Inzamam Mashood Nasir¹, Asima Bibi², Jamal Hussain Shah², Muhammad Attique Khan¹, Muhammad Sharif², Khalid Iqbal³, Yunyoung Nam⁴, Seifedine Kadry^5,*

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945

Abstract Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases. A fine-tuned,… More >

Muhammad Saboor Ahmed^*, Muhammad Tanvir Afzal

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1195-1214, 2021, DOI:10.32604/cmc.2020.012619

Abstract Citations play an important role in the scientific community by assisting in measuring multifarious policies like the impact of journals, researchers, institutions, and countries. Authors cite papers for different reasons, such as extending previous work, comparing their study with the state-of-the-art, providing background of the field, etc. In recent years, researchers have tried to conceptualize all citations into two broad categories, important and incidental. Such a categorization is very important to enhance scientific output in multiple ways, for instance, (1) Helping a researcher in identifying meaningful citations from a list of 100 to 1000 citations (2) Enhancing the impact factor… More >

Nazeer Muhammad¹, Rubab², Nargis Bibi³, Oh-Young Song⁴, Muhammad Attique Khan^5,*, Sajid Ali Khan⁶

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2199-2216, 2021, DOI:10.32604/cmc.2020.012257

Abstract Agriculture plays an important role in the economy of all countries. However, plant diseases may badly affect the quality of food, production, and ultimately the economy. For plant disease detection and management, agriculturalists spend a huge amount of money. However, the manual detection method of plant diseases is complicated and time-consuming. Consequently, automated systems for plant disease detection using machine learning (ML) approaches are proposed. However, most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data. To address the issue, this article proposes a fully… More >

Pallabi Sharma^1,*, Kangkana Bora², Kunio Kasugai³, Bunil Kumar Balabantaray¹

Oncologie, Vol.22, No.3, pp. 129-145, 2020, DOI:10.32604/oncologie.2020.013870

Abstract In this paper, we address a current problem in medical image processing, the detection of colorectal cancer from colonoscopy videos. According to worldwide cancer statistics, colorectal cancer is one of the most common cancers. The process of screening and the removal of pre-cancerous cells from the large intestine is a crucial task to date. The traditional manual process is dependent on the expertise of the medical practitioner. In this paper, a two-stage classification is proposed to detect colorectal cancer. In the first stage, frames of colonoscopy video are extracted and are rated as significant if it contains a polyp, and… More >

Tao Liao^1,‡, Weicheng Fu^1,†, Shunxiang Zhang^1,*, Zongtian Liu^2,§

Computer Systems Science and Engineering, Vol.35, No.5, pp. 311-319, 2020, DOI:10.32604/csse.2020.35.311

Abstract Event trigger recognition is a sub-task of event extraction, which is important for text classification, topic tracking and so on. In order to improve the effectiveness of using word features as a benchmark, a new event trigger recognition method based on positive and negative weight computing is proposed. Firstly, the associated word feature, the part-of-speech feature and the dependency feature are combined. Then, the combination of these three features with positive and negative weight computing is used to identify triggers. Finally, the text classification is carried out based on the event triggers. Findings from our experiments show that the application… More >

Purushottam Sharma¹, Mohammed Alshehri^2,*, Richa Sharma¹, Osama Alfarraj³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 885-901, 2021, DOI:10.32604/cmc.2020.012251

Abstract Low back pain (LBP) is a morbid condition that has afflicted several citizens in Europe. It has negatively impacted the European economy due to several man-days lost, with bed rest and forced inactivity being the usual LBP care and management steps. Direct models, which incorporate various regression analyses, have been executed for the investigation of this premise due to the simplicity of translation. However, such straight models fail to completely consider the impact of association brought about by a mix of nonlinear connections and autonomous factors.In this paper, we discuss a system that aids decision-making regarding the best-suited support system… More >

Adel Assiri^*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 767-778, 2021, DOI:10.32604/cmc.2020.013813

Abstract Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks. Network-based intrusion detection systems (NIDSs) using machine learning (ML) methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks. Among several ML methods, random forest (RF) is a robust method that can be used in ML-based network intrusion detection solutions. However, the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy. Therefore, optimal parameter selection is a real problem in… More >

Sadeeq Jan^1,*, Omer Bin Tauqeer¹, Fazal Qudus Khan², George Tsaramirsis², Awais Ahmad³, Iftikhar Ahmad⁴, Imran Maqsood⁵, Niamat Ullah⁶

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 631-645, 2021, DOI:10.32604/cmc.2020.012831

Abstract Over the last decade, a significant increase has been observed in the use of web-based Information systems that process sensitive information, e.g., personal, financial, medical. With this increased use, the security of such systems became a crucial aspect to ensure safety, integrity and authenticity of the data. To achieve the objectives of data safety, security testing is performed. However, with growth and diversity of information systems, it is challenging to apply security testing for each and every system. Therefore, it is important to classify the assets based on their required level of security using an appropriate technique. In this paper,… More >

Zongshuai Liu¹, Xuyu Xiang^1,2,*, Jiaohua Qin¹, Yun Tan¹, Qin Zhang¹, Neal N. Xiong³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 457-466, 2021, DOI:10.32604/cmc.2020.012165

Abstract In recent years, with the development of machine learning and deep learning, it is possible to identify and even control crop diseases by using electronic devices instead of manual observation. In this paper, an image recognition method of citrus diseases based on deep learning is proposed. We built a citrus image dataset including six common citrus diseases. The deep learning network is used to train and learn these images, which can effectively identify and classify crop diseases. In the experiment, we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed,… More >

Anju Asokan¹, J. Anitha¹, Bogdan Patrut², Dana Danciulescu³, D. Jude Hemanth^1,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 373-388, 2021, DOI:10.32604/cmc.2020.012364

Abstract Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas. Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection. However, many deep learning framework based approaches do not consider both spatial and textural details into account. In order to handle this issue, a Convolutional Neural Network (CNN) based multi-feature extraction and fusion is introduced which considers both spatial and textural features. This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features. Then… More >