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Yuvaraj, V.
- Optimal Power Flow Using Evolutionary Programming viz., PSO, CPSO, HDE
Authors
1 V.I.T University, Vellore, Tamil Nadu, IN
2 Power Electronics Department, Anna University, Coimbatore, IN
Source
Programmable Device Circuits and Systems, Vol 1, No 6 (2009), Pagination: 136-148Abstract
This paper presents an approach to obtain the optimal load flow solution using three different intelligent techniques such as Particle Swarm Optimization (PSO), Crazy Particle Swarm Optimization (CPSO) and Hybrid Differential Evolution (HDE) subject to various system constraints. The above optimization techniques have a capability to provide global optimal solution in problem domains where a complete traversion of the whole search space is completely infeasible. The proposed method has been tested on Ward and Hale six bus system IEEE 14 bus test system and IEEE-30 bus test system. The solutions obtained are quite encouraging and useful in solving the optimal load flow problem. The algorithm and simulation are carried using Mat lab software.
Keywords
Particle Swarm Optimization, Crazy Particle Swarm Optimization, Hybrid Differential Evolution, Optimal Load Flow (OPF).- Gene Selection And Modified Long Short Term Memorynetworkbased Lung Cancer Classification Using Gene Expression Data
Authors
1 School of Computer Studies, Rathnavel Subramaniam College of Arts and Science, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 2 (2022), Pagination: 2572-2577Abstract
Lung cancer is one of the fatal forms of cancer worldwide. Genetic variability has been identified as influencing a person vulnerability to lung cancer in epidemiologic research. A new study undertaken by a team of experts from the United States National Cancer Institute on 14,000 Asian women discovered that Asian women, regardless of whether they smoke or not, are more likely to acquire cancer owing to genetic abnormalities. Early detection of this lethal disease is a novel clinical application of microarray data. Recent research establishes a model for the early diagnosis of lung cancer. Additionally, multilayer perceptron, random subspace, and Sequential Minimal Optimization (SMO) approaches are used for classification. While information acquisition is typically a good indicator of an attribute significance, it is not perfect. A noticeable issue develops when knowledge gain is applied to qualities that might take on many distinct values. This paper provides an efficient gene selection model based on the Improved Whale Optimization Algorithm (IWOA) to address these concerns. It saves time and identifies relevant genes from gene expression data, increasing lung cancer categorization accuracy. Then, a Modified Long Short-Term Memory (MLSTM) Network is used to classify lung cancer. It accepts specified genes as inputs and determines which class they belong to, such as lung cancer or normal subjects. As demonstrated by empirical observations, the suggested model is effective in precision, recall, accuracy, and f–measure.Keywords
Lung Cancer, Early Stage, Developing Cancer, Genetic Variations, Feature Selection, Information Gain Attribute, Whale Optimization, Long Short Term MemoryReferences
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