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Application of Convolution Neural Network for Plastics Waste Management Using TensorFlow 1

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Saputro, Immanuela Puspasari Sanger, Junaidy Budi Adrian, Angelia Melani Mokorisa, Gilberth Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Faculty of Engineering Universitas Katolik De La Salle Manado Manado95000 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Nowadays, plastic waste has become a global problem. Plastic waste can be found in the land, oceans, rivers, and even soil sediments. This problem has motivated various countries to overcome environmental pollution, especially plastic waste primarily through recycling programs. Plastic waste that is still good can be recycled into handicrafts or sold at the waste bank according to the current selling price. However, the waste management officer encountered difficulties classifying plastic waste since there are many forms of waste. Recently, a deep learning-based method, namely The Convolutional Neural Network (CNN), has been widely used for image processing tasks. In this study, we developed a mobile-based application by employing the CNN Algorithm to identify and classify plastic waste. The application will recognize and classify recyclable plastic waste such as plastic bags, plastic bottles, shampoo bottles, etc. There were 156 images collected manually by a smartphone camera and Google Image. All images are converted into jpg format with the size of 300 × 300 pixels. The dataset was divided into two parts, 106 images for data training and 50 images for data testing. From the experimental result, the model obtained an accuracy of 86%. Moreover, there are features to view price lists and tutorials for handicrafts from plastic waste. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Deep learning

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Machine Speech Chain with Emotion Recognition 11

Machine Speech Chain with Emotion Recognition

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11th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2024

作者： Naufal, Akeyla Pradia Lestari, Dessi Puji Purwarianti, Ayu Azizah, Kurniawati Tanaya, Dipta Sakti, Sakriani School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331520311

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics. © 2024 IEEE.

关键词： Chains

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Developing an Artificial Intelligence Tool for Image Generation Using a Unique Dataset with Image-to-Image Functionality 24

Developing an Artificial Intelligence Tool for Image Generat...

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25th International Conference on computer Systems and Technologies, CompSysTech 2024

作者： Toktosunova, Akbiike Ergeshov, Alymbek Esenalieva, Gulzada Ermakov, Andrei Isaev, Ruslan Department of Applied Mathematics and Informatics Ala-Too International University Kyrgyzstan Department of Compute Science Ala-Too International University Kyrgyzstan Faculty of Engineering and Computer Science Ala-Too International University Kyrgyzstan

ISBN: (纸本)9798400716843

This article describes the application of a unique artificial intelligence model, trained on the basis of a special dataset, for drawing images. Artificial intelligence is very important in the modern world because it is a key driver of progress, offering innovative solutions to improve processes in various fields. A feature of this work is the use of the Stable Diffusion model and the creation of the LoRa model to create a specific object. The use of a special dataset, which is a classified set of images, to train models based on a stable propagation mechanism. The application of the image-to-image method, which consists in completely generating an image by having the model reproduce all the objects in the image, trying to preserve the maximum amount of detail in the image. As a result, a tool has been developed that can generate images of characters of a certain style. © 2024 ACM.

关键词： Adversarial machine learning

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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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Evaluating the performance of metaheuristic-tuned weight agnostic neural networks for crop yield prediction

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Neural Computing and Applications 2024年第24期36卷 14727-14756页

作者： Jovanovic, Luka Zivkovic, Miodrag Bacanin, Nebojsa Dobrojevic, Milos Simic, Vladimir Sadasivuni, Kishor Kumar Tirkolaee, Erfan Babaee Faculty of Technical Sciences Singidunum University Danijelova 32 Belgrade11000 Serbia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Mathematics Saveetha School of Engineering SIMATS Tamilnadu Thandalam Chennai602105 India MEU Research Unit Middle East University Amman Jordan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Yuandong Rd. Zhongli Dist. Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Center for Advanced Materials Qatar University Doha Qatar Department of Industrial Engineering Istinye University Istanbul34396 Turkey Department of Industrial and Mechanical Engineering Lebanese American University Byblos Lebanon

This study explores crop yield forecasting through weight agnostic neural networks (WANN) optimized by a modified metaheuristic. WANNs offer the potential for lighter networks with shared weights, utilizing a two-layer cooperative framework to optimize network architecture and shared weights. The proposed metaheuristic is tested on real-world crop datasets and benchmarked against state-of-the-art algorithms using standard regression metrics. While not claiming WANN as the definitive solution, the model demonstrates significant potential in crop forecasting with lightweight architectures. The optimized WANN models achieve a mean absolute error (MAE) of 0.017698 and an R-squared (R2) score of 0.886555, indicating promising forecasting performance. Statistical analysis and Simulator for Autonomy and Generality Evaluation (SAGE) validate the improvement significance and feature importance of the proposed approach. © The Author(s) 2024.

关键词： Crops

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Using Web Server Logs to Identify and Comprehend Anomalous User Activity

Using Web Server Logs to Identify and Comprehend Anomalous U...

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International Conference on Telecommunications (ConTEL)

作者： Lenka Benova Ladislav Hudec Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

This research paper presents a study for identifying user anomalies in large datasets of web server requests. Using a cybersecurity company's network of web servers as a case study, we propose a technique for analyzing user activity in NGINX logs. The proposed method does not require a labeled dataset and is capable of efficiently identifying different user anomalies in large datasets with millions of daily requests. The results of the analysis provided a deeper understanding of user behavior when seeking updates through web requests and aided in interpreting the findings. Clustering the anomalies helped to produce typical clusters and further supported the interpretation of the results. This work provides valuable insights into user behavior in web server networks and highlights the importance of efficient anomaly detection techniques in large datasets. The findings have potential real-world applications in the field of cybersecurity, particularly in providing network security analysts with an automated and more objective approach to threat analysis. This study showcases the importance of automated methods for analyzing user activity in web server networks and provides a more objective and efficient approach to detecting user anomalies in large datasets. This approach contributes to the development of more effective and precise cybersecurity systems, ultimately improving the protection of network infrastructures from malicious attacks.

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Stock Price Forecasting in Finance with Artificial Neural Networks 5

Stock Price Forecasting in Finance with Artificial Neural Ne...

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5th Asia Conference on Information engineering, ACIE 2025

作者： Daloglu, Turgut Osi, Garibe Aslan, Murat Beskirli, Mehmet Cizre District Directorate of National Education Şirnak University Şirnak Turkey Şirnak University Department of Computer Engineering Şirnak Turkey Karamanoǧlu Mehmetbey University Faculty of Medicine Departmen of Medical Education and Informatics Karaman Turkey

ISBN: (纸本)9798331528409

This paper examines the applications of Artificial Neural Networks (ANNs) in the field of finance, particularly in stock price forecasting. Artificial neural networks are mathematical models that mimic biological nervous systems and are used to solve complex problems such as financial data analysis. Stock price forecasting is an important topic for investors and finance professionals. Artificial neural networks have been widely used in this field due to their ability to process large amounts of financial data, learn complex relationships and predict future price movements. This study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. Neural networks attempt to predict future price movements by learning the complex relationships between these data. Stock price forecasting with artificial neural networks can offer a more flexible and adaptive approach than traditional statistical methods. However, the success of the model depends on factors such as the quality of the data set used, network architecture and training parameters. This study was conducted to understand the potential of artificial neural networks in finance and to evaluate their effectiveness in stock price forecasting. The purpose of this study is to explore how artificial neural networks can be used in financial data analysis, and in particular their potential in stock price forecasting. The study is designed to understand the advantages of applying ANN in finance, to evaluate the stock price forecasting performance of this technology and to provide a new perspective that can influence future investment strategies. © 2025 IEEE.

关键词： Neural network models

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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Synthesis of Batik Motifs using a Diffusion - Generative Adversarial Network

arXiv

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arXiv 2023年

作者： Octadion, One Yudistira, Novanto Kurnianingtyas, Diva Informatics Engineering Faculty of Computer Science Universitas Brawijaya Indonesia

Batik, a unique blend of art and craftsmanship, is a distinct artistic and technological creation for Indonesian society. Research on batik motifs is primarily focused on classification. However, further studies may extend to the synthesis of batik patterns. Generative Adversarial Networks (GANs) have been an important deep learning model for generating synthetic data, but often face challenges in the stability and consistency of results. This research focuses on the use of StyleGAN2-Ada and Diffusion techniques to produce realistic and high-quality synthetic batik patterns. StyleGAN2-Ada is a variation of the GAN model that separates the style and content aspects in an image, whereas diffusion techniques introduce random noise into the data. In the context of batik, StyleGAN2-Ada and Diffusion are used to produce realistic synthetic batik patterns. This study also made adjustments to the model architecture and used a well-curated batik dataset. The main goal is to assist batik designers or craftsmen in producing unique and quality batik motifs with efficient production time and costs. Based on qualitative and quantitative evaluations, the results show that the model tested is capable of producing authentic and quality batik patterns, with finer details and rich artistic variations. The use of the Wasserstein loss function tends to produce batik motifs that are relatively new but less neat than the use of the StyleGAN2-Ada loss. The quality of the dataset also has a positive impact on the quality of the resulting batik patterns. Overall, this research contributes to the integration of Diffusion-GAN technology with traditional arts and culture, especially in the synthesis of batik motifs. However, there is still room for further development in increasing skill and accuracy in producing more detailed batik motifs. The dataset and code can be accessed here: https://***/octadion/diffusion-stylegan2-ada-pytorch © 2023, CC BY-NC-SA.

关键词： Diffusion

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