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In the realm of network intrusion detection using machine learning, feature selection aims for computational efficiency, enhanced performance, and model interpretability, preventing overfitting and optimizing data visualization. This paper proposes a filtering method for feature selection, which optimizes information quantity and linear correlation between resultant features. The method identifies Pareto dominant pairs of informative and correlated features, constructs a graph, and selects key features based on betweenness centrality in its connected components. The proposal yields a more concise and informative dataset representation. Experimental results, using three diverse datasets, demonstrate that the proposal achieves more than 95% accuracy in classifying network attacks with just 14% of the total number features in original datasets. [ABSTRACT FROM AUTHOR]

A critical aspect of software engineering is Software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. [ABSTRACT FROM AUTHOR]

A Mobile Ad-hoc Network (MANET) is a self-organizing collection of mobile devices communicating in a distributed fashion across numerous hops. MANETs are an appealing technology for many applications, including rescue operations, environmental monitoring, tactical operations, and so on, because they let people communicate without the usage of permanent infrastructure. This flexibility, however, creates additional security vulnerabilities. Because of its benefits and expanding demand, MANETs have attracted a lot of interest from the scientific community. They do, however, seem to be more vulnerable to numerous attacks that wreak havoc on their performance than any network. Traditional cryptography techniques cannot entirely defend MANETs in terms of fresh attacks and vulnerabilities due to the distributed architecture of MANETs; however, these issues can be overcome by using machine learning approaches-based intrusion detection systems (IDS). IDS, typically screening system processes and identifying intrusions, are commonly employed to supplement existing security methods because preventative techniques are never enough. Because MANETs are continually evolving, their highly limited nodes, and the lack of central observation stations, intrusion detection is a complex and tough process. Conventional IDSs are difficult to apply to them. Existing methodologies must be updated for MANETs or new approaches must be created. This paper aims to present a novel concept founded on deep belief networks (DBN) and long shortterm memory (LSTM) for MANET attack detection. The experimental analysis was performed on the probe, root to local, user to root, and denial of service (DoS) attacks. In the first phase of this paper, particle swarm optimization was used for feature selection, and subsequently, the DBN and LSTM were used for the classification of attacks in the MANET. The experimental results gave an accuracy reaching 99.46%, a sensitivity of 99.52%, and a recall of 99.52% for DBN and LSTM accuracy reaching 99.75%, a sensitivity of 99.79%, and a recall of 99.79%. [ABSTRACT FROM AUTHOR]

To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At last, we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals. Therefore, the application of ISMO is validated by ten benchmark functions and feature selection. It is proved that the proposed method can resolve the problem of feature selection. [ABSTRACT FROM AUTHOR]

Functional End-of-Line Testing is a powerful but expensive approach for industrial quality assurance. A major cost driver of End-of-Line Testing is the often overlong and overcomplex design of industrial test cycles. To tackle this challenge, the paper proposes a data-driven approach based on machine learning for the optimization of the End-of-Line test cycle of a hydraulic pump. As each test cycle comprises a multivariate and multi-phased time series, the optimization can be interpreted as a form of feature selection that aims to select the minimal subset of relevant and non-redundant testing sensors and cycle phases. We define the problem as a multi-objective optimization task balancing the goals of maximizing the test cycle sensitivity and specificity while minimizing the test cycle complexity. The general idea of the optimization approach is inspired by the iterative concept of wrapper feature selection. Our approach was validated in an experiment by applying it to real-world End-of-Line Testing data of an hydraulic axial piston pump. The results have shown that the approach could be successfully used to find an improved test cycle design for the pump and, especially, Backward Elimination performed well as solution search algorithm. [ABSTRACT FROM AUTHOR]

As the digital and physical worlds connect, machine-learning tools are increasingly being used by businesses and institutions, including archaeological research institutes and museums. The article presents research that, based on the evolution of human scripts, provides a tool for identifying script variants used in old manuscripts and stone inscriptions. This makes the work of paleography institutes and individual researchers more efficient. Using this research, it is possible to provide some information about millions of hitherto unidentified inscriptions using machine learning. The developed and tested method applies the biological phylogenetics tool to script evolution, an area that has yet to be investigated with machine learning tools. The final aim of the research is to use these tools to clarify the connections between many Arabic, Aramaic and Central Iranian script variants. The described procedure gives a new solution to one of the fundamental issues of phylogenetic analysis, feature selection, the effectiveness of which is verified with test runs. The paper presents a new feature selection method to identify the strength or effect of features before phylogenetic processing. This type of dimensionality reduction removes unnecessary features from the dataset for these analyses and interpretations. The method was applied to some versions of Arabic, Aramaic and Middle Iranian scripts, but the procedure is not script-specific. [ABSTRACT FROM AUTHOR]

Text mining presents unique challenges in extracting meaningful information from the vast volumes of digital documents. Traditional filter feature selection methods often fall short in handling the complexities of short text data. To address this issue, this paper presents a novel approach to feature selection in text classification, aiming to overcome challenges posed by high dimensionality and reduced accuracy in the face of increasing digital document volumes. Unlike traditional filter feature selection techniques, the proposed method, Multivariate Relevance Frequency Analysis, offers a tailored solution for diverse text data types. By integrating positive, negative, and dependency relevance computations, the proposed approach effectively prunes features, enhancing classification performance. Extensive experimental analysis has been performed for the proposed model and compared with several standard existing feature selection models on five datasets involving short and long texts using four standard classifiers. The results indicate that the proposed model has the highest macro-F1 score of 94% for the SMS dataset, 78.1% for the SLS dataset, 89.4% for the AYSC dataset, 71.32% for the Reuters dataset, and 98.63% for the 20Newsgroup dataset. The statistical analysis also indicates that the proposed model provides better performance with both short texts such as messages and reviews as well as long texts containing documents, with superior performance for short-text data. The comparative analysis shows that the proposed model offers better performance than many other standard filtration models. [ABSTRACT FROM AUTHOR]

The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm algorithm, fusing the capabilities of the Artificial Bee Colony (ABC) and Firefly algorithms, aimed at improving feature selection in gene expression classification. The proposed method undergoes rigorous validation through statistical machine learning techniques and quantitative parameter evaluation, with comprehensive comparisons to established techniques in the field. The findings underscore the superiority of the hybrid Swarm Intelligence approach for feature selection in gene expression classification, offering promising prospects for enhancing cancer diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

Early-stage prediction of a disease is an important and challenging task. The application of machine learning techniques is playing an important role in this era. Thyroid is one of the chronic endocrine diseases, and approximately 42 million people in India are affected by this disease. This paper presents a comprehensive investigation into the enhancement of classification performance through the novel 'FeatureBoostThyro' (FBT) model. The study evaluates various machine learning algorithms, including stochastic gradient descent (SGD), K nearest neighbor (KNN), logistic regression (LR), naive bayes (NB), and support vector machine (SVM), in conjunction with diverse feature selection methods. The research systematically explores the impact of feature selection techniques such as information gain, relief F, chi-square, gini index, forward selection, backward selection, recursive feature elimination, and LASSO on model performance across the chosen algorithms. The analysis reveals notable variations in performance metrics, including accuracy, precision, recall, and F1-score, providing valuable insights into the interplay between algorithm and feature selection. One main contribution of this research is the introduction of the FBT model, which consistently outperforms other models across various feature selection methods, making it a promising tool for addressing complex classification tasks. The findings contribute to a broader understanding of model selection and optimization in machine learning applications. The proposed model undergoes evaluation using two distinct datasets: the primary dataset acquired from Lata Mangeshkar Hospital in Nagpur and the secondary dataset obtained from the UCI dataset. [ABSTRACT FROM AUTHOR]