Shabana R. Ziyad^1,*, Liyakathunisa², Eman Aljohani², I. A. Saeed³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1515-1534, 2023, DOI:10.32604/cmc.2023.040874

Abstract Autism spectrum disorder (ASD), classified as a developmental disability, is now more common in children than ever. A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children. Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years. This research study aims to develop an automated tool for diagnosing autism in children. The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition, feature selection, and classification phases. The most deterministic features are… More >

Shaik Mahaboob Basha^1,*, Victor Hugo C. de Albuquerque², Samia Allaoua Chelloug^3,*, Mohamed Abd Elaziz^4,5,6,7, Shaik Hashmitha Mohisin⁸, Suhail Parvaze Pathan⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1981-2004, 2024, DOI:10.32604/cmes.2023.031425

Abstract Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images into common lung infections, lung… More > Graphic Abstract

Asifa Mehmood Qureshi¹, Nouf Abdullah Almujally², Saud S. Alotaibi³, Mohammed Hamad Alatiyyah⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3707-3725, 2023, DOI:10.32604/cmc.2023.040738

Abstract Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density and worldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob detection was applied to mark… More >

Umar Abdulkadir Isa^*, Anil Fernando^*

Journal on Artificial Intelligence, Vol.5, pp. 31-42, 2023, DOI:10.32604/jai.2023.040213

Abstract Traditional machine learning metrics (TMLMs) are quite useful for the current research work precision, recall, accuracy, MSE and RMSE. Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%. We included in the prediction process, machine learning models (MLMs) with greater than 99% accuracy with a sensitivity of 95%–98% and specifically in the database. We need to explain the model to domain specialists through the MLMs. Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model. This is achieved by creating a model-independent, locally accurate explanation… More >

Ammar Almomani^1,2,*, Iman Akour³, Ahmed M. Manasrah^4,5, Omar Almomani⁶, Mohammad Alauthman⁷, Esra’a Abdullah¹, Amaal Al Shwait¹, Razan Al Sharaa¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2499-2517, 2023, DOI:10.32604/iasc.2023.039687

Abstract The exponential growth of Internet and network usage has necessitated heightened security measures to protect against data and network breaches. Intrusions, executed through network packets, pose a significant challenge for firewalls to detect and prevent due to the similarity between legitimate and intrusion traffic. The vast network traffic volume also complicates most network monitoring systems and algorithms. Several intrusion detection methods have been proposed, with machine learning techniques regarded as promising for dealing with these incidents. This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base (Random Forest, Decision Tree, and k-Nearest-Neighbors). The proposed system employs pre-processing… More >

Jayashree Shetty¹, Nisha P. Shetty^1,*, Hrushikesh Kothikar¹, Saleh Mowla¹, Aiswarya Anand¹, Veeraj Hegde²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1345-1362, 2023, DOI:10.32604/iasc.2023.039206

Abstract Alzheimer’s disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people’s regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer’s or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different… More >

Urcel Kalenga Tshingomba^1,2, Magali Jouven², Lucile Sautot² , Imad Shaqura², Maguelonne Teisseire¹

Revue Internationale de Géomatique, Vol.30, No.2, pp. 245-277, 2020, DOI:10.3166/rig.2022.00112

Abstract In this paper we experiment an approach allowing to produce a coherent land use and cover of rangelands in the French Mediterranean areas represented by the Occitania and Provence-Alpes-Côte d’Azur regions. Four different data sources are used: the OSO land use and cover (OSO), the Registre Parcellaire Graphique (RPG), the BD-Forêt V.2.0 and Sentinel 2 L3A satellite data. The RPG of 2019 and the BD-Forêt updated in 2018 were used as the main source of reference data for training the models to classify the OSO 2019 low F-score class objects, after extraction of the reflectances, spectral and textural indices variables… More >

Tusongjiang Kari¹, Zhiyang He¹, Aisikaer Rouzi², Ziwei Zhang³, Xiaojing Ma^1,*, Lin Du¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 691-705, 2023, DOI:10.32604/iasc.2023.037617

Abstract Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning machine tuned by the Aquila… More >

Sait Can Yucebas^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871

Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different models, which is overlooked in… More >

Zhifang Liao¹, Bolin Zhang¹, Xuechun Huang¹, Song Yu^1,*, Yan Zhang²

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 687-704, 2023, DOI:10.32604/cmes.2023.027466

Abstract Currently, open-source software is gradually being integrated into industrial software, while industry protocols in industrial software are also gradually transferred to open-source community development. Industrial protocol standardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informal proposals, and different workflows will lead to increased operating costs. The open-source community maintenance team needs software that is more intelligent to guide the identification and classification of these issues. To solve the above problems, this paper proposes a PR review prediction model based on multi-dimensional features. We extract 43 features of PR and divide them into five dimensions: contributor,… More >