Efficient Classification Approach Research Articles

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network

ABSTRACT Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

Rapid Textile Fibers Classification Technology Based on Near-Infrared Spectroscopy and Machine Learning

The accurate identification of textile materials plays a crucial role in production control, quality inspection, and market regulation. In recent years, near-infrared (NIR) spectroscopy has been widely applied in the identification and classification of textile fibers due to its rapid, non-destructive, and efficient characteristics. Therefore, this study utilizes a handheld near-infrared spectrometer and chemometrics to provide a convenient, efficient, non-destructive, and green analytical method for the qualitative analysis of textiles. The article collects spectral data of fiber samples using a handheld NIR spectrometer and analyzes them with chemometric methods. Data preprocessing techniques, feature extraction algorithms, comprehensive sampling methods, and classification algorithms are employed to establish classification models. After ten-fold cross-validation, the optimal SG_RF model is obtained, with a classification accuracy of 93.7% on the test set and an AUC_macro of 0.928, demonstrating excellent performance. The article validates the feasibility of using near-infrared spectroscopy combined with chemometric methods for textile fiber classification, offering a rapid, non-destructive, and efficient approach for textile fiber classification.

MRI Brain Tumor Classification Using HOG Features Selected via Impurity Based Importance Measure

MRI is considered the primary method for confirming the diagnosis of brain tumors and choosing the appropriate treatment. Automating the process of detecting brain tumors in MRI images using deep models has become a popular trend in the scientific research community. However, deep neural networks require a large volume of data to avoid overfitting, which is not ideally available. This is where handcrafted features come in handy. In this paper, we present an efficient approach for brain tumor classification that can outperform deep CNN models. In the proposed system, the histogram of oriented gradients algorithm is used to extract feature descriptors from brain MRI images. The extracted features are processed using a random forest algorithm, where each decision tree performs the task of evaluating feature importances via the impurity metric, while all estimators collaborate in selecting the effective feature set. Finally, the Fine KNN algorithm is used to classify the types of brain tumors based on the numerical features obtained. The presented model achieved a high-test accuracy of 99.35% with an F1-score of 99.30%, outperforming many deep models.

An Efficient Ensemble Approach for Brain Tumors Classification Using Magnetic Resonance Imaging

Tumors in the brain can be life-threatening, making early and precise detection crucial for effective treatment and improved patient outcomes. Deep learning (DL) techniques have shown significant potential in automating the early diagnosis of brain tumors by analyzing magnetic resonance imaging (MRI), offering a more efficient and accurate approach to classification. Deep convolutional neural networks (DCNNs), which are a sub-field of DL, have the potential to analyze rapidly and accurately MRI data and, as such, assist human radiologists, facilitating quicker diagnoses and earlier treatment initiation. This study presents an ensemble of three high-performing DCNN models, i.e., DenseNet169, EfficientNetB0, and ResNet50, for accurate classification of brain tumors and non-tumor MRI samples. Our proposed ensemble model demonstrates significant improvements over various evaluation parameters compared to individual state-of-the-art (SOTA) DCNN models. We implemented ten SOTA DCNN models, i.e., EfficientNetB0, ResNet50, DenseNet169, DenseNet121, SqueezeNet, ResNet34, ResNet18, VGG16, VGG19, and LeNet5, and provided a detailed performance comparison. We evaluated these models using two learning rates (LRs) of 0.001 and 0.0001 and two batch sizes (BSs) of 64 and 128 and identified the optimal hyperparameters for each model. Our findings indicate that the ensemble approach outperforms individual models, having 92% accuracy, 90% precision, 92% recall, and an F1 score of 91% at a 64 BS and 0.0001 LR. This study not only highlights the superior performance of the ensemble technique but also offers a comprehensive comparison with the latest research.

Adaptive threshold based outlier detection on IoT sensor data: A node-level perspective

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks.

Toward an Efficient Hybrid Clustering and Classification Approach for Fake News Detection in Social Network

Objectives: The spread of fake news on social media has become a pressing issue in recent times. Despite various organizations' efforts to address this problem, it continues to persist, necessitating finding more effective solutions. This study implements a machine learning-based approach for identifying fake news on social media with improved accuracy. Methods : The study's methodology utilizes a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to extract semantic features from news articles. Then, a hybrid clustering and classification approach is used, which combines K-means clustering and Artificial Neural Network (ANN) classifiers. Data used is a secondary data consisting of 23481 world news articles obtained from the Reuters.com website and 21417 unreliable news from polifact.com. During the training process, the number of units and layers on the model was tuned to optimize model performance. The model was compared to other baseline models such as KNN, SVM, Decision tree, and Boosted decision tree to establish the best performing model. Findings: The results from the two algorithms were weighted to final classifications using Bayesian probability theory. The proposed approach achieved an accuracy of 99.78%, a sensitivity of 100%, and specificity equal to 99.73%. The model's precision is 99.74%, indicating its ability to identify fake news. The F-score of the approach is 99.87%, indicating that the model strikes a good balance between correctly classifying fake news articles and reliable news articles. The approach outperformed other machine learning classifiers, including KNN, SVM, Decision Tree, and Boosted Decision Tree. Novelty : The study applies a hybrid approach with a classification and clustering algorithm to improve detection of fake news on social media, the approach is tested with varied real-world datasets to establish its robustness under different vocabularies and vocabulary sizes. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Hybrid clustering approach, Classification, CNN, LSTM, Boosted Decision Tree, Social platforms, K-means

Data-Driven Machine Fault Diagnosis of Multisensor Vibration Data Using Synchrosqueezed Transform and Time-Frequency Image Recognition with Convolutional Neural Network

Accurate vibration classification using inertial measurement unit (IMU) data is critical for various applications such as condition monitoring and fault diagnosis. This study proposes a novel convolutional neural network (CNN) based approach, the IMU6DoF-SST-CNN in six variants, for robust vibration classification. The method utilizes Fourier synchrosqueezed transform (FSST) and wavelet synchrosqueezed transform (WSST) for time-frequency analysis, effectively capturing the temporal and spectral characteristics of the vibration data. Additionally, was used the IMU6DoF-SST-CNN to explore three different fusion strategies for sensor data to combine information from the IMU’s multiple axes, allowing the CNN to learn from complementary information across various axes. The efficacy of the proposed method was validated using three datasets. The first dataset consisted of constant fan velocity data (three classes: idle, normal operation, and fault) at 200 Hz. The second dataset contained variable fan velocity data (also with three classes: normal operation, fault 1, and fault 2) at 2000 Hz. Finally, a third dataset of Case Western Reserve University (CWRU) comprised bearing fault data with thirteen classes, sampled at 12 kHz. The proposed method achieved a perfect validation accuracy for the investigated vibration classification task. While all variants of the method achieved high accuracy, a trade-off between training speed and image generation efficiency was observed. Furthermore, FSST demonstrated superior localization capabilities compared to traditional methods like continuous wavelet transform (CWT) and short-time Fourier transform (STFT), as confirmed by image representations and interpretability analysis. This improved localization allows the CNN to effectively capture transient features associated with faults, leading to more accurate vibration classification. Overall, this study presents a promising and efficient approach for vibration classification using IMU data with the proposed IMU6DoF-SST-CNN method. The best result was obtained for IMU6DoF-SST-CNN with FSST and sensor-type fusion.

An Efficient Approach for Automatic Fault Classification Based on Data Balance and One-Dimensional Deep Learning

Predictive maintenance (PdM) is implemented to efficiently manage maintenance schedules of machinery and equipment in manufacturing by predicting potential faults with advanced technologies such as sensors, data analysis, and machine learning algorithms. This paper introduces a study of different methodologies for automatically classifying the failures in PdM data. We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector Machines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. We reapply classification algorithms to the normalized dataset and present the performance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset groups by randomly selecting normal data and all faulty data for all fault types from the original dataset. The dataset groups are generated with 100 different repetitions, recording performance scores for each one and presenting the maximum scores. All methods utilized in the study are similarly employed on these groups. From these scores, the use of 1D-LeNet deep learning classifiers and feature normalization resulted in achieving the highest overall accuracy and F1-score performance of 98.50% and 98.32%, respectively. As a result, the goal of this study was to develop an efficient approach for automatic fault classification, leveraging data balance, and additionally, to provide an analysis of one-dimensional deep learning and shallow machine learning-based classification methods. In light of the experimentation and comparative analysis, this study successfully achieves its stated goal by demonstrating that one-dimensional deep learning and data balance collectively emerge as the optimal approach, offering good prediction accuracy.

GT-CHES: Graph transformation for classification in human evolutionary systems

While increasingly complex algorithms are being developed for graph classification in highly-structured domains, such as image processing and climate forecasting, they often lead to over-fitting and inefficiency when applied to human interaction networks where the confluence of cooperation, conflict, and evolutionary pressures produces chaotic environments. We propose a graph transformation approach for efficient classification in chaotic human systems that is based on game theoretic, network theoretic, and chaos theoretic principles. Graph structural properties are compiled into time-series that are then transposed into the frequency domain to offer a dynamic view of the system for classification. We propose a set of benchmark data sets and show through experiments that the approach is efficient and appropriate for many dynamic networks in which agents both compete and cooperate, such as social media networks, stock markets, political campaigns, legislation, and geopolitical events.

A deep learning-based approach for efficient detection and classification of local Ca²⁺ release events in Full-Frame confocal imaging

The release of Ca2+ ions from intracellular stores plays a crucial role in many cellular processes, acting as a secondary messenger in various cell types, including cardiomyocytes, smooth muscle cells, hepatocytes, and many others. Detecting and classifying associated local Ca2+ release events is particularly important, as these events provide insight into the mechanisms, interplay, and interdependencies of local Ca2+release events underlying global intracellular Ca2+signaling. However, time-consuming and labor-intensive procedures often complicate analysis, especially with low signal-to-noise ratio imaging data.Here, we present an innovative deep learning-based approach for automatically detecting and classifying local Ca2+ release events. This approach is exemplified with rapid full-frame confocal imaging data recorded in isolated cardiomyocytes.To demonstrate the robustness and accuracy of our method, we first use conventional evaluation methods by comparing the intersection between manual annotations and the segmentation of Ca2+ release events provided by the deep learning method, as well as the annotated and recognized instances of individual events. In addition to these methods, we compare the performance of the proposed model with the annotation of six experts in the field. Our model can recognize more than 75 % of the annotated Ca2+ release events and correctly classify more than 75 %. A key result was that there were no significant differences between the annotations produced by human experts and the result of the proposed deep learning model.We conclude that the proposed approach is a robust and time-saving alternative to conventional full-frame confocal imaging analysis of local intracellular Ca2+ events.

An Efficient Deep Learning Approach for DNA-Binding Proteins Classification from Primary Sequences

As the number of identified proteins has expanded, the accurate identification of proteins has become a significant challenge in the field of biology. Various computational methods, such as Support Vector Machine (SVM), K-nearest neighbors (KNN), and convolutional neural network (CNN), have been proposed to recognize deoxyribonucleic acid (DNA)-binding proteins solely based on amino acid sequences. However, these methods do not consider the contextual information within amino acid sequences, limiting their ability to adequately capture sequence features. In this study, we propose a novel approach to identify DNA-binding proteins by integrating a CNN with bidirectional long-short-term memory (LSTM) and gated recurrent unit (GRU) as (CNN-BiLG). The CNN-BiLG model can explore the potential contextual relationships of amino acid sequences and obtain more features than traditional models. Our experimental results demonstrate a validation set prediction accuracy of 94% for the proposed CNN-BiLG, surpassing the accuracy of machine learning models and deep learning models. Furthermore, our model is both effective and efficient, exhibiting commendable classification accuracy based on comparative analysis.

Manifold-Based Verbalizer Space Re-embedding for Tuning-Free Prompt-Based Classification

Prompt-based classification adapts tasks to a cloze question format utilizing the [MASK] token and the filled tokens are then mapped to labels through pre-defined verbalizers. Recent studies have explored the use of verbalizer embeddings to reduce labor in this process. However, all existing studies require a tuning process for either the pre-trained models or additional trainable embeddings. Meanwhile, the distance between high-dimensional verbalizer embeddings should not be measured by Euclidean distance due to the potential for non-linear manifolds in the representation space. In this study, we propose a tuning-free manifold-based space re-embedding method called Locally Linear Embedding with Intra-class Neighborhood Constraint (LLE-INC) for verbalizer embeddings, which preserves local properties within the same class as guidance for classification. Experimental results indicate that even without tuning any parameters, our LLE-INC is on par with automated verbalizers with parameter tuning. And with the parameter updating, our approach further enhances prompt-based tuning by up to 3.2%. Furthermore, experiments with the LLaMA-7B&13B indicate that LLE-INC is an efficient tuning-free classification approach for the hyper-scale language models.

Building large-scale registries from unstructured clinical notes using a low-resource natural language processing pipeline

Building clinical registries is an important step in clinical research and improvement of patient care quality. Natural Language Processing (NLP) methods have shown promising results in extracting valuable information from unstructured clinical notes. However, the structure and nature of clinical notes are very different from regular text that state-of-the-art NLP models are trained and tested on, and they have their own set of challenges. In this study, we propose Sentence Extractor with Keywords (SE-K), an efficient and interpretable classification approach for extracting information from clinical notes and show that it outperforms more computationally expensive methods in text classification. Following the Institutional Review Board (IRB) approval, we used SE-K and two embedding based NLP approaches (Sentence Extractor with Embeddings (SE-E) and Bidirectional Encoder Representations from Transformers (BERT)) to develop comprehensive registry of anterior cruciate ligament surgeries from 20 years of unstructured clinical data at a multi-site tertiary-care regional children's hospital. The low-resource approach (SE-K) had better performance (average AUROC of 0.94 ± 0.04) than the embedding-based approaches (SE-E: 0.93 ± 0.04 and BERT: 0.87 ± 0.09) for out of sample validation, in addition to minimum performance drop between test and out-of-sample validation. Moreover, the SE-K approach was at least six times faster (on CPU) than SE-E (on CPU) and BERT (on GPU) and provides interpretability. Our proposed approach, SE-K, can be effectively used to extract relevant variables from clinic notes to build large-scale registries, with consistently better performance compared to the more resource-intensive approaches (e.g., BERT). Such approaches can facilitate information extraction from unstructured notes for registry building, quality improvement and adverse event monitoring.

An efficient approach for breast cancer classification using machine learning

Breast cancer, a life-threatening disease affecting millions worldwide, poses significant challenges due to its time-consuming manual determination process, potential risks, and human errors. It is a condition where cells of the breast develop unnaturally and uncontrollably, resulting in a mass called a tumor. If lumps in the breast are not addressed, they can spread to other regions of the body, including the bones, liver, and lungs. Early diagnosis is crucial for effective treatment and improved patient outcomes. In this research paper, we focus on employing machine learning models to achieve quick identification of breast cancer tumors as benign or malignant. The primary objective is to develop a decision-making visualization pattern using swarm plots and heat maps. To accomplish this, we utilized the Light GBM (Gradient Boosting Machine) algorithm and compared its performance against other established machine learning models, namely Logistic Regression, Gradient Boosting Algorithm, Random Forest Algorithm, and XG Boost Algorithm. Ultimately, our study demonstrates that the Light GBM Algorithm exhibits the highest accuracy of 96.98% in distinguishing between benign and malignant breast tumors.

An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm

Glaucoma, a neuro-degenerative eye disease, is the result of an increase in intraocular pressure inside the retina. It is the second-leading cause of blindness worldwide, and if an early diagnosis is not made, it can lead to total blindness. There is a critical need to develop a system that can work well without a lot of equipment, qualified medical professionals, and requires less time about this core issue. This article provides a thorough examination of the main machine learning (ML) techniques employed in the processing of retinal images for the identification and diagnosis of glaucoma. Machine learning (ML) has been demonstrated to be a crucial technique for the development of computer-assisted technology. Machine learning (ML) techniques can be used to construct predictive models for the early diagnosis of glaucoma. Our objective is to develop a machine learning algorithm that can accurately forecast the likelihood of developing glaucoma using patient data. Ophthalmologists have also conducted a significant amount of secondary research over the years. Such characteristics emphasise the importance of ML while analysing retinal pictures.

An Efficient Privacy-Preserving Randomization-Based Approach for Classification Upon Encrypted Data in Outsourced Semi-Honest Environment

An Efficient Privacy-Preserving Randomization-Based Approach for Classification Upon Encrypted Data in Outsourced Semi-Honest Environment

RSS-LSTM: A Metaheuristic-Driven Optimization Approach for Efficient Text Classification

RSS-LSTM: A Metaheuristic-Driven Optimization Approach for Efficient Text Classification

Highly Efficient Machine Learning Approach for Automatic Disease and Color Classification of Olive Fruits

Highly Efficient Machine Learning Approach for Automatic Disease and Color Classification of Olive Fruits

An Efficient Approach for Coffee Leaf Disease Classification and Severity Prediction

An Efficient Approach for Coffee Leaf Disease Classification and Severity Prediction

An efficient context-aware approach for whole-slide image classification

Computational pathology for gigapixel whole-slide images (WSIs) at slide level is helpful in disease diagnosis and remains challenging. We propose a context-aware approach termed WSI inspection via transformer (WIT) for slide-level classification via holistically modeling dependencies among patches on WSI. WIT automatically learns feature representation of WSI by aggregating features of all image patches. We evaluate classification performance of WIT and state-of-the-art baseline method. WIT achieved an accuracy of 82.1% (95% CI, 80.7%-83.3%) in the detection of 32 cancer types on the TCGA dataset, 0.918 (0.910-0.925) in diagnosis of cancer on the CPTAC dataset, and 0.882 (0.87-0.890) in the diagnosis of prostate cancer from needle biopsy slide, outperforming the baseline by 31.6%, 5.4%, and 9.3%, respectively. WIT can pinpoint the WSI regions that are most influential for its decision. WIT represents a new paradigm for computational pathology, facilitating the development of digital pathology tools.