Quantifying the performance of quantum machine learning algorithms for heart valve detection using H-Bert classifier

Machine Learning (ML) techniques find value in healthcare due to its ability to process huge data sets and convert them into clinical insights. These insights help physicians and healthcare providers in planning, quick decision making and providing timely care to patients with higher accuracy, lower costs and increased customer satisfaction. This paper evaluates the performance of ML algorithms for cancer detection and diagnosis. It compares the performance of different cancer detection algorithms for different types of datasets. It also investigates how the performance of the ML algorithms improve with the use of feature extraction methods, specifically for lung cancer. Developing generic feature extraction methods still remain as a challenge. Healthcare providers need to reorient their treatment approach for diseases like cancer with more focus on detecting them at a very early stage so as to maximize the chances of recovery for the patients.

The success of blockchain projects depends, to a large extent, on the developer's involvement. Source code-commit privilege to the project is typically restricted to a few developers known as committers. Project leaders continuously search for new committers to evolve into a high-quality blockchain. However, promoting developers into committers is risky, mainly when the promoted committers exhibit low involvement behaviour. Hence, the committer assessment process is critical to the successful evolution of blockchain projects. The phenomenon of developer induction as code committers has previously been explored in the literature. However, previous studies employed objective measures such as the number of bugs report and code patches to appraise the developers activities for promotion purposes. Although these approaches are significant, behavioural tendencies influencing developers' activities are ignored. There have, however, been comparatively few investigations on a subjective measure that evaluate developers' perception of their activities for promotion purposes. This study aims to appraise blockchain developers' perceptions for predicting future committers in blockchain projects. In this study, 173 blockchain developers’ perceptions were gathered. The study employed hybrid analyses such as Structural Equation Modelling (SEM) to first analyse the survey data and Machine Learning (ML) algorithms to predict future committers. The performance of various ML algorithms was evaluated compared to classification performance indicators such as the F1 score, accuracy, precision, and AUC. In addition, the study also investigates the most important factors that predict future committers. The results indicate that XGBoost algorithm achieves the best performance with an accuracy of 0.94. Moreover, the most important factors in predicting future committers in a blockchain project are project desertion, developer involvement, decision-right delegation and system integration.

Automatic detection of fake news, which could negatively affect individuals and the society, is an emerging research area attracting global attention. The problem has been approached in this paper from Natural Language Processing and Machine Learning perspectives. The evaluation is carried out for three standard datasets with a novel set of features extracted from the headlines and the contents. Performances of seven machine learning algorithms in terms of accuracies and F1 scores are compared. Gradient Boosting outperformed other classifiers with mean accuracy of 88% and F1-Score of 0.91.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect bias and is discussed in this article from the cognitive psychology perspective. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> ML algorithms are common components in electrical engineering, computer science, and data science curricula. Learning ML is challenging not only due to its complex mathematical and algorithmic aspects, but also due to: 1) the complexity of using these algorithms in the context of real-life situations correctly and 2) the comprehension of related social and ethical issues. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Question:</i> To what extent do students fail to correctly interpret the performance of ML algorithms due to the domain neglect bias and how can this bias be explained? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> A questionnaire containing two questions was designed, in which the participants were asked to compare the performance of ML algorithms in the contexts of different application domains. The questions were designed to trigger the domain neglect bias which in turn, allowed quantification of its prevalence. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> One third of the students failed to recognize the outperforming ML algorithm due to the domain neglect bias. Educators should note this bias and develop new pedagogical methods for teaching ML performance in order to mitigate the bias.

The liver, a life-sustaining organ, plays a substantial role in many body functions. Liver diseases have become an important world health problem in terms of prevalence, incidences, and mortalities. Liver fibrosis/cirrhosis is great of importance, because if not treated in time liver cancer could be occurred and spread to other parts of the body. For this reason, early diagnosis of liver fibrosis/cirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosis/cirrhosis based on demographic and blood values. In this context, random forest, k nearest neighbour, C4.5 decision tree, K-star, random tree and reduced error pruning tree algorithms were used. Two distinct approaches were employed to evaluate the performances of machine learning algorithms. In the first approach, the entire features of dataset were utilized, while in the second approach, only the features selected through principal component analysis were used. Each approach was rigorously assessed using both 10-fold cross-validation and data splitting (70% train and 30% test) techniques. By conducting separate evaluations for each approach, a comprehensive understanding of the effectiveness of utilizing all features versus extracted features based principal component analysis was attained, providing valuable insights into the impact of feature dimensionality reduction on model performance. In this study, all analyses were implemented on WEKA data mining tool. In the first approach, the classification accuracies of random forest algorithm were 89.72% and 90.75% with the application of data splitting (70%-30%) and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis technique, the accuracy values obtained from data splitting and cross-validation techniques of random forest algorithm were 88.61% and 88.83%, respectively. The obtained results revealed out that random forest algorithm outperformed for both approaches. Besides, the application of principal component analysis technique negatively affected the classification performance of used machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosis/cirrhosis, potentially leading to death in its advanced stages.

A hybrid agent-based machine learning method for human-centred energy consumption prediction

Wireless community networks, WCN, have proliferated around the world. Cheap off-the-shelf WiFi devices have enabled this new network paradigm where users build their own network infrastructure in a do-it-yourself alternative to traditional network operators. The fact that users are responsible for the administration of their own nodes makes the network very dynamic. There are frequent reboots of the networking devices, and users that join and leave the network. In addition, the unplanned deployment of the network makes it very heterogeneous, with both high and low capacity links. Therefore, anomaly detection in such dynamic scenario is challenging. In this paper we provide a dataset gathered from a production WCN. The data was obtained from a central server that collects data from the mesh nodes that build the network. In total, 63 different nodes were encountered during the data collection. The WCN is used daily to access the Internet from 17 subscribers of the local ISP available on the mesh. We have produced a dataset gathering a large set of features related not only to traffic, but other parameters such as CPU and memory. Furthermore, we provide the network topology of each sample in terms of the adjacency matrix, routing table and routing metrics. In the data we provide there is a known unprovoked gateway failure. Therefore, the dataset can be used to investigate the performance of unsupervised machine learning algorithms for fault detection in WCN. To our knowledge, this is the first dataset that allows fault detection to be investigated from a production WCN.

This review paper provides an overview of precision healthcare analytics, focusing on the integration of machine learning (ML) techniques for automating image interpretation, disease detection, and prognosis prediction across various medical imaging modalities, including X-rays, MRIs, and CT scans. Drawing upon existing literature and empirical evidence, we assess the impact of ML-driven automated image interpretation on diagnostic accuracy, highlighting its superiority over traditional methods. Additionally, we examine the effectiveness of ML algorithms in disease detection, emphasizing their potential for early intervention and improved patient outcomes. Furthermore, we explore the prognostic capabilities of ML-based models in forecasting disease progression and guiding treatment strategies. Through a comprehensive synthesis of research findings, we identify key factors influencing the performance of ML algorithms in healthcare applications and discuss strategies for addressing challenges related to data quality, interpretability, and scalability. By critically evaluating current trends and advancements in precision healthcare analytics, this review aims to provide insights into the potential benefits and limitations of ML integration in medical practice, contributing to the ongoing discourse on enhancing patient care and healthcare delivery.

Diagnostic performance of radiomics and machine learning algorithms in differentiating grade 2-3 gliomas from glioblastomas among adult-type diffuse gliomas.

Racial and Ethnic Disparities in Predictive Accuracy of Machine Learning Algorithms Developed Using a National Database for 30-Day Complications Following Total Joint Arthroplasty

The field of quantum computing aims to speed up the solution for certain computational problems using quantum computers. The key milestone in this field is the claim that quantum supremacy can be achieved using quantum computers which shows that the quantum speedup is achievable in a real time system. Quantum computing can improve the performance of machine learning algorithms based on the quantum algorithms by producing better solutions for machine learning tasks than classical machine learning approaches. This chapter provides an overview on quantum computing, quantum supremacy and recent research and inventions in the field. The paper additionally illustrates the research conducted with respect to impact of quantum computing on machine learning.

Runtime evaluation of cognitive systems for non-deterministic multiple output classification problems

Earthquake is one of the natural disasters that have a big impact on society. Currently, there are many studies on earthquake detection. However, the vibrations that were detected by sensors were not only vibrations caused by the earthquake, but also other vibrations. Therefore, this study proposed an earthquake multi-classification detection with machine learning algorithms that can distinguish earthquake and non-earthquake, and vandalism vibration using acceleration seismic waves. In addition, velocity and displacement as integration products of acceleration have been considered additional features to improve the performances of machine learning algorithms. Several machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) have been used to develop the best algorithm for earthquake multi-classification detection. The results of this study indicate that the ANN algorithm is the best algorithm to distinguish between earthquake and non-earthquake, and vandalism vibrations. Moreover, it's also more resistant to various input features. Furthermore, using velocity and displacement as additional features has been proven to increase the performance of every model.

Machine and deep learning identified metabolites and clinical features associated with gallstone disease

Performances of machine learning algorithms for individual thermal comfort prediction based on data from professional and practical settings