MRI Kidney Tumor Image Classification with SMOTE Preprocessing and SIFT-tSNE Features using CNN

Kidney tumor detection is a challenging task due to the complexity of tumor characteristics and variability in imaging modalities. In this paper, we propose a deep learning-based approach for detecting kidney tumors with 98.5% accuracy. Our method addresses the issue of an imbalanced dataset by applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the distribution of images. SMOTE generates synthetic samples of the minority class to increase the number of samples, thus providing a balanced dataset. We utilize a convolutional neural network (CNN) architecture that is trained on this balanced dataset of kidney tumor images. The CNN can learn and extract relevant features from the images, resulting in precise tumor classification. We evaluated our approach on a separate dataset and compared it with state-of-the-art methods. The results demonstrate that our method not only outperforms other methods but also shows robustness in detecting kidney tumors with a high degree of accuracy. By enabling early detection and diagnosis of kidney tumors, our proposed method can potentially improve patient outcomes. Additionally, addressing the imbalance in the dataset using SMOTE demonstrates the usefulness of this technique in improving the performance of deep learning-based image classification systems

Bibliometric Mapping of the Computational Intelligence Field

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation

Particle Swarm Optimizers for Pareto Optimization with Enhanced Archiving Techniques

During the last decades, numerous heuristic search methods for solving multi-objective optimization problems have been developed. Population oriented approaches such as evolutionary algorithms and particle swarm optimization can be distinguished into the class of archive-based algorithms and algorithms without archive. While the latter may lose the best solutions found so far, archive based algorithms keep track of these solutions. In this article a new particle swarm optimization technique, called DOPS, for multiobjective optimization problems is proposed. DOPS integrates well-known archiving techniques from evolutionary algorithms into particle swarm optimization. Modifications and extensions of the archiving techniques are empirically analyzed and several test functions are used to illustrate the usability of the proposed approach. A statistical analysis of the obtained results is presented. The article concludes with a discussion of the obtained results as well as ideas for further research

Analytical Report on Metaheuristic and Non-Metaheuristic Algorithms for Clustering in Wireless Networks

This analytical report delves into the comprehensive evaluation of both metaheuristic and non-metaheuristic algorithms utilized for clustering in wireless networks. Clustering techniques play a pivotal role in enhancing the efficiency and performance of wireless networks by organizing nodes into meaningful groups. Metaheuristic algorithms, inspired by natural processes, offer innovative solutions to complex optimization problems, while non-metaheuristic algorithms rely on traditional mathematical principles. This report systematically compares and contrasts the efficacy of various algorithms, considering key metrics such as convergence speed, scalability, robustness, and adaptability to dynamic network conditions. By scrutinizing both categories of algorithms, this report aims to provide a holistic understanding of their respective advantages, limitations, and applicability in wireless network clustering scenarios. The insights derived from this analysis can guide network engineers, researchers, and practitioners in selecting the most suitable algorithms based on specific network requirements, ultimately contributing to the advancement of wireless network clustering techniques

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed