25,274 results
Search Results
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. Research Paper Screening Tool: Automating Conference Paper Evaluation and Enhancement.
- Author
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Rathnasiri, Hansani Upeksha, Ishara Lakshani, L. A., Amarasinghe, Nipuni Nilakna, Dissanayake, Oshan Asinda, Nawinna, Dasuni, and Attanayaka, Buddima
- Subjects
TECHNOLOGICAL innovations ,ARTIFICIAL neural networks ,MACHINE learning ,ARTIFICIAL intelligence ,NATURAL language processing - Abstract
In this era of knowledge, academic researchers are growing every day, this also spikes a growth in published literature on the new innovations and findings. This leads to a problem where the reviewers at the conferences must go through many research papers to determine whether they are suitable for the conference or not. This problem has caused the necessity of an effective paper screening tool for optimizing the literature review process. This research presents a development of a new Paper Screening Tool (PST) aimed at increasing the efficiency and accuracy of the literature screening phase. Leveraging the NPL processing techniques this PST and reduces a lot of manual efforts. Through comprehensive evaluation using a diverse dataset, the tools provide high precision. The PST also has user friendly interfaces and customizable report generation which empowers the researchers screening process to their specific needs. This paper contributes to literature by solving the challenge of information overloading during the literature review. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. Review paper on research direction towards cancer prediction and prognosis using machine learning and deep learning models
- Author
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Murthy, Nimmagadda Satyanarayana and Bethala, Chaitanya
- Published
- 2023
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- View/download PDF
5. Hydrolytic and soil degradation of cellulosic material (paper): optimization of parameters using ANN and RSM.
- Author
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Girish, Bandi, Rakshith, Golluri Ricky, Paul, Atanu Kumar, Raja, Vinoth Kumar, and Chakraborty, Gourhari
- Subjects
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ARTIFICIAL neural networks , *SOLID waste management , *SOIL degradation , *RESPONSE surfaces (Statistics) , *SOLID waste - Abstract
This work reports the degradation of cellulosic material, i.e., paper, under different environmental conditions (soil and hydrolytic). Models were developed using central composite design (CCD) within the framework of response surface methodology (RSM) and artificial neural network (ANN) techniques. It is done as part of a solid waste management system to eliminate the waste produced by excessive paper usage and obtain optimized degradation conditions of paper. In view of the real environments where paper-based solid wastes are usually exposed, soil degradation and hydrolytic degradation of paper were investigated. The factors pH (4–10), compost ratio (2–5), CaCl2 (5–15 ppm), and time (7–20 days) were independent factors that varied for the study, and degradation conversion was measured as the dependent variable for soil degradation of paper. The factors pH (4–10), CaCl2 (2–5 ppm), tripotassium phosphate (TPP, 2–5 ppm), and time (6–16 days) were independent variables that varied for the study, and degradation conversion was the dependent variable for hydrolytic degradation of paper. The optimal conditions for soil degradation were determined to be a pH of 4, a compost ratio of 5, a salt (CaCl2) addition of 5 ppm, and a duration of 20 days, resulting in the highest observed conversion rate. The coefficient of regression (R2) for CCD is 87.24%, whereas it is 94.82% for ANN. The optimal conditions for achieving maximum conversion in hydrolytic degradation include a pH of 10, a CaCl2 salt concentration of 5 ppm, a TPP salt concentration of 2 ppm, and a duration of 16 days. The coefficient of regression for CCD is 75.15%, while the coefficient of regression for ANN is 93.91%. In both deterioration scenarios, the data exhibited a higher degree of fit when modeled using ANN compared to CCD (RSM). [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. 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
- Full Text
- View/download PDF
7. 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
- Full Text
- View/download PDF
8. 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
9. 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
10. Synergistic effect of cellulo-xylanolytic and laccase enzyme consortia for improved deinking of waste papers.
- Author
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Gupta, Guddu Kumar, Kapoor, Rajeev Kumar, Chhabra, Deepak, Bhardwaj, Nishi Kant, and Shukla, Pratyoosh
- Subjects
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ARTIFICIAL neural networks , *WASTE paper , *FUNGAL enzymes , *HYDROPHOBIC compounds , *GENETIC algorithms , *XYLANASES - Abstract
• Enhanced cellulo-xylanolytic consortium production from Hypocrea lixii GGRK4 using MOGA-ANN is reported. • The production of CMCase (9.43 fold) and xylanase (4.40 fold) higher than un-optimized process. • The improved deinking efficiency and brightness is reported for photocopier paper and newspaper. • The physical strength of the waste papers were enhanced whereas double fold property was decreased proving its reusability. • A significant fungal enzyme consortium preparation for improved waste paper deinking achieved. This study reports the cellulo-xylanolytic cocktail production from Hypocrea lixii GGRK4 using multi-objective genetic algorithm-artificial neural network tool, resulting in 8.32 ± 1.07 IU/mL, 51.53 ± 3.78 IU/mL activity of CMCase and xylanase, respectively with more than 85 % residual activity at 60 °C and pH 6.0. Interestingly, metal ions viz. K+ and Ca2+ stimulated the enzyme activity, whereas Fe2+ and Cu2+ reduced the activity. Significant amounts of hydrophobic compounds, chromophores, and phenolics were released after wastepapers deinking. The deinking efficiency of 73.60 ± 2.45 % and 38.60 ± 1.34 % was obtained for photocopier paper and newspaper, respectively, whereas brightness of 89.90 ± 2.10 % ISO and 44.90 ± 1.63 % ISO was reported for both types of waste papers. The physical strength of deinked photocopier paper and newspapers, i.e., tensile index (3.10 and 0.50 %), tearing index (7.10 and 4.83 %), and burst factor (8.61) were enhanced whereas double fold property was decreased proving wastepaper reusability. This consortium showed effective and significant enzymatic deinking efficiency for recycled wastepapers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
11. 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
- Full Text
- View/download PDF
12. 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
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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
- View/download PDF
13. 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
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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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- View/download PDF
14. 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
15. Preparation and application of an olfactory visualization freshness sensor array based on microfluid paper‐based chip.
- Author
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Wang, Guannan, Cheng, Jiawei, Ma, Jun, Huang, Shaoyun, Zhang, Tao, Li, Yanwei, and Li, Houbin
- Subjects
SENSOR arrays ,CTENOPHARYNGODON idella ,FISHER discriminant analysis ,ARTIFICIAL neural networks ,VISUALIZATION ,FILTER paper ,ELECTRONIC noses - Abstract
This paper introduced a novel olfactory visualization freshness sensor array prepared by dropping pH indicators on UV lithography hydrophilic–hydrophobic paper under room temperature. The sensor array was applied in Grass carp spoilage classification. The sensor array can generate different discoloration patterns that respond to the pH change of the fish spoil; therefore, it can distinguish Grass carp freshness using pattern recognition. Classification models were built with linear discriminant analysis (LDA) and backpropagation artificial neural network (BP‐ANN) after extracting the sensor array's feature color information with principal component analysis (PCA). The performance of the BP‐ANN model was superior to that of the LDA model, with an optimum recognition rate of 95.45% (training set) and 90.90% (testing set). In conclusion, with a suitable algorithm choice and rational pH indicator arrangement, this olfactory visualization freshness sensor array can potentially monitor food freshness. We prepared Grass carp freshness sensor array through dropping pH‐indicators on hydrophilic–hydrophobic filter paper that fabricated by UV lithography under room temperature. The hydrophobic edges of the filter paper can maintain the sensor spots' shape and provide stable and consistent colorimetric change for Grass carp freshness recognition. Colorimetric classification models are built using LDA and BP‐ANN based on pattern recognition, and the recognition rate of the BP‐ANN model is above 90%. Novelty impact statement: We prepared Grass carp freshness sensor array through dropping pH‐indicators on hydrophilic‐hydrophobic filter paper that fabricated by UV lithography under room temperature. The hydrophobic edges of the filter paper can maintain the sensor spots' shape and provide stable and consistent colorimetric change for Grass carp freshness recognition. Colorimetric classification models are built using LDA and BP‐ANN based on pattern recognition, and the recognition rate of the BP‐ANN model is above 90 %. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
16. Abstract of the papers presented at the First International Congress and Workshop on Industrial Artificial Intelligence 2021 (IAI 2021) .
- Subjects
BALLAST (Railroads) ,ARTIFICIAL intelligence ,DIGITAL communications ,DEEP learning ,CONFERENCES & conventions ,ACOUSTIC reflection ,ARTIFICIAL neural networks ,CYBER physical systems - Published
- 2022
17. Transform paper-based cadastral data into digital systems using GIS and end-to-end deep learning techniques.
- Author
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Mango, Joseph, Wang, Moyang, Mu, Senlin, Zhang, Di, Ngondo, Jamila, Valerian-Peter, Regina, Claramunt, Christophe, and Li, Xiang
- Subjects
- *
DEEP learning , *GEOGRAPHIC information systems , *VECTOR data , *INTERNET stores , *ARTIFICIAL neural networks - Abstract
Digital systems storing cadastral data in vector format are considered effective due to their ability of offering interactive services to citizens and other land-related systems. The adoption of such systems is ubiquitous, but when adopted, they create two non-compatible systems with paper-based cadastral systems whose information needs to be digitised. This study proposes a new approach that is fast and accurate for transforming paper-based cadastral data into digital systems. The proposed method involves deep-learning techniques of the LCNN and ResNet-50 for detecting cadastral parcels and their numbers, respectively, from the cadastral plans. It also contains four functions defined to speed up transformations and compilations of the cadastral plan's data in digital systems. The LCNN is trained and validated with 968 samples. The ResNet-50 is trained and validated with 106,000 samples. The Structural-Average-Precision ( sAP 10 ) achieved with the LCNN was 0.9057. The Precision, Recall and F1-Score achieved with the ResNet-50 were 0.9650, 0.9648 and 0.9649, respectively. These results confirmed that the new method is accurate enough for implementation, and we tested it with a huge set of data from Tanzania. Its performance from the experimented data shows that the proposed method could effectively transform paper-based cadastral data into digital systems. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
18. 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
19. Seeking a paper for digital printing with maximum gamut volume: a lesson from artificial intelligence.
- Author
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Ataeefard, Maryam and Tilebon, Seyyed Mohamad Sadati
- Subjects
ARTIFICIAL intelligence ,ELECTRONIC paper ,DIGITAL printing ,ARTIFICIAL neural networks ,DIGITAL printing presses ,COMPUTER printers ,ATOMIC force microscopy - Abstract
The color gamut of imaging media is significant for the reproduction of color images because its magnitude directly affects the degree to which colors change during the printing process. Over the last few years, digital impression technology has started to play a substantial role in the printing industry due to the quest for short runs and variable information printing. The color gamut of electrophotographic digital printing depends on various parameters including the printer and toner, but especially the properties (whiteness, roughness, and gloss) of the paper, which influence the final printed color gamut and replication quality. Artificial intelligence approaches are applied herein for the first time to choose and predict the performance of a paper with appropriate properties to achieve the maximum color gamut. A genetic algorithm-based computer code is developed to optimize the architecture of an artificial neural network, thereby yielding an accurate model to predict the color gamut achievable in electrophotographic color printing. The gamut volume was generated using an Eye-One spectrophotometer, ProfileMaker, and ColorThink software. The properties of 11 dissimilar types of paper were assessed by atomic force microscopy, spectrophotometer, and goniophotometer. The results indicate that the reproducibility depended considerably on the features of the paper. Although high whiteness and gloss increased the color gamut volume, and high roughness decreased the reproducibility of the printing machine, the artificial intelligence approach provided the opportunity to achieve a high gamut volume with low gloss and high roughness. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
20. 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
21. Can We Read Neural Networks? Epistemic Implications of Two Historical Computer Science Papers.
- Author
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Offert, Fabian
- Subjects
- *
COMPUTER science research , *ARTIFICIAL intelligence , *ARTIFICIAL neural networks , *LANGUAGE models , *COMPUTER programming - Abstract
The article discusses two computer science research papers concerning artificial intelligence (AI). Topics explored include the susceptibility of deep convolutional neural networks to input pertubations acknowledged in the 2013 study "Intriguing Properties of Neural Networks," by Christian Szegedy and colleagues, and the capability of sequence-to-sequence language models to execute short computer programs reported in the 2014 study "Learning to Execute," by Wojciech Zaremba and Ilya Sutskever.
- Published
- 2023
- Full Text
- View/download PDF
22. Call for Papers—INFORMS Journal on Computing: Special Issue on Responsible AI and Data Science for Social Good.
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DATA science , *SOFTWARE architecture , *ARTIFICIAL intelligence , *MACHINE learning , *ARTIFICIAL neural networks , *SWARM intelligence - Published
- 2023
- Full Text
- View/download PDF
23. 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
24. Summaries of Wordle Results Outstanding Papers.
- Subjects
ARTIFICIAL neural networks ,RECURRENT neural networks ,GAUSSIAN mixture models - Abstract
This document provides summaries of research papers on the topic of Wordle, a popular puzzle game. The papers discuss various models and prediction frameworks used to analyze and improve the game. They explore factors such as word attributes, difficulty levels, and player behavior. The findings suggest that certain word attributes have an impact on the game's outcomes and difficulty. The models developed in these studies offer insights for game operators and suggestions for enhancing the game's popularity. [Extracted from the article]
- Published
- 2023
25. Utilizing Ultrasonic Guided Waves for the Early Age Assessment of Concrete Strength and Hardening: Review Paper.
- Author
-
Kumar, Pappu, Yadav, Onkar, and Dev, Pappu
- Subjects
CONCRETE ,MACHINE learning ,ARTIFICIAL intelligence ,TECHNOLOGICAL innovations ,ARTIFICIAL neural networks - Abstract
Monitoring the early age strength and hardening of concrete plays a crucial role in ensuring the structural integrity and durability of concrete structures. Ultrasonic guided waves have emerged as a promising nondestructive testing technique for assessing concrete properties. This review paper aims to provide an overview of the utilization of ultrasonic guided waves for the early age assessment of concrete strength and hardening. The paper begins by discussing the fundamental principles of ultrasonic guided waves and their interaction with concrete materials. It highlights the advantages of using guided waves, such as their ability to propagate over long distances and penetrate through concrete structures. The review then explores various techniques employed for generating and detecting guided waves, including piezoelectric transducers, air-coupled transducers, and laser-ultrasonics. Furthermore, the paper presents a comprehensive analysis of the different parameters that can be extracted from ultrasonic signals to assess the early age strength and hardening of concrete. These parameters include wave velocity, attenuation, reflection, and scattering characteristics. The influence of various factors, such as moisture content, temperature, and mixture proportions, on the ultrasonic response of concrete is also discussed. Moreover, the review discusses the challenges and limitations associated with the application of ultrasonic guided waves in early age concrete assessment. It addresses issues such as signal interpretation, wave dispersion, and the presence of air voids. Additionally, recent advancements in signal processing techniques and data interpretation methods are highlighted. In conclusion, the utilization of ultrasonic guided waves for the early age assessment of concrete strength and hardening shows great promise. This review paper provides valuable insights into the current state-of-the-art in this field and offers recommendations for future research directions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
26. Wet Paper Coding-Based Deep Neural Network Watermarking.
- Author
-
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
27. 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
28. Pet Adoption Speed Prediction.
- Author
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Akhil, B., Shashank, B., Ramalakshmi, Eliganti, and Prathima, T
- Subjects
PET adoption ,PETS ,DOG breeds ,SOCIAL media ,ARTIFICIAL neural networks ,CANIDAE ,WASTE paper - Abstract
Many stray animals throughout the world unfortunately do not get the opportunity to find the loving home that they deserve. In this classification task, we look to develop an algorithm to predict the speed of pet adoptions. The input to our algorithm includes animal type (dog or cat), breed, gender, colour, profile picture online and the descriptions about the pet, etc. We then use both traditional machine learning techniques (Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest and Gradient Boosting) and neural networks (fully connected neural networks and long short-term memory Model) to predict the adoption rate. In particular, we build feature vectors using information extracted from description scripts and feed them into neural network models. We are hoping to examine the results to develop strategies to help improve the overall adoption rate (i.e. what features lead to faster adoption). Every year, 3.3 million canines visit animal shelters, out of a total population of 200 million. Only 2% to 17% of pets are returned to their owners. The remaining animals are euthanized due to a shortage of room in shelters (killed). The present procedures for adapting or finding a pet are inefficient and haphazard. People disseminated leaflets to the general public and spread the word to others in the vicinity of the pet's disappearance. When individuals print fliers, they waste paper and money since there is no good impact. People also share their tales on social media sites like Instagram and Facebook. There are also several fraudsters who attempt to fraudulently claim the incentive for returning the pet to its legitimate owner. We want to provide an analysis on how fast pets(cats and dogs) can be adopted based on various factors like their health conditions, age, colour, breed etc. [ABSTRACT FROM AUTHOR]
- Published
- 2024
29. Paper-based colorimetric sensor using bimetallic Nickel-Cobalt selenides nanozyme with artificial neural network-assisted for detection of H2O2 on smartphone.
- Author
-
Lian, Meiling, Shi, Feiyu, Cao, Qi, Wang, Cong, Li, Na, Li, Xiao, Zhang, Xiao, and Chen, Da
- Subjects
- *
SELENIDES , *MOBILE apps , *ELECTRONIC data processing , *DETECTORS , *ARTIFICIAL neural networks , *THREE-dimensional printing , *SMARTPHONES - Abstract
An instrument-free, user-friendly, and cost-effective PAD colorimetric sensor that can quantitative detection of H 2 O 2 has been realized by using bimetallic nickel–cobalt selenides as highly active peroxidase mimic and smartphone integrated with dark cavity as detector. Integrated with an ANN model and a self-compiled easy-to-use smartphone APP, the intelligent and on-site detection of H 2 O 2 was constructed, which exhibited an ultra-wide dynamic range. [Display omitted] • An effective paper-based colorimetric sensor was prepared based on nanozyme. • The optimized Ni 0.75 Co 0.25 Se exhibited excellent peoxidase-mimetic activities. • The on-site detection of H 2 O 2 was constructed by a self-compiled smartphone APP. • The machine learning-assisted sensor can detect H 2 O 2 over a wide dynamic range. Paper-based analytical devices (PADs) integrated with smartphones have shown great potential in various fields, but they also face challenges such as single signal reading, complex data processing and significant environmental impacting. In this study, a colorimetric PAD platform has been proposed using bimetallic nickel–cobalt selenides as highly active peroxidase mimic, smartphone with 3D-printing dark-cavity as a portable detector and an artificial neural network (ANN) model as multi-signal processing tool. Notably, the optimized nickel–cobalt selenides (Ni 0.75 Co 0.25 Se with Ni to Co ratio of 3/1) exhibit excellent peoxidase-mimetic activities and are capable of catalyzing the oxidation of four chromogenic reagents in the presence of H 2 O 2. Using a smartphone with image capture function as a friendly signal readout tool, the Ni 0.75 Co 0.25 Se based four channel colorimetric sensing paper is used for multi-signal quantitative analysis of H 2 O 2 by determining the Grey, red (R), green (G) and blue (B) channel values of the captured pictures. An intelligent on-site detection method for H 2 O 2 has been constructed by combining an ANN model and a self-programmed easy-to-use smartphone APP with a dynamic range of 5 μM to 2 M. Noteworthy, machine learning-assisted smartphone sensing devices based on nanozyme and 3D printing technology provide new insights and universal strategies for visual ultrasensitive detection in a variety of fields, including environments monitoring, biomedical diagnosis and safety screening. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
30. Pulp and paper characterization by means of artificial neural networks for effluent solid waste minimization—A case study.
- Author
-
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
31. Enhancing the efficiency of a gas-fueled reheating furnace of the steelmaking industry: assessment and improvement
- Author
-
Sampaio Brasil, João Eduardo, Piran, Fabio Antonio Sartori, Lacerda, Daniel Pacheco, Morandi, Maria Isabel Wolf, Oliveira da Silva, Debora, and Sellitto, Miguel Afonso
- Published
- 2024
- Full Text
- View/download PDF
32. 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
33. Artificial intelligence-based method for forecasting flowtime in job shops
- Author
-
Modesti, Paulo, Ribeiro, Jhonatan Kobylarz, and Borsato, Milton
- Published
- 2024
- Full Text
- View/download PDF
34. Comment on papers using machine learning for significant wave height time series prediction: Complex models do not outperform auto-regression.
- Author
-
Jiang, Haoyu, Zhang, Yuan, Qian, Chengcheng, and Wang, Xuan
- Subjects
- *
ARTIFICIAL neural networks , *TIME series analysis , *PREDICTION models , *ARTIFICIAL intelligence , *MACHINE learning , *DECOMPOSITION method - Abstract
• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
35. Introduction to the virtual collection of papers on Artificial neural networks: applications in X‐ray photon science and crystallography.
- Author
-
Ekeberg, Tomas
- Subjects
- *
ARTIFICIAL neural networks , *DEEP learning , *CRYSTALLOGRAPHY , *ARTIFICIAL intelligence , *MACHINE learning , *PHOTONS - Abstract
Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
36. 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
37. Prediction of effective equivalent linear temperature gradients in bonded concrete overlays of asphalt pavements
- Author
-
Donnelly, Charles A., Sen, Sushobhan, DeSantis, John W., and Vandenbossche, Julie M.
- Published
- 2024
- Full Text
- View/download PDF
38. Data mining–based stock price prediction using hybridization of technical and fundamental analysis
- Author
-
Kaur, Jasleen and Dharni, Khushdeep
- Published
- 2023
- Full Text
- View/download PDF
39. 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
40. 50‐4: Invited Paper: Low‐Temperature Metal‐Oxide Thin‐Film Transistor Technology and the Realization of Electronic Systems on Flexible Substrates.
- Author
-
Shi, Runxiao, Hu, Yushen, Xie, Xinying, and Wong, Man
- Subjects
ARTIFICIAL neural networks ,ELECTRONIC systems ,FLEXIBLE electronics ,TRANSISTORS ,METALLIC oxides ,THIN film transistors - Abstract
A low‐temperature technology for fabricating thin‐film transistors (TFTs) is essential for the realization of electronic systems on flexible substrates. Presently reviewed are improved techniques for forming the source/drain regions and reducing the population of channel defects in a metal‐oxide TFT. These have been applied to the construction of different TFT structures, including ones with bottom gate, top gate, and dual gates. The utility of the improved 300‐°C technology has been demonstrated by the realization of a variety of electronic systems, such as a gate‐driver on array for active‐matrix displays, an analog front‐end for acquiring biopotential signals, and an artificial neural network for neuromorphic computing. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. PEERRec: An AI-based approach to automatically generate recommendations and predict decisions in peer review.
- Author
-
Bharti, Prabhat Kumar, Ghosal, Tirthankar, Agarwal, Mayank, and Ekbal, Asif
- Subjects
ARTIFICIAL intelligence ,ARTIFICIAL neural networks - Abstract
One key frontier of artificial intelligence (AI) is the ability to comprehend research articles and validate their findings, posing a magnanimous problem for AI systems to compete with human intelligence and intuition. As a benchmark of research validation, the existing peer-review system still stands strong despite being criticized at times by many. However, the paper vetting system has been severely strained due to an influx of research paper submissions and increased conferences/journals. As a result, problems, including having insufficient reviewers, finding the right experts, and maintaining review quality, are steadily and strongly surfacing. To ease the workload of the stakeholders associated with the peer-review process, we probed into what an AI-powered review system would look like. In this work, we leverage the interaction between the paper's full text and the corresponding peer-review text to predict the overall recommendation score and final decision. We do not envisage AI reviewing papers in the near future. Still, we intend to explore the possibility of a human–AI collaboration in the decision-making process to make the current system FAIR. The idea is to have an assistive decision-making tool for the chairs/editors to help them with an additional layer of confidence, especially with borderline and contrastive reviews. We use a deep attention network between the review text and paper to learn the interactions and predict the overall recommendation score and final decision. We also use sentiment information encoded within peer-review texts to guide the outcome further. Our proposed model outperforms the recent state-of-the-art competitive baselines. We release the code of our implementation here: https://github.com/PrabhatkrBharti/PEERRec.git. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Scanning the Issue.
- Author
-
Koul, Shiban K
- Subjects
WIRELESS LANs ,IEEE 802.16 (Standard) ,MICROGRIDS ,CLINICAL decision support systems ,ARTIFICIAL neural networks ,CURRENT conveyors ,COLONY-forming units assay ,INTEGRATED circuit design - Abstract
The components include E-shape single element slot antenna, Wilkinson Power Divider, Hybrid Directional Coupler, Loaded Line Phase Shifter, and Switched Line Phase Shifter. The following paper on "An Optimized DTW Algorithm using RMSE Approach to Classify the Liquids in Ka-Band", presents an algorithm to classify liquids with high level accuracy using transmission parameter of liquids with microwave spectroscopy. The authors study four types of textile antennas with wash cotton and denim substrate and compare the performance with standard substrate antenna for circular polarization, gain and return loss. The algorithm uses Dissolved Gas Analysis samples from real power transformers and genetic algorithm based fuzzy classifier. [Extracted from the article]
- Published
- 2022
- Full Text
- View/download PDF
43. Voice separation and recognition using machine learning and deep learning a review paper.
- Author
-
ibrahemm, Zaineb h. and Shihab, Ammar I.
- Subjects
ARTIFICIAL neural networks ,AUTOMATIC speech recognition ,DEEP learning ,MACHINE learning ,SPEECH perception ,SPEECH - Abstract
Voice isolation, a prominent research area in the field of speech processing, has garnered a great deal of attention due to its prospective implications in numerous domains. Deep neural networks (DNNs) have emerged as a potent instrument for addressing the challenges associated with vocal isolation. This paper presents a comprehensive study on the use of DNNs for voice isolation, focusing on speech recognition and speaker identification tasks. The proposed method uses frequency domain and time domain techniques to improve the separation of target utterances from background noise. The experimental results demonstrate the efficacy of the proposed method, revealing substantial improvements in voice isolation precision and robustness. This study's findings contribute to the increasing corpus of research on voice isolation techniques and provide valuable insights into the application of DNNs to improve speech processing tasks . [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. 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
45. Alternative method for determining basis weight in papermaking by using an interactive soft sensor based on an artificial neural network model.
- Author
-
Rodríguez-Álvarez, José L., López-Herrera, Rogelio, Villalón-Turrubiates, Iván E., García-Alcaraz, Jorge L., Díaz-Reza, José R., Arce-Valdez, Jesús L., Aragón-Banderas, Osbaldo, and Soto-Cabral, Arturo
- Subjects
ARTIFICIAL neural networks ,PAPERMAKING ,QUALITY control charts ,PAPER industry ,PROCESS control systems ,ANT algorithms - Published
- 2022
- Full Text
- View/download PDF
46. Multi-graph Attention Fusion Network for Paper Recommendation Considering Group Information in Scientific Social Networks.
- Author
-
Junqiao Gong, Li Zhou, Gang Wang, and Jian Ma
- Subjects
SOCIAL networks ,INFORMATION technology ,ARTIFICIAL intelligence ,TECHNOLOGICAL innovations ,DIGITAL technology ,ARTIFICIAL neural networks - Abstract
In scientific social networks, group information has become an important auxiliary information to enhance the performance of paper recommendation, as many researchers prefer to obtain interested papers by joining groups. However, the existing paper recommendation methods failed to make full use of group information. In this paper, a paper recommendation method considering group information with multi-graph attention fusion network (GI-MGAF) is proposed. Specifically, in the graph construction layer, we construct researcher-paper bipartite graph, group-researcher bipartite graph and group-paper bipartite graph. In the information propagation layer, graph attention networks (GAT) are used to learn the node representations on the constructed bipartite graphs. In the information fusion layer, the researcher-level attention and paper-level attention are developed to respectively fuse the representations of researchers and papers. Experiments were conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed GI-MGAF method. [ABSTRACT FROM AUTHOR]
- Published
- 2023
47. Editorial: Special Issue on Transfer Learning.
- Author
-
Chao, Guoqing, Zhu, Xingquan, Ding, Weiping, Bi, Jinbo, and Sun, Shiliang
- Subjects
HUMAN activity recognition ,TRANSFER of training ,FUZZY algorithms ,ARTIFICIAL neural networks ,NATURAL language processing ,LANGUAGE models ,ADAPTIVE fuzzy control - Abstract
Through call for Papers and the Review Process, the Special Issue has Accepted 22 Articles Covering Transfer Learning Model Designs and Applications. These 22 articles in this special issue provide a general picture of the development and applications of transfer learning method. Transfer Learning is a Machine Learning Paradigm which Reuses Previously Trained Models to help Solve a new Learning task. [Extracted from the article]
- Published
- 2023
- Full Text
- View/download PDF
48. 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
49. 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
50. 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
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