Fast Classification Research Articles

Application of the Naive Bayes Method for Determining the Quality of Crude Palm Oil (CPO) at PTPN 2 Sawit Seberang

The palm oil industry is a vital pillar of Indonesia's economy, with Crude Palm Oil (CPO) as one of its leading commodities. The quality of CPO significantly impacts its competitiveness and market price internationally. PTPN 2 Sawit Seberang, as a prominent CPO processing company, faces challenges in consistently maintaining product quality. Key factors affecting CPO quality include moisture content, free fatty acids, and impurity levels, which are difficult to manage manually. To address these challenges, this study applies the Naive Bayes method as an efficient and fast classification tool for determining CPO quality. Naive Bayes was chosen for its simplicity in probability calculations and its ability to handle data classification with reasonable accuracy. The data used in this study include moisture content, free fatty acids, and impurity levels measured between February and June 2024. The data was split into training data (80%) and testing data (20%) and analyzed using RapidMiner software. The results show that the Naive Bayes method achieved an accuracy rate of 66.6%, with precision and recall values of 50% each. Although the accuracy could be improved, the application of this method has significantly enhanced the efficiency of determining CPO quality. Thus, the implementation of the Naive Bayes method in determining CPO quality at PTPN 2 Sawit Seberang is an effective step towards improving operational efficiency, classification accuracy, and decision-making quality related to product standards, ultimately supporting the company's competitiveness in the global market.

Fast Hyperspectral Image Classification with Strong Noise Robustness Based on Minimum Noise Fraction

A fast hyperspectral image classification algorithm with strong noise robustness is proposed in this paper, aiming at the hyperspectral image classification problems under noise interference. Based on the Fast 3D Convolutional Neural Network (Fast-3DCNN), this algorithm enables the classification model to have good tolerance for various types of noise by using a Minimum Noise Fraction (MNF) as dimensionality reduction module for hyperspectral image input data. In addition, by introducing lightweight hybrid attention modules with the spatial and the channel information, the deep features extracted by the Convolutional Neural Network are further refined, ensuring that the model has high classification accuracy. Public dataset experiments have shown that compared to traditional methods, the MNF in this algorithm reduces the dimensionality of input spectral data, preserves information with higher signal-to-noise ratio(SNR) in the spectral bands, and aggregates spectral features into class feature vectors, greatly improving the noise robustness of the model. At the same time, based on a lightweight spectral–spatial hybrid attention mechanism, combined with fewer spectral dimensions, the model effectively avoids overfitting. With less loss in model training speed, it achieved better classification accuracy in small-scale training sample experiments, fully demonstrating the good generalization ability of this algorithm.

A self-iterative learning with dual attention mechanism fusion method for pumpkin seed quality equipment

To perform fast and accurate classification of pumpkin seeds, three batches of pumpkin seed images were taken. A self-iterative learning large kernel attention-Squeeze and Excitation-Residual Network34 (LKA-SE-ResNet34) model was proposed by adding dual attention mechanism fusion of large kernal attention (LKA) mechanism and Squeeze and Excitation (SE) attention mechanism to each residual module of ResNet34 for recognizing pumpkin seeds. ResNet34 network model was chosen as the basic framework by comparing GoogleNet, AlexNet, EfficientNet, MobileNet, DenseNet, ResNet34 and Residual Network50 (ResNet50) deep learning network models. The comparation result showed that the top two models with the highest accuracy (Acc) are ResNet50 network model and ResNet34 network model among the compared models. The experimental results showed that the self-iterative learning LKA-SE-ResNet34 model outperformed the above-mentioned other network models in pumpkin seed recognition with an accuracy of 91.8%. To further validate the effectiveness of the proposed model for small sample size of data, two-sided images and deformed images of pumpkin seeds were collected and used, and the experimental results still proved that the proposed model had good generalization ability and accuracy, with the accuracies of 93.63% and 94.15% for two sides of pumpkin seeds as well as an accuracy of 99.03% for deformed pumpkin seeds. A set of seed sorting machine and an application (APP) for non-destructive testing of seeds were initially developed.

Fast Accurate Detection and Classification of Kidney Diseases from CT Images using Hybrid Classifiers

Fast Accurate Detection and Classification of Kidney Diseases from CT Images using Hybrid Classifiers

An ensemble deep learning model for fast classification of Twitter spam

Twitter spam needs to be detected and arrested quickly. The paper examines methods for classification of spam in terms of determination of important features, comparative performance of classification models, and improvement in time performance for classification. It presents a conceptualization of several novel rich, deep, and naïve features. The extraction processes for rich and deep features increase the time complexity of spam classification. To address this, the proposed model selectively segregates and combines features to enable near real-time processing. This supersedes the time performance of standard machine learning and deep learning models, with no compromise on the quality of classification.

Electrochemical sensors for fast classification of different Cannabis sativa L. samples according to total Δ9-tetrahydrocannabinol content

In this work, we investigated the ability of an electrochemical sensor to recognize Cannabis sativa L. samples with different total content of Δ9-tetrahydrocannabinol (Δ9-THC), determined by the levels of the psychoactive cannabinoid and of its biosynthetic precursor Δ9-tetrahydrocannabinolic acid (Δ9-THCA), using a multivariate approach. The voltammetric responses recorded with screen-printed electrodes modified with carbon black reflected the compositional differences from the different samples, in terms of cannabinoids of the vegetal material. PLS-DA models allowed for the correct classification of most C. sativa samples into the classes of legal and illegal samples according to total Δ9-THC content, based on threshold limits defined by the EU/US (0.3 % w/w) and Italian (0.6 % w/w) regulations. Satisfactory results were achieved in both cases, obtaining classification efficiency values in prediction of the external test set equal to 85 % and 100 % for the EU/US and Italian thresholds, respectively. The obtained results suggest the possibility to consider the proposed method as a starting point for the implementation of an automated device for rapid prescreening of total Δ9-THC content directly on site.

Characterization of Mung Plants Treated with Iron Oxide Nanoparticles Using Raman and Ultraviolet-Visible Spectroscopy Coupled with Chemometrics

In order to meet the demands of the expanding population, nanotechnology-based materials are being investigated for a potential solution to enhance crop yield. This study examines the effect of different concentrations (0.2, 0.6, 1.0, and 1.4 mM) of iron oxide nanoparticles (Fe2O3 NPs) on mung plants using confocal micro Raman and ultraviolet-visible spectroscopy. The analysis shows that the biochemical profile of the plants is altered. The increased intensity values of carotenoids, pectin, lignin, proteins, cellulose, carbohydrates, and aliphatics observed in Raman spectroscopy, along with increased concentrations of photosynthetic pigments in ultraviolet-visible spectroscopy, suggest that Fe2O3 NPs positively impact mung. In agro-related studies, Raman spectroscopy in assistance with chemometric analysis provides a reliable, noninvasive, fast, and label-free recognition and classification of plants under biotic or abiotic stress. Principal component analysis (PCA), linear discriminant analysis (LDA), and principal component analysis-linear discriminant analysis (PCA-LDA) were applied to the Raman spectra of treated and untreated mung leaves for rapid categorization and precise prediction. The results show that PCA-LDA achieved superior predicting performance and visualization, with an accuracy of 70%, along with higher recall, precision, f1 score, and area under receiver operating characteristics curves compared to LDA. The study reveals that spectroscopic techniques combined with chemometrics have great potential for confirmatory, nondestructive, and label-free assessments of biotic and abiotic stresses in plants.

Fast Classification of Error Correcting Codes based on Syndrome a Posteriori Probability Spectrum for Intelligent Communication

Abstract Classification of channel codes has drawn widespread concern for a long time. In this letter, we propose a posteriori probability based approach to classify cyclic redundancy check codes and convolutional codes. We defined the posteriori probability spectrum to clarify the parity-check relationship between the intercepted codewords and the candidate parity-check vector generated by traversing. We theoretically explained that the posteriori probability spectrum of cyclic redundancy check codes and convolutional codes show clear distinctions. We further specify the feature parameter generated from the posteriori probability spectrum to do classification. Simulation results show that the posteriori probability spectrum for cyclic redundancy check codes and convolutional codes are significantly different. The proposed approach outperforms traditional Gauss-Jordan elimination through pivoting approach in classification accuracy.

Machine vision-based detection of forbidden elements in the high-speed automatic scrap sorting line

Highly efficient industrial sorting lines require fast and reliable classification methods. Various types of sensors are used to measure the features of an object to determine which output class it belongs to. One technique involves the use of an RGB camera and a machine learning classifier. The paper is focused on protecting the sorting process against prohibited and dangerous items potentially present in the sorted material that pose a threat to the sorting process or the subsequent metallurgical process. To achieve this, a convolutional neural network classifier was applied under real-life conditions to detect forbidden elements in copper-based metal scrap. A laboratory stand simulating the working conditions in a high-speed scrap sorting line was prepared. Using this custom stand, training and test sets for machine learning were gathered and labeled. An image preprocessing algorithm was designed to increase the robustness of the resulting forbidden element detector system. The performance of multiple neural network architectures and data set augmentations was analyzed. The highest accuracy of 98.03% and F1-score of 97.16% were achieved with a DenseNet-based classifier. The results of this paper show the feasibility of using the presented solution on a high-speed industrial line.

Enzyme-Mediated Duplex Exponential Amplification: A New Platform for Rapid Screening of <i>Hylurgus ligniperda</i>

As the world's second largest timber importer, wood demand in China has been growing extremely rapidly, leading to an increase of 163% from 2009 to 2018. The plant quarantine pest <i>H. ligniperda Fabricius, 1787</i> is an invasive species frequently intercepted at ports. <i>H. ligniperda</i> causes damage mainly to pine and spruce. To improve the efficiency of on-site inspection and the efficacy of early detection, tight quarantine in ports, time-effective identification, and a national surveillance program for high-risk invasive bark beetles are in urgent need. In this study, a simple, fast and accurate classification method for <i>H. ligniperda </i>is established based on the enzyme-mediated duplex exponential amplification (EmDEA) technique. Partial region from <i>inhibitor of apoptosis 2</i> (<I>IAP2)</I> gene was selected as the target and 6 primer/probe combinations were designed. Through selection, the combination of 3-HY-F3, 3-HY-R2 and 3-HY-RNA5 was chosen as the final primer-probe set, as it showed the lowest Ct with highest final fluorescence signal. Method validation and specificity test using 6 other beetle species living on coniferous wood showed that this method is result reliable and specific. Through parameter analysis with positive plasmid, the detection limit was calculated to be 13.6 copies/μL (9×10<sup>-7</sup> ng DNA/ reaction), much higher than conventional molecular methods such as PCR. The whole process including isothermal amplification, data analysis, and result output can be finished in 30 min, which is highly time-effective. Besides, the operation is simple and little training is needed for non-professionals. The application prospects of this rapid screening system include customs screening in ports, wild survey in non-lab situations and early warning system development. The new analysis platform EmDEA, can also be implemented in rapid detection and identification of other forestry pests.

Corynebacterium pseudotuberculosis: Whole genome sequencing reveals unforeseen and relevant genetic diversity in this pathogen.

Corynebacterium pseudotuberculosis (CPS) is an important bacterial animal pathogen. CPS causes chronic, debilitating and currently incurable infectious diseases affecting a wide range of livestock and wild herbivores including camelids worldwide. Belonging to the Corynebacterium diphtheriae complex, this pathogen can also infect humans. The classical characterization of CPS is typically based on the testing of nitrate reductase activity, separating the two biovars Equi and Ovis. However, more refined resolutions are required to unravel routes of infection. This was realized in our study by generating and analyzing whole genome sequencing (WGS) data. Using newly created core genome multilocus sequence typing (cgMLST) profiles we were the first to discover isolates grouping in a cluster adjacent to clusters formed by CPS biovar Equi isolates. This novel cluster includes CPS isolates from alpacas, llamas, camels and dromedaries, which are characterized by a lack of nitrate reductase activity as encountered in biovar Ovis. This is of special interest for molecular epidemiology. Nevertheless, these isolates bear the genes of the nitrate locus, which are characteristic of biovar Equi isolates. However, sequence analysis of the genes narG and narH of the nitrate locus revealed indels leading to frameshifts and inactivity of the enzymes involved in nitrate reduction. Interestingly, one CPS isolate originating from another lama with an insertion in the MFS transporter (narT) is adjacent to a cluster formed by ovine CPS isolates biovar Equi. Based on this knowledge, the combination of biochemical and PCR based molecular biological nitrate reductase detection can be used for a fast and uncomplicated classification of isolates in routine diagnostics in order to check the origin of camelid CPS isolates. Further analysis revealed that partial sequencing of the ABC transporter substrate binding protein (CP258_RS07935) is a powerful tool to assign the biovars and the novel genomovar.

MycoAI: Fast and accurate taxonomic classification for fungal ITS sequences.

Efficient and accurate classification of DNA barcode data is crucial for large-scale fungal biodiversity studies. However, existing methods are either computationally expensive or lack accuracy. Previous research has demonstrated the potential of deep learning in this domain, successfully training neural networks for biological sequence classification. We introduce the MycoAI Python package, featuring various deep learning models such as BERT and CNN tailored for fungal Internal Transcribed Spacer (ITS) sequences. We explore different neural architecture designs and encoding methods to identify optimal models. By employing a multi-head output architecture and multi-level hierarchical label smoothing, MycoAI effectively generalizes across the taxonomic hierarchy. Using over 5 million labelled sequences from the UNITE database, we develop two models: MycoAI-BERT and MycoAI-CNN. While we emphasize the necessity of verifying classification results by AI models due to insufficient reference data, MycoAI still exhibits substantial potential. When benchmarked against existing classifiers such as DNABarcoder and RDP on two independent test sets with labels present in the training dataset, MycoAI models demonstrate high accuracy at the genus and higher taxonomic levels, with MycoAI-CNN being the fastest and most accurate. In terms of efficiency, MycoAI models can classify over 300,000 sequences within 5 min. We publicly release the MycoAI models, enabling mycologists to classify their ITS barcode data efficiently. Additionally, MycoAI serves as a platform for developing further deep learning-based classification methods. The source code for MycoAI is available under the MIT Licence at https://github.com/MycoAI/MycoAI.

PAFE: A lightweight visualization-based fast malware classification method

With the development of automated malware toolkits, cybersecurity faces evolving threats. Although visualization-based malware analysis has proven to be an effective method, existing approaches struggle with challenging malware samples due to alterations in the texture features of binary images during the visualization preprocessing stage, resulting in poor performance. Furthermore, to enhance classification accuracy, existing methods sacrifice prediction time by designing deeper neural network architectures. This paper proposes PAFE, a lightweight and visualization-based rapid malware classification method. It addresses the issue of texture feature variations in preprocessing through pixel-filling techniques and applies data augmentation to overcome the challenges of class imbalance in small sample datasets. PAFE combines multi-scale feature fusion and a channel attention mechanism, enhancing feature expression through modular design. Extensive experimental results demonstrate that PAFE outperforms the current state-of-the-art methods in both efficiency and effectiveness for malware variant classification, achieving an accuracy rate of 99.25 % with a prediction time of 10.04 ms.

Comparison of Binary Logistic Regression and SVM to Classify Diabetes Sufferers

Diabetes is a chronic metabolic disorder characterized by high levels of glucose in the blood due to disruption of the insulin hormone which functions as a regulator of the balance of blood sugar levels. This disease continues to increase in prevalence in various countries, making it a global health problem. Diabetes has trigger factors that contribute to the incidence of the disease, such as age, gender, smoking habits, healthy eating patterns, high blood pressure, and others. Diagnosis of diabetes can be done by carrying out a fasting blood sugar test, a 2-hour postprandial (PP) blood sugar test, and a random blood sugar test. However, it is very possible for diagnoses made by health workers to have errors due to subjectivity and different experiences, so a fast and precise classification method is needed to classify patients undergoing diabetes examination based on variables related to diabetes. The classification method used in this research is binary logistic regression and Support Vector Machine (SVM). A similar study carried out classification of diabetes sufferers using the Naive Bayes and KNN methods by comparing the results with SVM, so in this study the binary logistic regression method and SVM will be used to determine the performance of the classification method. The data used is secondary data. Next, the data is divided into training and testing data. The analysis results show that the SVM method is slightly superior in classification accuracy of testing data, namely 97.75%. With this research, it is hoped that decisions on patients undergoing diabetes examination will be faster, more precise and effective, and classification methods with better performance can be applied

ETSVF-COVID19: efficient two-stage voting framework for COVID-19 detection

COVID-19 disease, an outbreak in the spring of 2020, reached very alarming dimensions for humankind due to many infected patients during the pandemic and the heavy workload of healthcare workers. Even though we have been saved from the darkness of COVID-19 after about three years, the importance of computer-aided automated systems that support field experts in the fight against with global threat has emerged once again. This study proposes a two-stage voting framework called ETSVF-COVID19 that includes transformer-based deep features and a machine learning approach for detecting COVID-19 disease. ETSVF-COVID19, which offers 99.2% and 98.56% accuracies on computed tomography scan and X-radiation images, respectively, could compete with the related works in the literature. The findings demonstrate that this framework could assist field experts in making informed decisions while diagnosing COVID-19 with its fast and accurate classification role. Moreover, ETSVF-COVID19 could screen for chest infections and help physicians, particularly in areas where test kits and specialist doctors are inadequate.

Scaling-law mechanical marker for liver fibrosis diagnosis and drug screening through machine learning.

Studies of cell and tissue mechanics have shown that significant changes in cell and tissue mechanics during lesions and cancers are observed, which provides new mechanical markers for disease diagnosis based on machine learning. However, due to the lack of effective mechanic markers, only elastic modulus and iconographic features are currently used as markers, which greatly limits the application of cell and tissue mechanics in disease diagnosis. Here, we develop a liver pathological state classifier through a support vector machine method, based on high dimensional viscoelastic mechanical data. Accurate diagnosis and grading of hepatic fibrosis facilitates early detection and treatment and may provide an assessment tool for drug development. To this end, we used the viscoelastic parameters obtained from the analysis of creep responses of liver tissues by a self-similar hierarchical model and built a liver state classifier based on machine learning. Using this classifier, we implemented a fast classification of healthy, diseased, and mesenchymal stem cells (MSCs)-treated fibrotic live tissues, and our results showed that the classification accuracy of healthy and diseased livers can reach 0.99, and the classification accuracy of the three liver tissues mixed also reached 0.82. Finally, we provide screening methods for markers in the context of massive data as well as high-dimensional viscoelastic variables based on feature ablation for drug development and accurate grading of liver fibrosis. We propose a novel classifier that uses the dynamical mechanical variables as input markers, which can identify healthy, diseased, and post-treatment liver tissues.

Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning.

Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals.

A real-time CNN–BiLSTM-based classifier for patient-centered AR-SSVEP active rehabilitation exoskeleton system

The rehabilitation of stroke has brought a serious social burden to many countries. The augmented reality steady-state visually evoked potential (AR-SSVEP) exoskeleton rehabilitation system can effectively help stroke patients recover motor function and reduce the burden on society and rehabilitation physicians. However, the decoding accuracy and efficiency of AR-SSVEP in short time stimulus are low, and the types of trajectory decoded are few. It has a negative impact on the patient’s rehabilitation interest and efficiency. In this article, a patient-centered AR-SSVEP active rehabilitation exoskeleton system is developed based on the CNN–BiLSTM fast classification algorithm. The algorithm uses CNN architecture to improve model generalization, and uses BiLSTM to improve classification accuracy. The patient-centered interactive trajectory decoding logic based on a task-driven strategy is proposed to achieve patient-defined coordinated movement trajectory of the left and right upper limb shoulder elbow joints through the 6-class brain–computer interface (BCI). The offline and online experiments were conducted on the system. In offline experiments, under a 0.5 s time window, the classification accuracy rate was 98.5%, and the information transfer rate (ITR) reached 210 bits/min. In online experiment, the patient controlled the exoskeleton of the upper limb to perform four non-repetitive trajectories, with an average time of 319 s, which was 20% more than the standard reference time. The experimental results have shown the system can effectively improve the accuracy and efficiency of motion intention identification within short time stimulus, and decode patient-defined motion trajectory. In conclusion, this study will provide a new rehabilitation way for stroke patients.

Fast and space-efficient taxonomic classification of long reads with hierarchical interleaved XOR filters.

Metagenomic long-read sequencing is gaining popularity for various applications, including pathogen detection and microbiome studies. To analyze the large data created in those studies, software tools need to taxonomically classify the sequenced molecules and estimate the relative abundances of organisms in the sequenced sample. Because of the exponential growth of reference genome databases, the current taxonomic classification methods have large computational requirements. This issue motivated us to develop a new data structure for fast and memory-efficient querying of long reads. Here, we present Taxor as a new tool for long-read metagenomic classification using a hierarchical interleaved XOR filter data structure for indexing and querying large reference genome sets. Taxor implements several k-mer-based approaches, such as syncmers, for pseudoalignment to classify reads and an expectation-maximization algorithm for metagenomic profiling. Our results show that Taxor outperforms state-of-the-art tools regarding precision while having a similar recall for long-read taxonomic classification. Most notably, Taxor reduces the memory requirements and index size by >50% and is among the fastest tools regarding query times. This enables real-time metagenomics analysis with large reference databases on a small laptop in the field.

Iron ore rock classification and mine remote sensing inversion based on spectroscopy and improved extreme learning machine

The classification of iron ore rock plays a crucial role in assisting the mine to develop scientifically sound mining strategies. Given the limitations of traditional manual identification and chemical techniques in terms of cost and detection time, it is crucial to develop an economical, high-precision, and efficient classification method for iron ore rocks. This paper takes an iron mine in Anshan as the research center and aims to establish an iron ore rock classification model by combining the improved extreme learning machine (ELM) with near-infrared spectra (NIRS). The NIRS of iron ore rock is denoised and feature extracted using the Savitzky–Golay filter and competitive adaptive reweighted sampling (CARS) methods, respectively. In this paper, we propose the IAdaBoost algorithm by introducing the sample similarity relationship into the weight-updating strategy of the AdaBoost integration algorithm. The weighted extreme learning machine (WELM) is adopted as the base learner for the IAdaBoost algorithm. Furthermore, we employ a multi-strategy improved gray wolf optimization (IGWO) algorithm to optimize the learning rate and the number of integrated base learners in the IAdaBoost algorithm. Consequently, we propose the IGWO-IAdaBoost-WELM model. Compared to other machine learning models, our proposed model performs better and can classify iron ore rocks consistently and efficiently. Meanwhile, we also used the model to invert the remote sensing images of the iron ore mining area, and the results show that the model can achieve fast, efficient, and large-scale iron ore rock classification, which provides an important reference for mining.