The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.

A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications

Background: Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems. Objective: This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks. Methods: In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets. Results: The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems. Conclusions: From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.

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Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review

(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. (Method) In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts in mammogram images. First, we segmented the region-of-interest. Next, the weighted-type fractional Fourier transform (WFRFT) was employed to obtain the unified time-frequency spectrum. Third, principal component analysis (PCA) was introduced and used to reduce the spectrum to only 18 principal components. Fourth, feed-forward neural network (FNN) was utilized to generate the classifier. Finally, a novel algorithm-specific parameter free approach, Jaya, was employed to train the classifier. (Results) Our proposed WFRFT + PCA + Jaya-FNN achieved sensitivity of 92.26%±3.44%, specificity of 92.28%±3.58%, and accuracy of 92.27%±3.49%. (Conclusions) The proposed CAD system is effective in detecting abnormal breasts and performs better than 5 state-of-the-art systems. Besides, Jaya is more effective in training FNN than BP, MBP, GA, SA, and PSO. ∗Address for correspondence: Jiangsu Key Laboratory of 3D, Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China 192 S. Wang et al. / Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained

Abnormal Breast Detection in Mammogram Images by Feed-forward Neural Network Trained by Jaya Algorithm

Long-term exposure to stress may lead to serious health problems such as those related to the immune, cardiovascular, and endocrine systems Once having arisen, these problems require a considerable investment of time and money to recover from With early detection and treatment, however, these health problems may be nipped in the bud, thus improving quality of life We present an automatic stress detection and alleviation system, called SoDA, to address this issue SoDA takes advantage of emerging wearable medical sensors (WMSs), specifically, electrocardiogram (ECG), galvanic skin response (GSR), respiration rate, blood pressure, and blood oximeter, to continuously monitor human stress levels and mitigate stress as it arises It performs stress detection and alleviation in a user-transparent manner, ie, without the need for user intervention When it detects stress, SoDA employs a stress alleviation technique in an adaptive manner based on the stress response of the user We establish the effectiveness of the proposed system through a detailed analysis of data collected from 32 participants A total of four stressors and three stress reduction techniques are employed In the stress detection stage, SoDA achieves 958 percent accuracy with a distinct combination of supervised feature selection and unsupervised dimensionality reduction In the stress alleviation stage, we compare SoDA with the ‘no alleviation’ baseline and validate its efficacy in responding to and alleviating stress

Keep the Stress Away with SoDA: Stress Detection and Alleviation System

Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length. We investigate this problem in detail, and propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then use 3D point cloud encoders to predict the missing depth shift and focal length that allow us to recover a realistic 3D scene shape. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to enhance depth prediction models trained on mixed datasets. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot dataset generalization. Code is available at: this https URL

Learning to Recover 3D Scene Shape from a Single Image.

Investigating the impact of data normalization on classification performance

Baseline wandering can mask some important features of the Electrocardiogram (ECG) signal hence it is desirable to remove this noise for proper analysis and display of the ECG signal. This paper presents the implementation and evaluation of different methods to remove this noise. The parameters i. e. Power Spectral density (PSD), average Power & Signal to noise ratio (SNR) are calculated of signals to compare the performance of different filtering methods. IIR zero phase filtering has been proved efficient method for the removal of Baseline wander from ECG signal. The results have been concluded using Matlab software and MIT-BIH arrhythmia database.

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Comparison of different approaches for removal of baseline wander from ECG signal

Feature wise normalization: An effective way of normalizing data

The ECG Signal is a graphical representation of the electromechanical activity of the cardiac system. It is one of the most important physiological parameter, which is being extensively used for knowing the state of cardiac patients. For diagnostic quality ECG recordings, signal acquisition must be noise free. Since ECG signals are only of the order of 1 mV in amplitude, the ECG acquisition is susceptible to many types of noises and power line interference is one of them. Many types of analog and Digital Filters have been proposed/ suggested by the researchers. The present paper has proposed a combination technique of two methods i.e. the moving averages technique and IIR notch to reduce the power line interference. The results have clearly indicated that there is reduction in the power line noise in the ECG signal. The filter proposed in this paper has fewer coefficients and hence lesser computation time. So the above combination is applicable for real time processing. The results have been concluded using Matlab and MIT-BIH database (with 60 Hz powerline interference)

Powerline Interference Reduction in ECG Using Combination of MA Method and IIR Notch

The classification of high dimensional data is a challenging problem due to the presence of redundant and irrelevant features in a higher amount. These unwanted features degrade accuracy and increase the computational complexity of machine learning algorithms. In this paper, we propose a hybrid method that integrates the complementary strengths of feature selection and feature weighting approaches for improving the classification of high dimensional data on the Nearest Neighbor classifier. Specifically, we suggest four strategies that combine filter and wrapper methods of feature selection and feature weighting. Experiments are performed on 12 high dimensional datasets and outcomes are supported by Friedman as well as Holm statistical tests for validation. Extended Adjusted Ratio of Ratios is used to recognize the best method considering accuracy, feature selection, and runtime. The results show that two proposed strategies outperform other well-known methods in accuracy and features reduction. The hybrid feature selection-feature weighting wrapper method is the best among all in accuracy while the hybrid feature selection filter-feature weighting wrapper method is the most suitable for reducing features and runtime. Thus, the promising outcomes validate the importance of hybridizing feature selection and feature weighting while dealing with high dimensional data.

Birmohan Singh

Papers

Investigating the impact of data normalization on classification performance

Comparison of different approaches for removal of baseline wander from ECG signal

Feature wise normalization: An effective way of normalizing data

Powerline Interference Reduction in ECG Using Combination of MA Method and IIR Notch

Hybridization of feature selection and feature weighting for high dimensional data