Binary Classification Algorithm Research Articles

Mapping surface hoar from near-infrared texture in a laboratory

Abstract. Surface hoar crystals are snow grains that form when water vapor deposits on the snow surface. Once buried, surface hoar creates a weak layer in the snowpack that can later cause large avalanches to occur. The formation and persistence of surface hoar are highly spatiotemporally variable, making its detection difficult. Remote-sensing technology capable of detecting the presence and spatial distribution of surface hoar would be beneficial for avalanche forecasting, but this capability has yet to be developed. Here, we hypothesize that near-infrared (NIR) texture, defined as the spatial variability of reflectance magnitude, may produce an optical signature unique to surface hoar due to the distinct shape and orientation of the grains. We tested this hypothesis by performing reflectance experiments in a controlled cold laboratory environment to evaluate the potential and accuracy of surface hoar mapping from NIR texture using a near-infrared hyperspectral imager (NIR-HSI) and a lidar operating at 1064 nm. We analyzed 41 snow samples, three of which were surface hoar and 38 of which consisted of other grain morphologies. When using NIR-HSI under direct and diffuse illumination, we found that surface hoar displayed higher NIR texture relative to all other grain shapes across numerous spectral bands and a wide range of spatial resolutions (0.5–50 mm). Due to the large number of spectral- and spatial-resolution combinations, we conducted a detailed samplewise case study at 1324 nm spectral and 10 mm spatial resolution. The case study resulted in the median texture of surface hoar being 1.3 to 8.6 times greater than that of the 38 other samples under direct and diffuse illumination (p < 0.05 in all cases). Using lidar, surface hoar also exhibited significantly increased NIR texture in 30 out of 38 samples, but only at select (5–25 mm) spatial resolutions. Leveraging these results, we propose a simple binary classification algorithm to map the extent of surface hoar on a pixelwise basis using both the NIR-HSI and lidar instruments. The NIR-HSI under direct and diffuse illumination performed best, with a median accuracy of 96.91 % and 97.37 %, respectively. Conversely, the median classification accuracy achieved with lidar was only 66.99 %. Further, to assess the repeatability of our method and demonstrate its mapping capacity, we ran the algorithm on a new sample with mixed microstructures, with an accuracy of 99.61 % and 96.15 % achieved using NIR-HSI under direct and diffuse illumination, respectively. As NIR-HSI detectors become increasingly available, our findings demonstrate the potential of a new tool for avalanche forecasters to remotely assess the spatiotemporal variability of surface hoar, which would improve avalanche forecasts and potentially save lives.

Quality 4.0: Learning quality control, the evolution of SQC/SPC

This article marks a significant advancement in the field of quality management, specifically focusing on the evolution from traditional Statistical Quality Control (SQC) and Statistical Process Control (SPC) methods to a more advanced Learning Quality Control (LQC) approach. The research introduces Quality 4.0 (Q4.0) as a novel paradigm that fuses the technologies of the fourth industrial revolution, Manufacturing Big Data (MBD), Industrial Internet of Things (IIoT), Cloud Storage and Computing (CSC) and Artificial Intelligence (AI), with traditional quality management practices. The central theme of this study is exploring the limitations inherent in conventional quality control methods when faced with the complexities of modern manufacturing environments. The authors propose LQC systems as a solution, employing binary classification algorithms to predict and detect defects in manufacturing processes. This represents a shift from reactive to proactive quality measures enabled by AI’s real-time data processing capabilities. The document delves into the evolution of manufacturing data across industrial revolutions, highlighting the exponential growth of unstructured data and its challenges. Through case studies, the authors illustrate the practical applications of LQC systems, demonstrating their ability to learn complex patterns in hyperdimensional spaces and automate tasks traditionally performed visually.

Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively.

SecureNet: Network Intrusion Detection using Machine Learning and Deep Learning Techniques

Abstract: In the ever-evolving landscape of cybersecurity, the need for robust intrusion detection systems has become paramount. This paper introduces a cutting-edge intrusion detection algorithm designed to enhance network security through the integration of advanced machine learning and deep learning methodologies. The proposed algorithm capitalizes on the strengths of both paradigms to achieve a comprehensive and adaptive approach to identifying malicious activities within a network. This research focuses on enhancing network security through the development and evaluation of a novel intrusion detection system leveraging both deep learning and traditional machine learning approaches. Utilizing the NSL-KDD dataset, we employ the Long Short-Term Memory (LSTM) model, a superior version of Recurrent Neural Networks (RNNs), and the K-Nearest Neighbors (KNN) algorithm for binary and multi-class classification of network intrusion anomalies. The LSTM model excels in capturing temporal dependencies, enabling the detection of nuanced sequential patterns, while the KNN algorithm contributes to a comprehensive classification framework. Experimental results demonstrate the effectiveness of the hybrid methodology, showcasing improved accuracy, precision, and recall compared to traditional methods. This research underscores the potential of integrating deep learning and classical machine learning techniques to bolster the capabilities of intrusion detection systems in safeguarding against evolving cyber threats

Classification of Fall Out Boy Eras

This paper explored the use of machine learning techniques to differentiate between two different musical eras of the same rock band, including the technique of Logistic Regression. Logistic regression (LR) is a widely used statistical modeling method for binary classification in supervised machine learning. It is often used to predict whether a given event belongs to one of two categories. The process helps data scientists understand which variables are good predictors of class membership. Applications of logistic regression include loan classification in the financial industry and predicting susceptibility to disease in the medical field. In this particular project, a dataset was constructed using data from Spotify and Genius consisting of songs and lyrics written by the band Fall Out Boy. A logistic regression model was developed from scratch to classify the songs and lyrics into one of two eras of the band: before their 2009 hiatus and afterward. The study aimed to determine if a computer could differentiate between the two eras. The model was also tested against other binary classification algorithms, including Random Forest and Support Vector Machines.

Using machine learning algorithms based on patient admission laboratory parameters to predict adverse outcomes in COVID-19 patients

Amidst the global COVID-19 pandemic, the urgent need for timely and precise patient prognosis assessment underscores the significance of leveraging machine learning techniques. In this study, we present a novel predictive model centered on routine clinical laboratory test data to swiftly forecast patient survival outcomes upon admission. Our model integrates feature selection algorithms and binary classification algorithms, optimizing algorithmic selection through meticulous parameter control. Notably, we developed an algorithm coupling Lasso and SVM methodologies, achieving a remarkable area under the ROC curve of 0.9277 with the use of merely 8 clinical laboratory parameters collected upon admission. Our primary contribution lies in the utilization of straightforward laboratory parameters for prognostication, circumventing data processing intricacies, and furnishing clinicians with an expeditious and precise prognostic assessment tool.

Predicting Onset of Sand Production in Oil Wells using Machine Learning

Sand production in oil wells is a significant challenge that negatively impacts productivity and compromise equipment integrity. This study explores the application of Optimized Support Vector Machine (SVM) binary classification algorithm to predict the onset of sand production in oil wells. A dataset from 63 oil wells was utilized, and class labels were determined based on the bulk and shear modulus product. The model development incorporated geological and mechanical parameters that could influence sand detachment such as: Young’s modulus, Poisson’s ratio, minimum and maximum horizontal stresses, overburden pressure, pore pressure, depth, fracture gradient, and formation strength. Instances above the threshold of 8E+11 were classified as indicative of no sand production, while those below were considered potential sand production scenarios. The SVM model demonstrated remarkable accuracy in predicting sand production onset, trained and tested rigorously with field data. The model's accuracy was evaluated using statistical parameters, such as: accuracy (ACC), sensitivity (SE), specificity (SP), and Matthew's Correlation Coefficient (MCC). From the results, the model achieved a score of 1 across all parameters, indicating high reliability and accuracy in sand production prediction. The practical implications of this model are significant, offering assistance to completion engineers in making proactive decisions regarding sand control strategies. Furthermore, the integration of this model into oil and gas industry processes can optimize operational efficiency by foreseeing potential sand production events, hence, preventing production impairment and ensuring loss prevention.

Identification of Autoantigen Markers for SARS CoV-2 Infection with Machine Learning-based Feature Selection: An Insight into COVID Symptoms

Background and Introduction:: Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection has been shown to trigger autoimmunity, and the phenomenon leads to several chronic human diseases such as Type-1 diabetes, Crohn’s disease, vasculitis, Guillian-Barrė syndrome, etc. The mechanism underlying SARS CoV-2-induced autoimmune response is unknown and is an active area of interest for the researchers. Objective:: The primary objective of this study is to identify the autoantigen markers for the classification of SARS CoV-2 (COVID-19 positive and negative samples) that trigger an immune response leading to autoimmunity using a machine learning approach that provides information to obtain a more accurate diagnosis for COVID-induced diseases. Materials and Methods:: Our study reports the transcriptomic profile of the COVID patient's whole blood samples collected from 0 to 35th day of acute infection as described in the GSE215865 dataset. The binary classification algorithm from the sci-kit learn python library, namely logistic regression and random forest with 10-fold cross-validation, was applied to the processed data, followed by a selection of the 20 best gene features with recursive feature elimination from a set of 10,719 gene features to obtain the classification accuracy of 87%. Results:: The fidgetin, microtubule severing factor (FIGN), SH3 and cysteine-rich domain (STAC), Cadherin-6 (CDH6), docking protein 6 (DOK6), nuclear RNA export factor 3 (NXF3) and maternally expressed 3 (MEG3) are the autoantigens markers identified for classification of COVID-positive and negative samples. Conclusion:: The identified autoantigen markers from transcriptomic datasets using machine learning techniques provide a deeper understanding of COVID-induced diseases and may play an important role as potential diagnostic and drug targets for COVID-19.

PREDICTING FIRE ALARMS USING MULTI SENSOR DATA: A BINARY CLASSIFICATION APPROACH

Fires pose significant threats to human life, property, and the environment. Early detection of fire incidents is crucial to prevent extensive damage and to ensure the safety of occupants. Traditional fire alarm systems typically rely on a single type of sensor, such as smoke detectors or heat sensors, to detect specific fire indicators. These systems operate based on predefined thresholds and triggers. However, they can be prone to false alarms triggered by non-fire-related events (e.g., cooking fumes or dust) and may not provide early warning signs in certain scenarios. To address these limitations, researchers and engineers have turned to advanced technologies, such as multi-sensor data analysis and machine learning algorithms, to develop more reliable and efficient fire alarm prediction systems. On the other hand, the need for a more robust and accurate fire alarm prediction system stems from the shortcomings of traditional methods. False alarms not only lead to wasted resources but also desensitize occupants, potentially leading them to ignore genuine alarms. Additionally, a delayed response to a fire incident can result in severe consequences, making it essential to develop an intelligent system that can effectively and timely predict fire events. Therefore, this work presents the utilization of multi-sensor data and binary classification to develop a more reliable fire alarm prediction system. The experiments are conducted using a dataset collected from various sensor inputs, including air temperature, humidity, CO2 concentration, molecular hydrogen, ethanol gas, and air pressure etc. Then applied binary classification algorithm to learn patterns from the data and classify fire-related events accurately. The results showed promising improvements in prediction accuracy, reduced false alarm rates, and early detection of fire incidents.

An overlapping minimization-based over-sampling algorithm for binary imbalanced classification

Imbalanced learning is an important branch of machine learning. It addresses the challenge of improving classifier accuracy for minority classes in imbalanced data sets. Currently, the mainstream methods for handling imbalanced learning are the synthetic minority oversampling technique (SMOTE) and its variants, which generate synthetic minority class samples to balance the dataset. However, existing methods suffer from issues such as increased sample overlap, exacerbated intra-class imbalance, and are sensitive to parameter settings. These issues make it challenging to generate high-quality minority class samples and can adversely affect the dataset. To address these challenges, this study proposes a novel overlapping minimization-based over-sampling (OMOS) algorithm for binary imbalanced classification. The OMOS algorithm consists of four steps: clustering, filtering, auto-encoding, and oversampling. In the clustering step, the mean shift algorithm is utilized to cluster the original dataset and identify clusters that belong to the minority class. In the filtering step, safe samples are selected that maintain consistent labels before and after clustering. Then, in the auto-encoding step, autoencoders are utilized to capture the distribution characteristics of safe samples within each minority class cluster. Finally, in the last step, minority class samples are generated based on the probability distribution learned from safe samples. Furthermore, OMOS introduces a novel approach to compute suitable sampling rates for each minority class cluster to handle intra-class imbalance. Experimental results show that the proposed OMOS algorithm outperforms six state-of-the-art SMOTE-based oversampling algorithms on 20 real-world imbalanced datasets and four classifiers: naive Bayes classifier, support vector machine, logistic regression, and decision trees. This demonstrates that OMOS is effective for binary imbalanced classification tasks.

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental health (MH) and substance use disorders (SUD) among women. By employing various modeling algorithms for binary classification, including Random Forest, Gradient Boosted Trees, XGBoost, Extra Trees, SGD, Deep Neural Network, Single-Layer Perceptron, K Nearest Neighbors (grid), and a super learning model (constructed by combining the predictions of a Random Forest model and an XGBoost model), the research aims to provide healthcare practitioners with a powerful tool for earlier identification, intervention, and personalised support for women at risk. The present research presents a machine learning (ML) methodology for more accurately predicting the co-occurrence of mental health (MH) and substance use disorders (SUD) in women, utilising the Treatment Episode Data Set Admissions (TEDS-A) from the year 2020 (n = 497,175). A super learning model was constructed by combining the predictions of a Random Forest model and an XGBoost model. The model demonstrated promising predictive performance in predicting co-occurring MH and SUD in women with an AUC = 0.817, Accuracy = 0.751, Precision = 0.743, Recall = 0.926 and F1 Score = 0.825. The use of accurate prediction models can substantially facilitate the prompt identification and implementation of intervention strategies.

Ensemble learning based approach for traffic incident detection and multi-category classification

Traffic incident is one of the important causes of road congestion. Traffic incident detection plays a crucial role in the safety application of intelligent transportation systems, which provides timely information for traffic management departments and reducing losses. Despite many researches on incident detection approach, the identification of different incident categories is not enough. In addition, traffic incident detection is still a challenging task due to the problem of data imbalance and feature selection. In this study, we propose a two-stage traffic incident detection framework based on ensemble learning. In the first stage, a binary classification algorithm based on XGBoost (eXtreme Gradient Boosting) is established to detect whether there is a traffic incident, and 24 feature variables are determined by model feature selection. In the second stage, three resampling algorithms are utilized to reconstruct and balance the dataset. Through comparative analysis, SMOTE (Synthetic Minority Over-sampling Technique)-XGBoost is the best method for incident multi-category classification with precision of 87.27%, 78.52% and 92.54%, respectively. Moreover, the baseline comparison experiments are conducted to evaluate our model performance with real-word datasets. The proposed model achieves the highest average accuracy of 93.45% in the first stage and the macro-precision of 86.11% in the second stage. The results indicate that the proposed method outperforms baselines and the two-stage framework can accurately realize the incident detection and multi-category classification.

Vocal music curriculum design and teaching quality evaluation in colleges and universities based on interactive structure model

Abstract Aiming at the current phenomenon of low teacher-student interaction in vocal music teaching, this paper combines the interaction structure model for the innovation and practice of college vocal music courses. Then, this paper uses Euclidean distance to improve the binary tree SVM multi-class classification algorithm, calculates the distance between the teaching samples of each vocal music course, classifies the samples for processing, and obtains the DBT-SVM multi-class classification algorithm. Finally, the DBT-SVM algorithm was used to establish an evaluation model for vocal course teaching quality in colleges and universities to assess the quality of vocal course teaching models. Each three-level index of teaching motivationality has an evaluation score above 80, which is a good level. The evaluation score for the pedagogical index is 89.44, which is almost excellent. The average score of the second-level index of interpersonal interaction is 74.52, which needs to be further improved. The teaching model of the college vocal music course proposed in this paper lays the foundation for the interactive transformation of vocal music teaching and provides a reference model for the improvement of the learning effect of college students’ vocal music courses.

Research on objective evaluation methods of labor education teaching for college students in higher education

Abstract Labor education in colleges and universities in the new era is the fundamental task of implementing the principle of establishing morality and educating people, as well as an essential hand in cultivating college students with all-round development of morality, intelligence, physicality, aesthetics, and labor. This paper focuses on the objective evaluation method of college students’ labor education to conduct research. Using the improved binary tree support vector machine multi-class classification algorithm, an evaluation model for college labor education based on a binary tree support vector machine has been constructed, and an evaluation system for labor education has been developed. The results show that the majors that have compulsory labor education courses, elective labor education courses, and have added labor education modules in their major courses are all art and sports, accounting for more than 50% on average. The percentages of liberal arts, science, economics and management, art and sports, medicine and science, and agriculture, which did not offer labor education-related courses, were 26.35%, 22.16%, 19.91%, 13.59%, 11.03%, and 13.19%, respectively. It is evident that labor education courses are not distributed equally in colleges and universities, and there are still some majors that do not offer labor education courses. In terms of labor education, most of the labor education through Civics and Political Science classes is provided to students. Managers are those who choose the most specialized courses. Students also tend to participate in bicultural education and social practice activities, accounting for 29.02% and 43.01%, respectively. It shows that the algorithm in this paper can objectively and effectively evaluate labor education in colleges and universities and identify the deficiencies in education.

Hardware Design of Lightweight Binary Classification Algorithms for Small-Size Images on FPGA

Hardware Design of Lightweight Binary Classification Algorithms for Small-Size Images on FPGA

Evaluating Binary Classification Algorithms on Data Lakes Using Machine Learning

Evaluating Binary Classification Algorithms on Data Lakes Using Machine Learning

A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXGBLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established evolutionary computation algorithms and the recently developed bio-inspired metaheuristic algorithm called Zebra Optimisation Algorithm. These algorithms include the Particle Swarm Optimisation Algorithm, the Bio-inspired Algorithms, Bat Optimisation Algorithm, Firefly Optimisation Algorithm, and Monarch Butterfly Optimisation Algorithm, as well as the Genetic Algorithm as an Evolutionary Algorithm. The dimensionality curse has been mitigated by using these metaheuristic methods for feature selection, and the results are compared with the wrapper-based feature selection XGBoost algorithm. The proposed algorithm uses the CSE-CIC -IDS2018 dataset, which contains the latest network attacks. XGBoost outperformed the other FS algorithms and was used as the feature selection algorithm. In evaluating the effectiveness of the newly proposed HXGBLSTM, binary and multi-class classifications are considered. When comparing the performance of the proposed HXGBLSTM for cyber threat detection, it outperforms seven innovative deep learning algorithms for binary classification and four of them for multi-class classification. Other evaluation criteria such as recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy for binary classification is 99.8\%, with F1-score of 99.83\%, precision of 99.85\%, and recall of 99.82\%, in extensive and detailed experiments conducted on a real dataset. The best accuracy, F1-score, precision, and recall for multi-class classification were all around 100\%, which does give the proposed algorithm an advantage over the compared ones.

Ordinal-ResLogit: Interpretable deep residual neural networks for ordered choices

This study presents an Ordinal version of Residual Logit (Ordinal-ResLogit) model to investigate the ordinal responses. We integrate the standard ResLogit model into COnsistent RAnk Logits (CORAL) framework, classified as a binary classification algorithm, to develop a fully interpretable deep learning-based ordinal regression model. As the formulation of the Ordinal-ResLogit model enjoys the Residual Neural Networks concept, our proposed model addresses the main constraint of machine learning algorithms, known as black-box. Moreover, the Ordinal-ResLogit model, as a binary classification framework for ordinal data, guarantees consistency among binary classifiers. We showed that the resulting formulation is able to capture underlying unobserved heterogeneity from the data as well as being an interpretable deep learning-based model. Formulations for market share, substitution patterns, and elasticities are derived. We compare the performance of the Ordinal-ResLogit model with an Ordered Logit Model using a stated preference (SP) dataset on pedestrian wait time and a revealed preference (RP) dataset on travel distance. Our results show that Ordinal-ResLogit outperforms the traditional ordinal regression model. Furthermore, the results obtained from the Ordinal-ResLogit RP model show that travel attributes such as driving and transit cost have significant effects on choosing the location of non-mandatory trips. In terms of the Ordinal-ResLogit SP model, our results highlight that the road-related variables and traffic condition are contributing factors in the prediction of pedestrian waiting time such that the mixed traffic condition significantly increases the probability of choosing longer waiting times.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes.

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Comparison of Data Classification Results in Serious Gaming for Rehabilitation of Rheumatoid Arthritis.

Rheumatoid arthritis is a common disease which affects the joints of the wrist, fingers, feet and in the end the daily activities. Nowadays, gestures and virtual reality are used in many activities supporting recovery, games, learning as technology is present more and more in different fields. This paper presents results related to the grip movement detected by a Leap Motion device using binary classification and machine learning algorithms. We used 2 models to compare the results: Naïve Bayes and Random Forest Classifier. The metrics for comparison were: accuracy, precision, recall and f1-score. Also, we create a confusion matrix for a clear visualization of the results. We used 5000 data to train the algorithm and 1500 data to test. The accuracy and the precision were bigger than 97% in all the cases.