Mustaqeem, Soonil Kwon^*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 4039-4059, 2021, DOI:10.32604/cmc.2021.015070

Abstract Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications, such as robotics, virtual reality, behavior assessments, and emergency call centers. Recently, researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches, but the recognition rate is still not convincing. Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations. In this paper, we suggested a new technique, which is a one-dimensional dilated convolutional neural network (1D-DCNN) for… More >

S. M. Atiqure Rahman^1,*, Hegazy Rezk^2,3, Mohammad Ali Abdelkareem^1,4, M. Enamul Hoque⁵, Tariq Mahbub⁶, Sheikh Khaleduzzaman Shah⁷, Ahmed M. Nassef^2,8

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3143-3160, 2021, DOI:10.32604/cmc.2021.015048

Abstract The main goal of this research is to develop and apply a robust Artificial Neural Networks (ANNs) model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact dryer. Concentrated salt and sugar solutions were used as the osmotic solutions at 27C. Series of experiments were performed at various temperatures of 35C, 40C, and 55C for conduction heat input under vacuum ( −760 mm Hg) condition. Some experiments were also performed in a pure vacuum without heat addition. Dimensionless moisture content (DMC), effective moisture diffusivity, and mass flux were… More >

Nadia Tabassum¹, Allah Ditta², Tahir Alyas³, Sagheer Abbas⁴, Hani Alquhayz⁵, Natash Ali Mian⁶, Muhammad Adnan Khan^7,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3129-3141, 2021, DOI:10.32604/cmc.2021.014729

Abstract Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged… More >

K. Lakshminarayanan¹, N. Muthukumaran¹, Y. Harold Robinson², Vimal Shanmuganathan³, Seifedine Kadry⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3045-3055, 2021, DOI:10.32604/cmc.2021.014370

Abstract Hookworm is an illness caused by an internal sponger called a roundworm. Inferable from deprived cleanliness in the developing nations, hookworm infection is a primary source of concern for both motherly and baby grimness. The current framework for hookworm detection is composed of hybrid convolutional neural networks; explicitly an edge extraction framework alongside a hookworm classification framework is developed. To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework, pooling layers are proposed. The hookworms display different profiles, widths, and bend directions. These challenges make it difficult for… More >

Emad-ul-Haq Qazi, Muhammad Hussain^*, Hatim A. AboAlsamh

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3329-3348, 2021, DOI:10.32604/cmc.2021.013589

Abstract The detection of alcoholism is of great importance due to its effects on individuals and society. Automatic alcoholism detection system (AADS) based on electroencephalogram (EEG) signals is effective, but the design of a robust AADS is a challenging problem. AADS’ current designs are based on conventional, hand-engineered methods and restricted performance. Driven by the excellent deep learning (DL) success in many recognition tasks, we implement an AAD system based on EEG signals using DL. A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain… More >

Sheng Bai, Feng Bao^*, Fengzhi Zhao

FDMP-Fluid Dynamics & Materials Processing, Vol.17, No.1, pp. 39-47, 2021, DOI:10.32604/fdmp.2021.010376

Abstract The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology. Assuming a problem of aircraft design as the workhorse, a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet (Visual Geometry Group Net) and FCN (Fully Convolutional Network) techniques. As shown by the results, the model displays a strong fitting ability, and there is almost no over-fitting in training. Moreover, the model has good accuracy and convergence. For different input data and different grids, the model basically achieves convergence, showing… More >

Hamid A. Jalab¹, Ala’a R. Al-Shamasneh¹, Hadil Shaiba², Rabha W. Ibrahim^3,4,*, Dumitru Baleanu^5,6,7

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2061-2075, 2021, DOI:10.32604/cmc.2021.015170

Abstract Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Rényi entropy, and MRI Kidney deep segmentation. The proposed… More >

Gulzar Ahmad¹, Saad Alanazi², Madallah Alruwaili², Fahad Ahmad^3,6, Muhammad Adnan Khan^4,*, Sagheer Abbas¹, Nadia Tabassum⁵

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2585-2600, 2021, DOI:10.32604/cmc.2021.015080

Abstract Security is a significant issue for everyone due to new and creative ways to commit cybercrime. The Closed-Circuit Television (CCTV) systems are being installed in offices, houses, shopping malls, and on streets to protect lives. Operators monitor CCTV; however, it is difficult for a single person to monitor the actions of multiple people at one time. Consequently, there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study, we have designed a new Intelligent Ammunition Detection and Classification (IADC) system using… More >

Wael Alosaimi^1,*, Hashem Alyami², M. Irfan Uddin³

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1665-1677, 2021, DOI:10.32604/cmc.2021.014950

Abstract To meet the food requirements of the seven billion people on Earth, multiple advancements in agriculture and industry have been made. The main threat to food items is from diseases and pests which affect the quality and quantity of food. Different scientific mechanisms have been developed to protect plants and fruits from pests and diseases and to increase the quantity and quality of food. Still these mechanisms require manual efforts and human expertise to diagnose diseases. In the current decade Artificial Intelligence is used to automate different processes, including agricultural processes, such as automatic harvesting. Machine Learning techniques are becoming… More >

Yasir Shahzad^1,*, Huma Javed¹, Haleem Farman², Jamil Ahmad², Bilal Jan³, Abdelmohsen A. Nassani⁴

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2463-2480, 2021, DOI:10.32604/cmc.2021.014904

Abstract Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling technique. The proposed model is… More >