Jahanzaib Latif ¹, Ahsan Wajahat¹, Alishba Tahir², Anas Bilal^3,*, Mohammed Zakariah⁴, Abeer Alnuaim⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 539-567, 2025, DOI:10.32604/cmes.2025.062301 - 11 April 2025

Abstract Glaucoma, a leading cause of blindness, demands early detection for effective management. While AI-based diagnostic systems are gaining traction, their performance is often limited by challenges such as varying image backgrounds, pixel intensity inconsistencies, and object size variations. To address these limitations, we introduce an innovative, nature-inspired machine learning framework combining feature excitation-based dense segmentation networks (FEDS-Net) and an enhanced gray wolf optimization-supported support vector machine (IGWO-SVM). This dual-stage approach begins with FEDS-Net, which utilizes a fuzzy integral (FI) technique to accurately segment the optic cup (OC) and optic disk (OD) from retinal images, even More >

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 >

Yeonwoo Lee^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2313-2329, 2023, DOI:10.32604/cmc.2023.031133 - 06 February 2023

Abstract In the context of both the Virtual Power Plant (VPP) and microgrid (MG), the Energy Management System (EMS) is a key decision-maker for integrating Distributed renewable Energy Resources (DERs) efficiently. The EMS is regarded as a strong enabler of providing the optimized scheduling control in operation and management of usage of disperse DERs and Renewable Energy reSources (RES) such as a small-size wind-turbine (WT) and photovoltaic (PV) energies. The main objective to be pursued by the EMS is the minimization of the overall operating cost of the MG integrated VPP network. However, the minimization of… More >

Ayman Altameem¹, Sandeep Kumar², Ramesh Chandra Poonia³, Abdul Khader Jilani Saudagar^4,*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4719-4736, 2022, DOI:10.32604/cmc.2022.022177 - 14 January 2022

Abstract Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes More >

Ayesha Maqbool^1,*, Farkhanda Afzal², Tauseef Rana³, Alina Mirza⁴

Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 653-667, 2021, DOI:10.32604/iasc.2021.017506 - 20 April 2021

Abstract This paper presents a co-operative, self-organisation method for Multiple Autonomous Vehicles aiming to share surveillance responsibilities. Spatial organization or formation configuration of multiple vehicles/agents’ systems is crucial for a team of agents to achieve their mission objectives. In this paper we present simple yet efficient thermodynamic inspired formation control framework. The proposed method autonomously allocates region of surveillance to each vehicle and also re-adjusts the area of their responsibilities during the mission. It provides framework for heterogeneous UAVs to scatter themselves optimally in order to provide maximum coverage of a given area. The method is… More >

Julius Beneoluchi Odili^a, Mohd Nizam Mohmad Kahar^a, A. Noraziah^a,b, M. Zarina^c, Riaz Ul Haq^a

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 759-769, 2018, DOI:10.1080/10798587.2017.1334370

Abstract This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm… More >