Bin Li^*, Linghui Kong, Xiangyi Zhang, Bochuo Kou, Hui Yu, Bowen Liu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3267-3282, 2024, DOI:10.32604/cmc.2024.047193

Abstract The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing… More >

Qazi Mazhar ul Haq¹, Fahim Arif^2,3, Khursheed Aurangzeb⁴, Noor ul Ain³, Javed Ali Khan⁵, Saddaf Rubab⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172

Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature selection, which is used to… More >

Usman Khan¹, Muhammad Khalid Khan¹, Muhammad Ayub Latif¹, Muhammad Naveed^1,2,*, Muhammad Mansoor Alam^2,3,4, Salman A. Khan¹, Mazliham Mohd Su’ud^2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2967-3000, 2024, DOI:10.32604/cmc.2024.045101

Abstract Recently, there has been a notable surge of interest in scientific research regarding spectral images. The potential of these images to revolutionize the digital photography industry, like aerial photography through Unmanned Aerial Vehicles (UAVs), has captured considerable attention. One encouraging aspect is their combination with machine learning and deep learning algorithms, which have demonstrated remarkable outcomes in image classification. As a result of this powerful amalgamation, the adoption of spectral images has experienced exponential growth across various domains, with agriculture being one of the prominent beneficiaries. This paper presents an extensive survey encompassing multispectral and hyperspectral images, focusing on their… More >

M. S. Vivekanandan¹, T. Jesudas^2,*

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 109-111, 2024, DOI:10.32604/iasc.2024.051758

Abstract This article has no abstract. More >

M. Adimoolam¹, K. Maithili², N. M. Balamurugan³, R. Rajkumar⁴, S. Leelavathy⁵, Raju Kannadasan⁶, Mohd Anul Haq^7,*, Ilyas Khan⁸, ElSayed M. Tag El Din⁹, Arfat Ahmad Khan¹⁰

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 33-55, 2024, DOI:10.32604/iasc.2024.039009

Abstract At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using… More >

Irfan Haider¹, Muhammad Attique Khan^1,*, Muhammad Nazir¹, Taerang Kim², Jae-Hyuk Cha²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

Seoyun Kim^1,#, Hyerim Yu^2,#, Jeewoo Yoon^1,3, Eunil Park^1,2,*

Computer Systems Science and Engineering, Vol.48, No.2, pp. 413-429, 2024, DOI:10.32604/csse.2023.041575

Abstract Given the increasing number of countries reporting degraded air quality, effective air quality monitoring has become a critical issue in today’s world. However, the current air quality observatory systems are often prohibitively expensive, resulting in a lack of observatories in many regions within a country. Consequently, a significant problem arises where not every region receives the same level of air quality information. This disparity occurs because some locations have to rely on information from observatories located far away from their regions, even if they may be the closest available options. To address this challenge, a novel approach that leverages machine… More >

Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN)… More >

Abdelrahman Maharek^1,2,*, Amr Abozeid^2,3, Rasha Orban¹, Kamal ElDahshan²

Computer Systems Science and Engineering, Vol.48, No.2, pp. 305-320, 2024, DOI:10.32604/csse.2024.039436

Abstract What causes object detection in video to be less accurate than it is in still images? Because some video frames have degraded in appearance from fast movement, out-of-focus camera shots, and changes in posture. These reasons have made video object detection (VID) a growing area of research in recent years. Video object detection can be used for various healthcare applications, such as detecting and tracking tumors in medical imaging, monitoring the movement of patients in hospitals and long-term care facilities, and analyzing videos of surgeries to improve technique and training. Additionally, it can be used in telemedicine to help diagnose… More >

Amr Ismail¹, Walid Hamdy^1,2, Aya M. Al-Zoghby³, Wael A. Awad³, Ahmed Ismail Ebada³, Yunyoung Nam⁴, Byeong-Gwon Kang^4,*, Mohamed Abouhawwash^5,6

Computer Systems Science and Engineering, Vol.48, No.2, pp. 273-285, 2024, DOI:10.32604/csse.2023.038192

Abstract Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has… More >