K. Muthumayil¹, M. Buvana², K. R. Sekar³, Adnen El Amraoui^4,*, Issam Nouaouri⁴, Romany F. Mansour⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1159-1175, 2022, DOI:10.32604/cmc.2022.017178 - 07 September 2021

Abstract The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network i.e., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing… More >

Harshita Patel¹, Dharmendra Singh Rajput^1,*, Ovidiu Petru Stan², Liviu Cristian Miclea²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 73-89, 2022, DOI:10.32604/cmc.2022.017114 - 07 September 2021

Abstract Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes. The Imbalanced distribution of data is a natural occurrence in real world datasets, so needed to be dealt with carefully to get important insights. In case of imbalance in data sets, traditional classifiers have to sacrifice their performances, therefore lead to misclassifications. This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue. We have adapted the ‘existing algorithm modification solution’ to learn… More >

Rawan Sulaiman Howyan^1,2,*, Emad Sami Jaha¹

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 555-580, 2022, DOI:10.32604/iasc.2022.019533 - 03 September 2021

Abstract Human face is one of the most widely used biometrics based on computer-vision to derive various useful information such as gender, ethnicity, age, and even identity. Facial age estimation has received great attention during the last decades because of its influence in many applications, like face recognition and verification, which may be affected by aging changes and signs which appear on human face along with age progression. Thus, it becomes a prominent challenge for many researchers. One of the most influential factors on age estimation is the type of features used in the model training… More >

Husam Ali Abdulmohsin^1,*, Hala Bahjat Abdul Wahab², Abdul Mohssen Jaber Abdul Hossen³

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 339-355, 2022, DOI:10.32604/iasc.2022.018045 - 03 September 2021

Abstract Classification is the last, and usually the most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as the main classification approach, regardless of time consumption. This study proposes a statistical feature classification cubic spline interpolation (FC-CSI) algorithm to classify emotions in speech using a curve fitting technique. FC-CSI is utilized in a speech emotion recognition system (SERS). The idea is to sketch the cubic spline interpolation (CSI) for each audio file in a dataset and the mean cubic spline interpolations (MCSIs) representing each emotion in the dataset. CSI interpolation… More >

Qazi Mudassar Ilyas¹, Muneer Ahmad^2,*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 241-254, 2022, DOI:10.32604/iasc.2022.018042 - 03 September 2021

Abstract The recent COVID-19 pandemic has had lasting and severe impacts on social gatherings and interaction among people. Local administrative bodies enforce standard operating procedures (SOPs) to combat the spread of COVID-19, with mandatory precautionary measures including use of face masks at social assembly points. In addition, the World Health Organization (WHO) strongly recommends people wear a face mask as a shield against the virus. The manual inspection of a large number of people for face mask enforcement is a challenge for law enforcement agencies. This work proposes an automatic face mask detection solution using an… More >

D. Srinivasa Rao¹, Ramesh Babu Ch², V. Sravan Kiran¹, N. Rajasekhar^3,*, Kalyanapu Srinivas⁴, P. Shilhora Akshay¹, G. Sai Mohan¹, B. Lalith Bharadwaj¹

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 161-176, 2022, DOI:10.32604/iasc.2022.017706 - 03 September 2021

Abstract

Plant diseases have become a major threat in farming and provision of food. Various plant diseases have affected the natural growth of the plants and the infected plants are the leading factors for loss of crop production. The manual detection and identification of the plant diseases require a careful and observative examination through expertise. To overcome manual testing procedures an automated identification and detection can be implied which provides faster, scalable and precisive solutions. In this research, the contributions of our work are threefold. Firstly, a bi-linear convolution neural network (Bi-CNNs) for plant leaf disease

… More >

Abdul Muiz Fayyaz¹, Kawther A. Al-Dhlan², Saeed Ur Rehman¹, Mudassar Raza¹, Waqar Mehmood³, Muhammad Shafiq⁴, Jin-Ghoo Choi^4,*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 507-522, 2022, DOI:10.32604/iasc.2022.016392 - 03 September 2021

Abstract Crops are very important to the financial needs of a country. Due to various diseases caused by different pathogens, a large number of crops have been destroyed. As humanoids, our basic need is food for survival, and the most basic foundation of our food is agriculture. For many developing countries, it is mainly an important source of income. Bacterial diseases are one of the main diseases that cause improper production and a major economic crisis for the country. Therefore, it is necessary to detect the disease early. However, it is not easy for humans to… More >

Adi Alhudhaif^1,*, Ammar Saeed², Talha Imran², Muhammad Kamran³, Ahmed S. Alghamdi³, Ahmed O. Aseeri¹, Shtwai Alsubai¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 223-235, 2022, DOI:10.32604/csse.2022.018430 - 26 August 2021

Abstract Image classification is a core field in the research area of image processing and computer vision in which vehicle classification is a critical domain. The purpose of vehicle categorization is to formulate a compact system to assist in real-world problems and applications such as security, traffic analysis, and self-driving and autonomous vehicles. The recent revolution in the field of machine learning and artificial intelligence has provided an immense amount of support for image processing related problems and has overtaken the conventional, and handcrafted means of solving image analysis problems. In this paper, a combination of… More >

Muhammad Hasnain¹, Imran Ghani², Seung Ryul Jeong^3,*, Aitizaz Ali¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 327-339, 2022, DOI:10.32604/csse.2022.018300 - 26 August 2021

Abstract This paper presents a review of the ensemble learning models proposed for web services classification, selection, and composition. Web service is an evolutionary research area, and ensemble learning has become a hot spot to assess web services’ earlier mentioned aspects. The proposed research aims to review the state of art approaches performed on the interesting web services area. The literature on the research topic is examined using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) as a research method. The study reveals an increasing trend of using ensemble learning in the chosen papers More >

R. Muthaiyan^1,*, Dr M. Malleswaran²

Computer Systems Science and Engineering, Vol.40, No.1, pp. 299-312, 2022, DOI:10.32604/csse.2022.018034 - 26 August 2021

Abstract In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of… More >