Ishaani Priyadarshini^*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072343 - 12 January 2026

Abstract Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes. This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) architecture, optimized using the Firefly Optimization algorithm (FO). The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data, capturing both local patterns and sequential dependencies in diagnostic features, while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance. The approach is evaluated on two benchmark biomedical datasets: one comprising diagnostic data… More >

Adil Yousif^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2869-2892, 2025, DOI:10.32604/cmes.2025.059786 - 03 March 2025

Abstract The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive Firefly Algorithm (AFA) for… More >

Arkan Kh Shakr Sabonchi^*

Journal of Information Hiding and Privacy Protection, Vol.6, pp. 21-45, 2024, DOI:10.32604/jihpp.2024.058954 - 31 December 2024

Abstract Substitution boxes (S-boxes) are key components of symmetrical cryptosystems, acting as nonlinear substitution functions that hide the relationship between the encrypted text and input key. This confusion mechanism is vital for cryptographic security because it prevents attackers from intercepting the secret key by analyzing the encrypted text. Therefore, the S-box design is essential for the robustness of cryptographic systems, especially for the data encryption standard (DES) and advanced encryption standard (AES). This study focuses on the application of the firefly algorithm (FA) and metaheuristic lion optimization algorithm (LOA), thereby proposing a hybrid approach called the… More >

Arkan Kh Shakr Sabonchi^*

Journal on Artificial Intelligence, Vol.6, pp. 261-282, 2024, DOI:10.32604/jai.2024.056552 - 18 October 2024

Abstract The phenomenal increase in device connectivity is making the signaling and resource-based operational integrity of networks at the node level increasingly prone to distributed denial of service (DDoS) attacks. The current growth rate in the number of Internet of Things (IoT) attacks executed at the time of exchanging data over the Internet represents massive security hazards to IoT devices. In this regard, the present study proposes a new hybrid optimization technique that combines the firefly optimization algorithm with global searches for use in attack detection on IoT devices. We preprocessed two datasets, CICIDS and UNSW-NB15,… More >

Pirapat Arunyanart, Nithitorn Kongkaew, Supattarachai Sudsawat^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3379-3403, 2024, DOI:10.32604/cmc.2024.054337 - 15 August 2024

Abstract This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method (DEM) simulation, design of experiments (DOE), and metaheuristic optimization algorithms. Specifically, the study employs the firefly algorithm (FA), a metaheuristic optimization technique, to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions. The experimental methodology involves several key steps: screening experiments to identify significant factors affecting bucket elevator operation, central composite design (CCD) experiments to further explore these factors, and response surface methodology (RSM)… More >

Punam Gulande^*, R. N. Awale

Computer Systems Science and Engineering, Vol.48, No.4, pp. 937-952, 2024, DOI:10.32604/csse.2024.046123 - 17 July 2024

Abstract The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm More >

Shazia Shamas¹, Surya Narayan Panda^1,*, Ishu Sharma^1,*, Kalpna Guleria¹, Aman Singh^2,3,4, Ahmad Ali AlZubi⁵, Mallak Ahmad AlZubi⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712 - 17 November 2023

Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired… More >

Nitin Mittal¹, Rohit Salgotra^2,3, Abhishek Sharma⁴, Sandeep Kaur⁵, S. S. Askar⁶, Mohamed Abouhawwash^7,8,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3159-3177, 2023, DOI:10.32604/iasc.2023.041059 - 11 September 2023

Abstract The optimization of cognitive radio (CR) system using an enhanced firefly algorithm (EFA) is presented in this work. The Firefly algorithm (FA) is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies. It has already proved its competence in various optimization problems, but it suffers from slow convergence issues. To improve the convergence performance of FA, a new variant named EFA is proposed. The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions, and simulation results show its superior performance compared to biogeography-based optimization (BBO), bat algorithm, artificial bee More >

Anu^*, Anita Singhrova

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 199-219, 2023, DOI:10.32604/iasc.2023.035389 - 29 April 2023

Abstract Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things (IoT) applications in the proximity of the network. Fog computing offers computational and storage services between cloud and terminal devices. However, an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services. A large number of heterogeneous shareable resources makes fog computing a complex environment. In the sight of these issues, this paper has proposed an efficient levy flight firefly-based resource… More >

I. Mettildha Mary^1,*, K. Karuppasamy²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2667-2685, 2023, DOI:10.32604/csse.2023.031114 - 03 April 2023

Abstract CC’s (Cloud Computing) networks are distributed and dynamic as signals appear/disappear or lose significance. MLTs (Machine learning Techniques) train datasets which sometime are inadequate in terms of sample for inferring information. A dynamic strategy, DevMLOps (Development Machine Learning Operations) used in automatic selections and tunings of MLTs result in significant performance differences. But, the scheme has many disadvantages including continuity in training, more samples and training time in feature selections and increased classification execution times. RFEs (Recursive Feature Eliminations) are computationally very expensive in its operations as it traverses through each feature without considering correlations More >