Text length considered adaptive bagging ensemble learning algorithm for text classification

Research has shown that Convolutional Neural Networks (CNN) can be effectively applied to text classification as part of a predictive coding protocol. That said, most research to date has been conducted on data sets with short documents that do not reflect the variety of documents in real world document reviews. Using data from four actual reviews with documents of varying lengths, we compared CNN with other popular machine learning algorithms for text classification, including Logistic Regression, Support Vector Machine, and Random Forest. For each data set, classification models were trained with different training sample sizes using different learning algorithms. These models were then evaluated using a large randomly sampled test set of documents, and the results were compared using precision and recall curves. Our study demonstrates that CNN performed well, but that there was no single algorithm that performed the best across the combination of data sets and training sample sizes. These results will help advance research into the legal profession's use of machine learning algorithms that maximize performance.

The majority of the data is preserved as text (about 75%), hence It is believed that text mining has a significant commercial potential. Unstructured texts continue to be the most readily available source of knowledge, despite the fact that knowledge may be accessed in many other places. Text classification that assigns documents to predetermined categories. Machine learning approaches can categories texts more accurately. The goal of this work is to introduce text classification, give a description of the text classification technique, a general review of the classifiers, and a comparison of a few of the current classifiers. It is based on performance, time complexity, and other factors. On the basis of speed, accuracy, benefits, and drawbacks of existing classification methods such as Decision Tree, Naive Bayes, Support Vector Machine, and k-Nearest Neighbours are compared.

In the past decades, the speed development of the Web and a large amount of data published through the Web have made it the largest public data source in the world. The network has become a carrier of massive information. How to efficiently classify text for the acquired massive information is a hot issue of current research. The traditional machine learning algorithms for text classification have many disadvantages such as inconspicuous text features, long training period and loss of word order. This article puts forward a BERT model based method for technology information text auto-Categoriz to improve the accuracy text classification of science and technology information. The results suggest that the using method has significantly improved accuracy, recall and fl_score, and has a good Chinese text classification effect.

A novel approach to supervised learning, called Lattice Machine, was proposed in [5]. In the Lattice Machine, it was assumed that data are structured as relations. In this paper we investigate the application of the Lattice Machine in the area of text classification, where textual data are unstructured. We represent a set of textual documents as a collection of Boolean feature vectors, where each vector corresponds to one document and each entry in a tuple indicates whether a particular term appears in the document. This is a common representation of textual documents. We show that using this representation, the Lattice Machine’s operations are simply set theoretic operations. In particular, the lattice sum operation is simply set intersection and the ordering relationship is simply set inclusion. Experiments show that the Lattice Machine, under this configuration, is quite competitive with state-of-the-art learning algorithms for text classification.

The text type is a vital undertaking within the realm of gadget-gaining knowledge of algorithms and can contain classifying text files into predefined classes. Text category is used in many herbal language processing obligations, sentiment evaluation, topic modeling, and record summarization. Design techniques for device mastering algorithms for textual content categories contain information preprocessing, feature engineering, model choice, and evaluation. Records preprocessing requires expertise in text information and includes the stairs of tokenizing the text records, removing stopwords, acting stemming and lemmatization, part-of-speech tagging, and vectorization. Feature engineering, which is a step that requires domain expertise, applies strategies consisting of feature selection, function extraction, and feature transformation. Version choice and optimization techniques, including hyperparameter tuning and version choice, are used to choose the most appropriate studying algorithm for the task. Lastly, assessment metrics evaluate the model's effectiveness, including accuracy, precision, remembering, and f-ratings. On average, these layout techniques are critical components while growing a successful text-type model.

This is a temporary expression of concern. After publication, IEEE learned that the article contains tortured, or nonsensical, phrases which may impact the reliability of the results. The article is under investigation by a duly constituted expert committee and this note will remain appended to the article until the review process is complete.

This is a temporary expression of concern. After publication, IEEE learned that the article contains tortured, or nonsensical, phrases which may impact the reliability of the results. The article is under investigation by a duly constituted expert committee and this note will remain appended to the article until the review process is complete.

Urban waste management poses significant challenges due to the growing volume and complexity of waste generated in cities. Effective classification of waste into degradable and non-degradable categories is critical for optimizing waste disposal, promoting recycling, and mitigating environmental impacts. This report presents a comprehensive exploration of an adaptive machine learning algorithm for image-based waste classification, designed to enhance municipal waste management systems. The proposed approach integrates advanced image processing techniques with machine learning models, such as Convolutional Neural Networks (CNNs) and their efficient variants, to classify waste with high accuracy. A citizen reporting interface allows users to capture and upload waste images, which are processed in real-time by a centralized system. The adaptive algorithm incorporates preprocessing steps, feature extraction, and model inference to deliver actionable insights, enabling municipal authorities to deploy appropriate waste collection mechanisms promptly. Through a detailed literature review, the report examines existing methodologies, benchmarks their performance, and identifies key challenges, including data quality, scalability, and integration with urban infrastructure. Case studies from global implementations illustrate the transformative potential of AI-driven waste management systems. Furthermore, the report outlines future directions, such as leveraging advanced deep learning architectures, real-time deployment using edge computing, and expanding classification to include hazardous and recyclable waste categories. This work contributes to the growing field of smart waste management by demonstrating how machine learning can address critical urban challenges. By fostering collaboration between technology and sustainability, the proposed framework lays the foundation for cleaner and more sustainable cities.

Abstract— Breast cancer remains one of the leading causes of mortality among women worldwide, making early and accurate diagnosis crucial for improving patient outcomes. Traditional machine learning models often struggle to maintain high classification accuracy due to data variability and the complex nature of tumor characteristics. This study proposes an adaptive voting ensemble learning algorithm to enhance breast cancer classification performance. The ensemble integrates multiple classifiers—such as Decision Trees, Support Vector Machines, and K-Nearest Neighbors—by assigning dynamic weights based on each model's real-time performance. The algorithm is evaluated using the Wisconsin Breast Cancer Dataset (WBCD), and its effectiveness is measured against individual classifiers using metrics like accuracy, precision, recall, and F1-score. Results demonstrate that the adaptive ensemble significantly outperforms standalone models, offering a more robust and reliable approach for breast cancer prediction. This method shows promise for application in clinical decision support systems, contributing to more accurate diagnostics and better treatment planning. Keywords- Breast Cancer Classification, Adaptive Voting, Ensemble Learning, Machine Learning, Wisconsin Breast Cancer Dataset (WBCD), Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Medical Diagnosis, Predictive Modeling

Isomap is a classic and representative manifold learning algorithm for nonlinear dimensionality reduction, which aims to circumvent the problem of “the curse of dimensionality” and attempts to recover the intrinsic structure hidden in high-dimensional data based on the assumption that data lie in or near a single manifold. However, Isomap fails to work when data set consists of multi-clusters or multiple sub-manifolds due to the requirement of preserving global geometric structure within data points. Several extended Isomap algorithms have been proposed to handle this issue, such as D-C Isoamp, M-Isomap, etc.. Here, we describe a new multi-manifolds learning algorithm by using a universal concept of neighbor: natural nearest neighbor (3N neighbor), which can be utilized automatically to construct a single or multiple adaptive neighborhood graphs that roughly coincide with the corresponding real multi-manifolds. The new algorithm preserves local adaptive neighborhood graph structure in terms of multi-manifolds as good as possible, in contrast to the previous algorithms, no category information about multi-manifolds are required. So it is a natural extension to the original Isomap, and also an adaptive multi-manifolds learning algorithm without the use of free parameter.

Software fault detection is crucial for ensuring reliable and high-quality software systems. However, traditional fault detection methods often rely on manual inspection or rule-based techniques, which are time-consuming and prone to human errors. In this research, the researchers propose an enhanced fault detection approach using an adaptive neural transfer learning algorithm. The goal is to leverage the power of neural networks and adaptability to improve fault detection accuracy and classification performance. The problem addressed in this research is the need for more effective fault detection methods that can handle the complexities of modern software systems. Existing fault detection techniques lack adaptability and struggle to cope with diverse software scenarios. Neural networks have shown promise in pattern recognition and classification tasks, making them suitable for fault detection. However, fixed architectures and training strategy limit their performance in different software contexts. To address this problem, the research proposes an adaptive neural transfer learning algorithm for fault detection. The algorithm dynamically adjusts its neural network architecture and training process based on the characteristics of the software under test. It incorporates adaptive mechanisms, such as adjusting learning rates and regularization techniques, to optimize performance. Real-time feedback and performance evaluation during the training process drive the adaptive mechanisms. To evaluate the proposed approach, the researchers conducted a series of experiments using diverse software systems and fault scenarios. The research compared the performance of the adaptive algorithm with traditional fault detection methods, including rule-based techniques and fixed neural network architectures. Evaluation metrics such as accuracy, precision, recall, and F1 score were used. The results consistently show that the adaptive neural transfer learning algorithm outperforms existing methods, achieving higher fault detection accuracy and improved classification performance.

An agent must learn and adapt quickly when playing against other agents. This process is challenging in particular when playing in stochastic environments against other learning agents. In this paper, we introduce a fast and adaptive learning algorithm for repeated stochastic games (FAL-SG). FAL-SG utilizes lossy game abstraction to reduce the state space of the game and facilitate learning and adapting rapidly. We analyze FAL-SG's performance by proving bounds on the abstraction loss and prediction mistakes and show that FAL-SG satisfies three criteria prescribed for multiagent learning algorithms. We successfully establish the robustness and scalability of FAL-SG with extensive theoretical and experimental results.

Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks adequate learning algorithm especially when it is used in a hybrid-type controller. Part I of this work was devoted to introduce a new CMAC adaptive learning algorithm. Part II will be directed to experimental application of new learning algorithm of a CMAC based hybrid-type real time controller. The proposed controller is applied for the trajectory tracking of a piezoelectric actuated tool post. It has been proven that the piezoelectric actuated tool post has hysteretic behavior. Extensive experiments have been carried out on the experimental setup to evaluate the proposed adaptive learning algorithm of CMAC. Only few experiments and their results are being presented. In these experiments, the performance of the piezoelectric actuated tool post has been examined and evaluated using different types of control algorithms and applying external load disturbance. The control performance of the proposed controller is compared with those of conventional controllers (PI controller and the conventional CMAC based controller). The experimental results showed that performance of the hybrid-type controller using the proposed learning algorithm is stable and more effective than that of the conventional controllers. Testing and comparing the learning ability of the proposed learning algorithm with that of the conventional CMAC learning algorithm indicated the effectiveness of the learning ability of the proposed algorithm. Finally, the response using the proposed hybrid-type controller is slightly better than using the conventional PI controller under the effect of external load disturbance.

Volumetric efficiency modelling of internal combustion engines based on a novel adaptive learning algorithm of artificial neural networks

The detection of zero-day vulnerabilities remains one of the most critical challenges in modern cybersecurity. Traditional detection systems, primarily reliant on signature-based methods, are ineffective against unknown or novel attacks. This book chapter explores the integration of adaptive machine learning algorithms for real-time zero-day vulnerability detection, highlighting the transition from conventional approaches to intelligent, dynamic solutions. Emphasis is placed on the evolution of machine learning techniques, including supervised, unsupervised, and semi-supervised learning, which enable the identification of previously unseen threats. The chapter also investigates the challenges faced in deploying machine learning models in real-time environments, such as high-dimensionality data, feature selection, and the need for continuous adaptation to emerging attack patterns. Additionally, it covers key tools and frameworks, such as TensorFlow, Apache Spark, and Apache Kafka, which support the development of scalable, low-latency detection systems. The potential of these frameworks to handle large-scale data streams while maintaining real-time performance is critical for enhancing the resilience of cybersecurity systems. By leveraging machine learning, organizations can significantly improve their capacity to identify and mitigate zero-day vulnerabilities before they cause substantial damage. This chapter provides an in-depth analysis of these techniques, offering insights into their practical applications and their contributions to advancing the field of cybersecurity.