Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (132)
  • Open Access

    ARTICLE

    Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

    Hadeel Alsolai1, Mohammed Rizwanullah2,*, Mashael Maashi3, Mahmoud Othman4, Amani A. Alneil2, Amgad Atta Abdelmageed2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 975-992, 2023, DOI:10.32604/cmc.2023.037936

    Abstract Soil classification is one of the emanating topics and major concerns in many countries. As the population has been increasing at a rapid pace, the demand for food also increases dynamically. Common approaches used by agriculturalists are inadequate to satisfy the rising demand, and thus they have hindered soil cultivation. There comes a demand for computer-related soil classification methods to support agriculturalists. This study introduces a Gradient-Based Optimizer and Deep Learning (DL) for Automated Soil Classification (GBODL-ASC) technique. The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches. In the… More >

  • Open Access

    ARTICLE

    Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

    Abdelwahed Motwakel1,*, Marwa Obayya2, Nadhem Nemri3, Khaled Tarmissi4, Heba Mohsen5, Mohammed Rizwanulla6, Ishfaq Yaseen6, Abu Sarwar Zamani6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1267-1281, 2023, DOI:10.32604/csse.2023.037735

    Abstract Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal… More >

  • Open Access

    ARTICLE

    Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

    G. Anitha1, N. Supriya2, Fayadh Alenezi3, E. Laxmi Lydia4, Gyanendra Prasad Joshi5, Jinsang You6,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 221-238, 2023, DOI:10.32604/csse.2023.036479

    Abstract Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief More >

  • Open Access

    ARTICLE

    Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

    Sultan Alahmari1, Saud Yonbawi2, Suneetha Racharla3, E. Laxmi Lydia4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6,*, Ayman Aljarbouh7, Samih M. Mostafa8

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 375-391, 2023, DOI:10.32604/csse.2023.036362

    Abstract Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial… More >

  • Open Access

    ARTICLE

    Improved Metaheuristics with Deep Learning Enabled Movie Review Sentiment Analysis

    Abdelwahed Motwakel1,*, Najm Alotaibi2, Eatedal Alabdulkreem3, Hussain Alshahrani4, Mohamed Ahmed Elfaki4, Mohamed K Nour5, Radwa Marzouk6, Mahmoud Othman7

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1249-1266, 2023, DOI:10.32604/csse.2023.034227

    Abstract Sentiment Analysis (SA) of natural language text is not only a challenging process but also gains significance in various Natural Language Processing (NLP) applications. The SA is utilized in various applications, namely, education, to improve the learning and teaching processes, marketing strategies, customer trend predictions, and the stock market. Various researchers have applied lexicon-related approaches, Machine Learning (ML) techniques and so on to conduct the SA for multiple languages, for instance, English and Chinese. Due to the increased popularity of the Deep Learning models, the current study used diverse configuration settings of the Convolution Neural… More >

  • Open Access

    ARTICLE

    Determination of AVR System PID Controller Parameters Using Improved Variants of Reptile Search Algorithm and a Novel Objective Function

    Baran Hekimoğlu*

    Energy Engineering, Vol.120, No.7, pp. 1515-1540, 2023, DOI:10.32604/ee.2023.029024

    Abstract Two novel improved variants of reptile search algorithm (RSA), RSA with opposition-based learning (ORSA) and hybrid ORSA with pattern search (ORSAPS), are proposed to determine the proportional, integral, and derivative (PID) controller parameters of an automatic voltage regulator (AVR) system using a novel objective function with augmented flexibility. In the proposed algorithms, the opposition-based learning technique improves the global search abilities of the original RSA algorithm, while the hybridization with the pattern search (PS) algorithm improves the local search abilities. Both algorithms are compared with the original RSA algorithm and have shown to be highly… More > Graphic Abstract

    Determination of AVR System PID Controller Parameters Using Improved Variants of Reptile Search Algorithm and a Novel Objective Function

  • Open Access

    ARTICLE

    Modeling of Combined Economic and Emission Dispatch Using Improved Sand Cat Optimization Algorithm

    Fadwa Alrowais1, Jaber S. Alzahrani2, Radwa Marzouk1, Abdullah Mohamed3, Gouse Pasha Mohammed4,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6145-6160, 2023, DOI:10.32604/cmc.2023.038300

    Abstract Combined Economic and Emission Dispatch (CEED) task forms multi-objective optimization problems to be resolved to minimize emission and fuel costs. The disadvantage of the conventional method is its incapability to avoid falling in local optimal, particularly when handling nonlinear and complex systems. Metaheuristics have recently received considerable attention due to their enhanced capacity to prevent local optimal solutions in addressing all the optimization problems as a black box. Therefore, this paper focuses on the design of an improved sand cat optimization algorithm based CEED (ISCOA-CEED) technique. The ISCOA-CEED technique majorly concentrates on reducing fuel costs More >

  • Open Access

    ARTICLE

    Sea Turtle Foraging Optimization-Based Controller Placement with Blockchain-Assisted Intrusion Detection in Software-Defined Networks

    Sultan Alkhliwi*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4735-4752, 2023, DOI:10.32604/cmc.2023.037141

    Abstract Software-defined networking (SDN) algorithms are gaining increasing interest and are making networks flexible and agile. The basic idea of SDN is to move the control planes to more than one server’s named controllers and limit the data planes to numerous sending network components, enabling flexible and dynamic network management. A distinctive characteristic of SDN is that it can logically centralize the control plane by utilizing many physical controllers. The deployment of the controller—that is, the controller placement problem (CPP)—becomes a vital model challenge. Through the advancements of blockchain technology, data integrity between nodes can be… More >

  • Open Access

    ARTICLE

    Hybrid Metaheuristics with Deep Learning Enabled Automated Deception Detection and Classification of Facial Expressions

    Haya Alaskar*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5433-5449, 2023, DOI:10.32604/cmc.2023.035266

    Abstract Automatic deception recognition has received considerable attention from the machine learning community due to recent research on its vast application to social media, interviews, law enforcement, and the military. Video analysis-based techniques for automated deception detection have received increasing interest. This study develops a new self-adaptive population-based firefly algorithm with a deep learning-enabled automated deception detection (SAPFF-DLADD) model for analyzing facial cues. Initially, the input video is separated into a set of video frames. Then, the SAPFF-DLADD model applies the MobileNet-based feature extractor to produce a useful set of features. The long short-term memory (LSTM) More >

  • Open Access

    ARTICLE

    Optimal Deep Learning Based Ransomware Detection and Classification in the Internet of Things Environment

    Manal Abdullah Alohali1, Muna Elsadig1, Fahd N. Al-Wesabi2, Mesfer Al Duhayyim3, Anwer Mustafa Hilal4,*, Abdelwahed Motwakel4

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3087-3102, 2023, DOI:10.32604/csse.2023.036802

    Abstract With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security concern is the most prominent. Different models were intended to address these security problems; still, several emergent variants of botnet attacks like Bashlite, Mirai, and Persirai use security breaches. The malware classification and detection in the IoT model is still a problem, as the adversary reliably generates a new variant… More >

Displaying 11-20 on page 2 of 132. Per Page