Hyun Yoo¹, Seo-El Lee², Kyungyong Chung^3,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1343-1359, 2023, DOI:10.32604/cmc.2023.039263

Abstract Deep learning-based action classification technology has been applied to various fields, such as social safety, medical services, and sports. Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions. There are various related studies on the real-time classification of actions in an image. However, existing deep learning-based action classification models have prolonged response speeds, so there is a limit to real-time analysis. In addition, it has low accuracy of action of each object if multiple objects appear in the image. Also, it needs to be improved since it… More >

Umair Muneer Butt^1,2,*, Rimsha Arif², Sukumar Letchmunan^1,*, Babur Hayat Malik², Muhammad Adil Butt²

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2551-2570, 2023, DOI:10.32604/cmc.2023.039164

Abstract The detection of brain disease is an essential issue in medical and research areas. Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging (MRI) images. These techniques involve training neural networks on large datasets of MRI images, allowing the networks to learn patterns and features indicative of different brain diseases. However, several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques. This paper implements a Feature Enhanced Stacked Auto Encoder (FESAE) model to detect brain diseases. The standard stack auto encoder’s results are… More >

Peng Gong^1,2,3, Wenzhong Yang^2,3,*, Liejun Wang^2,3, Fuyuan Wei^2,3, KeZiErBieKe HaiLaTi^2,3, Yuanyuan Liao^2,3

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1439-1462, 2023, DOI:10.32604/cmc.2023.038878

Abstract Smart contracts have led to more efficient development in finance and healthcare, but vulnerabilities in contracts pose high risks to their future applications. The current vulnerability detection methods for contracts are either based on fixed expert rules, which are inefficient, or rely on simplistic deep learning techniques that do not fully leverage contract semantic information. Therefore, there is ample room for improvement in terms of detection precision. To solve these problems, this paper proposes a vulnerability detector based on deep learning techniques, graph representation, and Transformer, called GRATDet. The method first performs swapping, insertion, and symbolization operations for contract functions,… More >

Mehdi Hassan^1,2, Safdar Ali³, Jin Young Kim^2,*, Muhammad Sanaullah⁴, Hani Alquhayz⁵, Khushbakht Safdar⁶

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1643-1663, 2023, DOI:10.32604/cmc.2023.038578

Abstract Breast cancer resistance protein (BCRP) is an important resistance protein that significantly impacts anticancer drug discovery, treatment, and rehabilitation. Early identification of BCRP substrates is quite a challenging task. This study aims to predict early substrate structure, which can help to optimize anticancer drug development and clinical diagnosis. For this study, a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning (TL) for automatic deep feature (DF) extraction followed by classification with linear discriminant analysis algorithm (TLRNDF-LDA). This study utilized structural fingerprints, which are exploited by DF contrary to conventional molecular descriptors. The proposed in… More >

Ilyоs Abdullaev¹, Natalia Prodanova², K. Aruna Bhaskar³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Jungeun Kim^8,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1463-1477, 2023, DOI:10.32604/cmc.2023.038417

Abstract Recently, computation offloading has become an effective method for overcoming the constraint of a mobile device (MD) using computation-intensive mobile and offloading delay-sensitive application tasks to the remote cloud-based data center. Smart city benefitted from offloading to edge point. Consider a mobile edge computing (MEC) network in multiple regions. They comprise N MDs and many access points, in which every MD has M independent real-time tasks. This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization (TORA-DLSGO) algorithm. The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,… More >

Sani Salisu^1,2, A. S. A. Mohamed^1,*, M. H. Jaafar³, Ainun S. B. Pauzi¹, Hussain A. Younis^1,4

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2385-2400, 2023, DOI:10.32604/cmc.2023.035904

Abstract In this article, a comprehensive survey of deep learning-based (DL-based) human pose estimation (HPE) that can help researchers in the domain of computer vision is presented. HPE is among the fastest-growing research domains of computer vision and is used in solving several problems for human endeavours. After the detailed introduction, three different human body modes followed by the main stages of HPE and two pipelines of two-dimensional (2D) HPE are presented. The details of the four components of HPE are also presented. The keypoints output format of two popular 2D HPE datasets and the most cited DL-based HPE articles from… More >

Farah Mohammad^1,*, Saad Al-Ahmadi², Jalal Al-Muhtadi^1,2

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1423-1438, 2023, DOI:10.32604/cmc.2023.035741

Abstract Social media forums have emerged as the most popular form of communication in the modern technology era, allowing people to discuss and express their opinions. This increases the amount of material being shared on social media sites. There is a wealth of information about the threat that may be found in such open data sources. The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information. Despite the fact that several models for detecting cybersecurity events have been presented, it remains challenging to extract security… More >

So-Yeon Lee¹, Jihoon Park¹, Dae-Young Kim^2,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1497-1514, 2023, DOI:10.32604/cmc.2023.034914

Abstract Recently, to build a smart factory, research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology, a field of artificial intelligence. Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset. However, compared to numerical raw data, learning based on image data has the disadvantage that creating a training dataset is very time-consuming. Therefore, we devised a two-step data preprocessing… More >

Chong Xu¹, Hao Zhu¹, Haotian Zhu², Jirong Wang¹, Qinghai Zhao^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2567-2591, 2023, DOI:10.32604/cmes.2023.029152

Abstract A new and improved RRT^∗ algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm. This algorithm enables the robot to avoid obstacles, find the optimal path, and complete automatic charging docking. It maintains the global completeness and path optimality of the RRT algorithm while also improving the iteration speed and quality of generated paths in both 2D and 3D path planning. After finding the optimal path, the B-sample curve is used to optimize the rough path to create a smoother and more optimal path.… More > Graphic Abstract

B. Ramesh, Kuruva Lakshmanna^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2513-2528, 2023, DOI:10.32604/cmes.2023.028944

Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field. Type 2… More > Graphic Abstract