Batyrkhan Omarov^1,2,*, Nurbek Saparkhojayev², Shyrynkyz Shekerbekova³, Oxana Akhmetova¹, Meruert Sakypbekova¹, Guldina Kamalova³, Zhanna Alimzhanova¹, Lyailya Tukenova³, Zhadyra Akanova⁴

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2815-2833, 2022, DOI:10.32604/cmc.2022.019246

Abstract Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of… More >

Nazar Hussain¹, Muhammad Attique Khan¹, Usman Tariq², Seifedine Kadry^3,*, MuhammadAsfand E. Yar⁴, Almetwally M. Mostafa⁵, Abeer Ali Alnuaim⁶, Shafiq Ahmad⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3281-3294, 2022, DOI:10.32604/cmc.2022.019036

Abstract Agriculture is an important research area in the field of visual recognition by computers. Plant diseases affect the quality and yields of agriculture. Early-stage identification of crop disease decreases financial losses and positively impacts crop quality. The manual identification of crop diseases, which are mostly visible on leaves, is a very time-consuming and costly process. In this work, we propose a new framework for the recognition of cucumber leaf diseases. The proposed framework is based on deep learning and involves the fusion and selection of the best features. In the feature extraction phase, VGG (Visual Geometry Group) and Inception V3… More >

Ramya G. Franklin^1,*, B. Muthukumar²

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 835-851, 2022, DOI:10.32604/iasc.2022.019877

Abstract Electrocardiography (ECG) is a method for monitoring the human heart’s electrical activity. ECG signal is often used by clinical experts in the collected time arrangement for the evaluation of any rhythmic circumstances of a topic. The research was carried to make the assignment computerized by displaying the problem with encoder-decoder methods, by using misfortune appropriation to predict standard or anomalous information. The two Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) fully connected layer (FCL) have shown improved levels over deep learning networks (DLNs) across a wide range of applications such as speech recognition, prediction etc., As CNNs… More >

Faiz Ali Shah¹, Muhammad Attique Khan², Muhammad Sharif¹, Usman Tariq³, Aimal Khan⁴, Seifedine Kadry⁵, Orawit Thinnukool^6,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1491-1507, 2022, DOI:10.32604/cmc.2022.019490

Abstract Fruit diseases seriously affect the production of the agricultural sector, which builds financial pressure on the country's economy. The manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an expert. Hence, it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf images. According to the literature, many automated methods have been developed for the recognition of fruit diseases at the early stage. However, these techniques still face some challenges, such as the similar symptoms of different fruit diseases and… More >

Muhammad Zia Ur Rehman¹, Fawad Ahmed¹, Muhammad Attique Khan², Usman Tariq³, Sajjad Shaukat Jamal⁴, Jawad Ahmad^5,*, Iqtadar Hussain⁶

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1401-1417, 2022, DOI:10.32604/cmc.2022.019046

Abstract In recent years, the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits. This in turn has helped in improving the quality and production of vegetables and fruits. Citrus fruits are well known for their taste and nutritional values. They are one of the natural and well known sources of vitamin C and planted worldwide. There are several diseases which severely affect the quality and yield of citrus fruits. In this paper, a new deep learning based technique is proposed for citrus disease classification. Two different pre-trained deep learning… More >

Javaria Amin¹, Muhammad Almas Anjum², Muhammad Sharif³, Seifedine Kadry⁴, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 619-635, 2022, DOI:10.32604/cmc.2022.018562

Abstract As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore,… More >

VERA KOLYOVSKA^1,#, SONYA IVANOVA², DESISLAVA DRENSKA³, DIMITAR MASLAROV^3,4,#, RENETA TOSHKOVA^1,*

BIOCELL, Vol.45, No.6, pp. 1485-1494, 2021, DOI:10.32604/biocell.2021.016250

Abstract Glycosphingolipids (gangliosides) have been characterized as important biological molecules with a key role as regulators in many physiological processes on cellular, tissue, organ, and organism levels. The deviations in their normal amounts, production, and metabolism are very often related to the development of many multi-factor socially important diseases. GM3 ganglioside, as a small molecule, plays important roles in the cascade regulatory pathways in the pathology of many disorders like neurodegenerative diseases, autoimmune diseases, inflammation, diabetes, malignant transformation, and others. Ganglioside GM3 and its derivatives are membrane-bound glycosphingolipids composed of an oligosaccharide head structure containing one sialic acid residue. These molecules… More >

Muhammad Rizwan Latif¹, Muhamamd Attique Khan¹, Muhammad Younus Javed¹, Haris Masood², Usman Tariq³, Yunyoung Nam^4,*, Seifedine Kadry⁵

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2917-2932, 2021, DOI:10.32604/cmc.2021.017364

Abstract Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these diseases at their… More >

Muhammad Alyas Khan¹, Mushtaq Ali¹, Mohsin Shah², Toqeer Mahmood³, Muneer Ahmad⁴, NZ Jhanjhi⁵, Mohammad Arif Sobhan Bhuiyan^6,*, Emad Sami Jaha⁷

Intelligent Automation & Soft Computing, Vol.30, No.3, pp. 771-785, 2021, DOI:10.32604/iasc.2021.018039

Abstract Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using intensity adjustment and histogram equalization.… More >

K. K. Thyagharajan, I. Kiruba Raji^*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2061-2076, 2021, DOI:10.32604/cmc.2021.017591

Abstract This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew, Pseudocercospora leaf spot, leaf web… More >