GGA-MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron.

The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal value needs to be computed in order to improve the classification accuracy of an ANN. Two of the major parameters that need to be determined during the design of an ANN are weights and biases. Various gradient-based optimization algorithms have been proposed by researchers in the past to generate an optimal set of weights and biases. However, due to the tendency of gradient-based algorithms to get trapped in local minima, researchers have started exploring metaheuristic algorithms as an alternative to the conventional techniques. In this paper, we propose the GGA-MLP (Greedy Genetic Algorithm-Multilayer Perceptron) approach, a learning algorithm, to generate an optimal set of weights and biases in multilayer perceptron (MLP) using a greedy genetic algorithm. The proposed approach increases the performance of the traditional genetic algorithm (GA) by using a greedy approach to generate the initial population as well as to perform crossover and mutation. To evaluate the performance of GGA-MLP in classifying nonlinear input patterns, we perform experiments on datasets of varying complexities taken from the University of California, Irvine (UCI) repository. The experimental results of GGA-MLP are compared with the existing state-of-the-art techniques in terms of classification accuracy. The results show that the performance of GGA-MLP is better than or comparable to the existing state-of-the-art techniques.

Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic optimization algorithm.

Osteosarcoma is one of the most common malignant bone tumors mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and it is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify medical images, which reduces the dependency on experts and leads to faster processing. In this paper, an automatic detection system: Integrated Features-Feature Selection Model for Classification (IF-FSM-C) to detect osteosarcoma from the high-resolution whole slide images (WSIs) is proposed. The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted (HC) feature extraction techniques and deep learning models (DLMs) namely EfficientNet-B0 and Xception. To further improve the performance of the proposed system, feature selection (FS) is performed. Here, two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V are proposed to perform FS. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed to compare the performance of proposed IF-FSM-C to the classifiers which use HC or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection. The best overall accuracy of 96.08% is obtained when integrated features extracted using HC techniques and Xception are used. The overall accuracy is enhanced to 99.54% after applying BAOA-S for FS. Further, the application of BAOA-S for FS reduces the number of features with the best model having only 188 features compared to 2118 features if no FS is applied.

Synthesis, characterization, and visible-light-induced photocatalytic activity of powdered semiconductor oxides of bismuth and zinc toward degradation of Alizarin Red S.

Semiconductor oxides of bismuth and zinc have been synthesized using modified sol-gel method and sol-combustion method, respectively. The synthesized catalysts were characterized by X-ray powder diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and UV-vis spectroscopy. The photocatalytic activity of Bi2 O3 and ZnO was evaluated for the degradation of Alizarin Red S (ARS), as a model pollutant, at 20 mg/L level in water under visible light irradiation. The percentage of photocatalytic degradation was determined using UV-vis spectrophotometer. The photocatalytic results revealed that Bi2 O3 and ZnO could effectively degrade 73% and 53% of ARS, respectively, within 13 hr under visible light illumination, indicating that synthesized Bi2 O3 is a better photocatalyst than ZnO. Photodegradation of ARS with Bi2 O3 and ZnO is remarkably influenced by change in pH of the dye solution, and pH 8 was found to be the most favorable for maximum removal of ARS in case of both Bi2 O3 (75%) and ZnO (58%) photocatalyst. PRACTITIONER POINTS: Photocatalytic degradation of ARS dye depends on pH of the solution. Calcination temperature influences the crystallite size of prepared semiconductor oxides of bismuth and zinc. Bi2 O3 shows better photocatalytic degradation efficiency than ZnO under visible light illumination.