Neural Network Classifier Research Articles

Morphological Abnormalities Classification of Red Blood Cells Using Fusion Method on Imbalance Datasets.

Red blood cells (RBCs) or Erythrocytes are essential components of the human body and they transport oxygen from the lungs to the body's tissues, regulate balance, and support the immune system. Abnormalities in RBC shapes (Poikilocytosis) and sizes (Anisocytosis) can impede oxygen-carrying capacity, leading to conditions such as anemia, thalassemia, McLeod Syndrome, liver disease, and so on. Hematologists typically spend considerable time manually examining RBC's shapes and sizes using a microscope and it is time-consuming. The proposed LSTM based neural network (NN) deep-learning strategy helps to classify abnormal RBCs automatically and accurately and overcome blood-related disorders at an early stage. After data processing, traditional and high-level features are fused to clearly distinguish between abnormal RBC classes. Class imbalance favors the dominant class, resulting in biased forecasts. To address class imbalance, a custom loss function is generated by integrating class weights and loss functions before feeding fused features to the NN classifier. Specifically, the loss function is designed to assign higher penalties to the misclassification of underrepresented classes, ensuring that the model is more sensitive to these classes during training. This is achieved by integrating class weights directly into the cross-entropy loss calculation, thereby balancing the influence of each class on the model's learning process. The proposed approach's performance is evaluated using the publicly accessible Chula-PIC-Lab dataset and privately gathered dataset from the Cachar Cancer Hospital and Research Centre (CCHRC) in Assam, India. The proposed approach achieves an average of and -score and accuracy on the Chula-PIC-Lab dataset and an average of and -score and accuracy on the CCHRC dataset for and classes and surpasses benchmark models including Custom CNN, Custom LSTM, Efficient Net-B1, SMOTE, Hybrid NN, and HPKNN.

Detection of weeds in vegetables using image classification neural networks and image processing

Weed management presents a major challenge to vegetable growth. Accurate identification of weeds is essential for automated weeding. However, the wide variety of weed types and their complex distribution creates difficulties in rapid and accurate weed detection. In this study, instead of directly applying deep learning to identify weeds, we first created grid cells on the input images. Image classification neural networks were utilized to identify the grid cells containing vegetables and exclude them from further analysis. Finally, image processing technology was employed to segment the non-vegetable grid images based on their color features. The background grid cells, which contained no green pixels, were identified, while the remaining cells were labeled as weed cells. EfficientNet, GoogLeNet, and ResNet models achieved overall accuracies of over 0.956 in identifying vegetables in the testing dataset, demonstrating exceptional identification performance. Among these models, the ResNet model exhibited the highest computational efficiency, with a classification time of 12.76 ms per image and a corresponding frame rate of 80.31 fps, satisfying the requirement for real-time weed detection. Effectively identifying vegetables and differentiating weeds from soil significantly reduces the complexity of weed detection and improves its accuracy.

Exoplanet discovery with variational quantum circuits

This manuscript addresses the automatic identification of exoplanets with the data from the Kepler mission using variational quantum circuits (VQC) implemented with the Qiskit library. The system accuracy was assessed across various configurations, including different numbers of qubits, feature maps, ansatz structures, and training algorithms, providing valuable insights into the performance of VQCs. The results of the VQC-based approach were compared to those obtained using a classical artificial neural network, namely a multilayer perceptron. The findings demonstrate that VQCs are a viable and promising method for processing astronomical data and discovering exoplanets, offering a potential advantage over traditional machine learning techniques.

Developing picking route policies with genetic algorithms and order batching with deep neural networks in picker to part warehouses

ABSTRACT This paper proposes a hybrid approach to warehouse management, combining Genetic Algorithm (GA) optimization for picking routes with Deep Neural Network (DNN) classification for order batching. The GA-DNN method significantly reduces travel distances while lowering computational time. Experimental results demonstrate up to 69.01% reduction in travel distance in large warehouses, outperforming traditional methods. This study underscores the effectiveness of GA-DNN solutions in addressing modern logistics challenges.

Gaussian process latent variable models-ANN based method for automatic features selection and dimensionality reduction for control of EMG-driven systems

Electromyography (EMG) signals have gained significant attention due to their potential applications in prosthetics, rehabilitation, and human-computer interfaces. However, the dimensionality of EMG signal features poses challenges in achieving accurate classification and reducing computational complexity. To overcome such issues, this paper proposes a novel approach that integrates feature reduction techniques with an artificial neural network (ANN) classifier to enhance the accuracy of high-dimensional EMG classification. This approach aims to improve the classification accuracy of EMG signals while substantially reducing computational costs, offering valuable implications for all EMG-related processes on such data. The proposed methodology involves extracting time and frequency domain features from twelve channels of EMG signals, followed by dimensionality reduction using techniques such as PCA, LDA, PPCA, Lasso and GPLVM, and classification using an ANN. Our investigation revealed that LDA is not appropriate for this dataset. The dimensionality reduction models did not have any significant effect on the accuracy, but the computational cost decreased significantly. In individual comparisons, GPLVM had the shortest computational time (29 s), which was significantly less than that of all the other models (p < 0.05), with PCA following at approximately 35 s and Relief at approximately 57 s, while PPCA took approximately 69 s, and Lasso exhibited higher computational costs than all the models but lower computational costs than did the original set. Using the best-performing features, all possible sets of 2, 3, 4 and 5 features were tested, and the 5-feature set exhibited the best performance. This research demonstrates the effectiveness of dimensionality reduction and feature selection in improving the accuracy of movement recognition in myoelectric control.

P0556 A novel switching of artificial intelligence to generate simultaneously multimodal images to assess inflammation and predict outcomes in Ulcerative Colitis

Abstract Background Virtual Chromoendoscopy (VCE) is pivotal for assessing activity and predicting outcomes in Ulcerative Colitis (UC), though inter- and intra-observer variability and the need for expertise persist. Artificial intelligence (AI) has the potential to offer standardised VCE-based assessment. This study introduces a novel AI model to detect, generate and transition between various endoscopic modalities, enhancing AI-driven inflammation assessment and outcome prediction in UC. Methods Endoscopic videos in high-definition white-light (HD-WLE), iScan2, iScan3 and NBI modalities from UC patients of the international PICaSSO iScan and Narrow-Band Imaging (NBI) cohort (302 and 54 patients, respectively) were used to develop a neural network (NN) able to identify the acquisition modality of each frame and for inter-modality image switching. 2535 frames were switched to different endoscopic modalities and used to train a deep-learning model for inflammation assessment using single and multimodal inputs on 169 videos of the iScan cohort. Subsequently, the model was tested on a subset of the iScan and NBI cohort (72 and 51 videos, 1080 and 765 frames, respectively). The model performance in predicting endoscopic and histological activity and outcomes and the agreement with experts were evaluated. Results Table 1 details the diagnostic performance of the AI model for the prediction of endoscopic and histological remission. The AI model efficiently classified and converted images across modalities (92% NN classifier accuracy). It showed excellent performance in predicting endoscopic and histological remission, with the multimodal assessment outperforming the unimodal one in both iScan cohorts (accuracy 91.67 [95% CI 82.74-96.88] and 88.89 [80.4-97.73]; AUROC 0.96 and 0.90 by Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and Paddington International Virtual Chromoendoscopy (PICaSSO) score, respectively) and NBI cohort (accuracy 84.62 [95% CI 65.13-95.64] and 88.46 [69.85-97.55]; AUROC 0.92 by UCEIS and PICaSSO score, respectively). Moreover, it showed a remarkable ability to predict clinical outcomes in the iScan and NBI cohort (HR 3.18 [0.98-10.35] and 1.7 [0.7-4.11] by endoscopy; 5.75 [1.77-18-71] and 3.9 [1.15-13.28] by histology, respectively). Finally, the agreement with the assessment performed by endoscopists and pathologists was good. Conclusion Our multimodal "AI-switching" model innovatively detects, generates, and transitions between different endoscopic enhancement modalities and platforms, refining inflammation assessment, outcome prediction, and precise UC management by integrating model-derived images.

A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting

With the proliferation of smart home devices and the ever-increasing demand for household energy management, very-short-term load forecasting (VSTLF) has become imperative for energy usage optimization, cost saving and for sustaining grid stability. Despite recent advancements, VSTLF in the household scenario still poses challenges. For instance, some characteristics (e.g., high-frequency, noisy and non-stationary) exacerbate the data processing and model training procedures, and the heterogeneity in household consumption patterns causes difficulties for models with the generalization capability. Further, the real-time data processing requirement calls for both the high forecasting accuracy and improved computational efficiency. Thus, we propose a diffusion–attention-enhanced temporal (DATE-TM) model with multi-feature fusion to address the above issues. First, the DATE-TM model could integrate residents’ electricity consumption patterns with climatic factors. Then, it extracts the temporal feature using an encoder and meanwhile models the data uncertainty through a diffusion model. Finally, the decoder, enhanced with the attention mechanism, creates the precise prediction for the household load forecasting. Experimental results reveal that DATE-TM significantly surpasses classical neural networks such as BiLSTM and DeepAR, especially in handling the data uncertainty and long-term dependency.

A Spiking Neural Network Approach for Classifying Hand Movement and Relaxation from EEG Signal using Time Domain Features

High-performance prosthetic and exoskeleton systems based on EEG signals can improve the quality of life of hand-impaired people. Effective controlling of these assistive devices requires accurate EEG signal classification. Although there have been advancements in the assistive Brain-Computer Interface (BCI) systems, still classifying the EEG signals with high accuracy is a great challenge. The objective of this research is to investigate the accuracy of the EEG signal classification of the Spiking Neural Network (SNN) classifier for factual and exact control of prosthetic and exoskeleton systems for individuals with hand impairment. The EEG dataset has been taken from the BNCI Horizon 2020 website, which is for hand movement-relax events of a patient with high spinal cord injury (SCI) to operate a neuro-prosthetic device attached to the paralyzed right upper limb. The fusion of Dispersion Entropy (DE), Fuzzy Entropy (FE), and Fluctuation based Dispersion Entropy (FDE) with mean and skewness features are extracted from the Motor Imagery (MI) EEG signals and applied to the Spiking Neural Network (SNN) classifier. To compare the performance of this algorithm, these same features have been used in Convolutional Neural Network (CNN), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR) classifiers. It has been found that SNN has given the highest classification accuracy of 80% with a precision of 80.95%, recall of 77.28%, and F1-score of 79.07%. This indicates that SNN with these five features has greater potential in BCI system-based applications.

Assessment of using transfer learning with different classifiers in hypodontia diagnosis

BackgroundHypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make highly accurate diagnoses. The aim of this study was to classify single premolar agenesis, multiple premolar agenesis, and without tooth agenesis using various artificial intelligence approaches.MethodsOne thousand sixty-eight panoramic radiographs from pediatric patients aged between 6 and 12 years without systemic disease were sorted into three separate classes: single premolar agenesis (n = 336), multiple premolar agenesis (n = 324), and without tooth agenesis (n = 408). Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). The dataset was divided into 80% for training and 20% for testing. Performance was evaluated via accuracy, recall, precision, F1-score, specificity and area under the curve (AUC) parameters.ResultsAll of the data were classified via a VGG-19 model with a bilayered neural network classifier, which achieved 95.63% accuracy, 93.26% precision, 93.34% recall, 96.73% specificity, 93.25% F1-score and 95.03% AUC and was identified as the most successful model. The accuracy values for this model were distributed as follows: 96.72% for without tooth agenesis, 95.79% for multiple premolar agenesis, and 94.39% for single premolar agenesis.ConclusionsSuccessful results of pretrained models have been demonstrated for the radiographic diagnosis of hypodontia in pediatric patients. It is expected that artificial intelligence approaches will facilitate the diagnosis of hypodontia.

An insight from machine learning perspective for COVID-19 survival prediction in Malaysia based on demographic factor

Malaysia reported its first imported COVID-19 case on 23 January 2020, which marked the country’s first confirmed positive case. The first case in Malaysia was from eight close contacts in Johor. The global health landscape has been significantly impacted by the COVID-19 pandemic, with mortality or survival being critical outcomes of interest. This study aims to predict COVID-19 survival occurrences in Malaysia by utilizing machine learning approaches based on demographic factors. The dataset used in this study comprises demographic information of 2,151,315 COVID-19 patients, including nationality, regions, age groups, gender, medical history, vaccine brands, and the number of vaccine doses received between 2020 and 2022. Four machine learning algorithms, namely Logistic Regression, Naïve Bayes, Support Vector Machine, and Artificial Neural Network were employed to assess the relationship between demographic factors and COVID-19 survival. To evaluate the model performance, the datasets are categorized into imbalanced and balanced (down-sampling). The results indicate that the balanced dataset (down-sampling) outperforms the imbalanced dataset in terms of overall accuracy, sensitivity, specificity, precision, and Area Under the Curve (AUC). Based on the analysis, the Artificial Neural Network (ANN) classifier exhibited the highest performance with a specificity 95.2% on a balanced dataset. The model excels in accurately identifying survivors, thereby minimizing false mortality predictions and is selected as the best model for predicting COVID-19 survival. Its capacity to process larger sample sizes, combined with numerous interconnected nodes, enables it to identify complex patterns and extract meaningful insights from diverse datasets, such as demographic factors. Additionally, the optimization of parameters, including the number of layers, learning rate, and activation functions, significantly contributed to its superior accuracy. The study identifies that those of chronic diseases, male, and aged 45 and above as the notable factors associated with lower survival rates among COVID-19 patients. The findings underscore the importance of completing the vaccination series by obtaining at least the second dose, as the first dose alone may not offer sufficient protection. In conclusion, this study successfully achieves its objectives by identifying the optimal dataset configuration and predictive model for forecasting COVID-19 survival based on demographic factors. This network could serve as a benchmark model classifier, offering a valuable tool to predict and promote vaccinations, as well as optimize the general healthcare system during the pandemic outbreak. The study not only contributes to the theoretical understanding of effective COVID-19 prediction but also emphasizes the practical implications of integrating advanced machine learning techniques into pandemic management strategies. Future research can build upon these findings by exploring additional machine learning techniques and considering geographical and environmental factors to further enhance the accuracy of long-term predictions.

Design and Evaluation of a Leader–Follower Isomorphic Vascular Interventional Surgical Robot

Vascular interventional surgical robots (VISRs) can help doctors to avoid X-ray radiation. This paper proposes a leader–follower isomorphic robot where the structural form and operational logic are completely identical. The doctor’s operation on the leader robot is precisely replicated on the follower robot, enabling delivery and rotation capabilities. It can further achieve collaborative operation. This control system adopts a four-channel scheme based on acceleration and can achieve approximately ideal transparency. The leader–follower delivery error of the catheter/guidewire is less than 1 mm, and the leader–follower rotation error of the guidewire is less than 0.3° in an actual intervention task based on a human vascular model. Subsequently, the cumulative sum (CUSUM) method was used to evaluate the learning curve of the robot system, demonstrating that both operators could master the operation method within 10 trials. We classified operators with different operational experience using machine learning methods. The classification process includes time-frequency domain feature extraction, feature selection based on the Relief method and random forest (RF) method, and a BP neural network (NN) classifier. The results indicate that this method can achieve accuracy of 94%. This paper comprehensively discusses the robot system from the perspectives of the mechanism design, control methods, and evaluation methods, providing valuable insights for the design of related robotic systems.

A multi-level feature fusion artificial neural network for classification of acoustic emission signals.

In this paper, we introduce FUSION-ANN, a novel artificial neural network (ANN) designed for acoustic emission (AE) signal classification. FUSION-ANN comprises four distinct ANN branches, each housing anindependent multilayer perceptron. We extract denoised features of speech recognition such as linear predictive coding, Mel-frequency cepstral coefficient, and gammatone cepstral coefficient to represent AE signals. These features are concatenated to form a new feature called LMGC, which serves as input data for the four branches of FUSION-ANN. The network performs AE signal recognition and classification through forward propagation in each branch, utilizing multi-level feature fusion. We evaluate FUSION-ANN's performance on the ORION-AE benchmark dataset, which contains AE signals from various loading conditions simulating loosening phenomena in aeronautics, automotive, and civil engineering structures. Our results demonstrate an impressive average accuracy of 98% in AE signal classification. Additionally, FUSION-ANN boasts high training efficiency, robustness, and accuracy, making it suitable for reliable AE signal analysis. However, given the current limitations, we aim to conduct more comprehensive investigations in the future. Our plan includes further testing of the network's performance across various categories of AE signals to assess its generality. Additionally, we will select richer and more efficient feature sets to characterize these signals.

Prediction of Postpartum Depression With Dataset Using Hybrid Data Mining Classification Technique

Postpartum Depression is a condition or a state which usually affects the woman immediately after child birth. The birth of a baby not only brings delighted emotions such as excitement, but also fear and anxiety which may sometimes lead to depression. It is a period of physical, emotional and behavioral changes that happen in some woman immediately after the delivery. Apart from the chemical changes, there are many factors which affect a woman during and after pregnancy period. If PPD is not identified and treated at the earlier stages, it may lead to serious issues for mother and child. It is therefore of vital importance to sift through the woman at any early stage to prevent any consequences. The objective of this study is to find out the presence of PPD without getting worse. Data mining plays an important role in the health care industry with successful outcome. It helps to find out hidden patterns, trends and anomalies from large dataset to make the predictions. The proposed system is a combined classification technique for the prediction of postpartum depression that uses Support vector machine, Artificial Neural Network and Hybrid classifier algorithm to produce the best result.

Vision Transformers for Image Classification: A Comparative Survey

Transformers were initially introduced for natural language processing, leveraging the self-attention mechanism. They require minimal inductive biases in their design and can function effectively as set-based architectures. Additionally, transformers excel at capturing long-range dependencies and enabling parallel processing, which allows them to outperform traditional models, such as long short-term memory (LSTM) networks, on sequence-based tasks. In recent years, transformers have been widely adopted in computer vision, driving remarkable advancements in the field. Previous surveys have provided overviews of transformer applications across various computer vision tasks, such as object detection, activity recognition, and image enhancement. In this survey, we focus specifically on image classification. We begin with an introduction to the fundamental concepts of transformers and highlight the first successful Vision Transformer (ViT). Building on the ViT, we review subsequent improvements and optimizations introduced for image classification tasks. We then compare the strengths and limitations of these transformer-based models against classic convolutional neural networks (CNNs) through experiments. Finally, we explore key challenges and potential future directions for image classification transformers.

Multiple constraint network classification reveals functional brain networks distinguishing 0-back and 2-back task.

Working memory is associated with general intelligence and is crucial for performing complex cognitive tasks. Neuroimaging investigations have recognized that working memory is supported by a distribution of activity in regions across the entire brain. Identification of these regions has come primarily from general linear model analyses of statistical parametric maps to reveal brain regions whose activation is linearly related to working memory task conditions. This approach can fail to detect nonlinear task differences or differences reflected in distributed patterns of activity. In this study, we take advantage of the increased sensitivity of multivariate pattern analysis in a multiple-constraint deep learning classifier to analyze patterns of whole-brain blood oxygen level dependent (BOLD) activity in children performing two different conditions of the emotional n-back task. Regional (supervoxel) whole-brain activation patterns from functional imaging runs of 20 children were used to train a set of neural network classifiers to identify task category (0-back vs. 2-back) and activation co-occurrence probability, which encoded functional connectivity. These simultaneous constraints promote the discovery of coherent networks that contribute towards task performance in each memory load condition. Permutation analyses discovered the global activation patterns and interregional coactivations that distinguish memory load. Examination of model weights identified the brain regions most predictive of memory load and the functional networks integrating these regions. Community detection analyses identified functional networks integrating task-predictive regions and found distinct patterns of network activation for each task type. Comparisons to functional network literature suggest more focused attentional network activation during the 2-back task. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

LUNG LOBE SEGMENTATION AND LUNG CANCER DETECTION WITH HYBRID OPTIMIZATION-ENABLED DEEP LEARNING USING CT IMAGES

One of the deadly diseases and a leading cause of death worldwide is lung cancer. Compound tomography (CT) is commonly used to identify tumors and it classifies the phase of cancer in the human body. Detection of cancer disease in the lung at an early stage is quite difficult and is essential to increase the survival patient’s rate. In this research paper, a deep learning (DL)-based optimization approach is developed for the detection of lung cancer and lung lobe segmentation using CT scan images. Initially, an adaptive wiener filter is used to pre-process the input images and the segmentation process is done by the pyramid scene parsing network (PSPNet) classifier which is effectually trained using the developed honey badger golden search optimization algorithm (HBGSO). Grid-based scheme is used to identify lung nodules and then the features are extracted. Finally, lung cancer detection is done by the Shepard convolutional neural networks (ShCNN) classifier and is trained using the proposed fractional HBGSO (FHBGSO) algorithm. The FHBGSO-based ShCNN outperforms with the highest accuracy of 93.4%, f-measure of 91.2% and precision of 89.8%. Thus, lung cancer is detected in the earlier stages by the devised scheme.

CATIL: Customized Adversarial Training based on Instance Loss

Adversarial training is one of the most effective adversarial defense methods currently recognized. It enhances the robustness of deep neural network (DNN) classifiers by generating adversarial samples. However, current adversarial training methods cannot effectively trade off the robust accuracy and natural accuracy when training DNN classifiers, and are prone to overfit. To solve these problems, we propose Customized Adversarial Training based on Instance Loss (CATIL), aiming to improve robust accuracy and natural accuracy while alleviating overfitting. We first comprehensively consider the influencing factors of adversarial training and propose the comprehensive customization strategy (CCS), which crafts unique attack strategies for each sample, fine-tunes the classifier's decision boundary, and boosts the robustness of the DNN classifier without compromising its natural accuracy. Second, the loss adjustment strategy (LAS) is designed to update the attack strategy according to the loss value. This increases the fitting difficulty of the DNN classifier and alleviates the overfitting problem. Finally, numerous experiments show that CATIL can effectively enhance robust accuracy and alleviate the overfitting problem without significantly compromising natural accuracy. When evaluating CIFAR-10 on Wide ResNet, CATIL achieves the best performance in both natural and robust accuracy compared to all benchmarks.

Artificial Intelligence-Based Direct Power Control for Power Quality Improvement in a WT-DFIG System via Neural Networks: Prediction and Classification Techniques

This paper discusses the improvement of power quality injected into the AC grid. This approach is achieved by enhancing the quality of injected power signals and mastering the active and reactive power exchanged between the DFIG based wind turbine (WT-DFIG) and the electrical grid, resulting in an improvement of the overall system performance and efficiency. This study includes all WT-DFIG operating modes, successively and continuously, as well as all local reactive power compensation modes. Therefore, novel control strategies are proposed in this paper for wind energy conversion systems based on artificial intelligence techniques. These techniques include Neural Network Prediction (PNN-DPC) and Classification (CNN-DPC). They aim to eliminate the drawbacks and difficulties associated with conventional Direct Power Control (C-DPC), while retaining its advantages. The paper also provides a thorough explanation of the mathematical models for neural network techniques and WT-DFIG system models. The MATLAB/Simulink environment is used to investigate the performance of the proposed techniques under different conditions and operating modes related to different scenarios. The results reveal a significant reduction in the ripple of the generated active power and the compensated local reactive power, better quality of the generated signal currents and a remarkable reduction in the current total harmonic distortion (THD). Furthermore, compared to C-DPC, PNN-DPC achieves a reduction of 72.07% in active power ripples, 77.07% in reactive power ripples, and 76.79% in current Total Harmonic Distortion (THD). CNN-DPC shows similar improvements with 72.04%, 77.13%, and 76.54% of reductions respectively. In addition, CNN-DPC slightly outperforms PNN-DPC. Nevertheless, both proposed control techniques show significant improvements in all characteristics compared to other methods. Consequently, the proposed control strategies indicate that artificial intelligence has the potential to improve the power quality and performance of wind power conversion system.

Comparison of Classical, Neural Network and Hybrid Models for Hysteretic Single-tendon Catheter Kinematics.

While robotic control of catheter motion can improve tip positioning accuracy, hysteresis arising from tendon friction and flexural deformation degrades kinematic modeling accuracy. In this paper, we compare the capabilities of three types of models for representing the forward and inverse kinematic maps of a clinical single-tendon cardiac catheter. Classical hysteresis models, neural networks and hybrid combinations of the two are included. Our results show that modeling accuracy is best when models are trained using motions corresponding to the anticipated clinical motions. For sinusoidal motions, recurrent neural network models provide the best performance. For point-to-point motions, however, a simple backlash model can provide comparable performance to a recurrent neural network.

Investigations on segmentation-based fractal texture for texture classification in the presence of Gaussian noise.

Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier. However, computing the performance of these methods in the presence of various degradations is always an open area of discussion. Image noise is always a dominant factor among various image degradation factors, affecting the performance of these methods and making texture classification challenging. Therefore, it is essential to investigate the interpretation of these methods in the presence of prominent degradation factors such as noise. Applications for Segmentation-Based Fractal Texture Features (SFTF) include image classification, texture generation, and medical image analysis. They are beneficial for examining textures with intricate, erratic patterns that are difficult to characterize using conventional statistical techniques accurately. This paper assesses two texture feature extraction methods based on SFTF and statistical moment-based texture features in the presence and absence of Gaussian noise. The SFTF and statistical moments-based handcrafted features are passed to a multilayer feed-forward neural network for classification. These models are evaluated on natural textures from Kylberg Texture Dataset 1.0. The results show the superiority of segmentation-based fractal analysis over other approaches. The average accuracy rates using the SFTF are 99% and 97% in the absence and presence of Gaussian noise, respectively.