector machines

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Content Creation for Touristic Portals Using ChatGPT

Content Creation for Touristic Portals Using ChatGPT

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Papantoniou Lampros Voyiatzaki Eleni Hatzilygeroudis Ioannis Computer Engineering and Informatics Dept University of Patras Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Our research presents the development of an innovative web-based application designed to assist hoteliers in creating and managing multilingual content for their tourist accommodations for tourism portals. To address this need, our application leverages the advanced capabilities of OpenAI's ChatGPT for automated content generation and DeepL's API for high-quality translations, integrated within a user-friendly interface. Through an integrated development process, we leveraged the capabilities of combining natural language processing with artificial intelligence to meet the specific needs of the tourism and hospitality industry. Our findings indicate that the application significantly streamlines the content creation and translation process, offering hoteliers a competitive edge in a globally connected marketplace. By automating these tasks, the system not only saves time and resources but also ensures consistency and quality in accommodation listings across different languages. The application's impact is further evidenced by its adaptability to various accommodation types and the potential for enhancing guest satisfaction through improved information accessibility. The benefits of our application are supported by a medium scale evaluation.

关键词： Measurement Machine learning algorithms Databases Tourism industry Chatbots Market research Artificial intelligence Portals Standards Testing

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The Deep Learning-based Intelligent System for Extracting Information from e-KTP 2

The Deep Learning-based Intelligent System for Extracting In...

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2nd International Conference on Software engineering and Information Technology, ICoSEIT 2024

作者： Fuadi, Zaki Muchtar, Kahlil Subianto, Muhammad Riza, Hammam Oktiana, Maulisa Nizamuddin Universitas Syiah Kuala Department of Informatics Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Jakarta10340 Indonesia

ISBN: (纸本)9798350317503

This research implements an Intelligent Information System for Text Recognition on Indonesian Identity Cards (eKTP) based on Deep Learning architecture. The aim of this research is to develop a system capable of accurately and efficiently detecting and recognizing text on e-KTP images. The first phase of this research involves selecting ideal images from the e-KTP dataset. Subsequently, pre-processing is conducted to crop the image sides and merge them with background images to create a more diverse dataset and obtain the e-KTP image masks as training data. After selecting the ideal images, the total training data amounts to 144 images. The U-Net architecture is chosen as the deep learning method for image segmentation in this research, while for text detection and recognition, two neural networks, CRAFT and TRBA (TPS-ResNet-BiLSTM-Attention), are employed. The testing, based on accuracy values, Dice coefficients, and IoU for the segmentation process, results in a U-Net model accuracy of 99.52%. Text detection and recognition follow confidence values. © 2024 IEEE.

关键词： Intelligent systems

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A Procedure with MySQL Python for Developing Data Warehouse and Analytics Using Data from a Greek Soft Drinks Company

A Procedure with MySQL Python for Developing Data Warehouse ...

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International Conference on Applied Mathematics & computer Science (ICAMCS)

作者： Sotirios Tsakiridis Alkiviadis Tsimpiris Athanasios Angeioplastis Nikolaos Papaioannou Paris Mastorocostas Dimitrios Varsamis Dept. Computer Informatics and Telecommunications Engineering International Hellenic University Serres Greece Dept. Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (数字)9798350363807

ISBN: (纸本)9798350363814

The research focuses on the process of creating a data warehouse to meet the decision-making needs of a Greek beverage company. The data cover the period from 2018 to 2022. The developed data warehouse schema follows the star schema, with one fact table and five-dimension tables. Based on this structure, a web application was developed in PHP, which displays reports, charts, and analyses on the company's performance through interactive queries. Additionally, prediction functions tailored to the company's data were developed in Python. The process followed, the queries for creating the data warehouse, as well as the commands for creating analyses and predictions, serve as a useful guide for other companies that wish to upgrade the capabilities of their ERP systems.

关键词： Costs Data analysis Profitability Decision making Stars Companies Production Data warehouses Strategic planning Market research

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Chest Radiographs Enhancement with Contrast Limited Adaptive Histogram 17

Chest Radiographs Enhancement with Contrast Limited Adaptive...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Lee, Hyeri Choo, Hyunseung Le, Duc-Tai Bum, Junghyun Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781665453486

Among all the image contrast enhancement methods, histogram equalization is the most widely used. However, conventional histogram equalization causes the entire image to be too bright, resulting in noise generation as a consequence of excessive brightness changes and information loss when the histogram is intensively distributed into a specific amount of brightness. The Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is designed to address these limitations. To avoid overamplifying brightness variations, CLAHE clips the histogram of an input image to make it uniformly distributed. This study employs the CLAHE algorithm to improve contrast in chest radiographs, with the expected result that lung disease diagnosis will be more accurate. © 2023 IEEE.

关键词： Image reconstruction

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Real-time Monitoring Using AWS Cloud Platform For Agriculture Soil 1

Real-time Monitoring Using AWS Cloud Platform For Agricultur...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Tirtakusuma, Danar Ikram Sukaridhoto, Sritrusta Nourma Budiarti, Rizqi Putri Mulyo, Dwi Alfarezi, Fitratama Aminputra, Bima Briliandhika Hakim, Oktafian Sultan Politeknik Elektronika Negeri Surabaya Dept. of Creative Multimedia Technology Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Indonesia Universitas Nahdlatul Ulama Surabaya Indonesia

ISBN: (纸本)9798350365191

In the era of modern agriculture, real-time monitoring of soil conditions is crucial for increasing productivity and efficiency. This research proposes an agricultural soil monitoring system that utilizes the AWS Cloud platform to capture, analyze, and present real-time soil data. This system is designed to integrate various sensors that can measure humidity, pH, temperature and soil nutrients, all connected via the Internet of Things (IoT). The data collected by these sensors is sent to the AWS Cloud, where it is processed using cloud computing services such as AWS Lambda and Amazon EC2. The use of the AWS Cloud enables high scalability, reliability, and data security, and facilitates big data analysis using Amazon S3 and Amazon RDS. By using an intuitive dashboard, farmers can see soil analysis results in real-time and make informed decisions based on the data. Additionally, this system is equipped with machine learning algorithms that run on the AWS Cloud to predict soil needs and provide optimal fertilizer recommendations. This research shows how implementing AWS Cloud in agriculture can bring digital transformation to agriculture, enabling farmers to increase crop yields while reducing waste and operational costs. © 2024 IEEE.

关键词： Cloud platforms

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Action Localization and Recognition through Unsupervised I3D and TSN 3

Action Localization and Recognition through Unsupervised I3D...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Umran, Munzir Muchtar, Kahlil Abidin, Taufik Fuadi Arnia, Fitri Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia

ISBN: (纸本)9798350321487

Since CCTV technology was widely introduced as a video surveillance system, it has become very popular due to increased security concerns. However, detecting suspicious behavior is still done manually by humans, which is inefficient in terms of accuracy and manpower (prone to distraction and fatigue). Many studies use the entire frame of a video to recognize human action, whereas in this study first localize the area of object movement using the unsupervised inflated 3D (I3D) network which produces several frames that have been determined as the region of interest (ROI) from the total frames in the video. Then the collection of ROI frames is generated into a video which will then be classified to recognize human action through the Temporal Segment Networks (TSN). For efficiency purposes, the proposed pipeline can be run separately in the edge (for localization), and cloud environment (for recognition), respectively. The results show that there are 54 classes of ROI video data sets that have an average human action recognition prediction similarity rate above 84.6% which is the average of the prediction similarity rate of the entire ROI video data. In terms of prediction accuracy, 37 classes of data (36.63%) are better than the original class, including 25 classes of data (24.75%) above the threshold, and the processing speed of classification with ROI video data is on average 5 times faster than full-frame video data. © 2023 IEEE.

关键词： Forecasting

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A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic

arXiv

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arXiv 2024年

作者： Gogou, Ioanna C. Koutsomitropoulos, Dimitrios A. Dept. of Computer Engineering and Informatics University of Patras Patras Greece

Convolutional Neural Networks (CNN) are commonly used for the problem of object detection thanks to their increased accuracy. Nevertheless, the performance of CNN-based detection models is ambiguous when detection speed is considered. To the best of our knowledge, there has not been sufficient evaluation of the available methods in terms of the speed/accuracy trade-off in related literature. This work assesses the most fundamental object detection models on the Common Objects in Context (COCO) dataset with respect to this trade-off, their memory consumption, and computational and storage cost. Next, we select a highly efficient model called YOLOv5 to train on the topical and unexplored dataset of human faces with medical masks, the Properly-Wearing Masked Faces Dataset (PWMFD), and analyze the benefits of specific optimization techniques for real-time medical mask detection: transfer learning, data augmentations, and a Squeeze-and-Excitation attention mechanism. Using our findings in the context of the COVID-19 pandemic, we propose an optimized model based on YOLOv5s using transfer learning for the detection of correctly and incorrectly worn medical masks that surpassed more than two times in speed (69 frames per second) the state-of-the-art model SE-YOLOv3 on the PWMFD dataset while maintaining the same level of mean Average Precision (67%). © 2024, CC BY.

关键词： Object detection

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Generalization Evaluation of Seq2seq based Fog Computing Application Placement Algorithm on Limited Dataset 3

Generalization Evaluation of Seq2seq based Fog Computing App...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Pakpahan, Michael S. M. Nugroho, Lukito Edi Widyawan, Widyawan Wardhana, Ajie Kusuma Muhammad Ardian, R.A. Astagenta, Rangga Satria Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Dept. Informatics Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Addressing its growing number and vital role, decentralization of cloud computing becoming a necessity. Fog computing aims to bring application closer to the data source-typically at the network's edge by leveraging local resources to provide faster data processing and decision-making. Fog computing then has to place application strategically to use its limited fog resource to improve application performance metrics. This problem known as Fog Application Placement Problem (FAPP) has been approached using previous methods that rely on rules and prior knowledge that may not be adaptive, but rather being overly specialized to specific problems. Deep learning with its learning mechanism, can offer more adaptable and dynamic solutions for a wide range of scenarios, especially in fog network that continuously evolve. This research investigate Seq2seq placement model inherent limitations, notably the impracticality of generating every possible pattern from all potential request configurations. We aim to address and answer the following critical questions: 1) How does the model's performance vary when confronted with unseen requests or an augmented number of modules, especially considering the limitations in training data?;2) Can the seq2seq model, even with its training limitations, adhere to the heuristic rules of the dataset when dealing with unfamiliar problems? This research shows that in similar availability, there is a reduction of almost half of the response time with 183.03 ms, 2.93 number of hops, and 0.87 megabyte of transmitted messages against the hop3 algorithm. Moreover, we highlight the ability of seq2seq model to follow heuristic rules in unseen scenarios. © 2023 IEEE.

关键词： Fog computing

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Towards Personalized Fitness with Multi-modal Wearable Heart Rate Monitors: A Proposal 6

Towards Personalized Fitness with Multi-modal Wearable Heart...

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6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Nugraha, Alfian Dwi Puspitaningayu, Pradini Hidayatullah, Rahmat Pramukantoro, Eko Sakti Widayaka, Parama Diptya Rochmawati, Naim State University of Surabaya Dept. of Informatics Engineering Surabaya Indonesia State University of Surabaya Dept. of Electrical Engineering Surabaya Indonesia Brawijaya University Faculty of Computer Science Malang Indonesia

ISBN: (纸本)9798350326642

Nowadays, the trend for home workouts is getting more and more popular. The growth of wearable fitness trackers from basic accelerometers, pedometers, pulse detectors, and ECGs also supports the advancement of personal fitness monitoring. Everyone has a distinct level of fitness, in theory. The heart rate, which is correlated with physical activity, such as sports, demonstrates how this organ functions. As the muscles work harder at a high intensity, the heart rate will also rise. This paper presents the design and implementation of a multimodal wearable heart rate monitoring system. The software is Android-based and is designed to be able to receive heart rate data sent by two different wearable heart rate monitors simultaneously using Bluetooth low-energy protocol. One uses an optical heart monitor and the other uses an electrocardiogram or ECG. The implementation also includes IoT for real-Time data acquisition and a web-based application for wider accessibility. The system is evaluated by collecting packets from two devices for 60 minutes. The results underscore the feasibility of the system in collecting users' heart rate data either using different devices. Furthermore, it serves as a foundation for future studies aiming to expand the personalization capabilities of wearable devices in healthcare and fitness. © 2023 IEEE.

关键词： Electrocardiograms

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