Adaptive Classification Research Articles

A controllable neural network-based method for optimal energy management of fuel cell hybrid electric vehicles

Neural Networks (NNs) can be used for energy management of hybrid vehicles, but they are hard to tune in inference to adapt to different driving conditions. To make the NN-based energy management strategy more flexible, this paper proposes a controllable NN for optimal energy management of fuel cell hybrid electric vehicles. Inspired by the equivalent factor in the Equivalent Consumption Minimization Strategy (ECMS), we introduce an adjustable target variable for the final state as an input to the NN-based strategy. During training, classification and regression networks with single-step and multi-step inputs are considered. An efficient shooting method and an adaptive method are then introduced to realize the precise control of the final state and online parameter adaptation. Simulations of the proposed method and the benchmarking method are carried out in different battery discharge modes. Results demonstrate that the proposed shooting neural classifier can achieve 99.7% fuel optimality of dynamic programming in a similar computational time to the shooting ECMS, and the proposed adaptive neural classifier can adapt to different driving conditions and has better fuel economy than the adaptive ECMS.

RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods.

A Competitive Segmentation of the Human Brain using Artificial Neural Network Approach Towards MRI

The diagnosis of brain abnormalities, prognosis monitoring, and treatment evaluation all rely heavily on the magnetic resonance scan of brain tissue segmentation. Although numerous automated or semi-automatic methods have been proposed in the literature to reduce the need for human intervention, the degree of accuracy is frequently still significantly lower than that of manual segmentation. We give a clever technique for fragmenting the cerebrum utilizing a managed counterfeit brain organization system called artificial neural network (ANN) and volumetric shape models. In the beginning, in addition to the usual spatial-based and intensity-based image features, a level-set oriented brain boundary fitting technique is used to accomplish this. This is controlled by the picture intensity. The ANN is then informed of the number of important structures. Additionally, rather than directly applying standard guidelines to local appearances, this ANN learns local adaptive feature classification conditions. The outcomes demonstrate that the proposed strategy achieves competitive results in a relatively shorter time spent training.

A Comprehensive Review on Email Spam Classification with Machine Learning Methods

This comprehensive review delves into the realm of email spam classification, scrutinizing the efficacy of various machine learning methods employed in the ongoing battle against unwanted email communication. The paper synthesizes a wide array of research findings, methodologies, and performance metrics to provide a holistic perspective on the evolving landscape of spam detection. Emphasizing the pivotal role of machine learning in addressing the dynamic nature of spam, the review explores the strengths and limitations of popular algorithms such as Naive Bayes, Support Vector Machines, and neural networks. Additionally, it examines feature engineering, dataset characteristics, and evolving threats, offering insights into the challenges and opportunities within the field. With a focus on recent advancements and emerging trends, this review aims to guide researchers, practitioners, and developers in the ongoing pursuit of robust and adaptive email spam classification systems.

ECGTransForm: Empowering adaptive ECG arrhythmia classification framework with bidirectional transformer

Cardiac arrhythmias, deviations from the normal rhythmic beating of the heart, are subtle yet critical indicators of potential cardiac challenges. Efficiently diagnosing them requires intricate understanding and representation of both spatial and temporal features present in Electrocardiogram (ECG) signals. This paper introduces ECGTransForm, a deep learning framework tailored for ECG arrhythmia classification. By embedding a novel Bidirectional Transformer (BiTrans) mechanism, our model comprehensively captures temporal dependencies from both antecedent and subsequent contexts. This is further augmented with Multi-scale Convolutions and a Channel Recalibration Module, ensuring a robust spatial feature extraction across various granularities. We also introduce a Context-Aware Loss (CAL) that addresses the class imbalance challenge inherent in ECG datasets by dynamically adjusting weights based on class representation. Extensive experiments reveal that ECGTransForm outperforms contemporary models, proving its efficacy in extracting meaningful features for arrhythmia diagnosis. Our work offers a significant step towards enhancing the accuracy and efficiency of automated ECG-based cardiac diagnoses, with potential implications for broader cardiac care applications. The source code is available at https://github.com/emadeldeen24/ECGTransForm.

Adaptive intuitionistic fuzzy neighborhood classifier

Adaptive intuitionistic fuzzy neighborhood classifier

Robust and adaptive terrain classification and gait event detection system

Real-time gait event detection (GED) system can be utilized for gait analysis and tracking fitness activities. GED for various types of terrains (e.g., stair-walk, uneven surfaces, etc.) is still an open research problem. This study presents an inertial sensor-based approach for real-time GED system that works for diverse terrains in an uncontrolled environment. The GED system classifies three types of terrains, i.e., flat-walk, stair-ascend and stair-descend, with an average classification accuracy of 99%. It also accurately detects various gait events, including, toe-strike, heel-rise, toe-off, and heel-strike. It is computationally efficient, implemented on a low-cost microcontroller, works in real-time and can be used in portable rehabilitation devices for use in dynamic environments.

A Review, Synthesizing Frameworks, and Future Research Agenda: Use of AI & ML Models in Cardiovascular Diseases Diagnosis

Cardiovascular diseases (CVDs) continue to be a leading cause of morbidity and mortality worldwide. Early detection and accurate diagnosis of the initial phases of CVDs are crucial for effective intervention and improved patient outcomes. In recent years, advances in intelligent automation and machine learning (ML) techniques have shown promise in enhancing the accuracy and efficiency of CVD detection. This systematic review aims to comprehensively analyze and synthesize the existing literature on the application of intelligent automation and ML adaptive classifier models in the detection of the initial phase of cardiovascular disease within the realm of medical science. The review follows a rigorous systematic methodology, including comprehensive literature search, study selection, data extraction, and quality assessment. A wide range of scholarly articles from the reputed journal were searched to identify relevant studies published over a specified period. The selected studies were critically evaluated for methodological robustness and relevance to the research objective. The synthesis of findings reveals a diverse landscape of research endeavors focused on employing intelligent automation and ML adaptive classifier models for CVD detection. The review highlights the various types of ML algorithms utilized, such as neural networks, decision trees, and support vector machines, and their potential to enhance the accuracy of diagnosis by analyzing complex and heterogeneous data sources, clinical records, and omics data. Furthermore, the review discusses challenges and limitations encountered in implementing these models, including data quality, interpretability, and ethical considerations. It also underscores the importance of interdisciplinary collaboration between medical practitioners, data scientists, and domain experts to ensure the seamless integration of these innovative technologies into clinical practice. In conclusion, this systematic review underscores the significant advancements made in the field of intelligent automation and ML adaptive classifier models in the detection of the initial phase of cardiovascular disease. While acknowledging the potential of these approaches, it also emphasizes the need for further research, standardization, and validation to harness their full capabilities and contribute to more accurate, timely and personalized cardiovascular disease diagnosis and management.

Ensemble learning model identifies adaptation classification and turning points of river microbial communities in response to heatwaves.

Heatwaves are a global issue that threaten microbial populations and deteriorate ecosystems. However, how river microbial communities respond to heatwaves and whether and how high temperatures exceed microbial adaptation remain unclear. In this study, we proposed four types of pulse temperature-induced microbial responses and predicted the possibility of microbial adaptation to high temperature in global rivers using ensemble machine learning models. Our findings suggest that microbial communities in parts of South American (e.g., Brazil and Chile) and Southeast Asian (e.g., Vietnam) countries are likely to change due to heatwave disturbance from 25 to 37°C for consecutive days. Furthermore, the microbial communities in approximately 48.4% of the global river gauge stations are prone to fast stress inadaptation, with approximately 76.9% of these stations expected to exceed microbial adaptation after heatwave disturbances. If emissions of particulate matter with sizes not more than 2.5 μm (PM2.5, an indicator of human activities) increase by twofold, the number of global rivers associated with the fast stress adaptation type will decrease by ~13.7% after heatwave disturbances. Understanding microbial responses is crucially important for effective ecosystem management, especially for fragile and sensitive rivers facing heatwave events.

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Due to the large span of flight delay, numerous influencing factors and large number of flights, the collected flight delay data is characterized by large-scale and imbalance, which is considered as one of the largest challenges in balanced flight delay data classification by using conventional classification algorithms. To solve this problem, an adaptive multi-class classification approach of imbalanced flight delay data using synthetic minority over-sampling technique (SMOTE)-based convolutional neural network with sparrow search algorithm, namely SSA-LTCNN is proposed in this paper. Firstly, the SMOTE is used to reduce the imbalance between the classes of the flight delay data. Then, the qualitative analysis method is employed to determine the key parameters of the CNN, which are optimized by using SSA to establish an optimized CNN model. Finally, the optimized CNN model with more effective feature representation ability is employed to extract discriminative features from the balanced flight delay data automatically. The obtained features in the last fully connected layer of the optimized CNN model are used for flight delay classification. The MIT-BIH Arrhythmia Data is selected to verify the effectiveness of the proposed SSA-LTCNN method and the confusion matrix, classification accuracy, precision, recall and F1 score are also used to evaluate the performance of the proposed SSA-LTCNN method, which can get better performance than RF, DT, LR, CNN and QRSCNN. The actual flight delay data is processed to verify the effectiveness of the proposed classification approach, which can achieve higher accuracy than KNN, NB, UCNN, CNN, SSA-LCNN and SSA-TCNN in different time periods. The experimental results show that the SSA-LTCNN can fast and effectively realize the classification of MIT-BIH Arrhythmia Data and flight delay data.

Development of clustered machine learning technique for the modeling of scour profile induced by propeller jets

In this study novel clustered machine learning (CML) models were developed by applying different base learners including multi linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), classification and regression trees (CART), least-squares boosting (LSBoost), and random forest (RF) to predict scour profile induced by the jet of a propeller. Moreover, the results of CML models were compared with regular ML algorithms. The results show that the tree-based ensemble models, RF and LSBoost, are more accurate than CART. The ANIFS has the worst performance compared with other regular nonlinear methods, RMSE = 17.414 mm. The performance of MLR is also weak, RMSE = 35.63 mm and R2 = 0.088. However, the implementation of CML drastically increases the performance of MLR, RMSE = 15.053 mm, and R2 = 0.871. The CANFIS with RMSE = 8.757 mm, R2 = 0.976, and SI = 0 is ranked as the most accurate model. The results show that CLM enhances the performance of regular ML by up to 59.90%. The application of clustered ML is recommended for other complex problems. Finally, the results show that the derived models trained by other datasets very well predict measured scour profiles which datasets were not used in the training process.

Predicting body weight of South African Sussex cattle at weaning using multivariate adaptive regression splines and classification and regression tree data mining algorithms

ABSTRACT The use of multivariate adaptive regression splines (MARS) and classification and regression tree (CART) to estimate the live body weight at weaning age of the Sussex cattle breed remain poorly understood in South Africa. This study was conducted to examine the effect of linear body measurements on body weight at weaning using MARS and CART algorithms. The body weight and linear body measurements included sternum height, withers height, heart girth, hip height, body length, rump length and rump width were collected from 101 Sussex cattle (female = 57 and male = 44) at weaning. Goodness of fit criterions was used to select the best data mining algorithms. The results showed that MARS showed higher predictive performance in the criteria as compared to CART algorithm. The findings of the study suggest that MARS algorithm can be used to estimate the BW at weaning age in Sussex cattle breed. These findings might be helpful to cattle farmers in the selection criterions of breeding stock at weaning age.

Adaptation for Automated Drift Detection in Electromechanical Machine Monitoring.

Practical machine learning applications for streaming data can involve concept drift (the change in statistical properties of data over time), one-shot or few-shot learning (starting with only one or a few examples for each class), a scarcity of representative training data, and extreme verification latency (only the initial dataset has ground-truth labels). This work presents a framework for organizing signal processing and machine learning techniques to provide adaptive classification and drift detection. Nonintrusive load monitoring (NILM) serves as an ideal case study, as modern sensing solutions provide a wellspring of electromechanical data sources. There is a lack of training datasets that generalize across different load and fault scenarios. Accordingly, training must be accomplished with a limited set of data when deploying a NILM to a new power system. Also, loads can exhibit concept drift over time either due to faults or normal variation. NILM field data is used as an illustrative case study to demonstrate the proposed framework for adaptation and drift tracking.

An adaptive self-correction joint training framework for person re-identification with noisy labels

Current person re-identification (ReID) methods heavily rely on well-annotated training data, and their performance suffers from significant degradation in the presence of noisy labels that are ubiquitous in real-life scenes. The reason is that noisy labels not only affect the prediction results of the classifier, but also impede feature refinement, making it difficult to distinguish between different person features. To address these issues, we propose an Adaptive Self-correction Classification (ASC) loss and an Adaptive Margin Self-correction Triplet (AMSTri) loss. Specifically, ASC loss helps the network to produce better predictions by balancing annotations and prediction labels, and pays more attention to the minority samples with the help of a focusing factor. On the other hand, the AMSTri loss introduces an adaptive margin that varies with sample features to accommodate complex data variations, and utilizes predicted labels to generate reliable triples for feature refinement. We then present an end-to-end adaptive self-correction joint training framework incorporating ASC loss and AMSTri loss to achieve a robust ReID model. Our comprehensive experiments demonstrate that the proposed framework outperforms most existing counterparts.

Predicting body weight through biometric measurements in growing hair sheep using data mining and machine learning algorithms.

Determination of live weight, which is one of the most important features that determine meat production, is a very important issue for herd management and sustainable livestock. In this context, the necessity of finding alternative methods has emerged, especially in rural conditions, due to the difficulties to be experienced in finding the weighing tool. Especially for conditions with no weighing tool, it has been tried to establish relations between the information obtained from body measurements and live weight. Since these studies will differ from species to species and breed to breed, the need for new studies is extremely high. For this aim, it is to evaluate the body measurement information obtained with the present study using several statistical approaches. To implement this aim, several data mining and machine learning algorithms such as multivariate adaptive regression splines (MARS), classification and regression tree (CART), and support vector machine regression (SVR) algorithms were used for training (70%) and test (30%) sets. To predict final body weight, 280 hair sheep breeds (162 female and 118 male) ranging from 2 months to 3 years were used with different data mining and machine learning approaches. Various goodness-of-fit criteria were used to evaluate the performances of the aforementioned algorithms. Although the MARS and SVR algorithms gave the same and highest results in terms of R2 and r values for both the train and the test sets, the SVR algorithm is one of the methods to be recommended as a result of this study, especially when other goodness-of-fit criteria are evaluated. In conclusion, the usage of SVR algorithms may be a useful tool of machine learning approaches for detecting the hair sheep breed standards and may contribute to increasing the sheep meat quality in Mexico.

Прагматическая адаптация в кинопереводе (на материале художественного фильма К. Тарантино «Криминальное чтиво»)

The study aims to identify the features and ways of implementing pragmatic adaptation when translating Q. Tarantino’s film “Pulp Fiction” from English into Russian. The paper addresses the notion of pragmatic adaptation and emphasises the importance of its use in film translation. The scientific novelty of the study lies in the fact that the authors apply the classification of pragmatic text adaptation by V. N. Komissarov to the analysis of the film text and describe the ways of its implementation when translating Q. Tarantino’s film “Pulp Fiction” from English into Russian. As a result, it has been found that the English to Russian translation of Q. Tarantino’s film “Pulp Fiction” is characterised by pragmatic adaptation of two types – pragmatic adaptation in realia translation and pragmatic adaptation to preserve the stylistic features of the original film text. Based on the analysed material and using the method of quantitative analysis, conclusions are drawn about the most common ways of implementing pragmatic adaptation in translation.

Electroencephalograph Emotion Classification Using a Novel Adaptive Ensemble Classifier Considering Personality Traits.

The study explores the use of Electroencephalograph (EEG) signals as a means to uncover various states of the human brain, with a specific focus on emotion classification. Despite the potential of EEG signals in this domain, existing methods face challenges. Features extracted from EEG signals may not accurately represent an individual's emotional patterns due to interference from time-varying factors and noise. Additionally, higher-level cognitive factors, such as personality, mood, and past experiences, further complicate emotion recognition. The dynamic nature of EEG data in terms of time series introduces variability in feature distribution and interclass discrimination across different time stages. To address these challenges, the paper proposes a novel adaptive ensemble classification method. The study introduces a new method for providing emotional stimuli, categorizing them into three groups (sadness, neutral, and happiness) based on their valence-arousal (VA) scores. The experiment involved 60 participants aged 19-30 years, and the proposed method aimed to mitigate the limitations associated with conventional classifiers. The results demonstrate a significant improvement in the performance of emotion classifiers compared to conventional methods. The classification accuracy achieved by the proposed adaptive ensemble classification method is reported at 87.96%. This suggests a promising advancement in the ability to accurately classify emotions using EEG signals, overcoming the limitations outlined in the introduction. In conclusion, the paper introduces an innovative approach to emotion classification based on EEG signals, addressing key challenges associated with existing methods. By employing a new adaptive ensemble classification method and refining the process of providing emotional stimuli, the study achieves a noteworthy improvement in classification accuracy. This advancement is crucial for enhancing our understanding of the complexities of emotion recognition through EEG signals, paving the way for more effective applications in fields such as neuroinformatics and affective computing.

Unsupervised machine learning detection of iceberg populations within sea ice from dual-polarisation SAR imagery

Accurate quantification of iceberg populations is essential to inform estimates of Southern Ocean freshwater and heat balances as well as shipping hazards. The automated operational monitoring of icebergs remains challenging, largely due to a lack of generality in existing approaches. Previous efforts to map icebergs have often exploited synthetic aperture radar (SAR) data but the majority are designed for open water situations, require significant operator input, and are susceptible to the substantial spatial and temporal variability in backscatter that characterises SAR time-series. We propose an adaptive unsupervised classification procedure based on Sentinel 1 SAR data and a recursive Dirichlet Process implementation of Bayesian Gaussian Mixture Model. The approach is robust to inter-scene variability and can identify icebergs even within complex environments containing mixtures of open water, sea ice and icebergs of various sizes. For the study area in the Amundsen Sea Embayment, close to the calving front of Thwaites Glacier, our classifier achieved a mean pixel-wise F1 score against manual iceberg delineations from the SAR scenes of 0.960±0.018 with a corresponding object-level F1 score of 0.729±0.086. The method provides an excellent basis for estimation of total near-shore iceberg populations and has inherent potential for scalability that other approaches lack.

ABT-SVDD: A method for uncertainty handling in domain adaptation using belief function theory

Domain adaptation involves adapting a model trained on one domain to work effectively on another, which can have different statistical properties, such as distributions, correlations, and relationships between features. These heterogeneities can lead to uncertainty, impacting the model’s performance. Despite many studies that have been done on domain adaptation, most have ignored the adverse impact of uncertain and noisy data on adaptation and classification. To address this issue, the proposed method, Adaptive Belief-based Twin Support Vector Data Description (ABT-SVDD), extends the one-class support vector data description (SVDD) to an adaptive twin classifier and integrates it with a belief-based sample weighting approach. Also, it utilizes a combination of Hermite polynomial and Gaussian kernels to enhance the computational power of the linear objective function of the SVDD classifier while improving the generalization capability. The effectiveness of ABT-SVDD has been compared to the state-of-the-art methods on several tasks taken from two benchmark datasets. The experimental results demonstrate that ABT-SVDD significantly improves classification accuracy on various tasks with varying amounts of labeled data in the target domain. Specifically, in normal situations, ABT-SVDD outperforms competing methods by 6.33% to 9.08%, while in noisy situations, it achieves a more significant improvement of 9.87% compared to the competing methods. Besides, the Wilcoxon statistical test demonstrates the superiority of ABT-SVDD over state-of-the-art ones in terms of classification accuracy and computational time.

Retracted: Adaptive Classification Algorithm Design for Online Teaching Resources of Opera Singing

Retracted: Adaptive Classification Algorithm Design for Online Teaching Resources of Opera Singing