Farhad Mortezapour Shiri^*, Thinagaran Perumal, Norwati Mustapha, Raihani Mohamed

Journal on Artificial Intelligence, Vol.6, pp. 301-360, 2024, DOI:10.32604/jai.2024.054314 - 20 November 2024

Abstract Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent… More >

Nguyen Tho Thong¹, Nguyen Van Quyet^1,2, Cu Nguyen Giap^3,*, Nguyen Long Giang¹, Luong Thi Hong Lan⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4173-4196, 2024, DOI:10.32604/cmc.2024.054031 - 12 September 2024

Abstract Time-stamped data is fast and constantly growing and it contains significant information thanks to the quick development of management platforms and systems based on the Internet and cutting-edge information communication technologies. Mining the time series data including time series prediction has many practical applications. Many new techniques were developed for use with various types of time series data in the prediction problem. Among those, this work suggests a unique strategy to enhance predicting quality on time-series datasets that the time-cycle matters by fusing deep learning methods with fuzzy theory. In order to increase forecasting accuracy… More >

Karthick Panneerselvam^1,2,*, K. Mahesh¹, V. L. Helen Josephine³, A. Ranjith Kumar²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1047-1061, 2023, DOI:10.32604/csse.2023.030513 - 03 November 2022

Abstract Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing high-quality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different… More >

I. Muthumani^1,*, N. Malmurugan², L. Ganesan³

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 917-928, 2022, DOI:10.32604/iasc.2022.018030 - 22 September 2021

Abstract Text content in videos includes applications such as library video retrievals, live-streaming advertisements, opinion mining, and video synthesis. The key components of such systems include video text detection and acknowledgments. This paper provides a framework to detect and accept text video frames, aiming specifically at the cursive script of Tamil text. The model consists of a text detector, script identifier, and text recognizer. The identification in video frames of textual regions is performed using deep neural networks as object detectors. Textual script content is associated with convolutional neural networks (CNNs) and recognized by combining ResNet More >

Keshav Thapa, Zubaer Md. Abdhulla AI, Yang Sung-Hyun^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1653-1670, 2021, DOI:10.32604/cmc.2021.015660 - 21 July 2021

Abstract In this era, deep learning methods offer a broad spectrum of efficient and original algorithms to recognize or predict an output when given a sequence of inputs. In current trends, deep learning methods using recent long short-term memory (LSTM) algorithms try to provide superior performance, but they still have limited effectiveness when detecting sequences of complex human activity. In this work, we adapted the LSTM algorithm into a synchronous algorithm (sync-LSTM), enabling the model to take multiple parallel input sequences to produce multiple parallel synchronized output sequences. The proposed method is implemented for simultaneous human… More >

Wenxiao Wang^1,*, Xiaoyu Li^1,*, Yin Ding¹, Feizhou Wu², Shan Yang³

Journal of Quantum Computing, Vol.3, No.1, pp. 25-33, 2021, DOI:10.32604/jqc.2021.016346 - 20 May 2021

Abstract Due to the increase in the types of business and equipment in telecommunications companies, the performance index data collected in the operation and maintenance process varies greatly. The diversity of index data makes it very difficult to perform high-precision capacity prediction. In order to improve the forecasting efficiency of related indexes, this paper designs a classification method of capacity index data, which divides the capacity index data into trend type, periodic type and irregular type. Then for the prediction of trend data, it proposes a capacity index prediction model based on Recurrent Neural Network (RNN), More >