Israa Ismail^1,*, Ghada Eltaweel¹, Mohamed Meselhy Eltoukhy^1,2

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.043873

Abstract Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs. Super-resolution is of paramount importance in the context of remote sensing, satellite, aerial, security and surveillance imaging. Super-resolution remote sensing imagery is essential for surveillance and security purposes, enabling authorities to monitor remote or sensitive areas with greater clarity. This study introduces a single-image super-resolution approach for remote sensing images, utilizing deep shearlet residual learning in the shearlet transform domain, and incorporating the Enhanced Deep Super-Resolution network (EDSR). Unlike conventional approaches that estimate residuals between high and low-resolution images, the proposed approach… More >

Xiabin Zhang^1,2, Zhongyi Hu^1,2,*, Lei Xiao^1,2, Hui Huang^1,2

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.048725

Abstract Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease (AD). Most studies predominantly employ Convolutional Neural Networks (CNNs), which focus solely on local features, thus encountering difficulties in handling global features. In contrast to natural images, Structural Magnetic Resonance Imaging (sMRI) images exhibit a higher number of channel dimensions. However, during the Position Embedding stage of Multi Head Self Attention (MHSA), the coded information related to the channel dimension is disregarded. To tackle these issues, we propose the RepBoTNet-CESA network, an advanced AD-aided diagnostic model that is capable of learning local and global… More >

Muhammad Ahmad Nawaz Ul Ghani¹, Kun She^1,*, Muhammad Arslan Rauf¹, Shumaila Khan², Javed Ali Khan³, Eman Abdullah Aldakheel⁴, Doaa Sami Khafaga⁴

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.049611

Abstract The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing to solve privacy concerns while… More >

Shu-Yin Chiang^*, Ting-Yu Lin

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.049477

Abstract This research focuses on addressing the challenges associated with image detection in low-light environments, particularly by applying artificial intelligence techniques to machine vision and object recognition systems. The primary goal is to tackle issues related to recognizing objects with low brightness levels. In this study, the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images. The detection scenarios are categorized into normal brightness and low brightness situations. When the system determines a normal brightness environment, normal brightness images are recognized using deep learning methods. In low-brightness situations, three methods are proposed for… More >

Syed Ayaz Ali Shah^1,*, Aamir Shahzad^1,*, Musaed Alhussein², Chuan Meng Goh³, Khursheed Aurangzeb², Tong Boon Tang⁴, Muhammad Awais⁵

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.047597

Abstract Diagnosing various diseases such as glaucoma, age-related macular degeneration, cardiovascular conditions, and diabetic retinopathy involves segmenting retinal blood vessels. The task is particularly challenging when dealing with color fundus images due to issues like non-uniform illumination, low contrast, and variations in vessel appearance, especially in the presence of different pathologies. Furthermore, the speed of the retinal vessel segmentation system is of utmost importance. With the surge of now available big data, the speed of the algorithm becomes increasingly important, carrying almost equivalent weightage to the accuracy of the algorithm. To address these challenges, we present a novel approach for retinal… More > Graphic Abstract

Imad Saud Al Naimi^1,2,*, Syed Alwee Aljunid Syed Junid¹, Muhammad lmran Ahmad^1,*, K. Suresh Manic^2,3

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.046461

Abstract Augmentation of abnormal cells in the brain causes brain tumor (BT), and early screening and treatment will reduce its harshness in patients. BT’s clinical level screening is usually performed with Magnetic Resonance Imaging (MRI) due to its multi-modality nature. The overall aims of the study is to introduce, test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans, facilitating improved accuracy. The research intends to devise a reliable framework for detecting the BT region in the two-dimensional (2D) MRI slice, and identifying its class with improved accuracy. The methodology for the… More >

Wajdi Alhakami^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.043935

Abstract The Kingdom of Saudi Arabia (KSA) has achieved significant milestones in cybersecurity. KSA has maintained solid regulatory mechanisms to prevent, trace, and punish offenders to protect the interests of both individual users and organizations from the online threats of data poaching and pilferage. The widespread usage of Information Technology (IT) and IT Enable Services (ITES) reinforces security measures. The constantly evolving cyber threats are a topic that is generating a lot of discussion. In this league, the present article enlists a broad perspective on how cybercrime is developing in KSA at present and also takes a look at some of… More >

Jiakai Li, Jianpeng Hu^*, Geng Zhang

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.050005

Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first propose an innovative attention interaction… More >

Ahmed Alhussen¹, Arshiya S. Ansari^2,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.049260

Abstract Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data acquired from various sources within… More >

Qiong Yang¹, Li Ma^1,2,*, Shanshan Tu¹, Sami Ullah³, Muhammad Waqas^4,5, Hisham Alasmary⁶

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2024.049970

Abstract Border Gateway Protocol (BGP) is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations. The BGP protocol exhibits security design defects, such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes, easily triggering prefix hijacking, path forgery, route leakage, and other BGP security threats. Meanwhile, the traditional BGP security mechanism, relying on a public key infrastructure, faces issues like a single point of failure and a single point of trust. The decentralization, anti-tampering, and traceability advantages of blockchain offer… More >