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The socio-economic factor analyses by using the Logit and Probit model have the limitation of representing random taste variation for the unobserved factors and the issues of temporally correlated errors respectively. In socio-economic factor analysis, the observed data are essential in the random distribution for the adequate representation of the random components associated with various factors and lead to poor prediction in the case of the Logit and Probit model. In this work, Extra- trees classifier machine learning model based socio-economic factors selection has been used and found capable to find out the socio-economic factors that contain relevant information to the target variable agricultural productivity. In addition to this, the voting classifiers ensemble learning approach is used for the prediction of agricultural productivity of respondents (farmers) from their socio-economic profiles. This proposed work has been evaluated by using the test case of analyzing the socio-economic factors of the farmers affecting agricultural productivity in Sambalpur District, in Odisha State, India. In this study, the farmers’ socio-economic data are collected by using structured interviews through questionnaires that are in line with standard Participatory Rural Appraisal. It is found that, the proposed approach of socio-economic factor identification is found efficient for finding the relationships between socio-economic factors and agricultural productivity and the proposed ensemble learning-based approach is efficient in terms of agricultural productivity prediction.

With the advent of the 20th century, the popularity of digital service usages is increasing every day. The internet has always been a popular communication method, and phishing webpages have been a challenging issue for more than two decades. Especially, E-commerce and other global companies face enormous challenges due to phishing of websites. Many developed countries have reported substantial economic loss due to unwanted phishing activities. With the exponential increase of digital communications, these phishing activities are going to be increased. There is a need for an effective intrinsic phishing detection technique. Phishing websites have some unique features by which they can be identified. In this research, a Light gradient boosting machine-based phishing email detection model using phisher websites' features of mimic URLs has been proposed. The primary objective is to develop a highly secured and accurate model for successful identification of security breach through websites phishing. With the performance comparison of other ensemble as well as state-of-the-art machine learning models, the proposed model resulted high performance accuracy and proved to a robust approach for phishing activity.

For two decades, the dominance of nature-inspired optimization algorithms has been irresistible in solving many complex problems. In fact, most of these algorithms are integrated with some other intelligent technique to prove the effectiveness of each method. Out of these algorithms, the last decade has witnessed a good research contribution for Teaching–Learning Based optimization and its different variations as well as advances in many engineering domains. In this research, a variation of the TLBO technique has been integrated with the Functional Link Artificial Neural Network to classify the nonlinear data. With suitable parameter adjustments, the proposed model can classify the data efficiently. For learning, the Gradient Descent method has been adopted for obtaining the optimal weight units of the neural network. Simulation results reveal that the proposed hybrid approach is superior in terms of considered performance parameters as compared to other competitive methods.

Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.

Purpose The appearance of the 2019 novel coronavirus (Covid-19), for which there is no treatment or a vaccine, formed a sense of necessity for new drug discovery advances. The pandemic of NCOV-19 (novel coronavirus-19) has been engaged as a public health disaster of overall distress by the World Health Organization. Different pandemic models for NCOV-19 are being exploited by researchers all over the world to acquire experienced assessments and impose major control measures. Among the standard techniques for NCOV-19 global outbreak prediction, epidemiological and simple statistical techniques have attained more concern by researchers. Insufficiency and deficiency of health tests for identifying a solution became a major difficulty in controlling the spread of NCOV-19. To solve this problem, deep learning has emerged as a novel solution over a dozen of machine learning techniques. Deep learning has attained advanced performance in medical applications. Deep learning has the capacity of recognizing patterns in large complex datasets. They are identified as an appropriate method for analyzing affected patients of NCOV-19. Conversely, these techniques for disease recognition focus entirely on enhancing the accurateness of forecasts or classifications without the ambiguity measure in a decision. Knowing how much assurance present in a computer-based health analysis is necessary for gaining clinicians’ expectations in the technology and progress treatment consequently. Today, NCOV-19 diseases are the main healthcare confront throughout the world. Detecting NCOV-19 in X-ray images is vital for diagnosis, treatment, and evaluation. Still, analytical ambiguity in a report is a difficult yet predictable task for radiologists. Method In this paper, an in-depth analysis has been performed on the significance of deep learning for Covid-19 and as per the standard search database, this is the first review research work ever made concentrating particularly on Deep Learning for NCOV-19. Conclusion The main aim behind this research work is to inspire the research community and to innovate novel research using deep learning. Moreover, the outcome of this detailed structured review on the impact of deep learning in covid-19 analysis will be helpful for further investigations on various modalities of diseases detection, prevention and finding novel solutions.

Despite the popularity of the applicability of Internet of Things (IoT) devices in many applications, security and anomaly detection has always been a rising concern for any domain of research. In the current smart era, the increased use of IoT and related applications in almost all domains tune us for the alarming situations of security measures. Many such insecure threats such as the denial of service, malicious control, and operations can be the real harm for any IoT devices. Compared to the initial days of developments in IoT control, many advanced techniques based on machine learning are designed for effective control of such malicious activities. In this paper, a stacked ensemble meta-learning (SEM) model has been developed to enhance the performance of the base machine learning model used for anomaly detection in IoT devices. The proposed model learns from the prediction errors of the base classifiers to build a more accurate prediction model. The proposed SEM constructs a higher-level prediction model over the predictions of weak base classifiers.

Background/Objectives: In socio-economic factor analysis, the observed data are essential in the random distribution for the adequate representation of the random components associated with various factors and lead to poor prediction in the case of the Logit and Probit model. The objective of this work is to have machine learning based model for socio-economic factors analysis and ensemble learning based model for efficient prediction of agricultural productivity. Methods: In this work, extra-tree classifier machine learning model based socio-economic factors selection has been used and found capable to evaluate the socio-economic factors that contain relevant information to the target variable agricultural productivity. In addition to this, the multi-class adaptive boosting ensemble learning approach is used for the prediction of agricultural productivity of respondents (farmers) from their socio-economic profiles. This proposed research has been evaluated by using the test case of analyzing the socio-economic factors of the farmers affecting agricultural productivity in Sambalpur District, of Odisha State, India.The farmers’ socio-economic data are collected by using structured interviews through questionnaires that are in line with standard Participatory Rural Appraisal. Findings: It is found that the proposed approach of socio-economic factor identification is efficient for computing the relationships between socioeconomic factors and agricultural productivity. Novelty: In this application domain of socio-economic factor analysis, the proposed method employs extra-tree classifier and boosting ensemble learning for socio-economic factor analysis towards agricultural productivity which is found efficient than other existing approaches such as Logit, Probit, Linear Regression, Linear Discriminant Analysis, Naive Baise, and other counterparts. Keywords: Socio-economic factor analysis; multiclass adaptive boosting; ensemble learning; extra-tree classifier; Probit; Logit

Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

Hand gesture is considered as one of the natural ways to interact with computers. The utility of hand gesture-based application is a recent trend and is an effective method for human–computer interaction. Though many static and other intelligent approaches using Machine learning (ML) are developed, still there is a marginal tradeoff between the computational cost and accuracy. In this paper, a Lightboost based Gradient boosting machine (LightGBM) is proposed for efficient hand gesture recognition. The hyper-parameters of the LightGBM are optimized with an improved memetic firefly algorithm. We have introduced a perturbation operator and incorporated it in the proposed memetic firefly algorithm for avoiding the local optimal solution in the traditional firefly algorithm. With comparative analysis among the proposed method and other competitive ML methods, the performance of the proposed method is found to be better in terms of various performance metrics such as accuracy, precision, recall, F1 score, and ROC–AUC. The proposed memetic firefly-based boosting approach is dominant over all the other considered methods with an accuracy of 99.36% and is robust for accurate hand gesture recognition.

In the past 20 years, many swarm intelligence, evolutionary, as well as nature-inspired algorithms have been developed in order to solve many real life problems in several fields. Compared to the earlier algorithms, it has been a recent trend to develop a new optimization algorithm for solving any problem. However, it is worthy to note that any novel algorithm may not be able to solve all diverse real domain problems. Recently, a new algorithm based on the concept of the human kidney was developed and is able to optimize many standard functions. In this paper, an attempt has been made to integrate the kidney-inspired optimization and the fuzzy c-means algorithm to solve nonlinear problems of data mining. It has always been a challenge for researchers to solve the data clustering problem, as they are highly nonlinear in nature. So, hybridization of two efficient algorithms may result in a competitive solution. Inspired by this, this paper proposes a hybrid method of two intelligent techniques to solve the clustering problem, particularly in biomedical data. The experimental results proved that the proposed method efficiently outperforms the other compared methods.