Mesfer Al Duhayyim^1,*, Saud S. Alotaibi², Shaha Al-Otaibi³, Fahd N. Al-Wesabi⁴, Mahmoud Othman⁵, Ishfaq Yaseen⁶, Mohammed Rizwanullah⁶, Abdelwahed Motwakel⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3315-3332, 2023, DOI:10.32604/cmc.2023.033250

Abstract Proper waste management models using recent technologies like computer vision, machine learning (ML), and deep learning (DL) are needed to effectively handle the massive quantity of increasing waste. Therefore, waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process. This study concentrates on the design of hazardous waste detection and classification using ensemble learning (HWDC-EL) technique to reduce toxicity and improve human health. The goal of the HWDC-EL technique is to detect the multiple classes of wastes, particularly hazardous and non-hazardous wastes.… More >

Mofreh A. Hogo^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2947-2966, 2022, DOI:10.32604/cmc.2022.027300

Abstract Data size plays a significant role in the design and the performance of data mining models. A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy. Features selection algorithms aim at selecting the best features and eliminating unnecessary ones, which in turn simplifies the structure of the data mining model as well as increases its performance. This paper introduces a robust features selection algorithm, named Features Ranking Voting Algorithm FRV. It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly; based… More >

Yazan Al-Issa¹, Ali Mohammad Alqudah^2,*, Hiam Alquran^3,2, Ahmed Al Issa⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 311-326, 2022, DOI:10.32604/cmc.2022.025750

Abstract Pulmonary diseases are common throughout the world, especially in developing countries. These diseases include chronic obstructive pulmonary diseases, pneumonia, asthma, tuberculosis, fibrosis, and recently COVID-19. In general, pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert radiologists. In recent years, many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung diseases. In this paper, the performance of four popular pretrained models (namely VGG16, DenseNet201, DarkNet19, and XceptionNet) in distinguishing between different pulmonary diseases was analyzed. To the best of our knowledge, this is the… More >

Mujeeb Ur Rehman^1,*, Maha Driss^2,3, Abdukodir Khakimov⁴, Sohail Khalid¹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5681-5697, 2022, DOI:10.32604/cmc.2022.025840

Abstract Lungs are a vital human body organ, and different Obstructive Lung Diseases (OLD) such as asthma, bronchitis, or lung cancer are caused by shortcomings within the lungs. Therefore, early diagnosis of OLD is crucial for such patients suffering from OLD since, after early diagnosis, breathing exercises and medical precautions can effectively improve their health state. A secure non-invasive early diagnosis of OLD is a primordial need, and in this context, digital image processing supported by Artificial Intelligence (AI) techniques is reliable and widely used in the medical field, especially for improving early disease diagnosis. Hence, this article presents an AI-based… More >

Shahid Hussain Danwar, Javed Ahmed Mahar^*, Aneela Kiran

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 417-440, 2022, DOI:10.32604/cmc.2022.023846

Abstract Election allows the voter of a country to select the most suitable group of candidates to run the government. Election in Pakistan is simply paper-based method but some certain political and socio-economic issues turn that simple process in complicated and disputes once. Solutions of such problems are consisting of many methods including the e-voting system. The e-voting system facilitates the voters to cast their votes by electronic means with very easy and convenient way. This also allows maintaining the security and secrecy of the voter along with election process. Electronic voting reduces the human-involvement throughout the process from start to… More >

Amal Abdullah Mohammed Alteaimi^1,*, Mohamed Tahar Ben Othman^1,2

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 577-595, 2022, DOI:10.32604/cmc.2022.023591

Abstract The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with a 100% recognition rate. We… More >

Saba Awan¹, Zahid Mehmood^2,*, Hassan Nazeer Chaudhry³, Usman Tariq⁴, Amjad Rehman⁵, Tanzila Saba⁵, Muhammad Rashid⁶

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4609-4626, 2022, DOI:10.32604/cmc.2022.019404

Abstract To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different… More >

Olutomilayo Olayemi Petinrin¹, Faisal Saeed², Xiangtao Li¹, Fahad Ghabban², Ka-Chun Wong^1,3,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4745-4762, 2022, DOI:10.32604/cmc.2022.020523

Abstract Bioactive compounds in plants, which can be synthesized using N-arylation methods such as the Buchwald-Hartwig reaction, are essential in drug discovery for their pharmacological effects. Important descriptors are necessary for the estimation of yields in these reactions. This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation. The algorithms were evaluated based on computational time and the number of selected descriptors. Analyses show that robust performance is obtained with more descriptors, compared to cases where fewer descriptors are selected. The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted… More >

A. Arunkumar^1,*, D. Surendran²

Computer Systems Science and Engineering, Vol.41, No.1, pp. 389-404, 2022, DOI:10.32604/csse.2022.020256

Abstract Difficulty in communicating and interacting with other people are mainly due to the neurological disorder called autism spectrum disorder (ASD) diseases. These diseases can affect the nerves at any stage of the human being in childhood, adolescence, and adulthood. ASD is known as a behavioral disease due to the appearances of symptoms over the first two years that continue until adulthood. Most of the studies prove that the early detection of ASD helps improve the behavioral characteristics of patients with ASD. The detection of ASD is a very challenging task among various researchers. Machine learning (ML) algorithms still act very… More >

Abdullah Ali Salamai¹, El-Sayed M. El-kenawy², Ibrahim Abdelhameed^3,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3749-3766, 2021, DOI:10.32604/cmc.2021.018179

Abstract Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems, which integrate the supply chain’s manufacturing operations, information technology, and telecommunication processes. Although supply chain 4.0 aims to improve supply chains’ production systems and profitability, it is subject to different operational and disruptive risks. Operational risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT systems. This paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group (SCDG) algorithm.… More >