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2. Forecasting Türkiye's Paper and Paper Products Sector Import Using Artificial Neural Networks.
- Author
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GÜR, Yunus Emre and EŞİDİR, Kamil Abdullah
- Subjects
- *
PAPER products industry , *ARTIFICIAL neural networks , *RADIAL basis functions , *MULTILAYER perceptrons , *INDEPENDENT variables - Abstract
The paper and paper products sector is a crucial component of the Turkish economy, characterized by significant interactions with various other sectors. Türkiye imports substantial amounts of paper, playing a vital role in the growth and sustainability of this sector. Accurate import forecasting is essential for strategic planning and resource management. This study aims to forecast the imports of the Turkish paper sector for the period from April 2023 to March 2024 using two artificial neural network (ANN) models: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The dataset, obtained from the Turkish Statistical Institute (TurkStat), covers 219 months of data from 2005 to 2023. The dependent variable is Türkiye’s monthly import value of paper and paper products, while the independent variables include the monthly average US Dollar exchange rate, monthly imports of Türkiye, the Manufacturing Industry Production Index, the Paper Production Index, and the monthly exports of paper and paper products from Türkiye. The MLP model forecasts that the monthly imports of paper and paper products will range between 270 to 300 million USD, while the RBF model predicts values between 268 and 321 million USD. These findings underscore the efficacy of ANNs in providing accurate and reliable forecasts. This study addresses a gap in the literature by applying ANN methods to forecast imports in the paper and paper products sector, presenting a novel approach that can assist companies in making better-informed decisions regarding inventory management, production planning, and marketing strategies. By leveraging the advanced computational power and pattern recognition capabilities of ANNs, the study aims to enhance the strategic planning processes in the paper and paper products industry. The traditional methods often used in trade data analysis and forecasting are limited in capturing the complex and non-linear relationships present in economic data. This study's application of ANNs offers a significant advancement by utilizing models that can better handle such complexities. The accuracy of the MLP and RBF models highlights their potential as valuable tools for economic forecasting, providing insights that are crucial for optimizing supply chain operations and improving market responsiveness. The results indicate that companies can achieve better operational performance and increased customer satisfaction by effectively forecasting future import requirements. The originality of this study lies in its methodological approach, utilizing ANN models to forecast import values in a sector where traditional methods have been predominant. This innovative approach not only contributes to the existing body of knowledge but also offers practical applications for businesses within the sector. The detailed analysis of the data, combined with the robust modeling techniques employed, provides a comprehensive framework for understanding the dynamics of paper imports and making strategic decisions based on accurate predictions. In conclusion, the study demonstrates the significant success of artificial neural networks in predicting import values for the Turkish paper and paper products sector. The findings provide valuable information that can aid companies in strategic planning, enhancing their ability to manage inventory, plan production, and develop effective market strategies. The research contributes to the literature by filling a gap with its innovative approach, offering new perspectives and practical applications for improving decision-making processes in the industry. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
3. An Artificial Neural Network (ANN) Modelling Approach for Evaluating Turbidity Properties of Paper Recycling Wastewater.
- Author
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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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- View/download PDF
4. Prediction of Values of Borsa Istanbul Forest, Paper, and Printing Index Using Machine Learning Methods.
- Author
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Akyüz, İlker, Polat, Kinyas, Bardak, Selahattin, and Ersen, Nadir
- Subjects
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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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5. Automatic Test Paper Generation Technology for Mandarin Based on Hilbert Huang Algorithm.
- Author
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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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6. Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries.
- Author
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Folks, Ryan D., Naik, Bhiken I., Brown, Donald E., and Durieux, Marcel E.
- Subjects
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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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7. A novel artificial neural network approach for residual life estimation of paper insulation in oil‐immersed power transformers.
- Author
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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
- Full Text
- View/download PDF
8. Is There a Difference between Paper and Electronic Chinese Signatures?
- Author
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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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9. Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition.
- Author
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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
- Full Text
- View/download PDF
10. Detecting Human Behavioral Pattern in Rock, Paper, Scissors Game Using Artificial Intelligence
- Author
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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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11. Special Issue "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023".
- Author
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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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12. Knowledge Infused Representations Through Combination of Expert Knowledge and Original Input
- Author
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Biermann, Daniel, Goodwin, Morten, Granmo, Ole-Christoffer, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zouganeli, Evi, editor, Yazidi, Anis, editor, Mello, Gustavo, editor, and Lind, Pedro, editor
- Published
- 2022
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13. Comparative analysis of saturated–unsaturated shear strength under undrained loading: Experimental validation and ANN prediction of clayey soils.
- Author
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Pande, Prashant, Giri, Jayant, Ali, Mohd Sajid, Mohammad, Faruq, Raut, Jayant, Raut, Sanjay, Sathish, T., and Giri, Pallavi
- Subjects
SHEAR strength of soils ,ARTIFICIAL neural networks ,CLAY soils ,SHEAR strength ,FILTER paper - Abstract
Geotechnical designs and analyses of earth structures and foundations exclusively involve the assessment and consideration of unsaturated soil shear strength. The laboratory testing equipment and methods for predicting the unsaturated soil shear strength are complicated and more expensive. The experimental program attempted to involve undrained triaxial and filter paper for evaluating the unsaturated soil shear strength of identically compacted clayey soil. This study undertakes a comparison of shear strength in clayey soil under undrained loads, examining both saturated and unsaturated conditions. A 60 kPa air entry suction value is the key point at which linearity between the unsaturated shear strength parameter Ø
b and effective friction Ø′ with 15° linear slopes turns to non-linearity. Unsaturated shear strength increased by 22.76% in optimally wet conditions, 52.68% in optimum conditions, and 77.81% in optimally dry conditions as compared to saturated shear strength. This study utilizes an artificial neural network (ANN) to predict clayey soil's unsaturated shear strength, finding that the optimal ANN configuration (2-5-1 topology, Levenberg–Marquard optimization, and logsig transfer function) achieved high reliability with a correlation coefficient (R) of 0.9289 and mean square error values of 2.22, 7.12, and 3.012 for training, testing, and validation, respectively. This experimental investigation improves our understanding of clayey soil shear strength and emphasizes the importance of saturation and moisture content in geotechnical assessments under undrained loading conditions. [ABSTRACT FROM AUTHOR]- Published
- 2024
- Full Text
- View/download PDF
14. Neural Network Applications in Stylometry: The "Federalist Papers"
- Author
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Tweedie, F. J., Singh, S., and Holmes, D. I.
- Published
- 1996
15. Reminder of the First Paper on Transfer Learning in Neural Networks, 1976.
- Author
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Bozinovski, Stevo
- Subjects
PATTERN recognition systems ,ARTIFICIAL neural networks ,GEOMETRIC modeling ,PAPER arts ,HEALTH care reminder systems - Abstract
Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2020
- Full Text
- View/download PDF
16. Biocompatible Potato-Starch Electrolyte-Based Coplanar Gate-Type Artificial Synaptic Transistors on Paper Substrates
- Author
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Hyun-Sik Choi, Young-Jun Lee, Hamin Park, and Won-Ju Cho
- Subjects
synaptic transistors ,potato starch ,artificial neural networks ,biocompatible ,biodegradable ,Biology (General) ,QH301-705.5 ,Chemistry ,QD1-999 - Abstract
In this study, we propose the use of artificial synaptic transistors with coplanar-gate structures fabricated on paper substrates comprising biocompatible and low-cost potato-starch electrolyte and indium–gallium–zinc oxide (IGZO) channels. The electrical double layer (EDL) gating effect of potato-starch electrolytes enabled the emulation of biological synaptic plasticity. Frequency dependence measurements of capacitance using a metal-insulator-metal capacitor configuration showed a 1.27 μF/cm2 at a frequency of 10 Hz. Therefore, strong capacitive coupling was confirmed within the potato-starch electrolyte/IGZO channel interface owing to EDL formation because of internal proton migration. An electrical characteristics evaluation of the potato-starch EDL transistors through transfer and output curve resulted in counterclockwise hysteresis caused by proton migration in the electrolyte; the hysteresis window linearly increased with maximum gate voltage. A synaptic functionality evaluation with single-spike excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), and multi-spike EPSC resulted in mimicking short-term synaptic plasticity and signal transmission in the biological neural network. Further, channel conductance modulation by repetitive presynaptic stimuli, comprising potentiation and depression pulses, enabled stable modulation of synaptic weights, thereby validating the long-term plasticity. Finally, recognition simulations on the Modified National Institute of Standards and Technology (MNIST) handwritten digit database yielded a 92% recognition rate, thereby demonstrating the applicability of the proposed synaptic device to the neuromorphic system.
- Published
- 2022
- Full Text
- View/download PDF
17. A Machine Learning Model to Predict Citation Counts of Scientific Papers in Otology Field.
- Author
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Alohali, Yousef A., Fayed, Mahmoud S., Mesallam, Tamer, Abdelsamad, Yassin, Almuhawas, Fida, and Hagr, Abdulrahman
- Subjects
DECISION trees ,SERIAL publications ,NATURAL language processing ,BIBLIOMETRICS ,MACHINE learning ,REGRESSION analysis ,RANDOM forest algorithms ,CITATION analysis ,DESCRIPTIVE statistics ,PREDICTION models ,ARTIFICIAL neural networks ,MEDICAL research ,MEDICAL specialties & specialists ,ALGORITHMS - Abstract
One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
18. Call for Papers—INFORMS Journal on Computing: Special Issue on Responsible AI and Data Science for Social Good.
- Subjects
- *
DATA science , *SOFTWARE architecture , *ARTIFICIAL intelligence , *MACHINE learning , *ARTIFICIAL neural networks , *SWARM intelligence - Published
- 2023
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19. Guest Editorial: Selected papers from the 8th Biennial Colloquium & 6th International Workshop on Optical Wireless Communications.
- Author
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de Figueiredo, Mónica Jorge Carvalho, Zvanovec, Stanislav, Pérez‐Jiménez, Rafael, and Alves, Luis Filipe Mesquita Nero Moreira
- Subjects
OPTICAL communications ,WIRELESS communications ,FREE-space optical technology ,DIGITAL communications ,MULTISPECTRAL imaging ,ARTIFICIAL neural networks ,TELECOMMUNICATION systems ,PULSE amplitude modulation - Abstract
These developments have the potential to foster additional innovation, facilitate future problem-solving and optimisation strategies, and ultimately contribute to mature OWC technologies. Keywords: optical communication; underwater optical wireless communication EN optical communication underwater optical wireless communication 87 90 4 08/24/23 20230801 NES 230801 Since 2011, optical wireless communication (OWC) technologies have gained momentum. These technologies include visible light communications (VLC), underwater VLC, Li-Fi, optical camera communications (OCC), visible light positioning, visible light sensing and free space optics, among others. [Extracted from the article]
- Published
- 2023
- Full Text
- View/download PDF
20. Biocompatible Potato-Starch Electrolyte-Based Coplanar Gate-Type Artificial Synaptic Transistors on Paper Substrates.
- Author
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Choi, Hyun-Sik, Lee, Young-Jun, Park, Hamin, and Cho, Won-Ju
- Subjects
- *
LONG-term synaptic depression , *POTATOES , *COPLANAR waveguides , *TRANSISTORS , *BIOLOGICAL neural networks , *NEUROPLASTICITY , *INTERNAL migration , *CAPACITANCE measurement , *NEURAL transmission - Abstract
In this study, we propose the use of artificial synaptic transistors with coplanar-gate structures fabricated on paper substrates comprising biocompatible and low-cost potato-starch electrolyte and indium–gallium–zinc oxide (IGZO) channels. The electrical double layer (EDL) gating effect of potato-starch electrolytes enabled the emulation of biological synaptic plasticity. Frequency dependence measurements of capacitance using a metal-insulator-metal capacitor configuration showed a 1.27 μF/cm2 at a frequency of 10 Hz. Therefore, strong capacitive coupling was confirmed within the potato-starch electrolyte/IGZO channel interface owing to EDL formation because of internal proton migration. An electrical characteristics evaluation of the potato-starch EDL transistors through transfer and output curve resulted in counterclockwise hysteresis caused by proton migration in the electrolyte; the hysteresis window linearly increased with maximum gate voltage. A synaptic functionality evaluation with single-spike excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), and multi-spike EPSC resulted in mimicking short-term synaptic plasticity and signal transmission in the biological neural network. Further, channel conductance modulation by repetitive presynaptic stimuli, comprising potentiation and depression pulses, enabled stable modulation of synaptic weights, thereby validating the long-term plasticity. Finally, recognition simulations on the Modified National Institute of Standards and Technology (MNIST) handwritten digit database yielded a 92% recognition rate, thereby demonstrating the applicability of the proposed synaptic device to the neuromorphic system. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
21. Wet Paper Coding-Based Deep Neural Network Watermarking.
- Author
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Wang, Xuan, Lu, Yuliang, Yan, Xuehu, and Yu, Long
- Subjects
- *
DIGITAL watermarking , *INTELLECTUAL property , *WATERMARKS , *INTELLECTUAL property infringement , *ARTIFICIAL neural networks - Abstract
In recent years, the wide application of deep neural network models has brought serious risks of intellectual property rights infringement. Embedding a watermark in a network model is an effective solution to protect intellectual property rights. Although researchers have proposed schemes to add watermarks to models, they cannot prevent attackers from adding and overwriting original information, and embedding rates cannot be quantified. Therefore, aiming at these problems, this paper designs a high embedding rate and tamper-proof watermarking scheme. We employ wet paper coding (WPC), in which important parameters are regarded as wet blocks and the remaining unimportant parameters are regarded as dry blocks in the model. To obtain the important parameters more easily, we propose an optimized probabilistic selection strategy (OPSS). OPSS defines the unimportant-level function and sets the importance threshold to select the important parameter positions and to ensure that the original function is not affected after the model parameters are changed. We regard important parameters as an unmodifiable part, and only modify the part that includes the unimportant parameters. We selected the MNIST, CIFAR-10, and ImageNet datasets to test the performance of the model after adding a watermark and to analyze the fidelity, robustness, embedding rate, and comparison schemes of the model. Our experiment shows that the proposed scheme has high fidelity and strong robustness along with a high embedding rate and the ability to prevent malicious tampering. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
22. Special Issue "International Conference Wood Science and Engineering in the Third Millennium—ICWSE 2023".
- Author
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Gurau, Lidia, Campean, Mihaela, and Salca, Emilia-Adela
- Subjects
SCIENCE conferences ,ENGINEERED wood ,WOOD waste ,DATE palm ,ARTIFICIAL neural networks ,PLYWOOD ,CONFERENCES & conventions ,COTTON ,NATURAL resources - Abstract
This article discusses a special issue of the journal Applied Sciences that focuses on the International Conference Wood Science and Engineering (ICWSE) held in November 2023. The conference covered various topics related to wood, including wood structure and properties, wood constructions, wood drying and heat treatments, conservation and restoration of wooden objects, wood-based materials, mechanical wood processing, and surface quality. The articles in the special issue explore different aspects of these topics, such as the characteristics and potential uses of different wood species, innovative wood construction techniques, optimal drying conditions for specific wood species, and the production and properties of wood-based materials. The research presented aims to promote the production of high-value wood products and contribute to the preservation of cultural heritage. The text provides a summary of various research papers related to wood science and engineering, covering topics such as the utilization potential of different tree species, wood material properties, wood preservation and coating techniques, and furniture design. The research findings highlight the importance of factors such as printing parameters, surface quality, adhesion of varnish coatings, and light-induced color changes in wood. The authors hope that these studies contribute to a better understanding of the scientific potential of wood and will be a starting point for further research in the field. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
23. [Abstracts of Papers Presented at the Conference on Applied Statistics in Ireland, 1993]
- Published
- 1995
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- View/download PDF
24. Application of Neural Networks for Estimation of Paper Properties Based on Refined Pulp Properties.
- Author
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Ciesielski, Krzysztof and Olejnik, Konrad
- Subjects
PAPER industry ,ARTIFICIAL neural networks ,PRODUCT quality ,PAPERMAKING ,DECISION making ,TENSILE strength - Abstract
Copyright of Fibres & Textiles in Eastern Europe is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2014
25. Pulp and paper characterization by means of artificial neural networks for effluent solid waste minimization—A case study.
- Author
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Almonti, Daniele, Baiocco, Gabriele, and Ucciardello, Nadia
- Subjects
- *
ARTIFICIAL neural networks , *WASTE minimization , *SOLID waste , *PAPER pulp , *MANUFACTURING processes - Abstract
Paper mills are among the most polluting industries, responsible for many organic and inorganic compounds emissions. The fibres electro-kinetic features strongly affect the ability to retain fillers since the fillers–fibres interactions are charge induced. The control and the prediction of these parameters would represent a precious aid for process management, allowing the fillers retention enhancement, a lower environmental impact and the paper sheet properties streamlining. The work presented deals with the implementation and training of four artificial neural networks (ANNs) for the prediction of the main electrochemical and physical features of cellulose pulp and paper. First, two ANNs predict the electrochemical parameters. Following, they were applied to predict the paper sheet properties and fillers retention. The neural models implemented showed outstanding prediction performance, with R2 in the order of 0.999 and a low mean error. The results demonstrate how Artificial Neural Networks may be a valuable instrument for paper mill pollutant reduction. However, they suggest a more inclusive investigation for a better fibres behaviour representation. • Main process parameters of an industrial papermaking process were identified. • Experimental datasets were achieved during industrial production. • Artificial Neural Networks were trained for process parameters prediction. • Accurate predictions of papermaking process were obtained. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
26. MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper.
- Author
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Rapp, Martin, Amrouch, Hussam, Lin, Yibo, Yu, Bei, Pan, David Z., Wolf, Marilyn, and Henkel, Jorg
- Subjects
- *
MACHINE learning , *CIRCUIT complexity , *COMPUTER-aided design , *ARTIFICIAL neural networks , *INTEGRATED circuits , *CONFIGURATION space , *MULTICASTING (Computer networks) - Abstract
Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in CAD that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
27. A Comprehensive Overview of the Temperature-Dependent Modeling of the High-Power GaN HEMT Technology Using mm-Wave Scattering Parameter Measurements (Invited Paper)
- Author
-
Giovanni Crupi, Mariangela Latino, Giovanni Gugliandolo, Zlatica Marinković, Jialin Cai, Gianni Bosi, Antonio Raffo, Enza Fazio, and Nicola Donato
- Subjects
Computer Networks and Communications ,artificial neural networks ,equivalent circuit ,GaN ,HEMT ,gate recurrent units ,high-power ,high-temperature ,machine learning technique ,millimeter-wave frequency ,scattering parameter measurements ,NO ,Hardware and Architecture ,Control and Systems Engineering ,Signal Processing ,Electrical and Electronic Engineering - Abstract
The gallium-nitride (GaN) high electron-mobility transistor (HEMT) technology has emerged as an attractive candidate for high-frequency, high-power, and high-temperature applications due to the unique physical characteristics of the GaN material. Over the years, much effort has been spent on measurement-based modeling since accurate models are essential for allowing the use of this advanced transistor technology at its best. The present analysis is focused on the modeling of the scattering (S-) parameter measurements for a 0.25 μm GaN HEMT on silicon carbide (SiC) substrate at extreme operating conditions: a large gate width (i.e., the transistor is based on an interdigitated layout consisting of ten fingers, each with a length of 150 μm, resulting in a total gate periphery of 1.5 mm), a high ambient temperature (i.e., from 35 °C up to 200 °C with a step of 55 °C), a high dissipated power (i.e., 5.1 W at 35 °C), and a high frequency in the millimeter-wave range (i.e., from 200 MHz up to 65 GHz with a step of 200 MHz). Three different modeling approaches are investigated: the equivalent-circuit model, artificial neural networks (ANNs), and gated recurrent units (GRUs). As is shown, each modeling approach has its pros and cons that need to be considered, depending on the target performance and their specifications. This implies that an appropriate selection of the transistor modeling approach should be based on discerning and prioritizing the key features that are indeed the most important for a given application.
- Published
- 2023
28. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system.
- Author
-
Wan, Jinquan, Huang, Mingzhi, Ma, Yongwen, Guo, Wenjie, Wang, Yan, Zhang, Huiping, Li, Weijiang, and Sun, Xiaofei
- Subjects
FORECASTING ,EFFLUENT quality ,PAPER mills ,WASTEWATER treatment ,ADAPTIVE control systems ,FUZZY sets ,INFERENCE (Logic) ,ARTIFICIAL neural networks - Abstract
Abstract: Advanced neuro-fuzzy modeling, namely an adaptive network-based fuzzy inference system (ANFIS), was employed to develop models for the prediction of suspended solids (SS) and chemical oxygen demand (COD) removal of a full-scale wastewater treatment plant treating process wastewaters from a paper mill. In order to improve the network performance, fuzzy subtractive clustering was used to identify model''s architecture and optimize fuzzy rule, meanwhile principal component analysis (PCA) was applied to reduce the input variable dimensionality. Input variables were reduced from six to four for COD and SS models, by considering PCA results and linear correlation matrices among input and output variables. The results indicate that reasonable forecasting and control performances have been achieved through the developed system. The minimum mean absolute percentage errors of 1.003% and 0.5161% for COD
eff and SSeff could be achieved using ANFIS. The maximum correlation coefficient values for CODeff and SSeff were 0.9912 and 0.9882, respectively. The minimum mean square errors of 1.2883 and 0.0342, and the minimum RMSEs of 1.135 and 0.1849 for CODeff and SSeff could also be achieved. [Copyright &y& Elsevier]- Published
- 2011
- Full Text
- View/download PDF
29. SEM-neural network analysis for mobile commerce adoption in Vietnamese small and medium-sized enterprises
- Author
-
Chau, Ngoc Tuan, Deng, Hepu, and Tay, Richard
- Published
- 2024
- Full Text
- View/download PDF
30. Automatic Segmentation with Deep Learning in Radiotherapy.
- Author
-
Isaksson, Lars Johannes, Summers, Paul, Mastroleo, Federico, Marvaso, Giulia, Corrao, Giulia, Vincini, Maria Giulia, Zaffaroni, Mattia, Ceci, Francesco, Petralia, Giuseppe, Orecchia, Roberto, and Jereczek-Fossa, Barbara Alicja
- Subjects
DIGITAL image processing ,DEEP learning ,NATURAL language processing ,ARTIFICIAL intelligence ,AUTOMATION ,RADIOTHERAPY ,ARTIFICIAL neural networks ,ONCOLOGY - Abstract
Simple Summary: Automatic segmentation of organs and other regions of interest is a promising approach for reducing the workload of doctors in radiotherapeutic planning, but it can be hard for doctors and researchers to keep up with current developments. This review evaluates 807 papers and reveals trends, commonalities, and gaps in the existing corpus. A set of recommendations for conducting effective segmentation studies is also provided. This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
31. Biocompatible Potato-Starch Electrolyte-Based Coplanar Gate-Type Artificial Synaptic Transistors on Paper Substrates
- Author
-
Won-Ju Cho, Hyun-Sik Choi, Hamin Park, and Young Jun Lee
- Subjects
Inorganic Chemistry ,synaptic transistors ,potato starch ,artificial neural networks ,biocompatible ,biodegradable ,Organic Chemistry ,General Medicine ,Physical and Theoretical Chemistry ,Molecular Biology ,Spectroscopy ,Catalysis ,Computer Science Applications - Abstract
In this study, we propose the use of artificial synaptic transistors with coplanar-gate structures fabricated on paper substrates comprising biocompatible and low-cost potato-starch electrolyte and indium–gallium–zinc oxide (IGZO) channels. The electrical double layer (EDL) gating effect of potato-starch electrolytes enabled the emulation of biological synaptic plasticity. Frequency dependence measurements of capacitance using a metal-insulator-metal capacitor configuration showed a 1.27 μF/cm2 at a frequency of 10 Hz. Therefore, strong capacitive coupling was confirmed within the potato-starch electrolyte/IGZO channel interface owing to EDL formation because of internal proton migration. An electrical characteristics evaluation of the potato-starch EDL transistors through transfer and output curve resulted in counterclockwise hysteresis caused by proton migration in the electrolyte; the hysteresis window linearly increased with maximum gate voltage. A synaptic functionality evaluation with single-spike excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), and multi-spike EPSC resulted in mimicking short-term synaptic plasticity and signal transmission in the biological neural network. Further, channel conductance modulation by repetitive presynaptic stimuli, comprising potentiation and depression pulses, enabled stable modulation of synaptic weights, thereby validating the long-term plasticity. Finally, recognition simulations on the Modified National Institute of Standards and Technology (MNIST) handwritten digit database yielded a 92% recognition rate, thereby demonstrating the applicability of the proposed synaptic device to the neuromorphic system.
- Published
- 2022
32. Modelling of time related drying changes on matte coated paper with artificial neural networks
- Author
-
Şahïnbaşkan, Türkün and Köse, Erdoğan
- Subjects
- *
PAPER , *COLOR , *DRYING , *MATTES , *OFFSET printing , *ARTIFICIAL neural networks , *MATHEMATICAL models , *NUMERICAL analysis - Abstract
Abstract: In this study, the determinability of time related colour changes in prints made using ink that dries on matte coated paper with the offset printing technique and infrared method, has been investigated with artifical neural networks after analysis of the experimental and numerical methods. In the experimental part a ECI 2002 CMYK test scale is printed on a 135g/m2 matte coated. The prints obtained were measured with a spectrophotometer at certain intervals and by converting the reflection data obtained into the CIELAB colour description spectrum, time related changes in printed colours have been determined. In the empirical study however, colour changes in prints made on matte coated paper using inks that dry with the infrared method are modelled numerically. The average deviation has been found to be 0.4%. As a result, artificial neural networks can be used with the aim to digitally calculate time related colour changes in the offset printing system. [Copyright &y& Elsevier]
- Published
- 2010
- Full Text
- View/download PDF
33. Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill: Part II. Nonlinear approaches
- Author
-
Oliveira-Esquerre, Karla Patricia, Seborg, Dale E., Mori, Milton, and Bruns, Roy Edward
- Subjects
- *
PAPER mills , *INDUSTRIAL wastes , *OXYGEN , *LAGOONS - Abstract
Abstract: Neural networks can provide effective predictive models for complex processes that are poorly described by first principle models, such as wastewater biological treatment systems. In this paper multilayer perceptron (MLP) and functional-link neural networks (FLN) are developed to predict inlet and outlet biochemical oxygen demand (BOD) of an aerated lagoon operated by International Paper of Brazil. In Part I, predictive models for both inlet and outlet BOD for the aerated lagoon were developed using linear multivariate regression techniques. For the current case study, MLP networks are the best choice for the prediction models. When only a relatively small number of samples is available, substantial improvement in inlet and outlet BOD prediction is shown for both FLN and MLP modeling using a reduced input variable set that was generated using partial least squares (PLS). Thus, this paper provides a novel approach for developing PLS–FLN model structures. [Copyright &y& Elsevier]
- Published
- 2004
- Full Text
- View/download PDF
34. The Identification and Evaluation Model for Test Paper's Color and Substance Concentration.
- Author
-
Guan, Jinlan, Ou, Jiequan, Liu, Guanghua, Chen, Minna, and Lai, Yuting
- Subjects
- *
IDENTIFICATION , *ARTIFICIAL neural networks , *COLORS , *STATISTICAL correlation , *CHI-squared test - Abstract
The colorimetric method is usually used to test the concentration of substances. However, this method has a big error since different people have different sensitivities to colors. In this paper, in order to solve the identification problem of the color and the concentration of the test paper, firstly, we found out that the concentration of substance is correlated with the color reading by using the Pearson's Chi-squared test method. And by the concentration coefficient of Pearson correlation analysis, the concentration of substance and color reading is highly correlated. Secondly, according to the RGB value of the paper image, the color moments of the image are calculated as the characteristics of the image, and the Levenberg–Marquardt (LM) neural network is established to classify the concentration of the substance. The accuracy of the training set model is 94.5%, and the accuracy of the test set model is 87.5%. The model precision is high, and the model has stronger generalization ability. Therefore, according to the RGB value of the test paper image, it is effective to establish the LM neural network model to identify the substance concentration. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
35. Critical Appraisal of a Machine Learning Paper: A Guide for the Neurologist.
- Author
-
Vinny, Pulikottil W., Garg, Rahul, Srivastava, M. V. Padma, Lal, Vivek, and Vishnu, Venugoapalan Y.
- Subjects
- *
DEEP learning , *NEUROLOGISTS , *EVIDENCE-based medicine , *MACHINE learning , *BENCHMARKING (Management) , *TERMS & phrases , *ARTIFICIAL neural networks , *PREDICTION models , *ALGORITHMS - Abstract
Machine learning (ML), a form of artificial intelligence (AI), is being increasingly employed in neurology. Reported performance metrics often match or exceed the efficiency of average clinicians. The neurologist is easily baffled by the underlying concepts and terminologies associated with ML studies. The superlative performance metrics of ML algorithms often hide the opaque nature of its inner workings. Questions regarding ML model's interpretability and reproducibility of its results in real-world scenarios, need emphasis. Given an abundance of time and information, the expert clinician should be able to deliver comparable predictions to ML models, a useful benchmark while evaluating its performance. Predictive performance metrics of ML models should not be confused with causal inference between its input and output. ML and clinical gestalt should compete in a randomized controlled trial before they can complement each other for screening, triaging, providing second opinions and modifying treatment. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
36. Paper Tissue Softness Rating by Acoustic Emission Analysis.
- Author
-
Kraljevski, Ivan, Duckhorn, Frank, Tschöpe, Constanze, Schubert, Frank, and Wolff, Matthias
- Subjects
ACOUSTIC emission ,ARTIFICIAL neural networks ,HYGIENE products ,TISSUES - Abstract
Softness is one of the essential properties of hygiene tissue products. Reliably measuring it is of utmost importance to ensure the balance between customer expectations and cost-effective tissue production. This study presents a method for assessing softness by analyzing acoustic emissions produced while tearing a tissue specimen. The aim was to train neural network models using the corrected results of human panel tests as the ground truth labels and to predict the tissue softness in two- and three-class recognition tasks. We also investigate the possibility of predicting some production parameters related to the softness property. The results proved that tissue softness and production parameters could be reliably estimated only by the tearing noise. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Neural network model for paper-forming process.
- Author
-
Scharcanski, Jacob and Dodson, C.T.J.
- Subjects
- *
PAPER industry , *ARTIFICIAL neural networks - Abstract
Presents an approach to the controllable simulation of paper forming using artificial neural network methods. Problem of simulating the dynamics of paper forming; Model for simulation of the dynamics of paper forming; Artificial neural networks and simulation of paper forming; Experimental results of the study; Discussion of the results; Conclusions.
- Published
- 1997
- Full Text
- View/download PDF
38. Research Paper: Using Artificial Neural Network to Destroy the Process of Traffic Accident Victims in Yazd Province.
- Author
-
Omidi, Mohammad Reza, Eskandari, Meysam Jafari, and Omidi, Nabi
- Subjects
- *
ARTIFICIAL neural networks , *ARTIFICIAL intelligence , *TRAFFIC safety , *ACCIDENT prevention , *CRASH injuries - Abstract
Background: Road accidents are among the most important causes of death and severe personal and financial injuries. Also, its profound social, cultural, and economic effects threaten human societies. This study aimed to estimate the trend of traffic accident victims in Yazd Province, Iran, to predict the number of traffic accident victims in this province. Materials and Methods: Based on traffic casualty statistics referred to forensic medicine in Yazd Province within April 1989 and March 2017 referred to Forensic Medicine of Yazd Province and using an artificial neural network to predict the number of injured for 12 months ending in 2020 has been paid. The neural network used in this study had 12 inputs, one output, and 5 hidden layers. The network predicts the relationship between data after training and learning. The network is considered the MSE benchmark. Results: The number of injured in traffic accidents in Yazd Province in 2020 was equal to 7052 people, with the highest number in December with 832 people and the lowest in June with 414 people. The exact method of use was equal to 92 cases. Conclusion: The trend of traffic accident casualties in Yazd Province in 2020 will be declining. For future research, the exact method designed in this study can be examined with other methods for the best response level. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
39. Indian Research on Artificial Neural Networks: A Bibliometric Assessment of Publications Output during 1999-2018.
- Author
-
Gupta, B. M. and Dhawan, S. M.
- Subjects
SOFT computing ,ENVIRONMENTAL sciences ,MATERIALS science ,MEDICAL sciences ,ARTIFICIAL neural networks ,CHEMICAL engineers ,CITATION indexes - Abstract
The paper describes the quantitative and qualitative dimensions of artificial neural networks (ANN) in India in the global context. The study is based on research publications data (8260) as covered in the Scopus database during 1999-2018. ANN research in India registered 24.52% growth, averaged 11.95 citations per paper, and contributed 9.77% share to the global ANN research. ANN research is skewed as the top 10 countries account for 75.15% of global output. India ranks as the third most productive country in the world. The distribution of research by type of ANN networks reveals that Feed Forward Neural Network type accounted for the highest share (10.18% share), followed by Adaptive Weight Neural Network (5.38% share), Feed Backward Neural Network (2.54% share), etc. ANN research applications across subjects were the largest in medical science and environmental science (11.82% and 10.84% share respectively), followed by materials science, energy, chemical engineering and water resources (from 6.36% to 9.12%), etc. The Indian Institute of Technology, Kharagpur and the Indian Institute of Technology, Roorkee lead the country as the most productive organizations (with 289 and 264 papers). Besides, the Indian Institute of Technology, Kanpur (33.04 and 2.76) and Indian Institute of Technology, Madras (24.26 and 2.03) lead the country as the most impactful organizations in terms of citation per paper and relative citation index. P. Samui and T.N. Singh have been the most productive authors and G.P.S.Raghava (86.21 and 7.21) and K.P. Sudheer (84.88 and 7.1) have been the most impactful authors. Neurocomputing, International Journal of Applied Engineering Research and Applied Soft Computing topped the list of most productive journals. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
40. Evolution of Software Development Effort and Cost Estimation Techniques: Five Decades Study Using Automated Text Mining Approach.
- Author
-
Jadhav, Anil, Kaur, Mandeep, and Akter, Farzana
- Subjects
COMPUTER software development ,TEXT mining ,ARTIFICIAL neural networks ,SOFTWARE engineering ,SOFTWARE engineers - Abstract
Software development effort and cost estimation (SDECE) is one of the most important tasks in the field of software engineering. A large number of research papers have been published on this topic in the last five decades. Investigating research trends using a systematic literature review when such a large number of research papers are published is a very tedious and time-consuming task. Therefore, in this research paper, we propose a generic automated text mining framework to investigate research trends by analyzing the title, author's keywords, and abstract of the research papers. The proposed framework is used to investigate research trends by analyzing the title, keywords, and abstract of select 1015 research papers published on SDECE in the last five decades. We have identified the most popular SDECE techniques in each decade to understand how SDECE has evolved in the past five decades. It is found that artificial neural network, fuzzy logic, regression, analogy-based approach, and COCOMO methods are the most used techniques for SDECE followed by optimization, use case point, machine learning, and function point analysis. The NASA and ISBSG are the most used dataset for SDECE. The MMRE, MRE, and PRED are the most used accuracy measures for SDECE. Results of the proposed framework are validated by comparing it with the outcome of the previously published review work and we found that the results are consistent. We have also carried out a detailed bibliometric analysis and metareview of the review and survey papers published on SDECE. This research study is significant for the development of new models for cost and effort estimations. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
41. PREDICTION OF THE FINANCIAL RETURN OF THE PAPER SECTOR WITH ARTIFICIAL NEURAL NETWORKS.
- Author
-
Yildirim, Ibrahim, Ozsahin, Sukru, and Akyuz, Kadri C.
- Subjects
- *
PREDICTION models , *PRODUCT returns , *PAPER industry , *ARTIFICIAL neural networks , *STOCK exchanges , *TRADING companies - Abstract
The unknown nature of the future requires us to question our decisions and seek reliable methods. The artificial neural networks approach, which is one of the methods used to best predict the future and one that is important for decision making has been thought of, particularly in recent years, as a method with a high level of validity in the fields of economy and financial prediction. The Istanbul Stock Exchange (ISE), at which millions of national and international investors operate, is among the developed stock exchanges of the world. The ISE has the attributes of being appropriate for making predictions regarding financial returns, without any sector differentiation, as a whole. In this study, it was aimed to predict monthly stock yields of 14 different paper companies dealing with the ISE (Istanbul Stock Exchange) by using artificial neural network. Four different variables (the gold price, ISE daily trading volume, exchange rate purchase-sale average, and monthly deposit interest rates by utilizing) and 127 months data were used. Results show that the monthly stock yields of the paper sector can be predicted correctly to account for 95% of the variability of data with the artificial neural network model, and the average absolute percentage failure value was 6.85%. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
42. A New Method of Paper Defect Recognition Based on Wavelet Packet Decomposition and BP Neural Network*.
- Author
-
Xiaofang, Wang, Shubo, Qiu, and Qiang, Wang
- Subjects
COMPUTER algorithms ,WAVELETS (Mathematics) ,FEATURE extraction ,MATHEMATICAL decomposition ,ARTIFICIAL neural networks ,MATHEMATICAL proofs - Abstract
Abstract: A new approach of paper defect recognition is proposed. To implement this method, computer is used to acquire paper image and the subtraction algorithm is used to determine if the image contains defect at first. For the image with paper defect, wavelet packet decomposition is applied to extracting the feature vector of the defect. A BP neural network is designed for recognizing the defect type according to the feature vector. This approach provides a unified detection algorithm for different types of paper defects. The experiment data prove this method is valid and can be applied in modern paper defect inspection system. [Copyright &y& Elsevier]
- Published
- 2011
- Full Text
- View/download PDF
43. Guest Editorial: Operational and structural resilience of power grids with high penetration of renewables.
- Author
-
Lei, Shunbo, Zhang, Yichen, Shahidehpour, Mohammad, Hou, Yunhe, Panteli, Mathaios, Chen, Xia, Aydin, Nazli Yonca, Liang, Liang, Wang, Cheng, Wang, Chong, and She, Buxin
- Subjects
MICROGRIDS ,ELECTRIC power distribution grids ,CYBER physical systems ,MIXED integer linear programming ,DEEP reinforcement learning ,ARTIFICIAL neural networks ,REINFORCEMENT learning ,ELECTRIC power - Published
- 2024
- Full Text
- View/download PDF
44. Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II.
- Author
-
Jeon, Gwanggil
- Subjects
REMOTE sensing ,MACHINE learning ,ARTIFICIAL neural networks ,DEEP learning ,ARTIFICIAL intelligence ,DISTANCE education - Abstract
This document is a summary of a special issue on advanced machine learning and deep learning techniques for remote sensing. The issue includes 16 research papers that cover a range of topics, including hyperspectral image classification, moving point target detection, radar echo extrapolation, and remote sensing object detection. Each paper introduces a novel approach or model and provides extensive testing and evaluation to demonstrate its effectiveness. The insights shared in this special issue are expected to contribute to future advancements in artificial intelligence-based remote sensing research. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
45. From Paper to Digital Applications of the Pain Drawing: Systematic Review of Methodological Milestones.
- Author
-
Shaballout, Nour, Neubert, Till-Ansgar, Boudreau, Shellie, and Beissner, Florian
- Subjects
ELECTRONIC paper ,DIGITAL technology ,META-analysis ,ARTIFICIAL neural networks ,PAIN - Abstract
Background: In a pain drawing (PD), the patient shades or marks painful areas on an illustration of the human body. This simple yet powerful tool captures essential aspects of the subjective pain experience, such as localization, intensity, and distribution of pain, and enables the extraction of meaningful information, such as pain area, widespreadness, and segmental pattern. Starting as a simple pen-on-paper tool, PDs are now sophisticated digital health applications paving the way for many new and exciting basic translational and clinical applications. Objective: Grasping the full potential of digital PDs and laying the groundwork for future medical PD apps requires an understanding of the methodological developments that have shaped our current understanding of uses and design. This review presents methodological milestones in the development of both pen-on-paper and digital PDs, thereby offering insight into future possibilities created by the transition from paper to digital. Methods: We conducted a systematic literature search covering PD acquisition , conception of PDs , PD analysis , and PD visualization. Results: The literature search yielded 435 potentially relevant papers, from which 53 methodological milestones were identified. These milestones include, for example, the grid method to quantify pain area, the pain-frequency maps, and the use of artificial neural networks to facilitate diagnosis. Conclusions: Digital technologies have had a significant influence on the evolution of PDs, whereas their versatility is leading to ever new applications in the field of medical apps and beyond. In this process, however, there is a clear need for better standardization and a re-evaluation of methodological and technical limitations that no longer apply today. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
46. Guest Editorial: Artificial intelligence‐empowered reliable forecasting for energy sectors.
- Author
-
Mahmoud, Karar, Guerrero, Josep M., Abdel‐Nasser, Mohamed, and Yorino, Naoto
- Subjects
ENERGY industries ,ARTIFICIAL neural networks ,MACHINE learning ,FORECASTING ,QUANTILE regression ,CONVOLUTIONAL neural networks ,DEMAND forecasting - Abstract
This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
47. Annual Report 2023.
- Author
-
Xu Guo
- Subjects
DIGITAL twins ,ARTIFICIAL neural networks ,MACHINE learning ,MULTILAYER perceptrons ,DATA analysis - Abstract
The given document is the Annual Report for 2023 of AIMS Microbiology, an international Open Access journal focused on publishing high-quality, peer-reviewed papers in the field of microbiology. In 2023, the journal received over 240 manuscript submissions and published 40 papers, including research articles, review articles, editorials, and commentaries. The authors of these papers came from more than 20 countries, indicating increased international collaborations in microbiology research. The journal also established special issues and invited 18 new experts to join the Editorial Board. In terms of impact, AIMS Microbiology received its first Impact Factor of 4.8 and saw an increase in its CiteScore from 4.8 to 6.3. The report concludes by expressing the journal's commitment to publishing high-quality papers and its goal of attracting more excellent articles in the future. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
48. New Findings on Artificial Neural Networks Described by Investigators at University of Florida (Enhancing Pathogen Identification In Cheese With High Background Microflora Using an Artificial Neural Network-enabled Paper Chromogenic Array...).
- Subjects
ARTIFICIAL neural networks ,IDENTIFICATION ,FOOD science ,TECHNOLOGICAL innovations ,ESCHERICHIA coli O157:H7 ,PATHOGENIC microorganisms ,CHEESE - Abstract
Researchers at the University of Florida have developed a new technique using artificial neural networks to enhance pathogen identification in cheese. The method, called the artificial neural network-driven paper chromogenic array sensor (ANN-PCA), allows for the nondestructive and simultaneous detection of Salmonella Enteritidis and Escherichia coli O157:H7 in shredded cheddar cheese. The technique has shown promising potential for integration into a digitalized, smart, and resilient nondestructive surveillance system for real-time pathogen detection in food supply chains. The research was funded by the US Army and the University of Florida, among others. [Extracted from the article]
- Published
- 2024
49. A letter to editor addressing a methodological concern: A critical analysis of papers included in a systematic review on vertical root fractures.
- Author
-
Azarm, Ali and Ameri, Fatemeh
- Subjects
ENDODONTICS ,TOOTH roots ,PREDICTION models ,ARTIFICIAL intelligence ,SYSTEMS development ,ARTIFICIAL neural networks ,TOOTH fractures ,MACHINE learning - Abstract
The article focuses on a critical analysis of systematic review methodologies concerning vertical root fractures (VRFs), highlighting concerns about potential biases when including studies on broader categories of dental cracks in VRF-specific reviews. Topics include the definition and characteristics of VRFs, methodological variations in VRF detection studies, and the implications of these variations on the reliability of systematic reviews in dental research.
- Published
- 2024
50. Editorial Note for the Special Issue: Perspectives and Challenges in Doctoral Research—Selected Papers from the 10th Edition of the Scientific Conference of the Doctoral Schools from the "Dunărea de Jos".
- Author
-
Rusu, Eugen and Rapeanu, Gabriela
- Subjects
DEEP learning ,CONVOLUTIONAL neural networks ,FOOD science ,INDUSTRIAL engineering ,ARTIFICIAL neural networks ,TRIBO-corrosion ,AUTONOMOUS robots - Abstract
This editorial note is dedicated to the 10th Scientific Conference which was held on June 2022 in Galati, Romania, and was organized by the Council of Doctoral Schools of the "Dunarea de Jos" University of Galati (SCDS-UDJG). Three articles in this Special Issue present findings in the field of mechanical and industrial engineering. Four articles present studies related to artificial intelligence technologies, representing the majority of papers published in this Special Issue. [Extracted from the article]
- Published
- 2023
- Full Text
- View/download PDF
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