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Drosophila melanogaster is an important genetic model organism used extensively in medical and biological studies. About 61% of known human genes have a recognizable match with the genetic code of Drosophila flies, and 50% of fly protein sequences have mammalian analogues. Recently, several investigations have been conducted in Drosophila to study the functions of specific genes exist in the central nervous system, heart, liver, and kidney. The outcomes of the research in Drosophila are also used as a unique tool to study human-related diseases. This article presents a novel automated system to classify the gender of Drosophila flies obtained through microscopic images (ventral view). The proposed system takes an image as input and converts it into grayscale illustration to extract the texture features from the image. Then, machine learning (ML) classifiers such as support vector machines (SVM), Naive Bayes (NB), and K -nearest neighbour (KNN) are used to classify the Drosophila as male or female. The proposed model is evaluated using the real microscopic image dataset, and the results show that the accuracy of the KNN is 90%, which is higher than the accuracy of the SVM classifier., Competing Interests: There is no conflict of interest between the authors., (Copyright © 2022 Channabasava Chola et al.)

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate. [ABSTRACT FROM AUTHOR]

Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients' activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric. [ABSTRACT FROM AUTHOR]

Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes. [ABSTRACT FROM AUTHOR]

The fast spread of coronavirus disease (COVID-19) caused by SARSCoV- 2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners. Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22, 2020, to May 30, 2020. The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method. The results reveal that the GBR model achieves 0.00686 root mean square error, the lowest among several comparative models. [ABSTRACT FROM AUTHOR]

Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset. [ABSTRACT FROM AUTHOR]

GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete; this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods. [ABSTRACT FROM AUTHOR]