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  • Open Access

    ARTICLE

    Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm

    D. Vidyabharathi1,*, V. Mohanraj2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2559-2573, 2023, DOI:10.32604/iasc.2023.032255

    Abstract For training the present Neural Network (NN) models, the standard technique is to utilize decaying Learning Rates (LR). While the majority of these techniques commence with a large LR, they will decay multiple times over time. Decaying has been proved to enhance generalization as well as optimization. Other parameters, such as the network’s size, the number of hidden layers, dropouts to avoid overfitting, batch size, and so on, are solely based on heuristics. This work has proposed Adaptive Teaching Learning Based (ATLB) Heuristic to identify the optimal hyperparameters for diverse networks. Here we consider three More >

  • Open Access

    ARTICLE

    Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

    Saud Yonbawi1, Sultan Alahmari2, B. R. S. S. Raju3, Chukka Hari Govinda Rao4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6, José Varela-Aldás7,*, Samih M. Mostafa8

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2319-2335, 2023, DOI:10.32604/csse.2023.036721

    Abstract Artificial intelligence (AI) technologies and sensors have recently received significant interest in intellectual agriculture. Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture. Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques. Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist. With this motivation, this study develops a modified… More >

  • Open Access

    ARTICLE

    Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

    Saud Yonbawi1, Sultan Alahmari2, T. Satyanarayana Murthy3, Padmakar Maddala4, E. Laxmi Lydia5, Seifedine Kadry6,7,8,*, Jungeun Kim9

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1533-1547, 2023, DOI:10.32604/csse.2023.036296

    Abstract Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the… More >

  • Open Access

    ARTICLE

    Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

    Mohammed Maray1, Haya Mesfer Alshahrani2, Khalid A. Alissa3, Najm Alotaibi4, Abdulbaset Gaddah5, Ali Meree1,6, Mahmoud Othman7, Manar Ahmed Hamza8,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6587-6604, 2023, DOI:10.32604/cmc.2023.034176

    Abstract In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed… More >

  • Open Access

    ARTICLE

    Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

    Mohammed Maray1, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Saeed Masoud Alshahrani4,*, Najm Alotaibi5, Sana Alazwari6, Mahmoud Othman7, Manar Ahmed Hamza8

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5467-5482, 2023, DOI:10.32604/cmc.2023.033534

    Abstract The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The… More >

  • Open Access

    ARTICLE

    Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

    Mohammad Alamgeer1, Amal Al-Rasheed2, Ahmad Alhindi3, Manar Ahmed Hamza4,*, Abdelwahed Motwakel4, Mohamed I. Eldesouki5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2725-2738, 2023, DOI:10.32604/cmc.2023.029163

    Abstract Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In More >

  • Open Access

    ARTICLE

    Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

    Abdul Rahaman Wahab Sait1,*, Mohamad Khairi Ishak2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2553-2567, 2023, DOI:10.32604/csse.2023.029603

    Abstract Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural… More >

  • Open Access

    ARTICLE

    Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

    Ashit Kumar Dutta1,*, T. Meyyappan2, Basit Qureshi3, Majed Alsanea4, Anas Waleed Abulfaraj5, Manal M. Al Faraj1, Abdul Rahaman Wahab Sait6

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2701-2713, 2023, DOI:10.32604/csse.2023.028984

    Abstract Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives. It results in illegal access to users’ private data and compromises it. Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data. Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity. This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model. The major intention of the… More >

  • Open Access

    ARTICLE

    Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

    K. Shankar1, E. Laxmi Lydia2, Sachin Kumar1,*, Ali S. Abosinne3, Ahmed alkhayyat4, A. H. Abbas5, Sarmad Nozad Mahmood6

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4541-4557, 2022, DOI:10.32604/cmc.2022.031247

    Abstract Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert’s knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification… More >

  • Open Access

    ARTICLE

    Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter Data

    G. Indra1,*, N. Duraipandian2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1455-1470, 2023, DOI:10.32604/iasc.2023.027703

    Abstract A flood is a significant damaging natural calamity that causes loss of life and property. Earlier work on the construction of flood prediction models intended to reduce risks, suggest policies, reduce mortality, and limit property damage caused by floods. The massive amount of data generated by social media platforms such as Twitter opens the door to flood analysis. Because of the real-time nature of Twitter data, some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy. However, due to the shorter duration of… More >

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