Sagiraju Srinadhraju^*, Samaresh Mishra, Suresh Chandra Satapathy

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1641-1668, 2024, DOI:10.32604/csse.2024.055612 - 22 November 2024

Abstract This paper introduces the integration of the Social Group Optimization (SGO) algorithm to enhance the accuracy of software cost estimation using the Constructive Cost Model (COCOMO). COCOMO’s fixed coefficients often limit its adaptability, as they don’t account for variations across organizations. By fine-tuning these parameters with SGO, we aim to improve estimation accuracy. We train and validate our SGO-enhanced model using historical project data, evaluating its performance with metrics like the mean magnitude of relative error (MMRE) and Manhattan distance (MD). Experimental results show that SGO optimization significantly improves the predictive accuracy of software cost More >

Sameema Tariq¹, Ata-Ur- Rehman^2,3, Maria Abubakar², Waseem Iqbal⁴, Hatoon S. Alsagri⁵, Yousef A. Alduraywish⁵, Haya Abdullah A. Alhakbani^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2493-2515, 2024, DOI:10.32604/cmc.2024.055787 - 18 November 2024

Abstract In video surveillance, anomaly detection requires training machine learning models on spatio-temporal video sequences. However, sometimes the video-only data is not sufficient to accurately detect all the abnormal activities. Therefore, we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video data. This paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual data. The proposed model is trained to produce low reconstruction error… More >

Priyanka¹, Damodarakurup Sajeev², Shaik Ahmed³, Shankar Pidishety³, Ram Soorat^3,*

Journal of Quantum Computing, Vol.6, pp. 53-66, 2024, DOI:10.32604/jqc.2024.054000 - 14 November 2024

Abstract The present study delves into the application of investigating quantum state behaviour, particularly focusing on coherent and superposition states. These states, characterized by their remarkable stability and precision, have found extensive utility in various domains of quantum mechanics and quantum information processing. Coherent states are valuable for manipulating quantum systems with accuracy. Superposition states allow quantum systems to exist in numerous configurations at the same time, which paves the way for quantum computing’s capacity for parallel processing. The research accentuates the crucial role of quantum error correction (QEC) in ensuring the stability and reliability of… More >

Jing Gao^*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158 - 20 June 2024

Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

Mahatir Ahmed Tusher¹, Saket Choudary Kongara¹, Sagar Dhanraj Pande², SeongKi Kim^3,*, Salil Bharany^4,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4063-4080, 2024, DOI:10.32604/cmc.2024.049007 - 20 June 2024

Abstract The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting. The majority of currently available methods use either a generative adversarial network (GAN) or a recurrent neural network (RNN) to generate new handwriting styles. This is why these techniques frequently fall short of producing diverse and realistic text pictures, particularly for terms that are not commonly used. To resolve that, this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles. This network excels in generating conditional… More >

Haolun Zhang¹, Mengna Yang¹, Jie Wei², Yufeng Nie^2,*

Digital Engineering and Digital Twin, Vol.2, pp. 49-77, 2024, DOI:10.32604/dedt.2023.044180 - 31 January 2024

Abstract This paper mainly considers the formulation and theoretical analysis of the reduced-order numerical method constructed by proper orthogonal decomposition (POD) for nonlocal diffusion problems with a finite range of nonlocal interactions. We first set up the classical finite element discretization for nonlocal diffusion equations and briefly explain the difference between nonlocal and partial differential equations (PDEs). Nonlocal models have to handle double integrals when using finite element methods (FEMs), which causes the generation of algebraic systems to be more challenging and time-consuming, and discrete systems have less sparsity than those for PDEs. So we establish… More >

Zeshan Faiz¹, Iftikhar Ahmed¹, Dumitru Baleanu^2,3,4, Shumaila Javeed^1,5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1217-1238, 2024, DOI:10.32604/cmes.2023.029879 - 29 January 2024

Abstract The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model (FDTM) in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network (LM-NN) technique. The fractional dengue transmission model (FDTM) consists of 12 compartments. The human population is divided into four compartments; susceptible humans (S_h), exposed humans (E_h), infectious humans (I_h), and recovered humans (R_h). Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments: aquatic (eggs, larvae, pupae), susceptible, exposed, and infectious. We investigated three different cases of vertical transmission probability (η), namely when Wolbachia-free mosquitoes persist only… More >

Hong Qian^1,2, Haixin Wang^1,*, Guangji Wang³, Qingyun Yan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 679-698, 2024, DOI:10.32604/cmes.2023.043377 - 30 December 2023

Abstract This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period. The aim is to address the problems of scaling and corrosion, which lead to increased loss of cold resources. The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction. This methodology offers a precise means of determining the optimal timing for condenser cleaning, with the ultimate goal of improving its overall performance. The proposed approach involves the integration of the… More >

Sheetal Sharma^1,2, Kamali Gupta¹, Deepali Gupta¹, Shalli Rani^1,*, Gaurav Dhiman^3,4,5,6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2029-2059, 2024, DOI:10.32604/cmes.2023.029997 - 15 December 2023

Abstract The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments. To address… More > Graphic Abstract

Mohammed Abdalsalam^1,*, Chunlin Li¹, Abdelghani Dahou², Natalia Kryvinska³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1427-1467, 2024, DOI:10.32604/cmes.2023.029911 - 17 November 2023

Abstract One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included… More >