Tawfeeq Shawly¹, Ahmed Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561

Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved… More >

Jigna Patel¹, Anand Ruparelia¹, Sudeep Tanwar^1,*, Fayez Alqahtani², Amr Tolba³, Ravi Sharma⁴, Maria Simona Raboaca^5,6,*, Bogdan Constantin Neagu⁷

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1281-1301, 2023, DOI:10.32604/cmc.2023.038796

Abstract The overgrowth of weeds growing along with the primary crop in the fields reduces crop production. Conventional solutions like hand weeding are labor-intensive, costly, and time-consuming; farmers have used herbicides. The application of herbicide is effective but causes environmental and health concerns. Hence, Precision Agriculture (PA) suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants. Motivated by the gap above, we proposed a Deep Learning (DL) based model for detecting Eggplant (Brinjal) weed in this paper. The key objective of this study is to detect plant and non-plant (weed) parts from crop images.… More >

Uzair Aslam Bhatti^1,*, Sibghat Ullah Bazai², Shumaila Hussain¹, Shariqa Fakhar³, Chin Soon Ku^4,*, Shah Marjan⁵, Por Lip Yee⁶, Liu Jing¹

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 681-697, 2023, DOI:10.32604/cmc.2023.037958

Abstract Crop diseases have a significant impact on plant growth and can lead to reduced yields. Traditional methods of disease detection rely on the expertise of plant protection experts, which can be subjective and dependent on individual experience and knowledge. To address this, the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease identification. In this paper, we propose a novel approach that utilizes a convolutional neural network (CNN) model in conjunction with Inception v3 to identify plant leaf diseases. The research focuses on developing a mobile application that leverages… More >

Batyrkhan Omarov^1,*, Meirzhan Baikuvekov¹, Zeinel Momynkulov², Aray Kassenkhan³, Saltanat Nuralykyzy³, Mereilim Iglikova³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3745-3761, 2023, DOI:10.32604/cmc.2023.042627

Abstract Heart disease is a leading cause of mortality worldwide. Electrocardiograms (ECG) play a crucial role in diagnosing heart disease. However, interpreting ECG signals necessitates specialized knowledge and training. The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis. This research paper proposes a 3D Convolutional Long Short-Term Memory (Conv-LSTM) model for detecting heart disease using ECG signals. The proposed model combines the advantages of both convolutional neural networks (CNN) and long short-term memory (LSTM) networks. By considering both the spatial and temporal dependencies of ECG, the 3D Conv-LSTM model… More >

Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3097-3112, 2023, DOI:10.32604/cmc.2023.040914

Abstract Predicting traffic flow is a crucial component of an intelligent transportation system. Precisely monitoring and predicting traffic flow remains a challenging endeavor. However, existing methods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes, resulting in the loss of essential information and lower forecast performance. On the other hand, the availability of spatiotemporal data is limited. This research offers alternative spatiotemporal data with three specific features as input, vehicle type (5 types), holidays (3 types), and weather (10 conditions). In this study, the proposed model combines the advantages of the… More >

Wenran Jia¹, Simin Ma¹, Peng Geng¹, Yan Sun^2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3393-3411, 2023, DOI:10.32604/cmc.2023.040091

Abstract Retinal vessel segmentation in fundus images plays an essential role in the screening, diagnosis, and treatment of many diseases. The acquired fundus images generally have the following problems: uneven illumination, high noise, and complex structure. It makes vessel segmentation very challenging. Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network (U-Net) models, and they have many limitations and shortcomings, such as the loss of microvascular details at the end of the vessels. We address the limitations of convolution by introducing the transformer into retinal vessel segmentation. Therefore, we propose a hybrid method for retinal vessel… More >

Wen-Tsai Sung¹, Hao-Wei Kang¹, Sung-Jung Hsiao^2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3833-3858, 2023, DOI:10.32604/cmc.2023.040024

Abstract In the speech recognition system, the acoustic model is an important underlying model, and its accuracy directly affects the performance of the entire system. This paper introduces the construction and training process of the acoustic model in detail and studies the Connectionist temporal classification (CTC) algorithm, which plays an important role in the end-to-end framework, established a convolutional neural network (CNN) combined with an acoustic model of Connectionist temporal classification to improve the accuracy of speech recognition. This study uses a sound sensor, ReSpeaker Mic Array v2.0.1, to convert the collected speech signals into text or corresponding speech signals to… More >

S M Hasan Mahmud^1,2, Md Mamun Ali³, Mohammad Fahim Shahriar¹, Fahad Ahmed Al-Zahrani⁴, Kawsar Ahmed^5,6,*, Dip Nandi¹, Francis M. Bui⁵

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3933-3948, 2023, DOI:10.32604/cmc.2023.039020

Abstract Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build… More >

Mehwish Zafar¹, Javeria Amin², Muhammad Sharif¹, Muhammad Almas Anjum³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2779-2793, 2023, DOI:10.32604/cmc.2023.035860

Abstract Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized features fusion-based model is proposed,… More >

Sayyid Kamran Hussain¹, Ali Haider Khan^2,*, Malek Alrashidi³, Sajid Iqbal⁴, Qazi Mudassar Ilyas⁴, Kamran Shah⁵

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3763-3781, 2023, DOI:10.32604/cmc.2023.041722

Abstract As ocular computer-aided diagnostic (CAD) tools become more widely accessible, many researchers are developing deep learning (DL) methods to aid in ocular disease (OHD) diagnosis. Common eye diseases like cataracts (CATR), glaucoma (GLU), and age-related macular degeneration (AMD) are the focus of this study, which uses DL to examine their identification. Data imbalance and outliers are widespread in fundus images, which can make it difficult to apply many DL algorithms to accomplish this analytical assignment. The creation of effcient and reliable DL algorithms is seen to be the key to further enhancing detection performance. Using the analysis of images of… More >