Adaptive Machine Learning Techniques Research Articles

ADVANCES IN ULTRAPRECISION DIAMOND TURNING: TECHNIQUES, APPLICATIONS, AND FUTURE TRENDS

Advances in ultraprecision diamond turning have revolutionized manufacturing processes across various industries, offering unparalleled precision and surface quality in the fabrication of optical components, microfluidic devices, and advanced mechanical parts. This review delves into the techniques, applications, and future trends in ultraprecision diamond turning, highlighting recent advancements and potential trajectories. Techniques in ultraprecision diamond turning have evolved significantly, driven by innovations in machine design, tooling materials, and control systems. Diamond turning machines equipped with ultra-stiff structures, high-speed spindles, and advanced feedback mechanisms enable sub-nanometer level accuracy and surface finishes down to Angstrom levels. Additionally, advancements in single-point diamond turning (SPDT), fast tool servo (FTS), and deterministic microgrinding (DMG) techniques further enhance the versatility and precision of the process. Applications of ultraprecision diamond turning span a wide range of industries, including aerospace, automotive, biomedical, and telecommunications. In optics manufacturing, diamond turning facilitates the production of aspheric lenses, freeform optics, and diffractive optical elements with unprecedented accuracy, contributing to the development of high-performance imaging systems and laser applications. Moreover, in the biomedical field, diamond-turned microfluidic devices enable precise control over fluid flow and particle manipulation, empowering advancements in drug delivery systems and lab-on-a-chip technologies. Future trends in ultraprecision diamond turning are poised to address challenges related to scalability, multi-material processing, and in-situ metrology. Integration of adaptive control algorithms and machine learning techniques promises enhanced process stability and predictive maintenance, optimizing productivity and reducing downtime. Furthermore, the development of hybrid manufacturing approaches, combining diamond turning with additive manufacturing or laser processing, offers novel avenues for fabricating complex, multi-functional components with improved efficiency and cost-effectiveness. The ongoing advancements in ultraprecision diamond turning techniques, coupled with diverse applications across industries, underscore its pivotal role in advancing manufacturing capabilities. Anticipated future trends hold promise for further expanding the scope and impact of this technology, driving innovation and pushing the boundaries of precision engineering.
 Keywords: Ultraprecision, Diamond, Turning, Technique, Review.

Enhanced image diagnosing approach in medicine using quantum adaptive machine learning techniques

Enhanced image diagnosing approach in medicine using quantum adaptive machine learning techniques

Computer Hardware Fault Detection based on Machine Learning

In order to solve the computer fault detection problem of machine learning, the author proposes a computer hardware fault detection problem based on machine learning. The method combines mutual information and class separability to analyze their relationship, which improves classification accuracy. This study presents an adaptive machine learning technique for the adaptive fusion of data from multiple sources. In addition, the mCRC algorithm seeks for the optimal feature subset using the enhanced forward floating search method, thereby overcoming the limitation that the mRMR algorithm does not specify how to determine the final feature subset. The classification accuracy of the mCRC algorithm is approximately 1% better than that of the mRMR algorithm, and the size of the final feature subset of the mCRC algorithm is 22% smaller than that of the final subset of the mRMR algorithm. Conclusion: the ReMAE algorithm has a higher rate of accurate failure prediction.

Enhancing Signature Verification through Neural Network Ensemble

This study presents a signature authentication mechanism to prevent forgery. In the actual world, handling a large collection of data and detecting genuine signatures with reasonable accuracy is often difficult for any verification system. As a result, artificial intelligence techniques are used that can learn from a large data set during the training phase and reply effectively during the application phase without wasting a lot of storage memory space or processing time. It should also be able to refresh its expertise based on real-world encounters on a regular basis. A Multi-Layered Neural Network Model is one such adaptive machine learning technique that is used in this study. Initially, a massive amount of data is gathered by photographing several authentic and fake signatures. The image quality is increased by applying image processing, which is followed by the feature extraction phase, which extracts specific unique standard statistical features.

Adapting Machine Learning Techniques for Developing Automatic Q&A Interaction Module for Translation Robots based on NLP

Research on Automatic Q&A Interaction Module of Computer-based Translation Robot is a study that focuses on developing an automatic question and answer (Q&A) interaction module for computer-based translation robots. The goal of the research is to enhance the capability of translation robots to perform more human-like interactions with users, particularly in terms of providing more efficient and accurate translations. In this paper proposed a Conditional Random Field Discriminative Analysis (CRFDA) for feature extraction to derive translation robot with Q&A. The proposed CRFDA model comprises of the discriminative analysis for the CRF model. The estimation CRF model uses the bi-directional classifier for the estimation of the feature vector. Finally, the classification is performed with the voting-based classification model for feature extraction. The performance of the CRFDA model is examined based on the Name Entity (Nes) in the TempVal1 &2 dataset. The extraction is based on the strict and relaxed feature model for the exact match and slight variation. The simulation analysis expressed that proposed CRFDA model achieves a classification accuracy of 91% which is significantly higher than the state-of-art techniques.

Machine learning for modern power distribution systems: Progress and perspectives

The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.

Introducing Adaptive Machine Learning Technique for Solving Short-Term Hydrothermal Scheduling with Prohibited Discharge Zones

The short-term hydrothermal scheduling (STHTS) problem has paramount importance in an interconnected power system. Owing to an operational research problem, it has been a basic concern of power companies to minimize fuel costs. To solve STHTS, a cascaded topology of four hydel generators with one equivalent thermal generator is considered. The problem is complex and non-linear and has equality and inequality constraints, including water discharge rate constraint, power generation constraint of hydel and thermal power generators, power balance constraint, reservoir storage constraint, initial and end volume constraint of water reservoirs, and hydraulic continuity constraint. The time delays in the transport of water from one reservoir to the other are also considered. A supervised machine learning (ML) model is developed that takes the solution of the STHTS problem without PDZ, by any metaheuristic technique, as input and outputs an optimized solution to STHTS with PDZ and valve point loading (VPL) effect. The results are quite promising and better compared to the literature. The versatility and effectiveness of the proposed approach are tested by applying it to the previous works and comparing the cost of power generation given by this model with those in the literature. A comparison of results and the monetary savings that could be achieved by using this approach instead of using only metaheuristic algorithms for PDZ and VPL are also given. The slipups in the VPL case in the literature are also addressed.

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.

A selective neighbour channels in Wi-Fi networks based on adaptive machine learning techniques

Due to the growing commercial exploitation of WiFi-based technologies in recent years and the lack of solutions for effective WiFi orchestration, spectrum utilization and user performance are often sub-optimal. The integration of WiFi-based radio resource management (RRM) and radio environmental maps (REMs) may create a cost-effective Smart-WiFi solution that optimizes underlying spectrum utilisation and network performance. The REM enables effective use of radio environmental data such as device location, estimated channel models, real-time network interference levels, WiFi channel occupancies, and so on. In WiFi-related settings, this information may be used to make intelligent and optimum RRM decisions. This study provides a new REM-based RRM strategy for managing and optimizing commercial WiFi devices that makes use of the underlying radio environmental data. The research uses a commercially available platform to illustrate the suggested solution, which includes on-the-fly radio environmental data gathering and optimal WiFi RRM allocation. In comparison to traditional WiFi networks, the simulation study findings reveal that the proposed Smart-WiFi leverages considerable performance advantages for large-scale situations.

Optimal paper web weight control system based on the Pontryagin’s maximum principle

The paper describes the stages of paper production, considers the structure of a paper-making machine. Questions related to the proof and use of the Pontryagin’s maximum principle in the theory of optimal control are considered. Optimal paper web weight control system based on the Pontryagin’s maximum principle is presented. Adaptive learning methods for modeling nonlinear systems represent some of the latest advances in adaptive algorithms and machine learning techniques designed to model and identify nonlinear systems. Real-world problems always involve a certain degree of non-linearity, which makes linear models a suboptimal choice. This article may be of interest to research engineers and practitioners in the study and application of control systems using adaptive regulators. This book serves as an essential resource for researchers, graduate students and doctoral students working in the field of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, and computational intelligence. This book may also be of interest to the industry market and practitioners working with a wide range of nonlinear systems.

Adaptive diagnostic machine learning technique for classification of cell decisions for AKT protein

Artificial intelligence techniques can unravel clinically consistent information in clinical data which in turn assist in decision-making. With the help of state-of-the-art techniques, diagnostic systems are used to identify various diseases by using different machine learning (ML) techniques. In this paper, a combination of ML techniques such as Radial Basis Function (RBF) and Multiple Layer Perceptron (MLP) was used to predict cell decisions (cell survival/death) of AKT protein. AKT signalling networks have various downstream consequences on cellular metabolism either directly through the regulation of nutrient transporters, metabolic enzymes or indirectly through the control of transcription factors that regulate the expression of metabolic pathways which determine cell survival, cell growth, and cell death. Experimental analysis was performed in this work to examine the signalling networks that determine cell survival/death decisions by using an amalgamation of three proteins for ten different combinations in 13 different slices for a period of 0–24 h. P-P plot, Q-Q plot, and histogram tests were used for data visualization to determine which distribution the data fits. In addition, goodness of fit test was also employed using distribution functions such as Weibull, Exponential, and Normal distribution to determine whether the data fits a distribution of a certain population. The results were validated by calculating the MTTF values. The results of the analysis performed show that the Weibull distribution yields remarkable results. Also the results obtained with the Multiple Layer Perceptron, MLP 10-8-1 was found to perform better than other techniques giving an accuracy of 99.33% when the exponential activation function was used. The results of the experimental study indicate that it is possible to create self-consistent cell-signalling compendia based on AKT protein data that have been computationally simulated to provide valuable insights for cell survival/death regulation.

ALGA: Active Learning-Based Genetic Algorithm for Accelerating Structural Optimization

Optimization of complex structures using standard evolutionary algorithms, like genetic algorithm (GA), is known to be computationally expensive, because a very large number of finite element analysis (FEA) must be conducted for each possible structural design during the optimization. In the literature, different pretrained surrogate models have been used to replace the FEA for improving the computational performance of the structural optimization. However, the optimal solution found by the optimizer often depends on the accuracy of the pretrained surrogate model itself. Moreover, large datasets may be needed for getting the required accuracy of the surrogate model. An adaptive machine learning technique called active learning is used in this paper to accelerate the evolutionary optimization of complex structures. An active learner is a machine-learning-based model that can interactively query the outputs of certain data points, whenever the model would be uncertain about those outputs. In the presented approach, the active learner helps the GA by predicting if the possible design is going to be feasible or infeasible, meaning if it satisfies the required constraints or not. If the active learner is uncertain about the output, an actual FEA is conducted, and it improves its own accuracy for future evaluations. The approach does not need a trained surrogate model before the optimization. The active learner adaptively learns about the structure during the optimization to improve the computational performance. The approach is used to optimize the classic 10-bar truss problem, the Hesse function, curvilinearly stiffened panels under buckling and stress constraints, and some benchmark constrained test functions. The results show that the approach has the potential to reduce the total required constraint evaluations by more than 50%.

Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems

With the prospect of a smart society in the foreseeable future, humans are experiencing an increased link to electronics in the digital world, which can benefit our life and productivity drastically. In recent decades, advances in the Human Machine Interface (HMI) have improved from tactile sensors, such as touchpads and joysticks, to now include the accurate detection of dexterous body movements in more diversified and sophisticated devices. Advancements in highly adaptive machine learning techniques, neural interfaces, and neuromorphic sensing have generated the potential for an economic and feasible solution for next-generation applications such as wearable HMIs with intimate and multi-directional sensing capabilities. This review offers a general knowledge of HMI technologies beginning with tactile sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on how machine learning, neural interfaces, and neuromorphic electronics can be used to enhance next-generation HMIs in an upcoming 5 G infrastructure and advancements in the internet of things and artificial intelligence of things in the near future. The efficient interactions with kinetic and physiological signals from human body through the fusion of tactile sensor and neural electronics will bring a revolution to both the advanced manipulation and medical rehabilitation.

Core‐level cybersecurity assurance using cloud‐based adaptive machine learning techniques for manufacturing industry

AbstractCybersecurity is the domain that ensures safeness in both individual system and overall network systems. The classification and learning approaches used in different machine learning (ML) techniques improve the protection of the cyber systems against various attacks. Techniques such as support vector machine (SVM), neural networks (NN), principle component analysis (PCA), and reinforcement learning (RL) are used against various cyber threats. Applying these techniques at the front‐end services (either online or offline) makes less effect than back end process‐level services of any computer system. The proposed work analyzes the benefits of implementing customized ML and deep learning (DL) techniques on the core of the operating system than application level services, which in effect increases the speed and correctness of attack detection. The core (kernel) of the operating system has the capability to extract all internal attributes of process and file systems. The kernel space security activities can be improved by proposed work where the process level attributes classified using ML and DL techniques. The cloud service helps in sharing of the kernel abilities of the system ensuring core level security. The following work uses recurrent NN (RNN), SVM, PCA, and RL for analyzing the system data collected using Process Explorer. This technique finds application in manufacturing domain where the systems are protected from the various attacks to secure the data of the manufacturing company.

An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.

Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges

In the last few years, there has been an exponential increase in the usage of the autonomous vehicles across the globe. It is due to an exponential increase in the popularity and usage of the artificial intelligence techniques in various applications. Traffic flow predication is important for autonomous vehicles using which they decide their itinerary and take adaptive decisions (for example, turn let or right, move straight, lane change, stop, or accelerate) with respect to their surrounding objects. From the existing literature, it has been observed that research on autonomous vehicles has shifted from the traditional statistical models to adaptive machine learning techniques. However, existing machine learning models may not be directly applicable in this environment due to non-linear complex relationship between spatial and temporal data collected from the surroundings during the aforementioned adaptive decisions taken by the vehicles. So, with focus on these issues, in this article, we explore various deep learning models for traffic flow prediction in autonomous vehicles and compared these models with respect to their applicability in modern smart transportation systems. Various parameters are chosen to have a relative comparison among different deep learning models. Moreover, challenges and future research directions are also discussed in the article.

Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors.

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with breast cancer. The present study was conducted to develop a novel machine learning–based classification scheme for predicting the risk factors of SPCs in breast cancer survivors. The proposed scheme was based on the XGBoost classifier with the following four comparable strategies: transformation, resampling, clustering, and ensemble learning, to improve the training balanced accuracy. Results suggested that the best prediction accuracy for an empirical case is the XGBoost associated with the strategies of resampling and clustering. The experimental results showed that age, sequence of radiotherapy and surgery, surgical margins of the primary site, human epidermal growth factor, high-dose clinical target volume, and estrogen receptors are relatively more important risk factors associated with SPCs in patients with breast cancer. These risk factors should be monitored for the early detection of breast cancer. In conclusion, the proposed scheme can support the important influence of personality and clinical symptom representations in all phases of the primary treatment trajectory. Our results further suggested that adaptive machine learning techniques require the incorporation of significant variables for optimal predictions.

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears.

Visual inspection for the quantification of malaria parasitaemiain (MP) and classification of life cycle stage are hard and time taking. Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k-nearest neighbor, Naïve Bayes and multi-class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non-infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing.

Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques

AbstractHigh-frequency stock trend prediction using machine learners has raised substantial interest in literature. Nevertheless, there is no gold standard to select the inputs for the learners. This paper investigates the approach of adaptive input selection (AIS) for the trend prediction of high-frequency stock index price and compares it with the commonly used deterministic input setting (DIS) approach. The DIS approach is implemented through computation of technical indicator values on deterministic period parameters. The AIS approach selects the most suitable indicators and their parameters for the time-varying dataset using feature selection methods. Two state-of-the-art machine learners, support vector machine (SVM) and artificial neural network (ANN), are adopted as learning models. Accuracy and F-measure of SVM and ANN models with both the approaches are computed based on the high-frequency data of CSI 300 index. The results suggest that the AIS approach using t-statistics, information gain and ROC methods can achieve better prediction performance than the DIS approach. Also, the investment performance evaluation shows that the AIS approach with the same three feature selection methods provides significantly higher returns than the DIS approach.

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting

Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include (1) discarding those observations, (2) treating them as non-events, (3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system.