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  • Open Access

    ARTICLE

    Electroencephalography (EEG) Based Neonatal Sleep Staging and Detection Using Various Classification Algorithms

    Hafza Ayesha Siddiqa1, Muhammad Irfan1, Saadullah Farooq Abbasi2,*, Wei Chen1

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1759-1778, 2023, DOI:10.32604/cmc.2023.041970

    Abstract Automatic sleep staging of neonates is essential for monitoring their brain development and maturity of the nervous system. EEG based neonatal sleep staging provides valuable information about an infant’s growth and health, but is challenging due to the unique characteristics of EEG and lack of standardized protocols. This study aims to develop and compare 18 machine learning models using Automated Machine Learning (autoML) technique for accurate and reliable multi-channel EEG-based neonatal sleep-wake classification. The study investigates autoML feasibility without extensive manual selection of features or hyperparameter tuning. The data is obtained from neonates at post-menstrual age 37 ± 05 weeks.… More >

  • Open Access

    ARTICLE

    K-Hyperparameter Tuning in High-Dimensional Space Clustering: Solving Smooth Elbow Challenges Using an Ensemble Based Technique of a Self-Adapting Autoencoder and Internal Validation Indexes

    Rufus Gikera1,*, Jonathan Mwaura2, Elizaphan Muuro3, Shadrack Mambo3

    Journal on Artificial Intelligence, Vol.5, pp. 75-112, 2023, DOI:10.32604/jai.2023.043229

    Abstract k-means is a popular clustering algorithm because of its simplicity and scalability to handle large datasets. However, one of its setbacks is the challenge of identifying the correct k-hyperparameter value. Tuning this value correctly is critical for building effective k-means models. The use of the traditional elbow method to help identify this value has a long-standing literature. However, when using this method with certain datasets, smooth curves may appear, making it challenging to identify the k-value due to its unclear nature. On the other hand, various internal validation indexes, which are proposed as a solution to this issue, may be… More >

  • Open Access

    ARTICLE

    An Optimized Feature Selection and Hyperparameter Tuning Framework for Automated Heart Disease Diagnosis

    Saleh Ateeq Almutairi*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2599-2624, 2023, DOI:10.32604/csse.2023.041609

    Abstract Heart disease is a primary cause of death worldwide and is notoriously difficult to cure without a proper diagnosis. Hence, machine learning (ML) can reduce and better understand symptoms associated with heart disease. This study aims to develop a framework for the automatic and accurate classification of heart disease utilizing machine learning algorithms, grid search (GS), and the Aquila optimization algorithm. In the proposed approach, feature selection is used to identify characteristics of heart disease by using a method for dimensionality reduction. First, feature selection is accomplished with the help of the Aquila algorithm. Then, the optimal combination of the… More >

  • Open Access

    ARTICLE

    Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

    Niharika Gupta1, Baijnath Kaushik1, Mohammad Khalid Imam Rahmani2,*, Saima Anwar Lashari2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 347-366, 2023, DOI:10.32604/cmc.2023.038864

    Abstract Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer lives. Early prediction of diabetes is crucial to taking precautionary steps to avoid or delay its onset. In this study, we proposed a Deep Dense Layer Neural Network (DDLNN) for diabetes prediction using a dataset with 768 instances and nine variables. We also applied a combination of classical machine learning (ML) algorithms and ensemble learning algorithms for the effective prediction of the disease. The classical ML algorithms used were Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN),… More >

  • Open Access

    ARTICLE

    Energy Efficient Hyperparameter Tuned Deep Neural Network to Improve Accuracy of Near-Threshold Processor

    K. Chanthirasekaran, Raghu Gundaala*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 471-489, 2023, DOI:10.32604/iasc.2023.036130

    Abstract When it comes to decreasing margins and increasing energy efficiency in near-threshold and sub-threshold processors, timing error resilience may be viewed as a potentially lucrative alternative to examine. On the other hand, the currently employed approaches have certain restrictions, including high levels of design complexity, severe time constraints on error consolidation and propagation, and uncontaminated architectural registers (ARs). The design of near-threshold circuits, often known as NT circuits, is becoming the approach of choice for the construction of energy-efficient digital circuits. As a result of the exponentially decreased driving current, there was a reduction in performance, which was one of… 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) model is exploited for deception… More >

  • Open Access

    ARTICLE

    Applied Linguistics with Mixed Leader Optimizer Based English Text Summarization Model

    Hala J. Alshahrani1, Khaled Tarmissi2, Ayman Yafoz3, Abdullah Mohamed4, Manar Ahmed Hamza5,*, Ishfaq Yaseen5, Abu Sarwar Zamani5, Mohammad Mahzari6

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3203-3219, 2023, DOI:10.32604/iasc.2023.034848

    Abstract The term ‘executed linguistics’ corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems. The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights. The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users. The Automatic Text Summarization (ATS) process reduces the primary size of the text without losing any basic components of the data. The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning (ALTS-MLODL) model. The… More >

  • 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 architectures Recurrent Neural Networks (RNN),… 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 black widow optimization with a… 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 dispensable plant and identify the… More >

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