Nasser Alalwan^1,*, Ahmed I. Taloba², Amr Abozeid³, Ahmed Ibrahim Alzahrani¹, Ali H. Al-Bayatti⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2497-2517, 2024, DOI:10.32604/cmes.2023.029910

Abstract An illness known as pneumonia causes inflammation in the lungs. Since there is so much information available from various X-ray images, diagnosing pneumonia has typically proven challenging. To improve image quality and speed up the diagnosis of pneumonia, numerous approaches have been devised. To date, several methods have been employed to identify pneumonia. The Convolutional Neural Network (CNN) has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology. However, these methods are complex, inefficient, and imprecise to analyze a big number of datasets. In this paper, a new hybrid method for the automatic classification… More >

Shweta Singhal¹, Nishtha Jatana², Ahmad F Subahi³, Charu Gupta^4,*, Osamah Ibrahim Khalaf⁵, Youseef Alotaibi⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6755-6774, 2023, DOI:10.32604/cmc.2023.032308

Abstract Software needs modifications and requires revisions regularly. Owing to these revisions, retesting software becomes essential to ensure that the enhancements made, have not affected its bug-free functioning. The time and cost incurred in this process, need to be reduced by the method of test case selection and prioritization. It is observed that many nature-inspired techniques are applied in this area. African Buffalo Optimization is one such approach, applied to regression test selection and prioritization. In this paper, the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization. The proposed algorithm… More >

B. Jaishankar^1,*, Raghunathan Anitha², Finney Daniel Shadrach¹, M. Sivarathinabala³, V. Balamurugan⁴

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1823-1836, 2023, DOI:10.32604/csse.2023.022938

Abstract In the discipline of Music Information Retrieval (MIR), categorizing music files according to their genre is a difficult process. Music genre classification is an important multimedia research domain for classification of music databases. In the proposed method music genre classification using features obtained from audio data is proposed. The classification is done using features extracted from the audio data of popular online repository namely GTZAN, ISMIR 2004 and Latin Music Dataset (LMD). The features highlight the differences between different musical styles. In the proposed method, feature selection is performed using an African Buffalo Optimization (ABO), and the resulting features are… More >

Julius Beneoluchi Odili^1,*, A. Noraziah², Mohd Helmy Abd Wahab³

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 41-51, 2020, DOI:10.31209/2018.100000059

Abstract This paper presents the African Buffalo Optimization algorithm for collision avoidance among electric fishes. Collision-avoidance in electric fish finds correlation with the Travelling Salesman avoiding the cities he has earlier visited. Collision avoidance in electric is akin to collision-avoidance in modern day driverless cars being promoted by Google Incorporation and other similar companies. The concept of collision-avoidance is also very useful to persons with visual impairment as it will help them avoid collision with objects, vehicles, persons, especially other visually-impaired. After a number of experimental procedures using the concept of the travelling salesman’s problem to simulate collision-avoidance in electric fish,… More >