16,650 results
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2. 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
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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
- Full Text
- View/download PDF
3. 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
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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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4. 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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- View/download PDF
5. 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
- Full Text
- View/download PDF
6. 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
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7. 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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- View/download PDF
8. 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
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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
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9. 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
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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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10. Knowledge Infused Representations Through Combination of Expert Knowledge and Original Input
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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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11. 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
12. 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
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13. 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
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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
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- View/download PDF
14. Call for Papers—INFORMS Journal on Computing: Special Issue on Responsible AI and Data Science for Social Good.
- Subjects
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DATA science , *SOFTWARE architecture , *ARTIFICIAL intelligence , *MACHINE learning , *ARTIFICIAL neural networks , *SWARM intelligence - Published
- 2023
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15. 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
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16. 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
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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
17. Wet Paper Coding-Based Deep Neural Network Watermarking.
- Author
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Wang, Xuan, Lu, Yuliang, Yan, Xuehu, and Yu, Long
- Subjects
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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
18. 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
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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
19. 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
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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
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20. 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
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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
21. A Comprehensive Overview of the Temperature-Dependent Modeling of the High-Power GaN HEMT Technology Using mm-Wave Scattering Parameter Measurements (Invited Paper)
- Author
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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
22. SEM-neural network analysis for mobile commerce adoption in Vietnamese small and medium-sized enterprises
- Author
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Chau, Ngoc Tuan, Deng, Hepu, and Tay, Richard
- Published
- 2024
- Full Text
- View/download PDF
23. Automatic Segmentation with Deep Learning in Radiotherapy.
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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
24. Biocompatible Potato-Starch Electrolyte-Based Coplanar Gate-Type Artificial Synaptic Transistors on Paper Substrates
- Author
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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
25. Paper Tissue Softness Rating by Acoustic Emission Analysis.
- Author
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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
26. 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
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27. Guest Editorial: Operational and structural resilience of power grids with high penetration of renewables.
- Author
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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
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28. Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II.
- Author
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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
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29. Guest Editorial: Artificial intelligence‐empowered reliable forecasting for energy sectors.
- Author
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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
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30. 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
31. A letter to editor addressing a methodological concern: A critical analysis of papers included in a systematic review on vertical root fractures.
- Author
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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
32. 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
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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
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33. Women in Artificial Intelligence.
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Valls, Aida and Gibert, Karina
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ARTIFICIAL intelligence ,DEEP learning ,ARTIFICIAL neural networks ,CLINICAL decision support systems ,COMPUTATIONAL mathematics ,NATURAL language processing - Published
- 2022
- Full Text
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34. Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks.
- Author
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Ji, Mingfei, Lu, Jianrong, and Zhang, Xiaowei
- Subjects
LANDSCAPE design ,ARTIFICIAL neural networks ,MULTIMODAL user interfaces ,GARDEN design ,LANDSCAPE gardening ,LOGIC design ,ELECTRONIC paper - Abstract
Digitalization brings challenges and new opportunities to the development of landscape gardening, "smart gardening," which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent computing method and deep neural network machine learning algorithm, this paper adopts "digital landscape design logic" to analyze and research smart gardens and digital design. The digital landscape design process and methods are discussed based on design logic, design basis, environment analysis, and results presentation, and the greenery maintenance scheduling system is constructed. The paper focuses on the digital implementation of the environmental analysis of the site and uses Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The main theme of the whole paper is a logical approach to digital landscape design for smart gardens, using digital technology tools from the perspective of smart garden thinking, combining quantitative analysis and qualitative design, and intervening in digital landscape garden planning and design to explore the application of digital technology and tools. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
35. Research on Digital Steganography and Image Synthesis Model Based on Improved Wavelet Neural Network.
- Author
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Li, Xujie, Yao, Rujing, and Lee, Jonghan
- Subjects
DIGITAL images ,CRYPTOGRAPHY ,FEEDFORWARD neural networks ,ARTIFICIAL neural networks ,ELECTRONIC paper ,LINEAR network coding ,GENETIC algorithms - Abstract
Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
36. A Comprehensive Overview of the Temperature-Dependent Modeling of the High-Power GaN HEMT Technology Using mm-Wave Scattering Parameter Measurements (Invited Paper).
- Author
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Crupi, Giovanni, Latino, Mariangela, Gugliandolo, Giovanni, Marinković, Zlatica, Cai, Jialin, Bosi, Gianni, Raffo, Antonio, Fazio, Enza, and Donato, Nicola
- Subjects
ARTIFICIAL neural networks ,GALLIUM nitride ,CRANES (Birds) ,TRANSISTORS ,MODULATION-doped field-effect transistors - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Guest Editorial: Advances in AI‐assisted radar sensing applications.
- Author
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Vishwakarma, Shelly, Chetty, Kevin, Le Kernec, Julien, Chen, Qingchao, Adve, Raviraj, Gurbuz, Sevgi Zubeyde, Li, Wenda, Ram, Shobha Sundar, and Fioranelli, Francesco
- Subjects
ARTIFICIAL intelligence ,HUMAN activity recognition ,RADAR ,ARTIFICIAL neural networks ,RADAR signal processing ,RADAR targets - Abstract
This document is a guest editorial from the journal IET Radar, Sonar & Navigation. It discusses the advances in AI-assisted radar sensing applications and the challenges that hinder its adoption in this field. The special issue of the journal features nine papers that address these challenges and offer innovative ideas and experimental results. The papers cover a range of topics, including health monitoring, human activity recognition, voice identification, elderly care health monitoring, track-to-track association, signal pre-processing, traffic congestion alleviation, and target recognition. The authors express their gratitude to the contributors and reviewers and believe that the research presented will inspire further exploration and innovation in this field. [Extracted from the article]
- Published
- 2024
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- View/download PDF
38. Dielectric Insulation in Medium- and High-Voltage Power Equipment—Degradation and Failure Mechanism, Diagnostics, and Electrical Parameters Improvement.
- Author
-
Koltunowicz, Tomasz N.
- Subjects
PARTIAL discharges ,ARTIFICIAL neural networks ,CABLE structures ,MACHINE learning ,MONTE Carlo method ,DIELECTRICS ,LAMINATED composite beams - Abstract
This document discusses the degradation and failure mechanisms of dielectric insulation in medium- and high-voltage power equipment. It highlights the importance of controlling insulation material degradation to prevent equipment failures and reduce the risk of environmental pollution. The document includes several research papers that address various topics related to the measurement, monitoring, and improvement of power equipment components. These papers cover issues such as winding breakdown faults in transformers, overvoltages caused by vacuum circuit breaker interruptions, insulation resistance degradation in cables, temperature rise in composite insulators, partial discharges and their effects on insulation, and acoustic inspection for detecting partial discharges. The document also presents studies on the technical condition of on-load tap changers, the behavior of silicone elastomers under electrical and mechanical stress, and the percolation phenomenon in square matrices. [Extracted from the article]
- Published
- 2024
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- View/download PDF
39. Abstract papers from the Energy Informatics.Academy Conference 2022 (EI.A 2022).
- Subjects
LOAD forecasting (Electric power systems) ,ELECTRIC charge ,SUPERVISED learning ,DEEP learning ,ARTIFICIAL neural networks ,APPLIED sciences ,ENERGY consumption forecasting ,CONSUMER behavior - Published
- 2022
- Full Text
- View/download PDF
40. Cloud computing load prediction method based on CNN-BiLSTM model under low-carbon background.
- Author
-
Zhang, HaoFang, Li, Jie, and Yang, HaoRan
- Subjects
ARTIFICIAL neural networks ,CONVOLUTIONAL neural networks ,CARBON emissions ,LONG-term memory ,GREENHOUSE gas mitigation - Abstract
With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. SiamDCFF: Dynamic Cascade Feature Fusion for Vision Tracking.
- Author
-
Lu, Jinbo, Wu, Na, and Hu, Shuo
- Subjects
ARTIFICIAL neural networks - Abstract
Establishing an accurate and robust feature fusion mechanism is key to enhancing the tracking performance of single-object trackers based on a Siamese network. However, the output features of the depth-wise cross-correlation feature fusion module in fully convolutional trackers based on Siamese networks cannot establish global dependencies on the feature maps of a search area. This paper proposes a dynamic cascade feature fusion (DCFF) module by introducing a local feature guidance (LFG) module and dynamic attention modules (DAMs) after the depth-wise cross-correlation module to enhance the global dependency modeling capability during the feature fusion process. In this paper, a set of verification experiments is designed to investigate whether establishing global dependencies for the features output by the depth-wise cross-correlation operation can significantly improve the performance of fully convolutional trackers based on a Siamese network, providing experimental support for rational design of the structure of a dynamic cascade feature fusion module. Secondly, we integrate the dynamic cascade feature fusion module into the tracking framework based on a Siamese network, propose SiamDCFF, and evaluate it using public datasets. Compared with the baseline model, SiamDCFF demonstrated significant improvements. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Introduction to special issue on scientific and statistical data management in the age of AI 2021.
- Author
-
Zhu, Qiang, Zhu, Xingquan, and Tu, Yicheng
- Subjects
STATISTICS ,DATA management ,ARTIFICIAL intelligence ,ARTIFICIAL neural networks ,HIGH performance computing ,GRAPH algorithms ,QUESTION answering systems - Abstract
The paper introduces a natural language processing system designed to answer natural language questions across knowledge graphs for scientific data applications where no prior training data in the form of question-answer pairs is available. 59 papers, submitted by 243 authors from over 20 countries/regions to SSDBM 2021, have showcased the pervasiveness of AI/ML in the field of scientific and statistical data management, which covered a variety of related topics (among others) including: In-database machine learning. Recent advancement in deep neural networks, combined with the high performance computing power and Big Data, has profoundly brought artificial intelligence (AI) to nearly all fields of scientific disciplines. [Extracted from the article]
- Published
- 2022
- Full Text
- View/download PDF
43. A Conditionally Anonymous Linkable Ring Signature for Blockchain Privacy Protection.
- Author
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Quan Zhou, Yulong Zheng, Minhui Chen, and Kaijun Wei
- Subjects
BLOCKCHAINS ,ARTIFICIAL intelligence ,ARTIFICIAL neural networks ,CONVOLUTIONAL neural networks ,MACHINE learning - Abstract
In recent years, the issue of preserving the privacy of parties involved in blockchain transactions has garnered significant attention. To ensure privacy protection for both sides of the transaction, many researchers are using ring signature technology instead of the original signature technology. However, in practice, identifying the signer of anillegal blockchain transactiononce ithas beenplacedon the chainnecessitates a signature technique that offers conditional anonymity. Some illegals can conduct illegal transactions and evade the lawusing ring signatures,which offer perfect anonymity. This paper firstly constructs a conditionally anonymous linkable ring signature using the Diffie-Hellman key exchange protocol and the Elliptic Curve Discrete Logarithm, which offers a non-interactive process for finding the signer of a ring signature in a specific case. Secondly, this paper's proposed scheme is proven correct and secure under Elliptic Curve Discrete Logarithm Assumptions. Lastly, compared to previous constructions, the scheme presented in this paper provides a non-interactive, efficient, and secure confirmation process. In addition, this paper presents the implementation of the proposed scheme on a personal computer, where the confirmation process takes only 2, 16, and 24ms for ring sizes of 4, 24 and 48, respectively, and the confirmation process can be combined with a smart contract on the blockchain with a tested millisecond level of running efficiency. In conclusion, the proposed scheme offers a solution to the challenge of identifying the signer of an illegal blockchain transaction, making it an essential contribution to the field. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. Deep Neural Network Regression‐Assisted Pressure Sensor for Decoupling Thermal Variations at Different Operating Temperatures.
- Author
-
Bang, Joohyung, Baek, Keuntae, Lim, Jaeyoung, Han, Yongha, and So, Hongyun
- Subjects
ARTIFICIAL neural networks ,PRESSURE sensors - Abstract
Decoupling environment‐dependent response in sensing techniques is essential for the diverse practical applications. This work presents a novel thermal effect decoupling method for sponge pressure sensors based on a deep neural network (DNN) regression model, which is difficult to achieve owing to the material‐ and structure‐related complex effects of the sponge‐based pressure sensor. A poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate)‐based multifunctional device is fabricated with a both pressure and thermally responsive part and an only thermally responsive part; and a DNN model with two input features is adapted to implement the substantial pressure prediction system without thermal interference. Proposed model shows the robust decoupled pressure‐sensing capability with high accuracy of ≈96.23% using two input features. It also enables accurate pressure prediction under both the thermally steady and transition regions, which indicates significant potential for a precise measurement system. These results demonstrate the possibility of reliable pressure monitoring under varying thermal conditions, which is important for accurately measuring pressure in complex power plants, human–machine interfaces, and compact wearable platforms. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
45. Deep Neural Networks in Medical Imaging: Privacy Preservation, Image Generation and Applications.
- Author
-
Stoian, Diana Ioana, Leonte, Horia Andrei, Vizitiu, Anamaria, Suciu, Constantin, and Itu, Lucian Mihai
- Subjects
ARTIFICIAL neural networks ,DIGITAL preservation ,DIAGNOSTIC imaging ,IMAGE reconstruction ,IMAGE analysis ,NOSOLOGY - Abstract
This special issue of the journal "Applied Sciences" focuses on the use of deep neural networks in medical imaging. The introduction highlights the significance of medical imaging in disease management and the development of advanced image analysis algorithms. The issue includes papers on various topics, such as privacy-preserving learning, image generation, and applications in cardiovascular diseases and other areas. The papers cover a wide range of techniques and applications, including protecting sensitive data, image reconstruction, classification of heart disease risk, and predicting cardiovascular measurements from images. Additionally, the document features papers on optimizing radiological workload, improving breast cancer detection, and classifying gastrointestinal disorders. The authors of this document, Diana Ioana Stoian, Horia Andrei Leonte, Anamaria Vizitiu, Constantin Suciu, and Lucian Mihai Itu, declare that they have no conflicts of interest, which is important for library patrons conducting research to ensure the authors' impartiality. [Extracted from the article]
- Published
- 2023
- Full Text
- View/download PDF
46. Methods and Applications of Data Mining in Business Domains.
- Author
-
Amrit, Chintan and Abdi, Asad
- Subjects
DATA mining ,DEEP learning ,ARTIFICIAL neural networks ,MACHINE learning ,ARTIFICIAL intelligence ,DECISION support systems - Abstract
These papers collectively showcase the adaptability and effectiveness of data mining techniques, making substantial contributions to the broader realm of " I Methods and Applications of Data Mining in Business Domains i ". In a business context, the challenge is that one would like to see (i) how the algorithms can be repeatable in the real world, (ii) how the patterns mined can be utilized by the business, and (iii) how the resulting model can be understood and utilized in the business environment [[1]]. Additionally, they provide insights into factors influencing the adoption of business intelligence systems (BISs) in small and medium-sized enterprises (SMEs) [[26]], and conduct a systematic literature review on AI-based methods for automating business processes and decision support [[27]]. This Special Issue invited researchers to contribute original research in the field of data mining, particularly in its application to diverse domains, like healthcare, software development, logistics, and human resources. [Extracted from the article]
- Published
- 2023
- Full Text
- View/download PDF
47. Special Issue: Artificial Intelligence Technology in Medical Image Analysis.
- Author
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Szilágyi, László and Kovács, Levente
- Subjects
DEEP learning ,COMPUTER-assisted image analysis (Medicine) ,IMAGE analysis ,ARTIFICIAL intelligence ,DIAGNOSTIC imaging ,ARTIFICIAL neural networks - Abstract
This document is a summary of a special issue in the journal Applied Sciences titled "Artificial Intelligence Technology in Medical Image Analysis." The special issue explores the applications of artificial intelligence (AI) in medical imaging and its impact on diagnostic and therapeutic processes. The use of AI-powered tools in image interpretation has shown exceptional capabilities in detecting and diagnosing medical conditions from imaging data, particularly in radiology. AI also contributes to improving image quality, automating routine tasks, and streamlining healthcare workflows. However, challenges such as data privacy, ethics, and regulatory frameworks need to be addressed for responsible implementation. The special issue includes several research papers that present advancements in automated medical decision support, age estimation, quality assurance, orthotic insole recommendation, tumor identification, thalamus segmentation, medical image classification, hyperparameter optimization, lung disease classification, and thoracic cavity segmentation. These papers demonstrate the potential of AI in improving accuracy, efficiency, and personalized treatment in medical image analysis. The integration of AI into healthcare requires collaboration between AI researchers, healthcare professionals, and regulatory bodies to ensure responsible and effective deployment. The future of AI in medical image analysis holds promise for improved diagnostic accuracy, early disease detection, and personalized treatment strategies. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
48. Metallic Materials: Structure Transition, Processing, Characterization and Applications.
- Author
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Hu, Jing, He, Ze, and Liu, Xiliang
- Subjects
ALUMINUM alloys ,NONFERROUS metals ,SHAPE memory alloys ,PHASE transitions ,HEAT treatment ,ARTIFICIAL neural networks ,NODULAR iron ,NICKEL-titanium alloys - Abstract
This document is a special issue of the journal "Materials" that focuses on the recent progress in the structure transition, processing, characterization, and applications of metallic materials. The issue includes research papers on various topics such as the development of dispersion-strengthened copper alloys, the characterization of carbide precipitation in steel, the effect of magnetic fields on aluminum bronze, and the enhancement of impact toughness in cast iron through heat treatment. The issue aims to provide comprehensive and up-to-date information for researchers, engineers, and industry experts in the field of metallic materials. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
49. Recognition of food images based on transfer learning and ensemble learning.
- Author
-
Bu, Le, Hu, Caiping, and Zhang, Xiuliang
- Subjects
CONVOLUTIONAL neural networks ,IMAGE recognition (Computer vision) ,ARTIFICIAL neural networks ,FEATURE extraction ,LEARNING ability - Abstract
The recognition of food images is of great significance for nutrition monitoring, food retrieval and food recommendation. However, the accuracy of recognition had not been high enough due to the complex background of food images and the characteristics of small inter-class differences and large intra-class differences. To solve these problems, this paper proposed a food image recognition method based on transfer learning and ensemble learning. Firstly, generic image features were extracted by using the convolutional neural network models (VGG19, ResNet50, MobileNet V2, AlexNet) pre-trained on the ImageNet dataset. Secondly, the 4 pre-trained models were transferred to the food image dataset for model fine-tuning. Finally, different basic learner combination strategies were adopted to establish the ensemble model and classify feature information. In this paper, several kinds of experiments were performed to compare the results of food image recognition between single models and ensemble models on food-11 dataset. The experimental results demonstrated that the accuracy of the ensemble model was the highest, reaching 96.88%, which was superior to any base learner. Therefore, the convolutional neural network model based on transfer learning and ensemble learning has strong learning ability and generalization ability, and it is feasible and practical to apply the method to food image recognition. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. Editorial for the Special Issue "Data Science and Big Data in Biology, Physical Science and Engineering".
- Author
-
Mahmoud, Mohammed
- Subjects
PHYSICAL sciences ,BIG data ,DEEP learning ,ARTIFICIAL neural networks ,DATA science ,MACHINE learning ,REINFORCEMENT learning - Abstract
This document is an editorial for a special issue of the journal "Technologies" focused on data science and big data in various fields such as biology, physical science, and engineering. The editorial highlights the importance of analyzing large amounts of data generated by digital technologies and the need for data scientists to use artificial intelligence and machine learning to extract valuable knowledge. The special issue includes 12 papers covering topics such as machine learning techniques for customer churn prediction, agile program management in the U.S. Navy, deep learning for cybersecurity in Industry 5.0, self-directed learning during the COVID-19 era, decision tree-based neural networks for data classification, data-driven governance in technology companies, and more. The papers explore different approaches, models, and tools in the context of data science and big data. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
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