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103,506 results

2. An Artificial Neural Network (ANN) Modelling Approach for Evaluating Turbidity Properties of Paper Recycling Wastewater.

Author

Kardeş, Serkan, Özkan, Uğur, Bayram, Okan, and Şahin, Halil Turgut

Subjects
*ARTIFICIAL neural networks, *PAPER recycling, *RECYCLED paper, *TURBIDITY, *POLLUTANTS
Abstract

A pre-treatment process was evaluated in this work for wastewater from paper recycling using microwave technology followed by rapid precipitation of contaminants through centrifugation. Artificial neural networks (ANNs) were used to analyze and optimize the turbidity values. Thirty experimental runs were utilized including microwave (MW) power, duration, centrifuge time, and centrifuge speed as input variables, generated by the Central Composite Full Design (CCFD) approach. The experimental turbidity ranged from 8.1 to 19.7 NTU, while predicted values ranged from 8.4 to 19.7 NTU by ANN. The ANN model showed a robust prediction performance with low mean squared error values during training and testing. Moreover, high R² values showed a remarkable agreement between the experimental observations and ANN predictions. The results obtained from the input values (A:150.00, B:60.00, C:15.00, D:30.00) of sample 2, which gave the lowest turbidity value, showed the most removal of pollution. The results obtained from the input values (A:250.00, B:60.00, C:7.00, D:20.00) of sample 30, which gave the highest turbidity value, showed the least removal of pollution. The results showed that increasing RPM and time of the centrifugation process significantly affected the removal of pollution in wastewater. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Deep Reinforcement Learning for Credit Card Fraud Detection

Author

Kumari, Nancy, Acharjya, D. P., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish C., editor

Published
2024
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4. Movement Pattern Recognition in Boxing Using Raw Inertial Measurements

Author

Puchalski, Radosław, Giernacki, Wojciech, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Pereira, Ana I., editor, Mendes, Armando, editor, Fernandes, Florbela P., editor, Pacheco, Maria F., editor, Coelho, João P., editor, and Lima, José, editor

Published
2024
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5. Forecasting IT Project Completion Time: Artificial Neural Networks Approach

Author

Dinars, Konstantins, Stecenko, Inna, Mišņevs, Boriss, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabashkin, Igor, editor, Yatskiv, Irina, editor, and Prentkovskis, Olegas, editor

Published
2024
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6. Application of Machine Learning for Air Quality Analysis

Author

Ocaña, Jesús, Miñan, Guillermo, Chauca, Luis, Espínola, Karina, Leiva, Luis, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Botto-Tobar, Miguel, editor, Zambrano Vizuete, Marcelo, editor, Montes León, Sergio, editor, Torres-Carrión, Pablo, editor, and Durakovic, Benjamin, editor

Published
2024
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9. Effects of Feature Types on Donor Journey

Author

Lee, Greg, Raghavan, Ajith Kumar, Hobbs, Mark, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rocha, Ana Paula, editor, Steels, Luc, editor, and van den Herik, Jaap, editor

Published
2024
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10. Machine Learning for Increased Profits in the Cryptocurrency Market Through Pattern Recognition with Artificial Neural Networks

Author

Lazo Lazo, Juan G., Ruiz Cárdenas, Diego A., Esquives Bravo, Sebastián R., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Jat, Dharm Singh, editor, Mishra, Durgesh, editor, and Joshi, Amit, editor

Published
2024
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11. Adaptive Methods for the Structural Optimization of Neural Networks and Their Ensemble for Data Analysis

Author

Bukhtoyarov, Vladimir, Nelyub, Vladimir, Evsyukov, Dmitry, Nelyub, Sergei, Gantimurov, Andrey, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Jordan, Vladimir, editor, Tarasov, Ilya, editor, Shurina, Ella, editor, Filimonov, Nikolay, editor, and Faerman, Vladimir A., editor

Published
2024
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12. Research Paper Screening Tool: Automating Conference Paper Evaluation and Enhancement.

Author

Rathnasiri, Hansani Upeksha, Ishara Lakshani, L. A., Amarasinghe, Nipuni Nilakna, Dissanayake, Oshan Asinda, Nawinna, Dasuni, and Attanayaka, Buddima

Subjects
TECHNOLOGICAL innovations, ARTIFICIAL neural networks, MACHINE learning, ARTIFICIAL intelligence, NATURAL language processing
Abstract

In this era of knowledge, academic researchers are growing every day, this also spikes a growth in published literature on the new innovations and findings. This leads to a problem where the reviewers at the conferences must go through many research papers to determine whether they are suitable for the conference or not. This problem has caused the necessity of an effective paper screening tool for optimizing the literature review process. This research presents a development of a new Paper Screening Tool (PST) aimed at increasing the efficiency and accuracy of the literature screening phase. Leveraging the NPL processing techniques this PST and reduces a lot of manual efforts. Through comprehensive evaluation using a diverse dataset, the tools provide high precision. The PST also has user friendly interfaces and customizable report generation which empowers the researchers screening process to their specific needs. This paper contributes to literature by solving the challenge of information overloading during the literature review. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Prediction of Values of Borsa Istanbul Forest, Paper, and Printing Index Using Machine Learning Methods.

Author

Akyüz, İlker, Polat, Kinyas, Bardak, Selahattin, and Ersen, Nadir

Subjects
*ARTIFICIAL neural networks, *STOCK price indexes, *GOLD sales & prices, *STOCK index futures, *MONEY supply
Abstract

It is difficult to predict index values or stock prices with a single financial formula. They are affected by many factors, such as political conditions, global economy, unexpected events, market anomalies, and the characteristics of the relevant companies, and many computer science techniques are being used to make more accurate predictions about them. This study aimed to predict the values of the XKAGT index by using the monthly closing values of the Borsa Istanbul (BIST) Forestry, Paper and Printing (XKAGT) index between 2002 and 2023, and the machine learning techniques artificial neural networks (ANN), random forest (RF), k-nearest neighbor (KNN), and gradient boosting machine (GBM). Furthermore, the performances of four machine learning techniques were compared. Factors affecting stock prices are generally classified as macroeconomic and microeconomic factors. As a result of examining the studies on determining the macroeconomic factors affecting the stock markets, 10 macroeconomic factors were determined as input. The macroeconomic variables used were crude oil price, exchange rate of USD/TRY, dollar index, BIST100 index, gold price, money supply (M2), S&P 500 index, US 10-year bond interest, export-import coverage rate in the forest products sector, and deposits interest rate. It was determined that all machine learning techniques used in the study performed successfully in predicting the index value, but the k-nearest neighbor algorithm showed the best performance with R2=0.996, RMSE=71.36, and a MAE of 40.8. Therefore, in line with the current variables, investors can make analyzes using any of the ANN, RF, KNN, and GBM techniques to predict the future index value, which will lead them to accurate results. [ABSTRACT FROM AUTHOR]

Published
2024
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15. A Novel Classification Methodology for Investigation of Heart Disease

Author

Pasrija, Kanika, Mittal, Kavita, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Whig, Pawan, editor, Silva, Nuno, editor, Elngar, Ahmed A., editor, Aneja, Nagender, editor, and Sharma, Pavika, editor

Published
2023
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16. ANN Model to Predict Religiosity Based on Social Online Behaviors and Personality Traits

Author

Jablonska, Marta R., Das, Smruti Rekha, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Narayana, V. A., editor, Suganthan, P. N., editor, Anguera, Jaume, editor, Koppula, Vijaya Kumar, editor, and Gupta, Suneet Kumar, editor

Published
2023
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17. Time Series Analysis and Forecast Accuracy Comparison of Models Using RMSE–Artificial Neural Networks

Author

Chowdary, Nama Deepak, Hrushikesh, Tadepally, Varma, Kusampudi Madhava, Basha, Shaik Ali, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, Dutta, Paramartha, editor, and Piuri, Vincenzo, editor

Published
2023
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18. Smart Grid Analytics—Analyzing and Identifying Power Distribution Losses with the Help of Efficient Data Regression Algorithms

Author

Mishra, Amita, Verma, Babita, Agarwal, Smita, Spurjeon, K. Subhashini, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, Dutta, Paramartha, editor, and Piuri, Vincenzo, editor

Published
2023
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19. Surrogate Modeling for Stochastic Assessment of Engineering Structures

Author

Lehký, David, Novák, Lukáš, Novák, Drahomír, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Pardalos, Panos, editor, Di Fatta, Giuseppe, editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor

Published
2023
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20. Is There a Difference between Paper and Electronic Chinese Signatures?

Author

Luo, Ji-Feng, Pu, Yun-Zhu, Yin, Jie-Yang, Liu, Xiaohong, Tan, Tao, Zhang, Yudong, and Hu, Menghan

Subjects
ARTIFICIAL neural networks, ELECTRONIC paper, DIGITAL signatures, CONVOLUTIONAL neural networks, WILCOXON signed-rank test
Abstract

The purpose of this study is to investigate whether there are differences in handwritten Chinese signatures on different media including paper and electronic devices. Participants were asked to sign specified names on various types of media and the signatures were scanned or saved digitally for subsequent analysis. In this study, using convolutional neural networks and Siamese neural networks as classifiers and comparators, the performance plunge is revealed and thus considerable dissimilarity between the signatures on different media is implied. To further explore this, cubic Bézier curves are fitted to the signatures using the least square method for quantitative statistical analysis. By analyzing the visual changes in the morphology of strokes, several features of signatures are selected and computed, and the paired t‐test and the Wilcoxon signed‐rank test are implemented, which provides a deeper substantiation and explanation of the findings. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Automatic Test Paper Generation Technology for Mandarin Based on Hilbert Huang Algorithm.

Author

Wang, Lei

Subjects
ARTIFICIAL neural networks, ALGORITHMS, COMPUTER engineering, EMPLOYEE rights, HUMAN resources departments
Abstract

With the development of computer technology, automatic test paper generation systems have gradually become an effective tool for detecting and maintaining national machine security and protecting the rights and interests of workers. This article achieved multi-level oral scores for different types of questions through online scoring using artificial neural networks in recent years. Based on its specific situation and evaluation index requirements, an analysis module that is reasonable, efficient, and in line with the hierarchical structure and module requirements of national conditions has been designed to complete the research on automatic test paper generation technology, in order to help better manage and allocate human resources and improve production efficiency. Afterwards, this article conducted functional testing on the technical module. The test results showed that the scalability of the system was over 82%. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Colombian Sign Language Classification Based on Hands Pose and Machine Learning Techniques

Author

Vera, Anny, Pérez, Camilo, Sánchez, Juan José, Orjuela-Cañón, Alvaro D., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Narváez, Fabián R., editor, Urgilés, Fernando, editor, Bastos-Filho, Teodiano Freire, editor, and Salgado-Guerrero, Juan Pablo, editor

Published
2023
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25. Strategic Demand Forecasting with Machine Learning Algorithms in a Dyeing Company

Author

Alp, Vuslat, Ervural, Beyzanur Cayir, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Durakbasa, Numan M., editor, and Gençyılmaz, M. Güneş, editor

Published
2023
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28. Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries.

Author

Folks, Ryan D., Naik, Bhiken I., Brown, Donald E., and Durieux, Marcel E.

Subjects
*MEDICAL records, *ARTIFICIAL neural networks, *COMPUTER vision, *DIASTOLIC blood pressure, *MEDICAL personnel, *DEEP learning, *SYSTOLIC blood pressure
Abstract

Background: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. Methods: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. Results: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. Conclusions: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries. [ABSTRACT FROM AUTHOR]

Published
2024
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29. A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers.

Author

Nezami, Md. Manzar, Equbal, Md. Danish, Ansari, Md. Fahim, Alotaibi, Majed A., Malik, Hasmat, García Márquez, Fausto Pedro, and Hossaini, Mohammad Asef

Subjects
*ARTIFICIAL neural networks, *POWER transformers, *TRANSFORMER insulation, *ARTIFICIAL intelligence, *MATHEMATICAL optimization
Abstract

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition.

Author

Rodrigues, João Antunes, Farinha, José Torres, Mendes, Mateus, Mateus, Ricardo J. G., and Cardoso, António J. Marques

Subjects
*MACHINE learning, *PAPER pulp, *ELECTRIC currents, *ARTIFICIAL neural networks
Abstract

Forecasting has extreme importance in industry due to the numerous competitive advantages that it provides, allowing to foresee what might happen and adjust management decisions accordingly. Industries increasingly use sensors, which allow for large-scale data collection. Big datasets enable training, testing and application of complex predictive algorithms based on machine learning models. The present paper focuses on predicting values from sensors installed on a pulp paper press, using data collected over three years. The variables analyzed are electric current, pressure, temperature, torque, oil level and velocity. The results of XGBoost and artificial neural networks, with different feature vectors, are compared. They show that it is possible to predict sensor data in the long term and thus predict the asset's behaviour several days in advance. [ABSTRACT FROM AUTHOR]

Published
2022
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31. NOWCASTING THE GROWTH RATE OF THE ICT SECTOR: OECD DIGITAL ECONOMY PAPERS.

Subjects
INFORMATION & communication technologies, ELECTRONIC paper, HIGH technology industries, INDUSTRY classification, ARTIFICIAL neural networks, STATISTICAL learning
Abstract

This technical paper explores the use of Google Trends data to estimate the growth rate of the information and communication technology (ICT) sector. It discusses the statistical and machine learning methods employed to extract relevant information and make accurate predictions. The paper addresses challenges such as sampling noise and downward trends in search indexes, and suggests strategies to mitigate these issues. The results of the nowcasting model indicate that the ICT sector remained resilient during the COVID-19 pandemic. The paper concludes by suggesting potential enhancements to the model, including the incorporation of additional data sources and the exploration of alternative machine learning techniques. [Extracted from the article]

Published
2024

32. Detecting Human Behavioral Pattern in Rock, Paper, Scissors Game Using Artificial Intelligence

Author

Maryam Ghasemi, Gholam Hossein Roshani, and Abdolreza Roshani

Subjects
artificial neural networks, multi-layer perceptron (mlp), rock paper scissors game, human behavior pattern detection, intelligent player, Computer engineering. Computer hardware, TK7885-7895
Abstract

As entertainment tools, computer games are important phenomena in the world, which are considered as a popular medium, an effective educational solution and a considerable economy resource. In this paper, Multi-Layer perceptron (MLP) neural network was used to detect human behavior pattern in rock, paper, scissors game. The similarity of artificial neural networks (ANNs) to the human brain is the main motivation of this study. MATLAB software was used to implement the network code. These codes consisted of two phases: 1) training the ANN to learn the human behavioral pattern considering forty games. 2) real play against a human by doing ten games. After the implementation of the network, its effectiveness in detecting human behavioral patterns was investigated. The network was tested on 40 people (20 women and 20 men). Each player played with the target network in three stages. The results of this study showed that the win percentage of computers with MLP neural network was 57.5% for men and 60.8% for women. While the percentage of the computer without neural networks and with random selections in 60 games was 52.5% for men and 42.5% for women

Published
2020
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33. Special Issue "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023".

Author

Kim, Byung-Seo, Afzal, Muhammad Khalil, and Ullah, Rehmat

Subjects
*MULTICASTING (Computer networks), *INFORMATION technology, *SENSOR networks, *ARTIFICIAL neural networks, *DEEP learning, *BEAM steering, *INTEGRATED circuit design, *COMPUTER network security
Abstract

This document is a summary of a special issue of the journal Sensors, titled "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023." The special issue features selected papers from the 10th and 11th International Conferences on Green and Human Information Technology (ICGHITs), which were held in Korea and Thailand. The conferences focused on the theme of "Emerging Artificial Intelligent (AI)+X technology" and "Hyper Automation + Human AI" respectively. The selected papers cover various topics such as network security, routing protocols, signal detection, and clustering mechanisms, all incorporating AI-based methods. The issue also includes papers on topics like secure authentication, distance estimation in RFID systems, energy optimization in smart homes, blockchain technology, and radar signal detection. The authors emphasize the importance of both technology and humanity in advancing green and information technologies. [Extracted from the article]

Published
2024
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35. Integrating Machine Learning and Cognitive Modeling of Decision Making

Author

Rahgooy, Taher, Venable, K. Brent, Trueblood, Jennifer S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Gurney, Nikolos, editor, and Sukthankar, Gita, editor

Published
2022
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36. Unsupervised Brain Segmentation System Using K-Means and Neural Network

Author

Laudicella, Riccardo, Agnello, Luca, Comelli, Albert, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mazzeo, Pier Luigi, editor, Frontoni, Emanuele, editor, Sclaroff, Stan, editor, and Distante, Cosimo, editor

Published
2022
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37. Acute Lymphoblastic Leukemia Disease Detection Using Image Processing and Machine Learning

Author

Chavan, Abhishek D., Thakre, Anuradha, Chopade, Tulsi Vijay, Fernandes, Jessica, Gawari, Omkar S., Gore, Sonal, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Tyagi, Vipin, editor, Gupta, P. K., editor, Flusser, Jan, editor, and Ören, Tuncer, editor

Published
2022
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38. Design, Implementation, and Modeling of a LoRa Network Installed in a Freshwater Body

Author

Guerra-Londono, Mateo, Urrea, Gustavo, Botero-Valencia, Juan, Reyes-Vera, Erick, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Narváez, Fabián R., editor, Proaño, Julio, editor, Morillo, Paulina, editor, Vallejo, Diego, editor, González Montoya, Daniel, editor, and Díaz, Gloria M., editor

Published
2022
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39. AttnGAN: Realistic Text-to-Image Synthesis with Attentional Generative Adversarial Networks

Author

Mathesul, Shubham, Bhutkar, Ganesh, Rambhad, Ayush, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ardito, Carmelo, editor, Lanzilotti, Rosa, editor, Malizia, Alessio, editor, Larusdottir, Marta, editor, Spano, Lucio Davide, editor, Campos, José, editor, Hertzum, Morten, editor, Mentler, Tilo, editor, Abdelnour Nocera, José, editor, Piccolo, Lara, editor, Sauer, Stefan, editor, and van der Veer, Gerrit, editor

Published
2022
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42. A Decision Support System Based on Rainfall Nowcasting and Artificial Neural Networks to Mitigate Wastewater Treatment Plant Downstream Floods

Author

Termite, Loris Francesco, Bonamente, Emanuele, Garinei, Alberto, Bolpagni, Daniele, Menculini, Lorenzo, Marconi, Marcello, Biondi, Lorenzo, Chini, Andrea, Crespi, Massimo, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Klein, Cornel, editor, Jarke, Matthias, editor, Helfert, Markus, editor, Berns, Karsten, editor, and Gusikhin, Oleg, editor

Published
2022
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43. Understanding the Voice of Customers Using Artificial Neural Networks: A Study of Food Aggregators in the Cachar District of Assam

Author

Chanda, Dhritiman, Mazumder, Nilanjan, Ghosh, D., Dutta, Deepjyoti, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mukhopadhyay, Somnath, editor, Sarkar, Sunita, editor, Dutta, Paramartha, editor, Mandal, Jyotsna Kumar, editor, and Roy, Sudipta, editor

Published
2022
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45. Assessment of Network Intrusion Detection System Based on Shallow and Deep Learning Approaches

Author

Meena, Gaurav, Babita, Mohbey, Krishna Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Balas, Valentina E., editor, Sinha, G. R., editor, Agarwal, Basant, editor, Sharma, Tarun Kumar, editor, Dadheech, Pankaj, editor, and Mahrishi, Mehul, editor

Published
2022
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46. The Application of Artificial Intelligence to Nuclear Power Plant Safety

Author

Yavuz, Ceyhun, Şentürk Lüle, Senem, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, and Kayakutlu, Gülgün, editor

Published
2022
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48. Selection of Abandoned Areas for Implantation of Solar Energy Projects Using Artificial Neural Networks

Author

Franco, David Gabriel de Barros, Steiner, Maria Teresinha Arns, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Canciglieri Junior, Osiris, editor, Noël, Frédéric, editor, Rivest, Louis, editor, and Bouras, Abdelaziz, editor

Published
2022
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49. Training Artificial Neural Networks with Gradient and Coarse-Level Correction Schemes

Author

Vater, Nadja, Borzì, Alfio, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Jansen, Giorgio, editor, Pardalos, Panos M., editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor

Published
2022
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