Dina M. Ibrahim^1,2,*, Nada M. Elshennawy²

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 331-348, 2022, DOI:10.32604/cmes.2022.016419 - 24 January 2022

Abstract Dates are an important part of human nutrition. Dates are high in essential nutrients and provide a number of health benefits. Date fruits are also known to protect against a number of diseases, including cancer and heart disease. Date fruits have several sizes, colors, tastes, and values. There are a lot of challenges facing the date producers. One of the most significant challenges is the classification and sorting of dates. But there is no public dataset for date fruits, which is a major limitation in order to improve the performance of convolutional neural networks (CNN)… More >

Usra Yasmeen¹, Muhammad Attique Khan¹, Usman Tariq², Junaid Ali Khan¹, Muhammad Asfand E. Yar³, Ch. Avais Hanif⁴, Senghour Mey⁵, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3667-3684, 2022, DOI:10.32604/cmc.2022.022264 - 07 December 2021

Abstract Agriculture is the backbone of each country, and almost 50% of the population is directly involved in farming. In Pakistan, several kinds of fruits are produced and exported the other countries. Citrus is an important fruit, and its production in Pakistan is higher than the other fruits. However, the diseases of citrus fruits such as canker, citrus scab, blight, and a few more impact the quality and quantity of this Fruit. The manual diagnosis of these diseases required an expert person who is always a time-consuming and costly procedure. In the agriculture sector, deep learning… More >

Loan Trinh Van¹, Quang H. Nguyen^1,*, Thuy Dao Thi Le²

Computer Systems Science and Engineering, Vol.41, No.3, pp. 1083-1098, 2022, DOI:10.32604/csse.2022.021635 - 10 November 2021

Abstract For human-machine communication to be as effective as human-to-human communication, research on speech emotion recognition is essential. Among the models and the classifiers used to recognize emotions, neural networks appear to be promising due to the network’s ability to learn and the diversity in configuration. Following the convolutional neural network, a capsule neural network (CapsNet) with inputs and outputs that are not scalar quantities but vectors allows the network to determine the part-whole relationships that are specific 6 for an object. This paper performs speech emotion recognition based on CapsNet. The corpora for speech emotion… More >

Haleem Farman¹, Jamil Ahmad^1,*, Bilal Jan², Yasir Shahzad³, Muhammad Abdullah¹, Atta Ullah⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 2073-2089, 2022, DOI:10.32604/cmc.2022.018961 - 03 November 2021

Abstract Plant diseases are a major cause of degraded fruit quality and yield losses. These losses can be significantly reduced with early detection of diseases to ensure their timely treatment, particularly in developing countries. In this regard, an expert system based on deep learning model where the expert knowledge, particularly the one acquired by plant pathologist, is recursively learned by the system and is applied using a smart phone application for use in the target field environment, is being proposed. In this paper, a robust disease detection method is developed based on convolutional neural network (CNN),… More >

Abdul Majid¹, Muhammad Attique Khan¹, Majed Alhaisoni², Muhammad Asfand E. yar³, Usman Tariq⁴, Nazar Hussain¹, Yunyoung Nam^5,*, Seifedine Kadry⁶

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1387-1402, 2022, DOI:10.32604/cmc.2022.017701 - 03 November 2021

Abstract Agriculture has been an important research area in the field of image processing for the last five years. Diseases affect the quality and quantity of fruits, thereby disrupting the economy of a country. Many computerized techniques have been introduced for detecting and recognizing fruit diseases. However, some issues remain to be addressed, such as irrelevant features and the dimensionality of feature vectors, which increase the computational time of the system. Herein, we propose an integrated deep learning framework for classifying fruit diseases. We consider seven types of fruits, i.e., apple, cherry, blueberry, grapes, peach, citrus,… More >

Fahd N. Al-Wesabi^1,2,*, Amani Abdulrahman Albraikan³, Anwer Mustafa Hilal⁴, Majdy M. Eltahir¹, Manar Ahmed Hamza⁴, Abu Sarwar Zamani⁴

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6223-6238, 2022, DOI:10.32604/cmc.2022.021299 - 11 October 2021

Abstract Precision agriculture enables the recent technological advancements in farming sector to observe, measure, and analyze the requirements of individual fields and crops. The recent developments of computer vision and artificial intelligence (AI) techniques find a way for effective detection of plants, diseases, weeds, pests, etc. On the other hand, the detection of plant diseases, particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss. Besides, earlier and precise apple leaf disease detection can minimize the spread of the disease. Earlier works make use of traditional image processing techniques which cannot assure… More >

Debapriya Hazra, Wafa Shafqat, Yung-Cheol Byun^*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3151-3167, 2022, DOI:10.32604/cmc.2022.020143 - 27 September 2021

Abstract Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions. Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing. Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies. The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data. Another critical factor is balancing the data for enhanced prediction. Data Augmentation is a… More >

Juan Pablo Villa-Pulgarin¹, Anderson Alberto Ruales-Torres^1,2, Daniel Arias-Garzón¹, Mario Alejandro Bravo-Ortiz¹, Harold Brayan Arteaga-Arteaga¹, Alejandro Mora-Rubio¹, Jesus Alejandro Alzate-Grisales¹, Esteban Mercado-Ruiz¹, M. Hassaballah³, Simon Orozco-Arias^4,5, Oscar Cardona-Morales¹, Reinel Tabares-Soto^1,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2131-2148, 2022, DOI:10.32604/cmc.2022.019529 - 27 September 2021

Abstract Skin cancer is one of the most severe diseases, and medical imaging is among the main tools for cancer diagnosis. The images provide information on the evolutionary stage, size, and location of tumor lesions. This paper focuses on the classification of skin lesion images considering a framework of four experiments to analyze the classification performance of Convolutional Neural Networks (CNNs) in distinguishing different skin lesions. The CNNs are based on transfer learning, taking advantage of ImageNet weights. Accordingly, in each experiment, different workflow stages are tested, including data augmentation and fine-tuning optimization. Three CNN models More >

Shui-Hua Wang¹, Muhammad Attique Khan², Yu-Dong Zhang^3,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3081-3097, 2022, DOI:10.32604/cmc.2022.019447 - 27 September 2021

Abstract Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and… More >

G. Jayandhi^1,*, J.S. Leena Jasmine², S. Mary Joans²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 491-503, 2022, DOI:10.32604/csse.2022.016376 - 09 September 2021

Abstract The most common form of cancer for women is breast cancer. Recent advances in medical imaging technologies increase the use of digital mammograms to diagnose breast cancer. Thus, an automated computerized system with high accuracy is needed. In this study, an efficient Deep Learning Architecture (DLA) with a Support Vector Machine (SVM) is designed for breast cancer diagnosis. It combines the ideas from DLA with SVM. The state-of-the-art Visual Geometric Group (VGG) architecture with 16 layers is employed in this study as it uses the small size of 3 × 3 convolution filters that reduces… More >