Dan Xu^*, Jiale Ru, Jinlong Shi

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.045258

Abstract Video salient object detection (VSOD) aims at locating the most attractive objects in a video by exploring the spatial and temporal features. VSOD poses a challenging task in computer vision, as it involves processing complex spatial data that is also influenced by temporal dynamics. Despite the progress made in existing VSOD models, they still struggle in scenes of great background diversity within and between frames. Additionally, they encounter difficulties related to accumulated noise and high time consumption during the extraction of temporal features over a long-term duration. We propose a multi-stream temporal enhanced network (MSTENet) to address these problems. It… More >

Tayyaba Farhat^1,2, Sheeraz Akram^3,*, Hatoon S. AlSagri³, Zulfiqar Ali⁴, Awais Ahmad³, Arfan Jaffar^1,2

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.045022

Abstract Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by significant challenges in social interaction, communication, and repetitive behaviors. Timely and precise ASD detection is crucial, particularly in regions with limited diagnostic resources like Pakistan. This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context. The research involves experimentation with VGG16 and MobileNet models, exploring different batch sizes, optimizers, and learning rate schedulers. In addition, the “Orange” machine learning tool is employed to evaluate classifier performance and automated… More >

Mohammad Z. Masoud¹, Yousef Jaradat¹, Ahmad Manasrah², Mohammad Alia³, Khaled Suwais^4,*, Sally Almanasra⁴

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.044504

Abstract This work carried out a measurement study of the Ethereum Peer-to-Peer (P2P) network to gain a better understanding of the underlying nodes. Ethereum was applied because it pioneered distributed applications, smart contracts, and Web3. Moreover, its application layer language “Solidity” is widely used in smart contracts across different public and private blockchains. To this end, we wrote a new Ethereum client based on Geth to collect Ethereum node information. Moreover, various web scrapers have been written to collect nodes’ historical data from the Internet Archive and the Wayback Machine project. The collected data has been compared with two other services… More >

Hosam Alhakami^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.043439

Abstract The rapid growth of the Internet of Things (IoT) operations has necessitated the incorporation of quantum computing technologies to meet its expanding needs. This integration is motivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario. The combination of IoT and quantum computing creates new privacy and security problems. This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications. We investigate the incorporation of quantum computing approaches within IoT security frameworks, with a focus on developing… More >

Abdulwahab Alazeb^*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.043173

Abstract Data protection in databases is critical for any organization, as unauthorized access or manipulation can have severe negative consequences. Intrusion detection systems are essential for keeping databases secure. Advancements in technology will lead to significant changes in the medical field, improving healthcare services through real-time information sharing. However, reliability and consistency still need to be solved. Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption. Disruptions to data items can propagate throughout the database, making it crucial to reverse fraudulent transactions without delay, especially in the healthcare industry, where real-time… More >

Riza Altunay^1,2,*, Omer Faruk Bay³

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.042340

Abstract The use of fog computing in the Internet of Things (IoT) has emerged as a crucial solution, bringing cloud services closer to end users to process large amounts of data generated within the system. Despite its advantages, the increasing task demands from IoT objects often overload fog devices with limited resources, resulting in system delays, high network usage, and increased energy consumption. One of the major challenges in fog computing for IoT applications is the efficient deployment of services between fog clouds. To address this challenge, we propose a novel Optimal Foraging Algorithm (OFA) for task placement on appropriate fog… More >

Ronglei Hu¹, Chuce He¹, Yaping Chi², Xiaoyi Duan¹, Xiaohong Fan¹, Ping Xu¹, Wenbin Gao^1,*

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.042000

Abstract In the education archive sharing system, when performing homomorphic ciphertext retrieval on the storage server, there are problems such as low security of shared data, confusing parameter management, and weak access control. This paper proposes an Education Archives Sharing and Access Control (EduASAC) system to solve these problems. The system research goal is to realize the sharing of security parameters, the execution of access control, and the recording of system behaviors based on the blockchain network, ensuring the legitimacy of shared membership and the security of education archives. At the same time, the system can be combined with most homomorphic… More >

Ananthakrishnan Balasundaram^1,*, Anshuman Mohanty², Ayesha Shaik¹, Krishnadoss Pradeep², Kedalu Poornachary Vijayakumar², Muthu Subash Kavitha³

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.044374

Abstract Automated object detection has received the most attention over the years. Use cases ranging from autonomous driving applications to military surveillance systems, require robust detection of objects in different illumination conditions. State-of-the-art object detectors tend to fare well in object detection during daytime conditions. However, their performance is severely hampered in night light conditions due to poor illumination. To address this challenge, the manuscript proposes an improved YOLOv5-based object detection framework for effective detection in unevenly illuminated nighttime conditions. Firstly, the preprocessing strategies involve using the ZeroDCE++ approach to enhance lowlight images. It is followed by optimizing the existing YOLOv5… More >

Muhammad Suleman¹, Faizan Ullah¹, Ghadah Aldehim^2,*, Dilawar Shah¹, Mohammad Abrar^1,3, Asma Irshad⁴, Sarra Ayouni²

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.042365

Abstract The early detection of skin cancer, particularly melanoma, presents a substantial risk to human health. This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques. Nevertheless, the existing methods exhibit certain constraints in terms of accessibility, diagnostic precision, data availability, and scalability. To address these obstacles, we put out a lightweight model known as Smart MobiNet, which is derived from MobileNet and incorporates additional distinctive attributes. The model utilizes a multi-scale feature extraction methodology by using various convolutional layers. The ISIC 2019 dataset, sourced from the International Skin Imaging Collaboration,… More >

Bauyrzhan Omarov^1,*, Zhuldyz Sailaukyzy², Alfiya Bigaliyeva², Adilzhan Kereyev³, Lyazat Naizabayeva⁴, Aigul Dautbayeva⁵

CMC-Computers, Materials & Continua, Vol., , DOI:10.32604/cmc.2023.042469

Abstract In the face of escalating intricacy and heterogeneity within Internet of Things (IoT) network landscapes, the imperative for adept intrusion detection techniques has never been more pressing. This paper delineates a pioneering deep learning-based intrusion detection model: the One Dimensional Convolutional Neural Networks (1D-CNN) and Bidirectional Long Short-Term Memory (BiLSTM) Network (Conv-BiLSTM) augmented with an Attention Mechanism. The primary objective of this research is to engineer a sophisticated model proficient in discerning the nuanced patterns and temporal dependencies quintessential to IoT network traffic data, thereby facilitating the precise categorization of a myriad of intrusion types. Methodology: The proposed model amalgamates… More >