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89,083 results

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

Author

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

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

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

Published
2024
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3. An Artificial Neural Network (ANN) Modelling Approach for Evaluating Turbidity Properties of Paper Recycling Wastewater

Author

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

Subjects
recycled paper, wastewater, turbidity, artificial neural network, microwave technology, Biotechnology, TP248.13-248.65
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.

Published
2024

4. An Early Evaluation of the Long-Term Influence of Academic Papers Based on Machine Learning Algorithms

Author

Junping Qiu and Xiaolin Han

Subjects
Academic papers, multiple linear regression, artificial neural network, classification and regression tree, prediction model, research value, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

Published
2024
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10. Artificial Neural Network to Estimate Deterministic Indices in Control Loop Performance Monitoring

Author

Gómez-Múnera, John A., Díaz-Charris, Luis, Jiménez-Cabas, Javier, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Choi, Bong Jun, editor, Singh, Dhananjay, editor, Tiwary, Uma Shanker, editor, and Chung, Wan-Young, editor

Published
2024
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12. A Comparative Analysis of Android Malware Detection Using Deep Learning

Author

Sahu, Diptimayee, Tripathy, Satya Narayan, Kapat, Sisira Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Challa, Rama Krishna, editor, Aujla, Gagangeet Singh, editor, Mathew, Lini, editor, Kumar, Amod, editor, Kalra, Mala, editor, Shimi, S. L., editor, Saini, Garima, editor, and Sharma, Kanika, editor

Published
2024
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16. Harnessing Advanced Techniques for Image Steganography: Sequential and Random Encoding with Deep Learning Detection

Author

Al Zakitat, Mustafa Ali Sahib, Abdulrazzaq, Mohammed Majid, Ramaha, Nehad T. A., Mukhlif, Yasir Adil, Ismael, Omar ayad, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Márquez, Fausto Pedro, editor, Jamil, Akhtar, editor, Hameed, Alaa Ali, editor, and Segovia Ramírez, Isaac, editor

Published
2024
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17. Gender Recognition Using ANN and Forward Rajan Transform Inclusive of Transgender Identity

Author

Priya, K., Roomi, S. Mohamed Mansoor, Maheswari, P. Uma, Farook, Faazelah Mohamed, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Chakravarthi, Bharathi Raja, editor, B, Bharathi, editor, García Cumbreras, Miguel Ángel, editor, Jiménez Zafra, Salud María, editor, Subramanian, Malliga, editor, Shanmugavadivel, Kogilavani, editor, and Nakov, Preslav, editor

Published
2024
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18. An Improved Snow Ablation Optimizer for Stabilizing the Artificial Neural Network

Author

Maddaiah, Pedda Nagyalla, Narayanan, Pournami Pulinthanathu, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish C., editor

Published
2024
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19. SoftCut: A Fully Differentiable Relaxed Graph Cut Approach for Deep Learning Image Segmentation

Author

Bonfiglio, Alessio, Cannici, Marco, Matteucci, Matteo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Pardalos, Panos M., editor, and Umeton, Renato, editor

Published
2024
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20. Performance Evaluation of Metaheuristics-Tuned Deep Neural Networks for HealthCare 4.0

Author

Jovanovic, Luka, Golubovic, Sanja, Bacanin, Nebojsa, Kunjadic, Goran, Antonijevic, Milos, Zivkovic, Miodrag, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Aurelia, Sagaya, editor, J., Chandra, editor, Immanuel, Ashok, editor, Mani, Joseph, editor, and Padmanabha, Vijaya, editor

Published
2024
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21. An Attribute Selection Using Propagation-Based Neural Networks with an Improved Cuckoo-Search Algorithm

Author

Priyanka, Walia, Kirti, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Challa, Rama Krishna, editor, Aujla, Gagangeet Singh, editor, Mathew, Lini, editor, Kumar, Amod, editor, Kalra, Mala, editor, Shimi, S. L., editor, Saini, Garima, editor, and Sharma, Kanika, editor

Published
2024
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26. Qualitative determination of volatile substances in different flavored cigarette paper by using headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) combined with chemometrics

Author

Chao Li, Senlin Tian, Junheng You, Jinyun Liu, E’xian Li, Chunqiong Wang, Qinghua Wang, Zijian Zhu, Duoqing Fan, and Runtao Tian

Subjects
Flavored cigarette paper, Headspace-gas chromatography-ion mobility spectrometry, Volatile substances, Fingerprint, Artificial neural network, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

In order to investigate the difference of volatile substances among flavored cigarette paper, which are supplied by several manufacturers with different batches, the stability of the complex system of scented cigarette paper was analyzed and evaluated. In this study, Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was used to detect the aroma compounds of 23 flavored cigarette paper samples. Based on fingerprint analysis, the differences and changes of aroma compounds of different samples were studied in the form of data visualization. Principal component analysis, partial least squares regression analysis, cluster heatmap analysis and artificial neural network analysis were used to evaluate the stability of different cigarette paper. The results show that: A total of 29 volatile substances were identified from different scented cigarette paper. Fingerprint analysis revealed that the volatile substances of different cigarette paper samples were roughly the same, but not the content. The results of chemometrics analysis showed that there were significant differences in the characteristic aroma compounds of cigarette paper from different manufacturers. HS-GC-IMS technology combined with chemometrics method could be applied to determine the difference of volatile substances among different flavored cigarette paper, which theoretically and technically supported the quality stability maintenance and identification of flavored cigarette paper processed in different places.

Published
2023
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28. Nowcasting - Rain Intensity Prediction Using Machine Learning Models with Markov Chain Process

Author

Nagarajan, Sudharsan, Manickam, Sivagami, Lakshmanan, Jeganathan, Nachimuthu, Maheswari, Thimma Subramanian, Pradeep Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Kottursamy, Kottilingam, editor, Bashir, Ali Kashif, editor, Kose, Utku, editor, and Uthra, Annie, editor

Published
2023
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29. Sensitivity Analysis of Engineering Structures Utilizing Artificial Neural Networks and Polynomial Chaos Expansion

Author

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

Published
2023
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30. System for Monitoring and Managing Electric Vehicles Charging Using IoT

Author

Aledamat, Osama, Elshiekh, Noureldin, ALSalahi, Ahmed, Ismail, Loay, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Singh Jat, Dharm, editor, Mishra, Durgesh Kumar, editor, and Joshi, Amit, editor

Published
2023
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31. Improved Robust Neural Network for Sim2Real Gap in System Dynamics for End-to-End Autonomous Driving

Author

Pareigis, Stephan, Maaß, Fynn Luca, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Gini, Giuseppina, editor, Nijmeijer, Henk, editor, Burgard, Wolfram, editor, and Filev, Dimitar, editor

Published
2023
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32. Estimating the Tensile Strength of Strain-Hardening Fiber-Reinforced Concrete Using Artificial Neural Network

Author

Nguyen, Diu-Huong, Tran, Ngoc-Thanh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish Chand, editor

Published
2023
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33. Computational Models for Prognosis of Medication for Cardiovascular Diseases

Author

Prasad, Vinayak Krishan, Rishabh, R., Shenoy, Vikram, Meleet, Merin, Cholli, Nagaraj G., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish Chand, editor

Published
2023
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34. Prediction of Hydrodynamic Coefficients of Stratified Porous Structure Using Artificial Neural Network (ANN)

Author

Gupta, Abhishek, Shilna, K., Karmakar, D., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish Chand, editor

Published
2023
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35. Machine Learning Techniques in Cardiovascular Disease Prediction

Author

Srivastava, Rajat, Maji, Srabanti, Panda, Trinath, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Narayana, V. A., editor, Suganthan, P. N., editor, Anguera, Jaume, editor, Koppula, Vijaya Kumar, editor, and Gupta, Suneet Kumar, editor

Published
2023
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36. Some Modified Activation Functions of Hyperbolic Tangent (TanH) Activation Function for Artificial Neural Networks

Author

Kumar, Arvind, Sodhi, Sartaj Singh, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, Dutta, Paramartha, editor, and Piuri, Vincenzo, editor

Published
2023
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37. Influence of Packet Switching and Routing Methods on the Reliability of the Data Transmission Network and the Application of Artificial Neural Networks

Author

Davronbekov, D., Aripov, J., Jabbarov, Sh., Djuraev, R., Matkurbonov, D., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zaynidinov, Hakimjon, editor, Singh, Madhusudan, editor, Tiwary, Uma Shanker, editor, and Singh, Dhananjay, editor

Published
2023
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38. Effectiveness of Deep Learning Based Filtering Algorithm in Separation of Human Objects from Images

Author

Khalilov, S. P., Yusupov, I., Mannapova, M. G., Nasrullayev, N. B., Botirov, F., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zaynidinov, Hakimjon, editor, Singh, Madhusudan, editor, Tiwary, Uma Shanker, editor, and Singh, Dhananjay, editor

Published
2023
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39. Stock Price Prediction in Response to US Dollar Exchange Rate Using Machine Learning Techniques

Author

Saeed, Muhammad Atif, Jamil, Akhtar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Márquez, Fausto Pedro, editor, Jamil, Akhtar, editor, Eken, Süleyman, editor, and Hameed, Alaa Ali, editor

Published
2023
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42. Fundamental Study on Application of Artificial Neural Network Model to the Mixture Design of Soil Treated with Paper Sludge Ash-Based Stabilizer

Author

To, Phuong-Anh T., Hayano, Kimitoshi, Mochizuki, Yoshitoshi, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Duc Long, Phung, editor, and Dung, Nguyen Tien, editor

Published
2024
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43. Challenges in computational materials modelling and simulation: A case-study to predict tissue paper properties

Author

Flávia P. Morais and Joana M.R. Curto

Subjects
3D fiber-based simulator, Artificial neural network, Multiple linear regression, Tissue functional properties, Tissue paper materials, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

The growing demand for tissue papers worldwide encourages the paper industry to find new approaches to optimize the raw materials furnish management, and simultaneously to improve tissue paper performance. Softness, strength, and absorption are the key tissue properties that enhance the attention of both industry and consumers. Fiber morphology, fiber modification process steps, and structural properties affect these functional properties, and, therefore, the efforts to evaluate them and establish the relationship or models that describe them constitute a multifactorial challenge. For this purpose, we aimed to investigate the trade-off between the input variables (morphological, suspension, and structural properties) and the final properties. Key variables like the type of furnish raw materials, including the fiber mixture, mechanical and enzymatic treatments, additives incorporation, and the type of industrial base tissue papers were taken under consideration. To achieve these relationships, we used different data-driven modeling approaches including multiple linear regression (MLR), artificial neural networks (ANN), and a 3D fiber-based simulator. The MLR and ANN models were built by data collected from an experimental design, and isotropic laboratory structures were prepared and tested for changes in structural and functional properties. Moreover, a 3D fiber-based simulator was used to investigate the influence of fibers on structural properties. These results indicated that the realistic predictions enabled us to link fiber and tissue structure characteristics. In conclusion, this work has revealed that this computational modeling approach can be used to model the effect of fiber pulps parameters with final end-use tissue properties, allowing to design innovative tissue products.

Published
2022
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44. Multi-agent System for Weather Forecasting in India

Author

Sreedevi, A. G., Palaniappan, S., Shankar, P., Sugumaran, Vijayan, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Abramowicz, Witold, editor, Auer, Sören, editor, and Stróżyna, Milena, editor

Published
2022
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45. A Review on Speech Synthesis Based on Machine Learning

Author

Kumari, Ruchika, Dev, Amita, Kumar, Ashwni, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Dev, Amita, editor, Agrawal, S. S., editor, and Sharma, Arun, editor

Published
2022
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46. Prediction of Impact Energy of Steel Using Artificial Neural Network

Author

Rath, S., Gond, S. K., Kumar, P., Sahana, P., Thakur, S. K., Pathak, P., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sk, Arif Ahmed, editor, Turki, Turki, editor, Ghosh, Tarun Kumar, editor, Joardar, Subhankar, editor, and Barman, Subhabrata, editor

Published
2022
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47. A Neural-Symbolic Approach to Computer Vision

Author

Kroshchanka, Aliaksandr, Golovko, Vladimir, Mikhno, Egor, Kovalev, Mikhail, Zahariev, Vadim, Zagorskij, Aleksandr, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Golenkov, Vladimir, editor, Krasnoproshin, Viktor, editor, Golovko, Vladimir, editor, and Shunkevich, Daniil, editor

Published
2022
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48. Satellite Image Classification Using ANN

Author

Mathur, Pratistha, Kavita, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Joshi, Sandeep, editor, Bairwa, Amit Kumar, editor, Nandal, Amita, editor, Radenkovic, Milena, editor, and Avsar, Cem, editor

Published
2022
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