Adaptive Machine Learning Algorithm Research Articles

Predicting Future Birth Rates with the Use of an Adaptive Machine Learning Algorithm: A Forecasting Experiment for Scotland.

The total fertility rate is influenced over an extended period of time by shifts in population socioeconomic characteristics and attitudes and values. However, it may be impacted by macroeconomic trends in the short term, although these effects are likely to be minimal when fertility is low. With the objective of forecasting monthly deliveries, this study concentrates on the analysis of registered births in Scotland. Through this approach, we examine the significance of precisely forecasting fertility trends, which can subsequently aid in the anticipation of demand in diverse sectors by allowing policymakers to anticipate changes in population dynamics and customize policies to tackle emerging demographic challenges. Consequently, this has implications for fiscal stability, national economic accounts and the environment. In conducting our analysis, we incorporated non-linear machine learning methods alongside traditional statistical approaches to forecast monthly births in an out-of-sample exercise that occurs one step in advance. The outcomes underscore the efficacy of machine learning in generating precise predictions within this particular domain. In sum, this research will comprehensively demonstrate a cutting-edge model of machine learning that utilizes several attributes to assist in clinical decision-making, predict potential complications during pregnancy and choose the appropriate delivery method, as well as help in medical diagnosis and treatment.

A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments.

The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%-96% higher accuracy than the existing approaches.

Incorporating Evidence-Based Gamification and Machine Learning to Assess Preschool Executive Function: A Feasibility Study.

Computerized assessments and digital games have become more prevalent in childhood, necessitating a systematic investigation of the effects of gamified executive function assessments on performance and engagement. This study examined the feasibility of incorporating gamification and a machine learning algorithm that adapts task difficulty to individual children's performance into a traditional executive function task (i.e., Flanker Task) with children ages 3-5. The results demonstrated that performance on a gamified version of the Flanker Task was associated with performance on the traditional version of the task and standardized academic achievement outcomes. Furthermore, gamification grounded in learning science and developmental psychology theories applied to a traditional executive function measure increased children's task enjoyment while preserving psychometric properties of the Flanker Task. Overall, this feasibility study indicates that gamification and adaptive machine learning algorithms can be successfully incorporated into executive function assessments with young children to increase enjoyment and reduce data loss with developmentally appropriate and intentional practices.

ENHANCING ENTERPRISE ENERGY MANAGEMENT WITH IOT-BASED MONITORING SYSTEMS

This article offers an in-depth exploration of how IoT technology can revolutionize energy management within enterprises. It provides a comprehensive elucidation of the system's architectural framework, spotlighting its innate capacity for real-time data collection, intricate analysis, and responsive control. The discussion extends to the seamless integration of IoT-enabled sensors, cutting-edge data analytics, and adaptive machine learning algorithms, all orchestrated to orchestrate optimal energy consumption strategies.A pivotal facet of this study underscores the paramount importance of sustainability and cost-effectiveness in the contemporary enterprise landscape. It underscores how the proposed IoT-based system assumes the role of a catalyst in realizing sustainability objectives. It achieves this by vigilant monitoring of energy utilization, waste minimization, and meticulous resource allocation. Moreover, this article presents a panorama of tangible outcomes by examining real-world deployments and substantiated case studies of the IoT-driven energy supply monitoring system. It effectively illustrates the multifaceted benefits, which encompass substantial energy conservation, streamlined operational efficiency, and a markedly reduced carbon footprint.In conclusion, this research accentuates the transformative potential of IoT technology in the realm of enterprise energy supply monitoring. It proffers a comprehensive, forward-looking solution for businesses to not only elevate their sustainability quotient but also optimize their operational prowess, thus contributing substantively to a greener, more efficient future.

Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.

The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.

Adaptive secure malware efficient machine learning algorithm for healthcare data

AbstractMalware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT‐based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content‐aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content‐aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre‐ and post‐train malware models. AMDML is based on a rule called ‘federated learning,’ which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML outperforms machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes.

Robust Vibration Output-only Structural Health Monitoring Framework Based on Multi-modal Feature Fusion and Self-learning

Output-only structural health monitoring is a highly active research direction because it is a promising methodology for building digital twin applications providing near-real-time monitoring results of the structure. However, one of the technical bottlenecks is how to work effectively with multiple high-dimensional vibration signals. To address this question, this study develops a two-stage data-driven framework based on various advanced techniques, such as time-series feature extractions, self-learning, graph neural network, and machine learning algorithms. At first, multiple features in statistical, time, and spectral domains, are extracted from raw vibration data; then, they subsequently enter a graph convolution network to account for the spatial correlation of sensor locations. After that, the high-performance adaptive boosting machine learning algorithm is leveraged to assess structures' health states. This method allows for learning a lower-dimensional yet informative representation of vibration data; thus, the subsequent monitoring tasks could be performed with reduced time complexity and economical computational resources. The performance of the proposed method is qualitatively and quantitatively demonstrated through two examples involving both numerical and experimental structural data. Furthermore, comparison and robustness studies are carried out, showing that the proposed approach outperforms various machine learning/deep learning-based methods in terms of accuracy and noise/missing-robustness.

Combination of Adaptive Neuro Fuzzy Inference System and Machine Learning Algorithm for Recognition of Human Facial Expressions

Combination of Adaptive Neuro Fuzzy Inference System and Machine Learning Algorithm for Recognition of Human Facial Expressions

An adaptive machine learning algorithm for the resource-constrained classification problem

Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis, hiring decisions when filling a limited number of positions, and defect detection in manufacturing settings under a limited inspection budget. Typical classification algorithms treat the learning process and the resource constraints as two separate and sequential tasks. We develop an adaptive learning approach that considers resource constraints and learning jointly by iteratively fine-tuning misclassification costs. Via a structured experimental study using a publicly available data set, we evaluate a decision tree classifier that utilizes the proposed approach. The adaptive learning approach performs significantly better than alternative approaches, especially for difficult classification problems in which the performance of common approaches may be unsatisfactory. The suggested approach reaches similar classification decisions for different costs, thus it may be useful when misclassification costs are not known precisely or are costly to achieve. We envision the suggested learning approach as an important addition to the repertoire of techniques for handling resource-constrained classification problems.

Adaptive machine learning algorithm for human target detection in IoT environment

Adaptive machine learning algorithm for human target detection in IoT environment

Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence.

Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms.

Comparison of adaptive neuro-fuzzy inference system (ANFIS) and machine learning algorithms for electricity production forecasting

Combined cycle power plants (CCPP) are popular in the energy sector for the production of electricity, and are the union of two thermodynamic cycles, corresponding to the steam turbine and the gas turbine. This paper presents the application of several machine learning (ML) techniques and the adaptive neuro-fuzzy inference system (ANFIS) to predict the hourly electricity production in a CCPP. The models were developed using 5-fold cross-validation with the collected features of temperature, exhaust pressure, relative humidity, ambient pressure, and electricity production per hour (the target feature). The hyperparameters of the tested models were optimized. The correlation and determination coefficients of the models were higher than 92%, showing a significant performance. The ANFIS (r = 98% e R2 = 95%) model shows the lowest values in the evaluated error metrics, compared to the other ML models. Finally, the results showed the effectiveness of ANFIS in predicting the hourly production of electricity in CCPP.

QAM-DMT of Hybridly Integrated EAM-DFBLD Chip-on-Board With Adaptive Machine Learning Algorithm for 149.6-Gbit/s BtB and 138-Gbit/s 10-km-SMF Transmission

By using the generalized frequency division multiplexing (GFDM) and the machine learning algorithm for self-feedback amplitude/phase pre-distortion, the electro-absorption modulator integrated distributed-feedback semiconductor laser diode (EAM-DFBLD) is encoded with improved transmission quality and capacity. Both back-to-back (BtB) and 10-km single-mode fiber (SMF) transmissions are preliminarily demonstrated for inter-data-center applications. Optimizing the EAM-DFBLD transmitter at 1306.8 nm with a bias current of 100 mA and a bias voltage of −1.5 V can provide a 34.5-GHz bandwidth for 16-QAM discrete multitone (DMT) and GFDM formats. The parametric comparison reveals that the 16-QAM GFDM with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> = 16 can improve its performance from BtB to 10-km SMF transmission with decoding BER slightly increasing from 1.44 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> to 2.82 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . The EAM-DFBLD carried GFDM data also provides a better power penalty by -0.25 dB. This enables higher raw data rates of 149.6 Gbit/s under BtB case and 138 Gbit/s in 10-km SMF transmission link below the forward error correction (FEC) criterion. The GFDM format carried by the EAM-DFBLD transmitter thus becomes the more suitable candidate than the DMT to implement ultra-high-speed inter-data-center and data access link applications.

Detection of early-stage ovarian cancer using a liquid biopsy test based on extracellular vesicle proteins.

e17558 Background: We have recently conducted a study to evaluate the predictive value of proteins associated with extracellular vesicles (EVs) isolated using an alternating current electrokinetic (ACE) system, for the detection of early stage (I &amp; II) ovarian cancer cases. Ovarian cancer patients, typically, have few symptoms in stages I &amp; II; the disease is often undetected until it is advanced. Currently, only 15% of cases are detected at early stages when clinical treatment outcomes are improved. Methods: We tested the feasibility of detecting the presence of early stage (I-II) ovarian cancer using protein biomarkers carried by EVs in patient blood. Using an ACE microelectrode array, EVs were purified from ovarian cancer patient plasma (N = 70; Stage I = 51, Stage II = 19) and female non-cancer donors (N = 290) and tested for the presence of cancer biomarker proteins with multiple bead-based multiplex immunoassay kits. An adaptive boosted trees machine learning algorithm with 100x cross-validation was developed to identify the most important features for differentiation between cases and controls. Results: The machine learning algorithm identified relevant biomarkers for the detection of ovarian cancer with a receiver-operator characteristic area under the curve (AUC) of 0.952 (95% CI: 0.918 – 0.985). Overall sensitivity was 72.9% at 99% specificity, rising to 84.3% at 95% specificity. When further stratified, Stage I sensitivity was 70.6% and stage II sensitivity was 78.9% at the 99% specificity level, as expected. We also separated the cases into endometroid carcinoma (N = 24) and serous carcinoma (N = 36). At the 99% specificity level, the sensitivity was 87.5% and 63.9%, respectively. Conclusions: These results suggest that it is possible to detect early-stage ovarian cancer by analyzing the EV-bound proteins isolated with the ACE technology. EVs may carry the earliest signals of cancer development and a blood-based EV-protein biomarker test can yield high sensitivity and specificity. Stable biomarker detection with small input volumes may be possible. These results require further investigation in larger datasets to validate the current observations with the potential application for general, routine screening, as well as for populations at elevated disease risk.

A cryptic pocket in Ebola VP35 allosterically controls RNA binding

Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola’s replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins.

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Machine-learning-enabled adaptive signal decomposition for a brain-computer interface using EEG

Background and objectiveThe use of adaptive signal decomposition methods and machine learning (ML) algorithms have gained interest in biomedical applications. Brain-computer interfaces (BCIs) are one such application that uses electroencephalogram (EEG) signals to assist people with severe brain disabilities to communicate with their external environment. This paper uses recent adaptive signal decomposition methods such as empirical mode decomposition, empirical wavelet transform, variational mode decomposition, and variational non-linear chirp mode decomposition to decompose EEG signals into various modes. Three publicly available EEG datasets are employed. EEG datasets are decomposed into modes using the aforementioned methods. Linear and non-linear time-domain features are extracted from the modes. A feature set is obtained by selecting highly discriminant features using the Kruskal-Wallis test. Classification is performed using four recent ML-based algorithms. ResultsThe maximum classification accuracy achieved is 89.6 ± 4.6% and 61.1 ± 5.1% in binary and multiclass (seven classes) signals, respectively, for dataset 1. The effectiveness of the proposed model is measured by evaluating the performance parameters such as parameters recall (REC), specificity (SPEC), precision (PREC), F1-score, and area under the curve (AUC). For binary classification, REC, SPEC, PREC, F1-score, and AUC are 95%, 94%, 38%, 55%, and 89% (95% CI: [0.871–0.937]), respectively. Also, for multiclass classification, REC, SPEC, PREC, F1-score, and AUC are achieved as 60%, 93%, 60%, 60%, and 89% (95% CI: [0.873–0.971]), respectively. ConclusionsThe results show that the employed algorithms achieve excellent accuracy for the available datasets. The results also strengthen the possibility of using EEG signals for silent communication.

Forecasting asylum-related migration flows with machine learning and data at scale

The sudden and unexpected migration flows that reached Europe during the so-called ‘refugee crisis’ of 2015–2016 left governments unprepared, exposing significant shortcomings in the field of migration forecasting. Forecasting asylum-related migration is indeed problematic. Migration is a complex system, drivers are composite, measurement incorporates uncertainty, and most migration theories are either under-specified or hardly actionable. As a result, approaches to forecasting generally focus on specific migration flows, and the results are often inconsistent and difficult to generalise. Here we present an adaptive machine learning algorithm that integrates administrative statistics and non-traditional data sources at scale to effectively forecast asylum-related migration flows. We focus on asylum applications lodged in countries of the European Union (EU) by nationals of all countries of origin worldwide, but the same approach can be applied in any context provided adequate migration or asylum data are available. Uniquely, our approach (a) monitors drivers in countries of origin and destination to detect early onset change; (b) models individual country-to-country migration flows separately and on moving time windows; (c) estimates the effects of individual drivers, including lagged effects; (d) delivers forecasts of asylum applications up to four weeks ahead; (e) assesses how patterns of drivers shift over time to describe the functioning and change of migration systems. Our approach draws on migration theory and modelling, international protection, and data science to deliver what is, to our knowledge, the first comprehensive system for forecasting asylum applications based on adaptive models and data at scale. Importantly, this approach can be extended to forecast other social processes.

WITHDRAWN: Breast cancer detection using random forest classifier

Breast Cancer is a menacing disease and is commonly seen in women. Based on the severity, breast cancer is classified into duplet variants. One is Benign type of breast cancer, which can be detected at early stages and can be cured with the help of medication. Other is Malignant type of breast cancer, which shows severe affect and might lead to death. To detect breast cancer at early stages, wide variety of algorithm techniques are used such as Navie Bayes, Convolution Neural Network, KNN, adaptive voting ensemble machine learning algorithm and so on. Most latest algorithm that is under practice is adaptive voting ensemble machine learning algorithm. In this algorithm, Wisconsin Breast Cancer dataset and CNN algorithm is used to classify images and for object detection. But the major drawback of ensemble machine learning algorithm is lack of accuracy. It is proved that Neutral Network works more effective on humans mostly in analyzing data and to perform pre-diagnosis without medical knowledge. In this paper, we propose Random Forest Classifier algorithm to achieve more accuracy.

Fault Diagnosis and Asset Management of Power Transformer Using Adaptive Boost Machine Learning Algorithm

Dissolved Gas Analysis (DGA) data of liquid insulation used to find the incipient faults such as partial discharge, thermal faults of various temperatures, discharge of high and low energy faults, combination of electrical and thermal faults in transformers. The conventional approaches of DGA namely Gas Ratio method, Duval triangle method and the Neural Network seems to be time consuming and sometimes yield erroneous results. In this paper, Adaptive BOOST machine learning algorithm is proposed, which is effective in classifying the transformer incipient faults. The results of proposed algorithm is compared with the results of different other machine learning algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Ensembler algorithm for the same set of transformers data. From the comparison, it is evident that ADABOOST machine learning algorithm performs well.