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  • Open Access

    ARTICLE

    Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

    Donghun Wang, Jonghyun Lee, Minchan Kim, Insoo Lee*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2025-2040, 2023, DOI:10.32604/iasc.2023.034749

    Abstract Lithium-ion batteries are commonly used in electric vehicles, mobile phones, and laptops. These batteries demonstrate several advantages, such as environmental friendliness, high energy density, and long life. However, battery overcharging and overdischarging may occur if the batteries are not monitored continuously. Overcharging causes fire and explosion casualties, and overdischarging causes a reduction in the battery capacity and life. In addition, the internal resistance of such batteries varies depending on their external temperature, electrolyte, cathode material, and other factors; the capacity of the batteries decreases with temperature. In this study, we develop a method for estimating the state of charge (SOC)… More >

  • Open Access

    ARTICLE

    EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

    S. Kavi Priya*, K. Pon Karthika

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160

    Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory… More >

  • Open Access

    ARTICLE

    An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

    Y. C. A. Padmanabha Reddy1, Shyam Sunder Reddy Kasireddy2, Nageswara Rao Sirisala3, Ramu Kuchipudi4, Purnachand Kollapudi5,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6389-6409, 2023, DOI:10.32604/cmc.2023.034072

    Abstract The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages. The digital document needs to be evaluated physically through the Cross-Language Text Summarization (CLTS) involved in the disparate and generation of the source documents. Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents. The digital documents need to be processed with the contextual semantic data with the decoding scheme. This paper presented a multilingual cross-language processing of the documents with the abstractive and summarising of the documents. The proposed model is represented as the Hidden Markov… More >

  • Open Access

    ARTICLE

    A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

    Anitha Thangasamy*, Bose Sundan, Logeswari Govindaraj

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2553-2567, 2023, DOI:10.32604/csse.2023.032078

    Abstract The recent development of cloud computing offers various services on demand for organization and individual users, such as storage, shared computing space, networking, etc. Although Cloud Computing provides various advantages for users, it remains vulnerable to many types of attacks that attract cyber criminals. Distributed Denial of Service (DDoS) is the most common type of attack on cloud computing. Consequently, Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks. Since DDoS attacks have become increasingly widespread, it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish… More >

  • Open Access

    ARTICLE

    Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

    El-Sayed M. El-kenawy1, Abdelaziz A. Abdelhamid2,3, Fadwa Alrowais4,*, Mostafa Abotaleb5, Abdelhameed Ibrahim6, Doaa Sami Khafaga4

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2885-2899, 2023, DOI:10.32604/csse.2023.034206

    Abstract Rainfall plays a significant role in managing the water level in the reservoir. The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir. Many individuals, especially those in the agricultural sector, rely on rain forecasts. Forecasting rainfall is challenging because of the changing nature of the weather. The area of Jimma in southwest Oromia, Ethiopia is the subject of this research, which aims to develop a rainfall forecasting model. To estimate Jimma’s daily rainfall, we propose a novel approach based on optimizing the parameters of long short-term memory (LSTM) using Al-Biruni… More >

  • Open Access

    ARTICLE

    Effective and Efficient Video Compression by the Deep Learning Techniques

    Karthick Panneerselvam1,2,*, 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

    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 groups for data processing, using… More >

  • Open Access

    ARTICLE

    Adaptive Deep Learning Model for Software Bug Detection and Classification

    S. Sivapurnima*, D. Manjula

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1233-1248, 2023, DOI:10.32604/csse.2023.025991

    Abstract Software is unavoidable in software development and maintenance. In literature, many methods are discussed which fails to achieve efficient software bug detection and classification. In this paper, efficient Adaptive Deep Learning Model (ADLM) is developed for automatic duplicate bug report detection and classification process. The proposed ADLM is a combination of Conditional Random Fields decoding with Long Short-Term Memory (CRF-LSTM) and Dingo Optimizer (DO). In the CRF, the DO can be consumed to choose the efficient weight value in network. The proposed automatic bug report detection is proceeding with three stages like pre-processing, feature extraction in addition bug detection with… More >

  • Open Access

    ARTICLE

    An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

    Mohammed Hadwan1,2,*, Hamzah A. Alsayadi3,4, Salah AL-Hagree5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3471-3487, 2023, DOI:10.32604/cmc.2023.033457

    Abstract The attention-based encoder-decoder technique, known as the trans-former, is used to enhance the performance of end-to-end automatic speech recognition (ASR). This research focuses on applying ASR end-to-end transformer-based models for the Arabic language, as the researchers’ community pays little attention to it. The Muslims Holy Qur’an book is written using Arabic diacritized text. In this paper, an end-to-end transformer model to building a robust Qur’an vs. recognition is proposed. The acoustic model was built using the transformer-based model as deep learning by the PyTorch framework. A multi-head attention mechanism is utilized to represent the encoder and decoder in the acoustic… More >

  • Open Access

    ARTICLE

    Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

    Weifeng Liu1,2, Xin Yu1,*, Qinyang Zhao3, Guang Cheng2, Xiaobing Hou1, Shengqi He4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3199-3219, 2023, DOI:10.32604/cmc.2023.032595

    Abstract Time series forecasting and analysis are widely used in many fields and application scenarios. Time series historical data reflects the change pattern and trend, which can serve the application and decision in each application scenario to a certain extent. In this paper, we select the time series prediction problem in the atmospheric environment scenario to start the application research. In terms of data support, we obtain the data of nearly 3500 vehicles in some cities in China from Runwoda Research Institute, focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,… More >

  • Open Access

    ARTICLE

    Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

    Ahmed Abdu Alattab1,*, Mohammed Eid Ibrahim1, Reyazur Rashid Irshad1, Anwar Ali Yahya2, Amin A. Al-Awady3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2397-2413, 2023, DOI:10.32604/cmc.2023.030924

    Abstract This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the… More >

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