N. Rajagopal Reddy, S. Sharief Basha^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 579-596, 2023, DOI:10.32604/csse.2023.034527

Abstract The hesitancy fuzzy graphs (HFGs), an extension of fuzzy graphs, are useful tools for dealing with ambiguity and uncertainty in issues involving decision-making (DM). This research implements a correlation coefficient measure (CCM) to assess the strength of the association between HFGs in this article since CCMs have a high capacity to process and interpret data. The CCM that is proposed between the HFGs has better qualities than the existing ones. It lowers restrictions on the hesitant fuzzy elements’ length and may be used to establish whether the HFGs are connected negatively or favorably. Additionally, a CCM-based attribute DM approach is… More >

Naqqash Dilshad¹, Taimoor Khan², JaeSeung Song^1,*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 749-764, 2023, DOI:10.32604/csse.2023.034475

Abstract To prevent economic, social, and ecological damage, fire detection and management at an early stage are significant yet challenging. Although computationally complex networks have been developed, attention has been largely focused on improving accuracy, rather than focusing on real-time fire detection. Hence, in this study, the authors present an efficient fire detection framework termed E-FireNet for real-time detection in a complex surveillance environment. The proposed model architecture is inspired by the VGG16 network, with significant modifications including the entire removal of Block-5 and tweaking of the convolutional layers of Block-4. This results in higher performance with a reduced number of… More >

Mohamed Amine Ouamri^1,2, Reem Alkanhel^3,*, Daljeet Singh⁴, El-sayed M. El-kenaway⁵, Sherif S. M. Ghoneim⁶

Computer Systems Science and Engineering, Vol.46, No.1, pp. 73-92, 2023, DOI:10.32604/csse.2023.034461

Abstract Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand. To meet these access conditions and improve Quality of Service, resource allocation (RA) should be carefully optimized. Traditionally, RA problems are nonconvex optimizations, which are performed using heuristic methods, such as genetic algorithm, particle swarm optimization, and simulated annealing. However, the application of these approaches remains computationally expensive and unattractive for dense cellular networks. Therefore,… More >

Abdullah M. Basahel, Mohammad Yamin^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 209-224, 2023, DOI:10.32604/csse.2023.034449

Abstract Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution… More >

Imran Ali¹, Zohaib Mushtaq², Saad Arif³, Abeer D. Algarni^4,*, Naglaa F. Soliman⁴, Walid El-Shafai^5,6

Computer Systems Science and Engineering, Vol.46, No.1, pp. 303-319, 2023, DOI:10.32604/csse.2023.034374

Abstract Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on agricultural land with a spectral… More >

Deshun Li¹, Shaorong Sun², Qisen Wu², Shuhua Weng¹, Yuyin Tan², Jiangyuan Yao^1,*, Xiangdang Huang¹, Xingcan Cao³

Computer Systems Science and Engineering, Vol.46, No.1, pp. 433-443, 2023, DOI:10.32604/csse.2023.034368

Abstract As a critical infrastructure of cloud computing, data center networks (DCNs) directly determine the service performance of data centers, which provide computing services for various applications such as big data processing and artificial intelligence. However, current architectures of data center networks suffer from a long routing path and a low fault tolerance between source and destination servers, which is hard to satisfy the requirements of high-performance data center networks. Based on dual-port servers and Clos network structure, this paper proposed a novel architecture to construct high-performance data center networks. Logically, the proposed architecture is constructed by inserting a dual-port server… More >

Ahmed M. Elshewey¹, Mahmoud Y. Shams^2,*, Zahraa Tarek³, Mohamed Megahed⁴, El-Sayed M. El-kenawy⁵, Mohamed A. El-dosuky^3,6

Computer Systems Science and Engineering, Vol.46, No.1, pp. 765-781, 2023, DOI:10.32604/csse.2023.034324

Abstract Food choice motives (i.e., mood, health, natural content, convenience, sensory appeal, price, familiarities, ethical concerns, and weight control) have an important role in transforming the current food system to ensure the healthiness of people and the sustainability of the world. Researchers from several domains have presented several models addressing issues influencing food choice over the years. However, a multidisciplinary approach is required to better understand how various aspects interact with one another during the decision-making procedure. In this paper, four Deep Learning (DL) models and one Machine Learning (ML) model are utilized to predict the weight in pounds based on… More >

Maha Sabir¹, Jawad Ahmad^2,*, Daniyal Alghazzawi¹

Computer Systems Science and Engineering, Vol.46, No.1, pp. 57-72, 2023, DOI:10.32604/csse.2023.034277

Abstract The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based cyberattack detection framework. The proposed… More >

S. Prince Chelladurai^1,*, T. Rajagopalan²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 951-960, 2023, DOI:10.32604/csse.2023.034262

Abstract Cloud computing offers numerous web-based services. The adoption of many Cloud applications has been hindered by concerns about data security and privacy. Cloud service providers’ access to private information raises more security issues. In addition, Cloud computing is incompatible with several industries, including finance and government. Public-key cryptography is frequently cited as a significant advancement in cryptography. In contrast, the Digital Envelope that will be used combines symmetric and asymmetric methods to secure sensitive data. This study aims to design a Digital Envelope for distributed Cloud-based large data security using public-key cryptography. Through strategic design, the hybrid Envelope model adequately… More >

N. Vasudevan^*, T. Karthick

Computer Systems Science and Engineering, Vol.46, No.1, pp. 337-356, 2023, DOI:10.32604/csse.2023.034242

Abstract Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation, and a capsule neural network… More >