An Automatic and Adaptive Multi-manifolds Learning Algorithm

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.

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.

This paper proposes a hierarchical clustering algorithm based on Saturated Neighbor Graph -- hi-CLUBS and a new concept, natural nearest neighbor, which adopts a parameter-less algorithm of searching the natural neighbors for each point in a dataset. In the work, the Saturated Neighbor Graph is constructed by the natural nearest neighbor firstly. Then modularity is introduced into graph partitioning algorithm, with which the generated graph is partitioned into small sub-clusters without any parameters. Finally, these initial sub-clusters are repeatedly merged with another cluster according to similarity measurement based on connectivity and closeness, until the desired cluster number is reached. The results show that hi-CLUBS produces a set of final clusters achieves better quality than the traditional clustering algorithms.

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.

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

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.

Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm

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

For any newly developed mechanism the most challenging task is the controller. The controller is an algorithm that organizes the mechanism input energy to perform a specified task. Robot control usually requires the directing of signals or fluid paths of power elements to indicate robot end-effector dynamic behaviour. Furthermore, robot control can be divided into two main areas: kinematic control (the coordination of the links of the kinematic chain to produce desired motions of the robot) and dynamic control (driving the actuators of the mechanism to follow the commanded position velocities). These control strategies are widely used in most robots involving position coordination in Cartesian space by a direct kinematic method (Karlik and Aydin, 2000). In this chapter, an artificial neural network (ANN) adaptive learning algorithm has been implemented for dynamic behaviour control of a new two-degreeof-freedom (2DOF) serial ball-and-socket actuator. The ANN provides computer simulation of human brain activity that gives computers the ability to learn and predict a decision for a specific task. The ANN requires a specific network design followed by a training process. A variety of modifications could be carried out for the network design during the training process. For a robot control scheme there are many uncertainties in the parameters of both the actuators (hydraulic, pneumatic, and electric drivers) and the mechanical parts of the manipulators (Cheah et al., 2003, Tso & Law, 1993, Mills, 1994, Yang & Chu, 1993, Tsao & Tomizuka, 1994, Park & Cho, 1992, Ambrosino et al., 1998). Therefore, to cover the overall complexity of the robot control problem and the quest for a truly autonomous robot system, the application of an ANN to the robot control scheme has been considered (Ananthraman, 1991, Cruse & Bruwer, 1990, Kuperstein & Wang, 1990, Miller et al., 1990, Hasan et al., 2007, Abdelhameed, 1999, Sharkey & Noel, 1997, Brun-Picard et al., 1999). In addition, the proposed hydraulic power system is one with highly non-linear behaviour. Variations in parameters affect the hydraulic system operation and performance, e.g. the laminar and turbulent flows, channel geometry, friction results in the system equation, the relation between flow velocity and pressure, and oil viscosity (Knohl & Unbehauen, 2000). Hence, to 5

Text length considered adaptive bagging ensemble learning algorithm for text classification

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.

Learning boolean halfspaces with small weights from membership queries

Contextual language processing plays an important role for the post-processing of speech recognition. The purpose of the contextual language processing is to find the most plausible candidate for each syllable with the maximum likelihood probability. Generally, the performance of the probabilistic model is affected by two major errors, i.e., modeling error and estimation error in training corpus. In this paper, we focus on the problem of estimation error in training corpus. An adaptive learning algorithm is proposed to decrease the influences of variant run-time context domain. It shows which objects are to be adjusted and how to adjust their probabilities by a neural network model. The resulting techniques are greatly simplified and robust. The experimental results demonstrate the effects of the learning algorithm from generic domain to specific domain. In general, these techniques can be easily extended to various language models and corpus-based applications.

A problem of an adaptive neural network system synthesizes in a case of a nonlinear actuation channel's singularities is considered. The singularities can cause a loss of stability if the back propagation algorithm is used without modification. Known approaches of solving the problem are considered and a new modification of adaptive learning algorithm is proposed that can be applied to a wider class of processes.

Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object