C. S. S. Anupama¹, Rafina Zakieva², Afanasiy Sergin³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Chomyong Kim⁸, Yunyoung Nam^8,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1453-1468, 2023, DOI:10.32604/iasc.2023.038321

Abstract Gait is a biological typical that defines the method by that people walk. Walking is the most significant performance which keeps our day-to-day life and physical condition. Surface electromyography (sEMG) is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent. Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment. Several approaches are established in the works for gait recognition utilizing conventional and deep learning (DL) approaches. This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning… More >

Khalid M. O. Nahar¹, Ammar Almomani^2,3,*, Nahlah Shatnawi¹, Mohammad Alauthman⁴

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2037-2057, 2023, DOI:10.32604/iasc.2023.038235

Abstract This study presents a novel and innovative approach to automatically translating Arabic Sign Language (ATSL) into spoken Arabic. The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models. The image-based translation method maps sign language gestures to corresponding letters or words using distance measures and classification as a machine learning technique. The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs, with a translation accuracy of 93.7%. This research makes a significant contribution to the field of ATSL. It offers… More >

Ganjikunta Chakrapani, V. Sugumaran^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1513-1530, 2023, DOI:10.32604/iasc.2023.034597

Abstract Clutch is one of the most significant components in automobiles. To improve passenger safety, reliability and economy of automobiles, advanced supervision and fault diagnostics are required. Condition Monitoring is one of the key divisions that can be used to track the reliability of clutch and allied components. The state of the clutch elements can be monitored with the help of vibration signals which contain valuable information required for classification. Specific drawbacks of traditional fault diagnosis techniques like high reliability on human intelligence and the requirement of professional expertise, have made researchers look for intelligent fault diagnosis techniques. In this article,… More >

Md Saidul Islam^*

Journal on Internet of Things, Vol.4, No.3, pp. 127-140, 2022, DOI:10.32604/jiot.2022.037489

Abstract Internet of Things (IoT) technologies used in health have the potential to address systemic difficulties by offering tools for cost reduction while improving diagnostic and treatment efficiency. Numerous works on this subject focus on clarifying the constructs and interfaces between various components of an IoT platform, such as knowledge generation via smart sensors collecting biosignals from the human body and processing them via data mining and, in recent times, deep neural networks offered to host on cloud computing architecture. These approaches are intended to assist healthcare professionals in their daily activities. In this comparative research, we discuss the construction of… More >

Shah Faisal¹, Kashif Javed¹, Sara Ali¹, Areej Alasiry², Mehrez Marzougui², Muhammad Attique Khan^3,*, Jae-Hyuk Cha^4,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 895-914, 2023, DOI:10.32604/cmc.2023.039781

Abstract Citrus fruit crops are among the world’s most important agricultural products, but pests and diseases impact their cultivation, resulting in yield and quality losses. Computer vision and machine learning have been widely used to detect and classify plant diseases over the last decade, allowing for early disease detection and improving agricultural production. This paper presented an automatic system for the early detection and classification of citrus plant diseases based on a deep learning (DL) model, which improved accuracy while decreasing computational complexity. The most recent transfer learning-based models were applied to the Citrus Plant Dataset to improve classification accuracy. Using… More >

Deepthi K. Oommen^*, J. Arunnehru

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640

Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the younger onset. The research used… More >

Nadeem Jabbar Chaudhry^1,*, M. Bilal Khan², M. Javaid Iqbal¹, Siddiqui Muhammad Yasir³

Journal on Artificial Intelligence, Vol.4, No.4, pp. 245-259, 2022, DOI:10.32604/jai.2022.038875

Abstract Recently, outstanding identification rates in image classification tasks were achieved by convolutional neural networks (CNNs). to use such skills, selective CNNs trained on a dataset of well-known images of metal surface defects captured with an RGB camera. Defects must be detected early to take timely corrective action due to production concerns. For image classification up till now, a model-based method has been utilized, which indicated the predicted reflection characteristics of surface defects in comparison to flaw-free surfaces. The problem of detecting steel surface defects has grown in importance as a result of the vast range of steel applications in end-product… More >

Wei Sun¹, Zhanhe Du^2,*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1181-1195, 2023, DOI:10.32604/iasc.2023.037497

Abstract Currently, it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal, which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activity. Therefore, a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor (NIoTS) and transfer learning is proposed. First, the NIoTS is installed in the athlete’s leg muscles to collect activity signals. Second, the noise component in the activity signal is removed using the de-noising method based on mathematical morphology. Finally, the depth feature of the activity signal is extracted… More >

Xin Wang^*, Shuang Liu, Chang-Cai Zhou

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 221-234, 2023, DOI:10.32604/iasc.2023.036766

Abstract Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then the data set was divided… More >

Ling Wang, Jingchi Jiang^*, Jingwen Song, Jie Liu

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 833-848, 2023, DOI:10.32604/iasc.2023.036402

Abstract It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text. However, only some labeled data for agricultural knowledge graph domain training are available. Furthermore, labeling is costly due to the need for more data openness and standardization. This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction. Knowledge distillation between the target and source data domain is performed, where Bi-LSTM and CRF models are constructed for entity recognition. The experimental result is shown… More >