Weighted Teaching-Learning-Based Optimization for Global Function Optimization

Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.

Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review

Hyperspectral image(HSI)classification has been one of themost important tasks in the remote sensing community over the last few decades.Due to the presence of highly correlated bands and limited training samples in HSI,discriminative feature extraction was challenging for traditional machine learning methods.Recently,deep learning based methods have been recognized as powerful feature extraction tool and have drawn a significant amount of attention in HSI classification.Among various deep learning models,convolutional neural networks(CNNs)have shown huge success and offered great potential to yield high performance in HSI classification.Motivated by this successful performance,this paper presents a systematic review of different CNN architectures for HSI classification and provides some future guidelines.To accomplish this,our study has taken a few important steps.First,we have focused on different CNN architectures,which are able to extract spectral,spatial,and joint spectral-spatial features.Then,many publications related to CNN based HSI classifications have been reviewed systematically.Further,a detailed comparative performance analysis has been presented between four CNN models namely 1D CNN,2D CNN,3D CNN,and feature fusion based CNN(FFCNN).Four benchmark HSI datasets have been used in our experiment for evaluating the performance.Finally,we concluded the paper with challenges on CNN based HSI classification and future guidelines that may help the researchers to work on HSI classification using CNN.

Software Cost Estimation Using Social Group Optimization

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 models,offering valuable insights for project managers and practitioners in the field.However,the approach’s effectiveness may vary depending on the quality and quantity of available historical data,and its scalability across diverse project types and sizes remains a key consideration for future research.