Neural Network-based Approach Research Articles

Loan classification using a feed-forward neural network

Objectives. The purpose of the study is to construct and study the use of a feed-forward neural network to solve the problem of loan classification, as well as to conduct a comparative analysis of the neural networkbased approach with the existing approach based on logistic regression.Methods. Based on a feed-forward neural network using historical data on loans issued, the following metrics are calculated: cost function, Accuracy, Precision, Recall, and measure, calculated on Precision and Recall values. Polynomial parameters and the principal component method are used to determine the optimal set of input data for the studied neural network.Results. The impact of data normalization on the final result was analyzed, the influence of the number of units in the hidden layer on the outcome was evaluated using a two-stage method and the Monte Carlo method, the effect of balanced data use was determined, the optimal threshold value for output layer of the neural network under investigation was calculated, the optimal activation function for the hidden layer units was found, the effect of increasing input indicators through missing values imputation and the use of polynomials of varying degrees was studied and the redundancy in the existing set of input indicators was analyzed.Conclusion. Based on the results of the research, we can conclude that the use of a direct distribution network to solve problems of loan classification is appropriate. Compared to logistic regression, implementing a solution using a feed-forward neural network requires more time and computing resources. However, the obtained most important values of Accuracy and measure are higher than those calculated using logistic regression [1].

“An Extensive study of Symantic and Syntatic Approaches to Automatic Text Summarization”

Automatic text summarization (ATS) has emerged as a crucial research domain in the discipline of natural language processing (NLP) and information retrieval. The exponential growth of digital content has necessitated the need for efficient techniques that can automatically generate concise and informative summaries from lengthy documents. This article provided a comprehensive recap of automatic text summarization, covering both abstractive and extractive methods. Using extractive techniques, prime phrases or keywords from the original text are identified and chosen, while abstractive methods involve producing summaries by paraphrasing and synthesizing content in a more human-like manner. Discussed the advantages and limitations of each approach, including the challenge of ATS, which arises when summarizing content from external sources. Furthermore, reviews common evaluation metrics used for assessing the quality of summaries and discusses recent advancements in neural network-based approaches for text summarization. This survey aims to provide an overview of automatic text summarization which acts as a useful resource for researchers and practitioners in the fields of information retrieval and NLP.

Predictive Neural Network Modeling for Almond Harvest Dust Control.

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. Preprocessing steps like outlier removal and normalization were employed to refine the dataset for training. The network's architecture was designed with two hidden layers and optimized using tanh activation and MSE loss functions through the Adam algorithm, striking a balance between model complexity and predictive accuracy. The model was trained on extensive field data from an almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a mean squared error (MSE) of 0.02 and a mean absolute error (MAE) of 0.01, indicating high precision in forecasting PM2.5 levels. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

Fixed-time adaptive neural quantized formation control of USVs with tunnel prescribed performance

This paper investigates fixed-time adaptive neural quantized formation control of unmanned surface vehicles (USVs) with tunnel prescribed performance in the presence of leader’s information unknown, model dynamic uncertainties and input quantization. Initially, a fixed-time observer is constructed to rapidly estimate the leader’s unknown information. Following this, a control strategy is designed employing a fixed-time sliding mode differentiator-based backstepping technique combined with a neural network-based minimum parameter learning approach (MLP-NN). This strategy addresses the ”complexity explosion” issue arising from the differentiation of the virtual control law and model dynamic uncertainties separately. Moreover, a specialized prescribed performance function is incorporated to ensure that performance specifications of the formation tracking errors meet specific performance criteria. The integration of hysteresis quantizer aids in conserving save communication resources and significantly reduces the detrimental effects caused by quantization errors through the proposed control methodology. It is demonstrated that all closed-loop signals are practical fixed-time stable and formation tracking errors consistently adhere to the predetermined performance envelopes. The efficacy of proposed control strategy is validated through comprehensive numerical simulations.

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.

Data-Driven Accelerated Parameter Identification for Chaboche-Type Visco-Plastic Material Models to Describe the Relaxation Behavior of Copper Alloys

Abstract Background Calibrating material models to experimental measurements is crucial for realistic computational analysis of components. For complex material models, however, optimization-based identification procedures can become time-consuming, particularly if the optimization problem is ill-posed. Objective The objective of this paper is to assess the feasibility of using machine learning to identify the parameters of a Chaboche-type material model that describes copper alloys. Specifically, we apply and analyze this identification approach using short-term uniaxial relaxation tests on a C19010 copper alloy. Methods A genetic algorithm forms the basis for identifying the parameters of the Chaboche-type material model. The approach is accelerated by replacing the numerical simulation of the experimental setup by a neural network surrogate. The neural networks-based approach is compared against a classic approach using both, synthetic and experimental data. Results The results show that on the one hand, a sufficiently accurate identification of the material model parameters can be achieved by a classic but time-consuming genetic algorithm. On the other hand, it is shown that machine learning enables a much more time-efficient identification procedure, however, suffering from the ill-posedness of the identification problem. Conclusion Compared to classic parameter identification approaches, machine learning techniques can significantly accelerate the identification procedure for parameters of Chaboche-type material models with acceptable loss of accuracy.

Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography

Breast cancer, with its high mortality, faces diagnostic challenges due to variability in mammography quality and breast densities, leading to inconsistencies in radiological interpretations. Computer-aided diagnostic (CAD) systems, while helpful, struggle with accurately interpreting lesion characteristics such as morphology, density, and size. To address this, our study developed advanced deep-learning algorithms to improve the detection, localization, risk assessment, and classification of breast lesions, aiming to reduce false positives on human intervention and tackle slow convergence rates. Key innovations of the approach include preprocessing techniques, advanced filtering, and data augmentation strategies to optimize model performance, mitigating over- and under-fitting concerns. A significant development is the Chaotic Leader Selective Filler Swarm Optimization (cLSFSO) method, which effectively detects breast-dense lesions by extracting textural and statistical features. Additionally, the study adapted deep learning models like modified VGGNet and SE-ResNet152 through transfer learning, significantly enhancing their capability to distinguish between normal and suspicious mammography regions. The study also introduces hybrid deep neural network-based approaches, including CNN+LSTM and CNN+SVM, for diagnosing and grading cancerous polyps from the pre-segmented ROIs. Besides, the transfer learning paradigm is employed to boost the efficacy in classifying breast masses and reducing computing time by modifying the final layer of the proposed pre-trained models. The integration of Grad-CAM techniques further refines our analysis, leading to more accurate assessments and improved diagnosis of breast anomalies. Evaluated using benchmark and private datasets, our algorithms demonstrated a sensitivity of 0.99 and an overall AUC of 0.99, indicating significant improvements in mammogram analysis. These advancements aid radiologists, potentially improving patient outcomes and contributing to medical imaging and AI in healthcare.

A bilinear transformer interactive neural networks-based approach to fine-grained recognition and protection of plant diseases for gardening design

Landscape gardening design is an important part of urban ecology and urban image, which is of great significance for urban plant protection. Plant diseases are a major challenge that must be faced in the conservation of landscape plants. Having timely and comprehensive information about the types and developmental stages of plant diseases is crucial for effective disease control. Traditional plant disease differentiation, which relies on manual inspection by professionals, is both time-consuming and labor-intensive. The rapid development of deep artificial neural network technology, especially the rise of computer vision technology, provides efficient and accurate technical means for plant disease identification. In this paper, we introduce the Interactive Bilinear Transformer Network (IBTN) model, which utilizes fine-grained recognition technology for landscape plant disease identification. We validate the effectiveness of model using several public datasets, demonstrating its competitive performance.

New perspectives for the intelligent rolling stock classification in railways: an artificial neural networks-based approach

New perspectives for the intelligent rolling stock classification in railways: an artificial neural networks-based approach

A Hybrid Computer Vision and Machine Learning Approach for Robust Vortex Core Detection in Fluid Mechanics Applications

Abstract Vortex core detection remains an unsolved problem in the field of experimental and computational fluid dynamics. Available methods such as the Q, delta, and swirling strength criterion are based on a decomposed velocity gradient tensor but detect spurious vortices (false positives and false negatives), making these methods less robust. To overcome this, we propose a new hybrid machine learning approach in which we use a convolutional neural network to detect vortex regions within surface streamline plots and an additional deep neural network to detect vortex cores within identified vortex regions. Furthermore, we propose an automatic labeling approach based on K-means clustering to preprocess our input images. We show results for two classical test cases in fluid mechanics: the Taylor–Green vortex problem and two rotating blades. We show that our hybrid approach is up to 2.6 times faster than a pure deep neural network-based approach and furthermore show that our automatic K-means clustering labeling approach achieves within 0.45% mean square error of the more labour-intensive, manual labeling approach. At the same time, by using a sufficient number of samples, we show that we are able to reduce false positives and negatives entirely and thus show that our hybrid machine learning approach is a viable alternative to currently used vortex detection tools in fluid mechanics applications.

MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.

With a growing body of evidence establishing circular RNAs (circRNAs) are widely exploited in eukaryotic cells and have a significant contribution in the occurrence and development of many complex human diseases. Disease-associated circRNAs can serve as clinical diagnostic biomarkers and therapeutic targets, providing novel ideas for biopharmaceutical research. However, available computation methods for predicting circRNA-disease associations (CDAs) do not sufficiently consider the contextual information of biological network nodes, making their performance limited. In this work, we propose a multi-hop attention graph neural network-based approach MAGCDA to infer potential CDAs. Specifically, we first construct a multi-source attribute heterogeneous network of circRNAs and diseases, then use a multi-hop strategy of graph nodes to deeply aggregate node context information through attention diffusion, thus enhancing topological structure information and mining data hidden features, and finally use random forest to accurately infer potential CDAs. In the four gold standard data sets, MAGCDA achieved prediction accuracy of 92.58%, 91.42%, 83.46% and 91.12%, respectively. MAGCDA has also presented prominent achievements in ablation experiments and in comparisons with other models. Additionally, 18 and 17 potential circRNAs in top 20 predicted scores for MAGCDA prediction scores were confirmed in case studies of the complex diseases breast cancer and Almozheimer's disease, respectively. These results suggest that MAGCDA can be a practical tool to explore potential disease-associated circRNAs and provide a theoretical basis for disease diagnosis and treatment.

Advancing Handwritten Musical Notation Recognition Using Deep Learning: A Convolutional Neural Network-Based Approach with Improved Accuracy

The use of computers to read musical scores is referred to as optical music recognition (OMR). The recent advancements in artificial intelligence and big data have led to the development of deep learning approaches for recognizing musical notes. Previous research has shown that there is a lot of room for improvement in handwritten musical notation recognition systems due to differences in writing styles and the complex structure of musical symbols. The research described here aims to develop a deep learning-based system for recognizing handwritten musical notation. The system uses a convolutional neural network (CNN) to extract and learn pixel features of musical symbols and achieve a recognition accuracy of over 90%. The CNN model was trained using image samples from the HOMUS dataset and fine-tuned to minimize the loss function and reduce classification errors. The CNN model achieved an accuracy of 96.95% on the test samples, which is a significant improvement over the 86.0% accuracy from previous studies. The performance of the CNN model was also compared to five state-of-the-art deep learning methods, namely, quantum gray Wolf optimization (QGWO) algorithm, nonfully connected network (NFC-Net) classifier, nearest neighbor classifier, data augmentation and ensemble learning, and the CNN model outperformed four of them. However, the CNN model occasionally misclassified musical symbols with similar shapes, indicating that there is still room for improvement in the system’s performance. Future research could focus on improving the model’s performance on similar-shaped symbols. Overall, the research demonstrates the effectiveness of using a CNN model for handwritten musical notation recognition and highlights the potential of deep learning approaches in this area.

A back propagation neural network-based approach for inverting layered seabed acoustic parameters in shallow waters

IntroductionExisting methods primarily focus on earth acoustic parameters inversion under specific layered structures. However, they face challenges with experimental data from unknown seabed stratification, hindering accurate parameter inversion.MethodsTo address this, a novel algorithm combines Back Propagation Neural Network (BPNN) for distinguishing seabed stratification and inverting acoustic parameters. Simulated sound pressure data disturb seabed parameters as input, enabling feature recognition for training the neural network inversion model. Acoustic parameters are then estimated under identified stratification using the sound field model.ResultsThe inversion model is validated using simulation and pool shrinkage data. Results show the neural network model effectively stratifies simulation and experimental data, providing accurate inversion results for acoustic parameters corresponding to distinct layers.DiscussionThe neural network model's accuracy and practicality are confirmed through hierarchical judgment of scale test data and acoustic parameter inversion. This approach introduces a new perspective for shallow sea acoustic parameter inversion, offering a promising application scenario.

AMYGNN: A Graph Convolutional Neural Network-Based Approach for Predicting Amyloid Formation from Polypeptides.

There has been an increasing interest in the use of amyloids for constructing various functional materials. The design of amyloid-associated functional materials requires the identification of the core peptide sequences as the fundamental building block. The existing computational methods are limited in terms of delineating polypeptides, the typical non-Euclidean structural data, and they fail to capture the dynamic interactions between amino acids due to ignoring the contextual information from surrounding amino acids. Here, we first propose the use of a state-of-the-art graph convolutional neural network for predicting the trends of amyloid formation from specific peptide sequences (AMYGNN) by abstracting each polypeptide as a graph, in which the constituting amino acids are viewed as nodes and edges characterizing the connections between pairs of amino acids are established when they meet a given distance threshold (Cα-Cα ≤ 5 Å). Our model achieves high performance with accuracy (0.9208), G-mean (0.9203), MCC (0.8417), and F1 (0.9235) in determining the characteristic peptide sequences to form amyloid. 32 of 534 crucial amino acid properties that greatly contribute to the formation of amyloids are ascertained, and the β-folding-like graph structure of a polypeptide is believed to be essential for the formation of amyloid. Our model enables the mapping of polypeptides with underlying interactions between amino acids and provides a quick and precise predictive framework for directing the construction of amyloid-associated functional materials.

A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents

Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through incremental knowledge acquisition. Throughout the training phase, the constructive agent achieved a minimum of 40% greater rewards and executed 8% more actions when compared to other agents. In the subsequent testing phase, the constructive agent exhibited a 15% increase in the number of actions performed in contrast to its counterparts.

Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

Sensor-based human activity recognition (HAR) plays a fundamental role in various mobile application scenarios, but the model performance of HAR heavily relies on the richness of the dataset and the completeness of data annotation. To address the shortage of comprehensive activity types in collected datasets, we adopt the domain adaptation technique with a graph neural network-based approach by incorporating an adaptive learning mechanism to enhance the action recognition model’s generalization ability, especially when faced with limited sample sizes. To evaluate the effectiveness of our proposed approach, we conducted experiments using three well-known datasets: MHealth, PAMAP2, and TNDA. The experimental results demonstrate the efficacy of our approach in sensor-based HAR tasks, achieving impressive average accuracies of 98.88%, 98.58%, and 97.78% based on the respective datasets. Furthermore, we conducted transfer learning experiments to address the domain adaptation problem. These experiments revealed that our proposed model exhibits exceptional transferability and distinguishing ability, even in scenarios with limited available samples. Thus, our approach offers a practical and viable solution for sensor-based HAR tasks.

Balancing efficiency vs. effectiveness and providing missing label robustness in multi-label stream classification

Available works addressing multi-label classification in a data stream environment focus on proposing accurate prediction models; however, they struggle to balance effectiveness and efficiency. In this work, we present a neural network-based approach that tackles this issue and is suitable for high-dimensional multi-label classification. The proposed model uses a selective concept drift adaptation mechanism that makes it well-suited for a non-stationary environment. We adapt the model to an environment with missing labels using a simple imputation strategy and demonstrate that it outperforms a vast majority of the supervised models. To achieve these, a weighted binary relevance-based approach named ML-BELS is introduced. To capture label dependencies, instead of a chain of stacked classifiers, the proposed model employs independent weighted ensembles as binary classifiers, with the weights generated by the predictions of a BELS classifier. We present an extensive assessment of the proposed model using 11 prominent baselines, five synthetic, and 13 real-world datasets, all with different characteristics. The results demonstrate that the proposed approach ML-BELS is successful in balancing effectiveness and efficiency, and is robust to missing labels and concept drift.

Analysis of convolutional neural networks-based approaches in fruit disease detection for smart agriculture applications

Analysis of convolutional neural networks-based approaches in fruit disease detection for smart agriculture applications

A Neural Network-Based Approach for Real-Time Measurement of the Concentration of Gaseous Pollutants in Tehran Using MODIS

A Neural Network-Based Approach for Real-Time Measurement of the Concentration of Gaseous Pollutants in Tehran Using MODIS

Machine Translation and Natural Language Processing

Machine translation and natural language processing are powerful tools that allow for the automated translation of text from one language to another. Machine translation systems have evolved from rule-based systems to statistical and neural network-based approaches, with each approach having its own strengths and weaknesses. NLP, on the other hand, focuses on understanding the structure and meaning of natural language and includes tasks such as part-of-speech tagging, named entity recognition, and sentiment analysis. Thegoalofmachinetranslation,a subfield of natural language processing (NLP), is to create platform that translate text or speechautomatically.RBMT provides linguistic accuracy and control, but linguistic resources must be built and maintained meticulously. However, SMT makes use of large-scale parallel corpora to automatically learn translation patterns, making it adaptable to new language pairs and context-dependent translations.However, SMT may struggle with handling grammatical nuances and domain-specific vocabulary. Hybrid approaches combining the best of both RBMT andSMT and newer approaches like neural machine translation (NMT) are being explored to overcome the limitations and improve the quality of translation output.