Gi-Wan Hong¹, Jeong-Wook Kim¹, Hangbae Chang^2,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1489-1501, 2021, DOI:10.32604/cmc.2021.013344

Abstract Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information, but also the expansion of areas and assets to be protected. In terms of information security, it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets. Also, it has caused difficulties in managing those issues due to reasons such as mutual interference, countless security events and logs’ data, etc. Within this security environment, an organization should identify and classify assets based on the value of data and their security perspective, and… More >

Huaning Song¹, Jiansheng Peng^1,2,*, Nianyang Tuo¹, Haiying Xia², Yiyun Peng³

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 581-592, 2021, DOI:10.32604/iasc.2021.014152

Abstract Accurate identification of sugarcane buds, as one of the key technologies in sugarcane plantation, becomes very necessary for its mechanized and intelligent advancements. In the traditional methods of sugarcane bud recognition, a significant amount of algorithms work goes into the location and recognition of sugarcane bud images. A Convolutional Neural Network (CNN) for classifying the bud conditions is proposed in this paper. Firstly, we convert the colorful sugarcane images into gray ones, unify the size of them, and make a TFRecord format data set, which contains 1100 positive samples and 1100 negative samples. Then, a CNN mode is developed to… More >

Samina Amin^1,*, M. Irfan Uddin¹, M. Ali Zeb¹, Ala Abdulsalam Alarood², Marwan Mahmoud³, Monagi H. Alkinani⁴

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1317-1332, 2021, DOI:10.32604/cmc.2021.014733

Abstract Social media platforms have lately emerged as a promising tool for predicting the outbreak of epidemics by analyzing information on them with the help of machine learning techniques. Many analytical and statistical models are available to infer a variety of user sentiments in posts on social media. The amount of data generated by social media platforms, such as Twitter, that can be used to track diseases is increasing rapidly. This paper proposes a method for the classification of tweets related to the outbreak of dengue using machine learning algorithms. An artificial neural network (ANN)-based method is developed using Global Vector… More >

Muhammad Ashfaq Khan, Yangwoo Kim^*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 427-443, 2021, DOI:10.32604/cmc.2021.014682

Abstract Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a… More >

Shaheen Khatoon^1,*, Md Maruf Hasan¹, Amna Asif¹, Majed Alshmari¹, Yun-Kiam Yap²

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 595-612, 2021, DOI:10.32604/cmc.2021.014580

Abstract Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI) and computer vision allow researchers… More >

Mai Ramadan Ibraheem¹, Jilan Adel², Alaa Eldin Balbaa³, Shaker El-Sappagh⁴, Tamer Abuhmed^5,*, Mohammed Elmogy⁶

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 393-409, 2021, DOI:10.32604/cmc.2021.014446

Abstract Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classification. To this end, sEMG signals were acquired from five core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset,… More >

Prabakaran Rajamanickam¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril Raj²

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 709-722, 2021, DOI:10.32604/cmc.2021.014347

Abstract Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from… More >

Kashif Irshad¹, Muhammad Tanvir Afzal², Sanam Shahla Rizvi³, Abdul Shahid⁴, Rabia Riaz⁵, Tae-Sun Chung^6,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 877-894, 2021, DOI:10.32604/cmc.2021.014156

Abstract The growing collection of scientific data in various web repositories is referred to as Scientific Big Data, as it fulfills the four “V’s” of Big Data–-volume, variety, velocity, and veracity. This phenomenon has created new opportunities for startups; for instance, the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs. Traditionally, the content of the papers are compared to list the relevant papers from a repository. The conventional method results in a long list of papers that is often impossible to interpret productively. Therefore, the need for… More >

Mavra Mehmood¹, Ember Ayub¹, Fahad Ahmad^1,6,*, Madallah Alruwaili², Ziyad A. Alrowaili³, Saad Alanazi², Mamoona Humayun², Muhammad Rizwan¹, Shahid Naseem⁴, Tahir Alyas⁵

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 641-657, 2021, DOI:10.32604/cmc.2021.013774

Abstract Clinical image processing plays a significant role in healthcare systems and is currently a widely used methodology. In carcinogenic diseases, time is crucial; thus, an image’s accurate analysis can help treat disease at an early stage. Ductal carcinoma in situ (DCIS) and lobular carcinoma in situ (LCIS) are common types of malignancies that affect both women and men. The number of cases of DCIS and LCIS has increased every year since 2002, while it still takes a considerable amount of time to recommend a controlling technique. Image processing is a powerful technique to analyze preprocessed images to retrieve useful information… More >

Nakhim Chea¹, Yunyoung Nam^2,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 411-426, 2021, DOI:10.32604/cmc.2021.013390

Abstract Various techniques to diagnose eye diseases such as diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD), are possible through deep learning algorithms. A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects. However, multiple major eye diseases, such as DR, GLC, and AMD, could not be detected simultaneously by computer-aided systems to date. There were just high-performance-outcome researches on a pair of healthy and eye-diseased group, besides of four categories of fundus image classification. To have a better knowledge of multi-categorical classification of fundus photographs, we used optimal residual… More >