Article
Green & Sustainable Science & Technology
Syed Altan Haider, Muhammad Sajid, Hassan Sajid, Emad Uddin, Yasar Ayaz
Summary: This study utilizes statistical and Deep Learning techniques to forecast solar Global Horizontal Irradiance in Islamabad, Pakistan, aiming to promote renewable energy development for tackling global climate change. The research finds that ANN, CNN, and LSTM perform best for short-term forecasts, while SARIMAX and Prophet are efficient for long-term forecasts.
Article
Computer Science, Artificial Intelligence
Zhuang Ye, Jianbo Yu
Summary: Machine health assessment is crucial for prognostics and health management, and the proposed LSTMCAE demonstrates effectiveness in feature learning and generating health index using multivariate Gaussian distribution. Experimental results show the superiority of LSTMCAE in machine health assessment compared to other unsupervised learning methods.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics
Abdullah Alrasheedi, Abdulaziz Almalaq
Summary: This paper proposes hybrid DL methods to enhance the outcomes in Saudi SG load forecasting, aiming to develop reliable forecasting models and obtain knowledge of the relationships between various features and attributes in Saudi SGs.
Article
Computer Science, Artificial Intelligence
Ismail Shahin, Noor Hindawi, Ali Bou Nassif, Adi Alhudhaif, Kemal Polat
Summary: Recent research in speech emotion recognition has shown significant advancements by using MFCC's spectrogram features and novel classifier algorithms such as CapsNet. The proposed DC-LSTM COMP-CapsNet algorithm achieves a higher accuracy in emotion recognition compared to other known methods and classical classifiers, with an average accuracy of 89.3% in recognizing Arabic Emirati-accented speech.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Thermodynamics
Enbo Yu, Guoji Xu, Yan Han, Yongle Li
Summary: A short-term wind speed prediction model based on cross-channel data convolution, intelligent signal extension, and attention mechanisms is proposed in this study to enhance the prediction efficiency. The model improves the accuracy and efficiency of wind speed prediction by classifying and predicting the wind speed signal.
Article
Computer Science, Artificial Intelligence
Shuai Zhang, Kun Zhu, Wenyu Zhang
Summary: A novel deep learning model is proposed to represent the spatial correlations of traffic network more effectively through a new correlation matrix structure. The model calculates the correlations among sensors to construct speed, volume, and occupancy correlation matrices, and optimizes the placement of highly correlated sensors using an enhanced heuristic optimization algorithm. The three optimal correlation matrices are then combined to form a three-dimensional multivariate correlation matrix characterized by locally high correlation, which enables the exploitation of deep spatial features of traffic network.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Manuel Lopez-Martin, Soledad Le Clainche, Belen Carro
Summary: Deep learning models have not been fully applied to fluid dynamics predictions yet, but they are state-of-the-art solutions in other areas. By combining 3D convolutional layers with low-dimensional intermediate representations, a deep learning prediction model can successfully forecast the future states of dynamic systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Venkateswarlu Gundu, Sishaj P. Simon, Krishna Kumba
Summary: In this study, multiple deep neural network models and different weather parameters are used to forecast solar power generation. Through comparing the performance of these models and testing with actual data, it is found that the forecast model based on JAYA-based LSMN demonstrates superior predicting performance compared to conventional techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ziyu Sheng, Huiwei Wang, Guo Chen, Bo Zhou, Jian Sun
Summary: Research focused on using DRN with convolution structure for short-term load forecasting, finding that the model has higher prediction accuracy compared to existing models and can handle nonlinear regression problems effectively while achieving state-of-the-art results.
APPLIED INTELLIGENCE
(2021)
Article
Physics, Applied
Yihuan Qiao, Ya Wang, Changxi Ma, Ju Yang
Summary: The paper proposed a short-term traffic flow prediction method based on 1DCNN-LSTM, which improves prediction accuracy by capturing spatial and temporal information in traffic data and using it for regression predictions.
MODERN PHYSICS LETTERS B
(2021)
Article
Engineering, Civil
Yang Zhang, Dongrong Xin
Summary: This paper proposes a novel short-term traffic flow prediction method based on diverse ensemble deep learning, which includes spatial correlation measurement, LSTM-CNN model, and dynamic optimization. Experimental results show that this method can improve prediction algorithm performance and outperform classical methods even with small samples.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Mosbah Aouad, Hazem Hajj, Khaled Shaban, Rabih A. Jabr, Wassim El-Hajj
Summary: This paper introduces a deep learning approach to improve the accuracy of residential short-term load forecasting, achieving a significant performance improvement.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Mubarak S. Almutairi, Khalid Almutairi, Haruna Chiroma
Summary: The development and use of intelligent transportation systems within the concept of internet of vehicles is a growing trend. Deep learning algorithms, such as DRNN-LSTM, play a significant role in this field. The detection of rear end collision is a critical aspect in internet of vehicles, and previous approaches have limitations. In this research, a hybrid approach of DRNN-LSTM is proposed and shown to outperform other algorithms for rear end collision detection.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Nawaf Mohammad H. Alamri, Michael Packianather, Samuel Bigot
Summary: This paper aims to improve the performance of Deep Learning algorithms by optimizing LSTM parameters using the Bees Algorithm. It also explores the application of BA in CNN and its effectiveness in residual life prediction and classification problems.
APPLIED SCIENCES-BASEL
(2023)
Article
Thermodynamics
Ceyhun Yildiz, Hakan Acikgoz, Deniz Korkmaz, Umit Budak
Summary: This study introduces a novel deep learning method for wind power forecasting and demonstrates its competitive performance in very short-term wind power forecasting through experimental results.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Ali Alwehaibi, Marwan Bikdash, Mohammad Albogmi, Kaushik Roy
Summary: This paper proposes an optimized sentiment classification method based on deep learning for dialectal Arabic short text at the document level. The research results show significant performance improvement in Arabic text classification.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Ramit Kumar Roy, Himadri Mukherjee, Kaushik Roy, Umapada Pal
Summary: Accurately recognizing destination city names is crucial for postal documents to reach their intended addresses. In India, people often mix up scripts when writing addresses due to the country's multilingual and multi script nature. This paper presents a Convolutional Neural Network (CNN) based approach for recognizing handwritten multilingual multiscript Indian city names. The proposed scheme achieves high accuracy in both single script and multi script scenarios, with a maximum accuracy of 98.01%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Kha Gia Quach, Ngan Le, Chi Nhan Duong, Ibsa Jalata, Kaushik Roy, Khoa Luu
Summary: Group-level emotion recognition is a growing research area that is becoming increasingly important for assessing crowds of all sizes in the security and social media domains. This work extends previous research on group-level emotion recognition from single images or videos to fully investigate expression recognition in crowd videos through an effective deep feature level fusion mechanism.
PATTERN RECOGNITION
(2022)
Article
Business
Sahana Das, Sk Md Obaidullah, Kaushik Roy, Chanchal Kumar Saha
Summary: Cardiotocography (CTG) is a widely used technique to monitor fetal health. This study uses machine learning algorithms to accurately classify the baseline and compares the results with visual estimation by obstetricians, with FURIA algorithm achieving the highest accuracy.
INTERNATIONAL JOURNAL OF BUSINESS ANALYTICS
(2022)
Article
Computer Science, Information Systems
Payel Rakshit, Somnath Chatterjee, Chayan Halder, Shibaprasad Sen, Sk Md Obaidullah, Kaushik Roy
Summary: This paper discusses the application of popular Convolutional Neural Networks (CNNs) in Bangla handwritten character recognition and evaluates the performance of each network. The study shows the superior performance of CNN models in Bangla handwritten character recognition.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Information Systems
Trishita Ghosh, Shibaprasad Sen, Sk. Md. Obaidullah, K. C. Santosh, Kaushik Roy, Umapada Pal
Summary: The easy availability and rapid use of online devices have increased the demand for online handwriting recognition. This paper discusses various machine learning and deep learning approaches for recognizing online handwritten characters, words, and texts. The advantages and challenges of online handwriting recognition are also addressed.
COMPUTER SCIENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Payel Rakshit, Chayan Halder, Sk Md Obaidullah, Kaushik Roy
Summary: This paper presents a multi-script text line segmentation algorithm based on newly developed light projection, start point detection, and boundary tracking methods. The proposed approach overcomes the hindrance faced by state-of-the-art methods and achieves promising results on various public handwritten datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Mridul Ghosh, Himadri Mukherjee, Sk Md Obaidullah, Xiao-Zhi Gao, Kaushik Roy
Summary: Computational perception has experienced a significant transformation from handcrafted feature-based techniques to deep learning in the field of scene text identification and recognition. Over the past decade, there have been important developments and advancements in this area. The traditional handcrafted feature-based techniques have been replaced by deep learning-based techniques, leading to a new stage in scene text identification.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Ankita Dhar, Himadri Mukherjee, Kaushik Roy, K. C. Santosh, Niladri Sekhar Dash
Summary: This article introduces a hybrid approach that combines text-based and graph-based features to showcase the effectiveness of an automatic text categorization system. The approach was applied on 14,373 Bangla articles, collected from various online news corpora covering nine categories. The experiments also include the application of the features on two popular English datasets to test the system's robustness and language independency.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Medicine, General & Internal
Sahana Das, Himadri Mukherjee, Kaushik Roy, Chanchal Kumar Saha
Summary: Cardiotocography (CTG) is currently the only non-invasive and cost-effective tool for continuous fetal health monitoring. Automated analysis of CTG remains challenging due to the complex and dynamic patterns of fetal heart, which are poorly interpreted. In this study, a machine-learning-based model using SVM, RF, MLP, and bagging was proposed, achieving high accuracy and showing potential for integration into an automated decision support system.
Article
Computer Science, Information Systems
Debjyoti Basu, Himadri Mukherjee, Matteo Marciano, Shibaprasad Sen, Sajai Vir Singh, Sk Md Obaidullah, Kaushik Roy
Summary: This research proposes a machine learning-based approach to classify the dawn and dusk time ragas in music. Mel-frequency cepstral coefficients are used for feature extraction, and a two-stage classification technique is employed, achieving promising results.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ankan Bhattacharyya, Somnath Chatterjee, Shibaprasad Sen, S. K. M. D. Obaidullah, Kaushik Roy
Summary: Online handwritten word recognition is still a challenging task, especially for low-resource languages like Bangla. This study explores the use of different recurrent neural network architectures to recognize online handwritten Bangla words. The challenge lies in the variable number of strokes used to write words. The developed segmentation-free recognition module achieves high accuracy by leveraging stroke features and outperforms existing techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Somnath Chatterjee, Himadri Mukherjee, Shibaprasad Sen, Sk Md Obaidullah, Kaushik Roy
Summary: Postal documents are commonly used for official communication and online shopping. Delivery delays can occur due to various handwritten scripts, necessitating the use of postal sorting facilities. To address this problem, a Deep Learning-based system is proposed to recognize handwritten city names written in six major scripts. Experimental results show high accuracy rates in both script-dependent and independent approaches.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ankita Dhar, Himadri Mukherjee, Shibaprasad Sen, Md Obaidullah Sk, Amitabha Biswas, Teresa Goncalves, Kaushik Roy
Summary: Author identification is an important aspect of literary analysis, and this paper proposes a convolutional neural network-based system for identifying authors by visualizing their distinct writing styles. The system achieved high accuracy in experiments conducted on a dataset of literary articles.
Proceedings Paper
Computer Science, Theory & Methods
Taiwo Ojo, Hongmei Chi, Janei Elliston, Kaushik Roy
Summary: The probability of retrieving sensitive information from secondhand IoT devices has increased due to advancements in flash memory storage technology. This study investigates data retrieval methods from secondhand memory cards and finds that utilizing software tools is the best way to prevent data leakage.