Zhe Liu^1,2,3,*, Sijia Zhu⁴, Yulong Huang^1,*, Tapan Senapati^5,6,7, Xiangyu Li⁸, Wulfran Fendzi Mbasso⁹, Himanshu Dhumras¹⁰, Mehdi Hosseinzadeh^11,12,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2157-2188, 2025, DOI:10.32604/cmes.2025.072352 - 26 November 2025

Abstract Uncertainty and ambiguity are pervasive in real-world intelligent systems, necessitating advanced mathematical frameworks for effective modeling and analysis. Fermatean fuzzy sets (FFSs), as a recent extension of classical fuzzy theory, provide enhanced flexibility for representing complex uncertainty. In this paper, we propose a unified parametric divergence operator for FFSs, which comprehensively captures the interplay among membership, non-membership, and hesitation degrees. The proposed operator is rigorously analyzed with respect to key mathematical properties, including non-negativity, non-degeneracy, and symmetry. Notably, several well-known divergence operators, such as Jensen-Shannon divergence, Hellinger distance, and χ²-divergence, are shown to be special cases More >

Asmat Ullah¹, Yar Muhammad^1,*, Bakht Zada¹, Korhan Cengiz², Nikola Ivković^3,*, Mario Konecki³, Abid Yahya⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3767-3786, 2025, DOI:10.32604/cmc.2025.067145 - 23 September 2025

Abstract Low visibility conditions, particularly those caused by fog, significantly affect road safety and reduce drivers’ ability to see ahead clearly. The conventional approaches used to address this problem primarily rely on instrument-based and fixed-threshold-based theoretical frameworks, which face challenges in adaptability and demonstrate lower performance under varying environmental conditions. To overcome these challenges, we propose a real-time visibility estimation model that leverages roadside CCTV cameras to monitor and identify visibility levels under different weather conditions. The proposed method begins by identifying specific regions of interest (ROI) in the CCTV images and focuses on extracting specific… More >

Zhongshang Chen, Ji Feng^*, Fapeng Cai, Degang Yang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2031-2048, 2024, DOI:10.32604/cmc.2024.052114 - 15 August 2024

Abstract In clustering algorithms, the selection of neighbors significantly affects the quality of the final clustering results. While various neighbor relationships exist, such as K-nearest neighbors, natural neighbors, and shared neighbors, most neighbor relationships can only handle single structural relationships, and the identification accuracy is low for datasets with multiple structures. In life, people’s first instinct for complex things is to divide them into multiple parts to complete. Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures. Taking inspiration from this, we propose a novel neighbor method: Shared Natural Neighbors (SNaN). More >

Reem Alkanhel¹, Kalaiselvi Chinnathambi², C. Thilagavathi³, Mohamed Abouhawwash^4,5, Mona A. Al duailij⁶, Manal Abdullah Alohali⁷, Doaa Sami Khafaga^6,*

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1571-1583, 2023, DOI:10.32604/iasc.2023.033430 - 05 January 2023

Abstract Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are… More >

G. Divya Mohana Priya^1,*, M. Karthikeyan¹, K. Murugan²

Computer Systems Science and Engineering, Vol.43, No.2, pp. 471-486, 2022, DOI:10.32604/csse.2022.021023 - 20 April 2022

Abstract Optimizing the sensor energy is one of the most important concern in Three-Dimensional (3D) Wireless Sensor Networks (WSNs). An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus, the total consumption of energy is optimal. However, the computational complexity will be increased due to data dimension, and this leads to increase in delay in network data transmission and reception. For solving the above-mentioned issues, an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis (ILDA) is proposed for 3D hierarchical clustering WSNs. The major objective of… More >

A. K. M. Golam Sarwar¹, Md. Sabibul Haque^1,*, Md. Ekramul Haque², Md. Amir Hossain³, Md. Golam Azam⁴, Md. Nesar Uddin¹, Eldessoky S. Dessoky⁵, Mahmoud A. Basry⁶, Md. Alamgir Hossain¹

Phyton-International Journal of Experimental Botany, Vol.91, No.8, pp. 1721-1743, 2022, DOI:10.32604/phyton.2022.021069 - 14 April 2022

Abstract Linseed is a multipurpose crop and the crop needs further improvement to increase production and yield due to its high value and demand. This study aimed to assess the extent and pattern of genetic variability of forty linseed genotypes based on diverse agro–morphological and yield attributes. The field experiment was conducted following a Randomized Complete Block Design with three replications. Linseed germplasm showed a wide range of phenotypic expression, genetic variability and heritability for 30 studied traits. A low to high phenotypic coeffi- cient of variation (PCV) and genotypic coefficient of variation (GCV) were observed.… More >

Hatim Elhassan¹, Mohammed Abaker¹, Abdelzahir Abdelmaboud², Mohammed Burhanur Rehman^1,*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 259-273, 2022, DOI:10.32604/iasc.2022.023750 - 05 January 2022

Abstract The research community has well recognized the importance of requirement elicitation. Recent research has shown the continuous decreasing success rate of IS projects in the last five years due to the complexity of the requirement conflict refinement process. Requirement conflict is at the heart of requirement elicitation. It is also considered the prime reason for deciding the success or failure of the intended Information System (IS) project. This paper introduces the requirements conflict detection automation model based on the Mean shift clustering unsupervised machine learning model. It utilizes the advantages of Artificial Intelligence in detecting… More >

Ahmed F. Ibrahim¹, M. Hassaballah², Abdelmgeid A. Ali³, Yunyoung Nam^4,*, Ibrahim A. Ibrahim³

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2507-2524, 2022, DOI:10.32604/cmc.2022.018131 - 27 September 2021

Abstract Social networking sites in the most modernized world are flooded with large data volumes. Extracting the sentiment polarity of important aspects is necessary; as it helps to determine people’s opinions through what they write. The Coronavirus pandemic has invaded the world and been given a mention in the social media on a large scale. In a very short period of time, tweets indicate unpredicted increase of coronavirus. They reflect people’s opinions and thoughts with regard to coronavirus and its impact on society. The research community has been interested in discovering the hidden relationships from short… More >

M. Arun Manicka Raja^1,*, S. Swamynathan²

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1263-1280, 2022, DOI:10.32604/cmc.2022.019451 - 07 September 2021

Abstract News feed is one of the potential information providing sources which give updates on various topics of different domains. These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources. In this paper, the news summarization system is proposed for the news data streams from RSS feeds and Google news. Since news stream analysis requires live content, the news data are continuously collected for our experimentation. The major contributions of this work involve domain corpus based… More >

A. Sumathi^1,*, S. Meganathan¹, B. Vijila Ravisankar²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612 - 24 August 2021

Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the… More >