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  • Open Access

    ARTICLE

    Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

    Neelam Mughees1,2, Mujtaba Hussain Jaffery1, Abdullah Mughees3, Anam Mughees4, Krzysztof Ejsmont5,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564

    Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked,… More >

  • Open Access

    ARTICLE

    Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

    Po-Yuan Yang1,*, Yu-Cheng Liao2, Fu-I Chou2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2861-2880, 2023, DOI:10.32604/csse.2023.036810

    Abstract Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated… More >

  • Open Access

    ARTICLE

    Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

    Manar Ahmed Hamza1,*, Hala J. Alshahrani2, Khaled Tarmissi3, Ayman Yafoz4, Amal S. Mehanna5, Ishfaq Yaseen1, Amgad Atta Abdelmageed1, Mohamed I. Eldesouki6

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3303-3319, 2023, DOI:10.32604/csse.2023.034823

    Abstract The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the… More >

  • Open Access

    ARTICLE

    Deep Bimodal Fusion Approach for Apparent Personality Analysis

    Saman Riaz1, Ali Arshad2, Shahab S. Band3,*, Amir Mosavi4

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2301-2312, 2023, DOI:10.32604/cmc.2023.028333

    Abstract Personality distinguishes individuals’ patterns of feeling, thinking, and behaving. Predicting personality from small video series is an exciting research area in computer vision. The majority of the existing research concludes preliminary results to get immense knowledge from visual and Audio (sound) modality. To overcome the deficiency, we proposed the Deep Bimodal Fusion (DBF) approach to predict five traits of personality-agreeableness, extraversion, openness, conscientiousness and neuroticism. In the proposed framework, regarding visual modality, the modified convolution neural networks (CNN), more specifically Descriptor Aggregator Model (DAN) are used to attain significant visual modality. The proposed model extracts More >

  • Open Access

    ARTICLE

    IoT-Driven Optimal Lightweight RetinaNet-Based Object Detection for Visually Impaired People

    Mesfer Alduhayyem1,*, Mrim M. Alnfiai2,3, Nabil Almalki4, Fahd N. Al-Wesabi5, Anwer Mustafa Hilal6, Manar Ahmed Hamza6

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 475-489, 2023, DOI:10.32604/csse.2023.034067

    Abstract Visual impairment is one of the major problems among people of all age groups across the globe. Visually Impaired Persons (VIPs) require help from others to carry out their day-to-day tasks. Since they experience several problems in their daily lives, technical intervention can help them resolve the challenges. In this background, an automatic object detection tool is the need of the hour to empower VIPs with safe navigation. The recent advances in the Internet of Things (IoT) and Deep Learning (DL) techniques make it possible. The current study proposes IoT-assisted Transient Search Optimization with a… More >

  • Open Access

    ARTICLE

    Detection of Abnormal Network Traffic Using Bidirectional Long Short-Term Memory

    Nga Nguyen Thi Thanh, Quang H. Nguyen*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 491-504, 2023, DOI:10.32604/csse.2023.032107

    Abstract Nowadays, web systems and servers are constantly at great risk from cyberattacks. This paper proposes a novel approach to detecting abnormal network traffic using a bidirectional long short-term memory (LSTM) network in combination with the ensemble learning technique. First, the binary classification module was used to detect the current abnormal flow. Then, the abnormal flows were fed into the multilayer classification module to identify the specific type of flow. In this research, a deep learning bidirectional LSTM model, in combination with the convolutional neural network and attention technique, was deployed to identify a specific attack. More >

  • 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… 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… 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 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… More >

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