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Software quality is improved through early software flaw identification, which is made possible by software fault prediction (SFP). Early software problem identification reduces the amount of money and work needed to fix those vulnerabilities. This paper ...

Histopathological images play a significant role in selecting effective therapeutics and identifying disorders like cancer. Digital histopathology is a crucial advancement in contemporary medicine. The growth and spread of cancer cells within the body ...

As concerns about social bots online increase, studies have attempted to explore the discourse they produce, and its effects on individuals and the public at large. We argue that the common reliance on aggregated scores of binary classifiers for bot ...

In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this ...

Software fault prediction is a crucial task, especially with the rapid improvements in software technology and increasing complexity of software. As identifying and addressing bugs early in the development process can significantly minimize the costs and ...

The research established a central objective to implement an academic performance forecasting model based on artificial intelligence to determine the level of incidence factors in the management of academic performance and design the predictive model ...

In this paper, we propose two methods of application of federated learning to the construction of classifiers for the analysis of data related to predicting the death of patients suffering from the vasculitis. The paper contains results of ...

In this article we present the Amharic Speech Emotion Dataset (ASED), which covers four dialects (Gojjam, Wollo, Shewa, and Gonder) and five different emotions (neutral, fearful, happy, sad, and angry). We believe it is the first Speech Emotion ...

We propose a novel multivariate nonparametric multiple change point detection method using classifiers. We construct a classifier log-likelihood ratio that uses class probability predictions to compare different change point configurations. We propose a ...

Hate speech on social media post is running now a days. Social media like YouTube, Twitter, and Facebook etc. are responsible for hated speech. Hated speech spreads through digital media, causing individuals to get confused and adopt prejudiced ...

A lot of effort has been devoted to reading resistor colors using image processing and pattern recognition techniques. It is not so clear which classifier or machine learning is effective for classifying colors in reading a resistance of a resistor. ...

The article considers a compression framework for 2D/3D digital signals, including digital images and hyperspectral data. A compression framework is proposed that uses neural networks to exclude insignificant signal portions from the compression ...

Breast cancer is one of the diseases that led to a huge number of deaths in the recent decades. One of the major issues that affect the recovery procedure is the early detection of the disease. Thus, in this paper, several machine learning algorithms ...

Classification, a heavily-studied data-driven machine learning task, drives an increasing number of prediction systems involving critical human decisions such as loan approval and criminal risk assessment. However, classifiers often demonstrate ...

Phishing, a prevailing cyber-security issue, is one of the most common attacks to obtain user's sensitive information. To eradicate it, the users or software should detect it first. A popular approach to carry out phishing is through generating phishing ...

In the recent years, the human activity recognition gain popularity in ambient computing. The human activity recognition is composed of identifying the daily activities of the users by observing their actions. Action identification is a more complex task ...

In recent years, there has been an increasing amount of streaming information coming from time series. Learning from data appearing in real time is quite a call, due in part to the speed at which new data appears. Hidden data changes that are not ...

Park visual quality evaluation is one of the contents of park environmental performance evaluation. The purpose of this study is to establish a model for park quality evaluation using image feature extraction and machine learning. We selected three areas ...

In this work, we investigate whether malware understands the analysis environment. This analysis is carried out by executing a set of real malicious programs and benign samples on virtual and native machines. The result of execution is API sequence ...

Phishing websites trick honest users into believing that they interact with a legitimate website and capture sensitive information, such as user names, passwords, credit card numbers, and other personal information. Machine learning is a promising ...