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In the recent literature, many metaheuristic approaches has been developed to examine, interpret, and analyze high dimensional data. However, there is always a requirement to design a more productive and cost-effective technique. Many of the authors have acknowledged the capability of cuckoo search approach. Inspired from the blooming application of cuckoo search algorithm (CSA), the authors have proposed a binary cuckoo search algorithm (BCSA) for gene selection and classification of microarray data. With the objective of optimizing gene subset and classification result, BCSA has been implemented. BCSA is optimized using support vector machine (SVM) classifier, and significant gene set is extracted. This experimental study has been conducted over five microarray datasets. Superiority of this gene subset is shown using a few other classifiers such as k-nearest neighbor and artificial neural network. The model performance is also compared with a few other metaheuristic approaches such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and differential evolution.

Since the last few decades, Artificial Neural Networks have been the center of attraction of a large number of researchers for solving diversified problem domains. Due to its distinguishing features such as generalization ability, robustness and strong ability to tackle nonlinear problems, it appears to be more popular in financial time series modeling and prediction. In this paper, a Pi-Sigma Neural Network is designed for foretelling the future currency exchange rates in different prediction horizon. The unrevealed parameters of the network are interpreted by a hybrid learning algorithm termed as Shuffled Differential Evolution (SDE). The main motivation of this study is to integrate the partitioning and random shuffling scheme of Shuffled Frog Leaping algorithm with evolutionary steps of a Differential Evolution technique to obtain an optimal solution with an accelerated convergence rate. The efficiency of the proposed predictor model is actualized by predicting the exchange rate price of a US dollar against Swiss France (CHF) and Japanese Yen (JPY) accumulated within the same period of time.

Due to increased number of breast cancer cases, there is rise in concern about the disease. So, in today’s era, machine learning classification can play a vital role in classifying the degree of malignancy of the cancer. This paper uses four different classifiers such as KNN, logistic regression, naive Bayes and decision tree to classify the cancer into two classes of benign and malignant. The proposed model is simulated over Wisconsin diagnostic breast cancer dataset and evaluated by accuracy metric. The model classifies the disease based on the most accurate classifier. The KNN model shows significantly better result than the other classifier.

Due to the advancement of the Internet nowadays, a lot of people are mostly dependent on the Web to get the required information. As data is increasing exponentially, there is a high chance of duplication of data; it is difficult and tedious for the manual reading of all the documents as well as the rejection of the duplicates and extraction of useful information. One of the solutions to this issue is “Text Summarization,” through which a huge volume of data can be read quickly; but it is very hard to summarize documents manually, thus necessitating the use of an automatic tool to perform this task. Abstractive Text Summarization is one such automated technique of producing a short and accurate summary of a document while preserving essential information and comprehensive meaning. In this paper, Abstractive Text Summarization using Deep Learning with Attention Mechanism has been proposed. The designed framework removes duplicate data and generates new sentences by rephrasing them or adding words originally absent in the source text. Experimental results on the dataset, Amazon Fine Food Review, are evaluated by utilizing performance metrics such as Rouge scores.

An optimization algorithm gives an optimum solution by executing iteratively and comparing it with different solutions. Therefore, many optimization algorithms have been discussed and used in all kinds of fields in the past few years. In this study, a brief description of an optimization algorithm called Fruit Fly algorithm (FFOA) is presented. This algorithm is based on the concept of the fruit flies and their extraordinary methods in finding a way to their food. Due to this, it has been applied in many of the research fields like science, engineering, medical, agriculture, etc., in many forms.

Optimization is a process in which the system is being modified so that the features can work more effectively or by finding any necessity execution. This process is conducted through maximizing and minimizing the parameters that are being involved in the problem. Many successful optimization approaches are available in the recent scenario; however, this research work focuses on cuckoo search technique. A new higher level procedure optimization algorithm called cuckoo search (CS) was developed by Yang and Deb in 2009. Cuckoo search is a swarm intelligence (SI) algorithm. Swarm intelligence techniques are those techniques that are being originated from the behavior of swarm. Cuckoo search algorithm is being inspired by the bird called cuckoo. It is the inspiration of brooding parasitism of cuckoo species and implemented successfully in many areas for optimization. This paper represents a brief review on CS algorithm along with the optimization process implementing some standard test functions. This research work highlights test function optimization for the non constraint function. This work may give an inspiration to apply the CS algorithm to solve the engineering design optimization problems.

Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it is categorized as single document or multi document summarization. Rather than single document, multi document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in the study.

With the exponential growth of information in World Wide Web, extracting relevant information from huge amount of data has become a critical task. Text summarization has been appeared as one of the solution to such problem. As the main objective is to retrieve a condensed document that pertain the original information, so it can be considered as an optimization problem. In this paper, a comparative analysis of few meta-heuristic approaches such as Cuckoo Search (CS), Cat Swarm Optimization (CSO), Particle Swarm Optimization (PSO), Harmony Search (HS), and Differential Evolution (DE) algorithm is presented for single document summarization problem. The performance of all these algorithms are compared in terms of different evaluation metrics such as F score, true positive rate and positive predicate value to validate summary relevancy and non-redundancy over traditional and standard Document Understanding Conference (DUC) datasets.

In today's era, there are large numbers of documents or information that is present related to any particular field. There are many sources out of which we can gather a lot much information that will be pertinent to our field of search. Much information is available at various sources like the internet or various books. But, as we know that a huge amount of information cannot be always considered or taken into use. So, a precise amount of information is always considered and that information is drawn out from the original document that is huge in size. In other words, we can say that we pluck out the summary of the main document. A summary of any document is defined as a collection of essential data by collecting the brief statements accounting the main points of the original document. Therefore, Summarization of a text is a procedure of separating or getting the relevant data out of a very large document. It is the process of shortening the text document by using various technologies and methodologies to create a coherent summary including the major points of the original document. There are various methods by which the summarization process can be carried out.

Predicting future stock index price movement is equivalent to a binary classification problem with one class label for increasing movement and other for decreasing movement. In the literature, a wide range of classifiers are tested for this application, but the decision regarding a better technique varies with the choice of performance measures. Hence, assessing classifiers can be considered as a multicriteria decision-making (MCDM) problem. In this study, a TOPSIS-based MCDM framework is suggested for ranking five classifier models such as radial basis function, Naive Bayes, decision tree, support vector machine, and k-nearest neighbor with respect to four criteria in application to prediction of future stock index price movements. Historical stock index prices of two benchmark stock indices such as BSE SENSEX and S&P500 are taken for the empirical validation of the model. The results reveal that ranking a classifier with respect to multiple evaluation measures is better compared to selecting one considering single criterion.

In last few decades, Bio-inspired algorithms (BIAs) have gained a significant popularity to handle hard real world and complex optimization problem. The scope and growth of Bio Inspired algorithms explore new application areas and computing opportunities. This paper presents a review with the objective is to bring a better understanding and to motivate the research on BIAs based text summarization. Different techniques have been used for text summarization are genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), harmonic search (HS).

Exponential growth of information in the web became infeasible for user to sieve useful information very quickly. So solution to such problem now a day is text summarization. Text summarization is the process of creating condensed version of original text by preserving important information in it. This paper presents for the first time a nature inspired cuckoo search optimization algorithm for optimal selection of sentences as summary sentence of intelligent text summarizer. The key aspects of proposed summarizer focus on content coverage and length while reducing redundant information in the summaries. To solve this optimization problem, this model uses inter-sentence relationship and sentence-to-document relationship by considering widely used similarity measure cosine similarity. The inputs for this model are taken from DUC dataset. Whereas the result is evaluated by ROUGE tool and compared with state-of-the-art approaches, in which our model in multi-document summarization have shown significant result than others.

Gold Price Prediction has always been a fascinating area of study for researchers and decision makers who wish to determine its future value efficiently and accurately. In this study a predictor model is designed using a Pi-Sigma Neural Network (PSNN) for prediction of future gold price. Two evolutionary estimation paradigms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are suggested during training step of the Pi-Sigma Neural Network to optimize the tunable weights of the network. The model is evaluated based on certain performance evaluation criteria such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) over two dataset such as Gold/INR and Gold/AED accumulated within the same period of time. The result analysis illustrates the better prediction

With the exponential growth in the quantity and complexity of information sources, a number of computational intelligent-based techniques have developed in literature for document summarization. In this paper, a comparative study of two population-based stochastic optimization techniques has been proposed for document summarization. It specifies the relationship among sentences based on similarity and minimizes the weight of each sentence to extract summary sentences at different compression level. Comparison of both the optimization techniques based on fallout value of extracted sentences shows the good performance of PSO compared to DE on five different English corpus data.