Adelya Khayrova^*, Sergey Lopatin, Valery Varlamov

Journal of Renewable Materials, Vol.12, No.5, pp. 923-950, 2024, DOI:10.32604/jrm.2024.050033

Abstract Due to the expected rise in the world population, an increase in the requirements for quality and safety of food and feed is expected, which leads to the growing demand for new sources of sustainable and renewable protein. Insect protein is gaining importance as a renewable material for several reasons, reflecting its potential contributions to sustainability, resource efficiency, and environmental conservation. Some insect species are known to be able to efficiently convert organic waste into high-value products such as protein, requiring less land and water compared to traditional livestock. In addition, insect farming produces fewer… More > Graphic Abstract

Romaissa Kebache¹, Abdelkader Laouid^1,*, Ahcene Bounceur², Mostefa Kara^1,3, Konstantinos Karampidis⁴, Giorgos Papadourakis⁴, Mohammad Hammoudeh²

Computer Systems Science and Engineering, Vol.48, No.4, pp. 1055-1072, 2024, DOI:10.32604/csse.2024.048738

Abstract Internet cloud services come at a price, especially when they provide top-tier security measures. The cost incurred by cloud utilization is directly proportional to the storage requirements. Companies are always looking to increase profits and reduce costs while preserving the security of their data by encrypting them. One of the offered solutions is to find an efficient encryption method that can store data in a much smaller space than traditional encryption techniques. This article introduces a novel encryption approach centered on consolidating information into a single ciphertext by implementing Multi-Key Embedded Encryption (MKEE). The effectiveness… More >

Kaveh Heidary^*

Journal on Artificial Intelligence, Vol.6, pp. 153-175, 2024, DOI:10.32604/jai.2024.050563

Abstract This paper provides a detailed mathematical model governing the operation of feedforward neural networks (FFNN) and derives the backpropagation formulation utilized in the training process. Network protection systems must ensure secure access to the Internet, reliability of network services, consistency of applications, safeguarding of stored information, and data integrity while in transit across networks. The paper reports on the application of neural networks (NN) and deep learning (DL) analytics to the detection of network traffic anomalies, including network intrusions, and the timely prevention and mitigation of cyberattacks. Among the most prevalent cyber threats are R2L,… More >

Li-Woei Chen¹, Kun-Lin Tsai^2,*, Fang-Yie Leu³, Wen-Cheng Jiang², Shih-Ting Tseng²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2779-2794, 2024, DOI:10.32604/cmes.2024.052124

Abstract Various mobile devices and applications are now used in daily life. These devices require high-speed data processing, low energy consumption, low communication latency, and secure data transmission, especially in 5G and 6G mobile networks. High-security cryptography guarantees that essential data can be transmitted securely; however, it increases energy consumption and reduces data processing speed. Therefore, this study proposes a low-energy data encryption (LEDE) algorithm based on the Advanced Encryption Standard (AES) for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things (IoT) devices. In the proposed LEDE algorithm, the system time More >

Rui Wang¹, Zhihui Liu^2,*, Hongdi Liu³, Baozhong Su⁴, Chuanyi Ma⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3035-3049, 2024, DOI:10.32604/cmes.2024.050806

Abstract In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model’s receptive field is enhanced by introducing a multi-branch More >

Refah Alotaibi¹, Hoda Rezk², Ahmed Elshahhat^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3073-3103, 2024, DOI:10.32604/cmes.2024.050606

Abstract A new one-parameter Chris-Jerry distribution, created by mixing exponential and gamma distributions, is discussed in this article in the presence of incomplete lifetime data. We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry (CJ) distribution. When the indicated censored data is present, Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices, including the hazard rate and reliability functions. We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity. More >

Deepak Upreti¹, Eunmok Yang², Hyunil Kim^3,*, Changho Seo^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2239-2274, 2024, DOI:10.32604/cmes.2024.048932

Abstract Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise More >

Mojtaba Yari^1,*, Manoj Khandelwal², Payam Abbasi³, Evangelos I. Koutras⁴, Danial Jahed Armaghani^5,*, Panagiotis G. Asteris⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2207-2238, 2024, DOI:10.32604/cmes.2024.048071

Abstract Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to More >

Zhengyang Zhang^#, Weixue Liu^#, Yinran Huang^*, Ping Li^*

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1263-1274, 2024, DOI:10.32604/phyton.2024.050755

Abstract Plant antifreeze proteins (AFPs) are special proteins that can protect plant cells from ice crystal damage in low-temperature environments, and they play a crucial role in the process of plants adapting to cold environments. Proteins with these characteristics have been found in fish living in cold regions, as well as many plants and insects. Although research on plant AFPs started relatively late, their application prospects are broad, leading to the attention of many researchers to the isolation, cloning, and genetic improvement of plant AFP genes. Studies have found that the distribution of AFPs in different species… More >

Ibraheem Abu Falahah¹, Osama Al-Baik², Saleh Alomari³, Gulnara Bektemyssova⁴, Saikat Gochhait^5,6, Irina Leonova⁷, Om Parkash Malik⁸, Frank Werner^9,*, Mohammad Dehghani¹⁰

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3631-3678, 2024, DOI:10.32604/cmc.2024.053189

Abstract This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the unique hunting behavior of frilled lizards in their natural habitat. FLO draws its inspiration from the sit-and-wait hunting strategy of these lizards. The algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases: (i) an exploration phase, which mimics the lizard’s sudden attack on its prey, and (ii) an exploitation phase, which simulates the lizard’s retreat to the treetops after feeding. To assess FLO’s efficacy in addressing optimization problems, its performance is rigorously tested on fifty-two… More >