Binary Classification Algorithm Research Articles

Support vector machine based on the quadratic unconstrained binary optimization model

Abstract Support vector machine (SVM) is a powerful supervised machine learning model that is often used in binary classification algorithms. As Moore’s Law approaches its theoretical limits and the demand for machine learning to handle large-scale, high-dimensional data analysis intensifies, the necessity of adopting non-traditional computational approaches becomes evident. Quantum computing, in particular, emerges as a vital solution for the effective training of SVM models, providing capabilities beyond those of classical computing systems. To solve the above problems, a QUBO (quadratic unconstrained binary optimization) model is proposed to transform the SVM machine learning model into a quadratic unconstrained binary optimization problem so that they can be effectively trained on the D-Wave platform using adiabatic quantum computer. The results show that the QUBO model can transform the SVM model into a simple quadratic programming problem, which makes it suitable for adiabatic quantum computer processing. When processing large-scale and high-dimensional data, this transformation shows a natural advantage and significantly improves computational efficiency. The application potential of this transformation technology is huge in the medical field.

A novel binary data classification algorithm based on the modified reaction-diffusion predator-prey system with Holling-II function.

In this study, we propose a modified reaction-diffusion prey-predator model with a Holling-II function for binary data classification. In the model, we use u and v to represent the densities of prey and predators, respectively. We modify the original equation by substituting the term v with f-v to obtain a stable and clear nonlinear decision surface. By employing a finite difference method for numerical solution of the original model, we conduct various experiments in two-dimensional and three-dimensional spaces to validate the feasibility of the classifier. Additionally, with consideration for wide real applications, we perform classification experiments on electroencephalogram signals, demonstrating the effectiveness and robustness of the classifier in binary data classification.

Effective SQL Injection Detection: A Fusion of Binary Olympiad Optimizer and Classification Algorithm

Since SQL injection allows attackers to interact with the database of applications, it is regarded as a significant security problem. By applying machine learning algorithms, SQL injection attacks can be identified. Problem: In the training stage of machine learning methods, effective features are used to develop an optimal classifier that is highly accurate. The specification of the features with the highest efficacy is considered to be an NP-complete combinatorial optimization challenge. Selecting the most effective features refers to the procedure of identifying the smallest and most effective features in the dataset. The rationale behind this paper is to optimize the accuracy, precision, and sensitivity parameters of the SQL injection attack detection method. Method: In this paper, a method for identifying SQL injection attacks was suggested. In the first step, a particular training dataset that included 13 features was developed. In the second step, to specify the best features of the dataset, a specific binary variety of the Olympiad optimization algorithm was developed. Various machine learning algorithms were used to create the optimal attack detector. Results: Based on the experiments carried out, the suggested SQL injection detector using an artificial neural network and the feature selector can achieve 99.35% accuracy, 100% precision, and 100% sensitivity. Owing to selecting about 30% of the effective features, the proposed method enhanced the efficacy of SQL injection detectors.

Some notes on the basic concepts of support vector machines

Support vector machines (SVMs) are classic binary classification algorithms and have been shown to be a robust and well-behaved technique for classification in many real-world problems. However, there are ambiguities in the basic concepts of SVMs although these ambiguities do not affect the effectiveness of SVMs. Corinna Cortes and Vladimir Vapnik, who presented SVMs in 1995, pointed out that an SVM predicts through a hyperplane with a maximal margin. However existing literatures have two different definitions of the margin. On the other hand, Corinna Cortes and Vladimir Vapnik converted an SVM into an optimization problem that is much easier to solve. Nevertheless, existing papers do not explain how the optimization problem derives from an SVM well. These ambiguities may cause certain troubles in understanding the basic concepts of SVMs. For this purpose, this paper defines a separating hyperplane of a training data set and, hence, an optimal separating hyperplane of the set. The two definitions are reasonable since this paper proves that w0Tx+b0=0 is an optimal separating hyperplane of a training data set when w0 and b0 constitute a solution to the above optimization problem. Some notes on the above margin and optimization problem are given based on the two definitions. These notes should be meaningful for clarifying the basic concepts of SVMs.

Revisiting Winnow: A modified online feature selection algorithm for efficient binary classification

AbstractWinnow is an efficient binary classification algorithm that effectively learns from data even in the presence of a large number of irrelevant attributes. It is specifically designed for online learning scenarios. Unlike the Perceptron algorithm, Winnow employs a multiplicative weight update function, which leads to fewer mistakes and faster convergence. However, the original Winnow algorithm has several limitations. They include, it only works on binary data, and the weight updates are constant and do not depend on the input features. In this article, we propose a modified version of the Winnow algorithm that addresses these limitations. The proposed algorithm is capable of handling real‐valued data, updates the learning function based on the input feature vector. To evaluate the performance of our proposed algorithm, we compare it with seven existing variants of the Winnow algorithm on datasets of varying sizes. We employ various evaluation metrics and parameters to assess and compare the performance of the algorithms. The experimental results demonstrate that our proposed algorithm outperforms all the other algorithms used for comparison, highlighting its effectiveness in classification tasks.

Research on Electric Energy Metering Anomaly Detection and Classification Algorithm Under Multi-Source Data Fusion

The renewable energy grid is a distributed power grid, which needs to be parallel to the thermal power grid to ensure the stability of its own power generation. Therefore, renewable energy has the problem of multiple sources of power generation data and complex electric energy metering, which has always limited the development of renewable energy. In order to improve the grid-connected power supply quality of renewable energy and improve the accuracy of electric energy metering, this paper proposes a binary classification algorithm to collect, amplify and standardize multi-source data. Then, the Fourier function is used to normalize the multi-source data to shorten the differences between different devices. In particular, the attribute features are classified and the input data adjustment parameters are set. Finally, renewable energy's electric energy data value is measured, and the abnormal data characteristics are obtained through the second-order derivative to realize data mining and electric energy metering anomaly detection. The simulation results show that the binary classification can complete the metering and anomaly detection of electric energy in the distributed power grid, and the classification processing can simplify the complexity of multi-source data, improve the accuracy of metering anomaly detection, and make its dispersion more reasonable, and the results are between 90~95%, which is better than the online monitoring method. The time for anomaly result detection is 25s, which is shorter than the previous algorithm, and cross-domain data search is realized, with a cross-domain rate of 50~60%. Therefore, the classification algorithm proposed in this paper can meet the requirements of electric energy metering under multi-source data fusion and achieve rapid anomaly detection.

Tire Texture Monitoring (VGG 19 VS Efficient Net b7)

Tires are crucial components of vehicles, continuously in contact with the road. Monitoring tire conditions is vital for safety and performance, as degradation in tire treads and sidewalls can affect traction, fuel efficiency, longevity, and road noise. This research leverages both VGG19 and Efficient Net B7 algorithms to enhance tire image rendering, addressing limitations of traditional techniques. Using a binary classification algorithm, we classify tire images as healthy or cracked. By fine-tuning VGG19 and EfficientNet B7 on a specialized tire dataset, we achieve high-quality, photorealistic renderings. Our results demonstrate remarkable improvements in texture quality and visual realism compared to traditional methods. The rendered images exhibit finer details and more accurate representations of the tire’s tread patterns and material properties. This research contributes to the field of computer graphics by presenting a novel application of deep learning techniques to a specific industrial need, paving the way for future advancements in high-quality rendering of complex tire textures. Key Words: VGG19,Photorealistic rendering, deep learning.

Application of CNN for multiple phase corrosion identification and region detection

Corrosion is a significant issue that contributes negatively to the degradation of materials most especially metals. To ensure proper maintenance, enhance reliability and prevent breakdown, it is very essential to not only effectively detect corrosion but to also understand its locations and distributions on the materials. A Multiple phase Convolutional Neural Network (CNN) model is created for this purpose. The Multiple phase CNN model consists of custom designed deep learning algorithms at various stages. This created the opportunity to make use of binary classification, multi-label classification and patch distribution algorithm to detect and identify corrosion regions on metallic materials. Six (6) different labels of corrosion were modelled to represent different levels of degradation using 600 anonymized images. The images were used in the various stages of the framework for training the respective models. Results at the binary level shows 94.87 % of corrosion detection. The multiclass stage of the Multiple phase CNN records the highest accuracy of 92.1 %. The patch distribution stage recorded a highest accuracy of 96.5 % and 94.6 % for the Average Image and Average Pixel ROCAUC (Region of Concentration Area Under Cover). It also shows a region segment average accuracy detection of 91.5 % (image level) and 89.2 %(pixel level) for 9 distinct regions. The research provides a comprehensive and detailed reliability and maintenance information for Aerospace, Transport and Manufacturing Materials experts and non-experts. The framework shows a robust approach to detecting corrosion which is essential for critical and safety applications as well as preventing economic loss due to corrosion. This can also be extended to other domains beyond the corrosion case study.

Generalisation capabilities of machine-learning algorithms for the detection of the subthalamic nucleus in micro-electrode recordings.

Micro-electrode recordings (MERs) are a key intra-operative modality used during deep brain stimulation (DBS) electrode implantation, which allow for a trained neurophysiologist to infer the anatomy in which the electrode is placed. As DBS targets are small, such inference is necessary to confirm that the electrode is correctly positioned. Recently, machine learning techniques have been used to augment the neurophysiologist's capability. The goal of this paper is to investigate the generalisability of these methods with respect to different clinical centres and training paradigms. Five deep learning algorithms for binary classification of MER signals have been implemented. Three databases from two different clinical centres have also been collected with differing size, acquisition hardware, and annotation protocol. Each algorithm has initially been trained on the largest database, then either directly tested or fine-tuned on the smaller databases in order to estimate their generalisability. As a reference, they have also been trained from scratch on the smaller databases as well in order to estimate the effect of the differing database sizes and annotation systems. Each network shows significantly reduced performance (on the order of a 6.5% to 16.0% reduction in balanced accuracy) when applied out-of-distribution. This reduction can be ameliorated through fine-tuning the network on the new database through transfer learning. Although, even for these small databases, it appears that retraining from scratch may still offer equivalent performance as fine-tuning with transfer learning. However, this is at the expense of significantly longer training times. Generalisability is an important criterion for the success of machine learning algorithms in clinic. We have demonstrated that a variety of recent machine learning algorithms for MER classification are negatively affected by domain shift, but that this can be quickly ameliorated through simple transfer learning procedures that can be readily performed for new centres.

Identification of fraudulent financial statements through a multi‐label classification approach

SummaryAlthough the financial audit controls in companies have advanced over the years, the number of corporate fraud instances is growing, thus raising the need for investigating the factors that can be used as early warning signals and developing effective systems for identifying financial fraud. In this paper, financial statements from 133 Greek companies listed in the Athens Stock Exchange over the period 2014 to 2019 are investigated, based on the fraud diamond theory. Financial data and corporate governance variables are used as inputs to data mining techniques to develop models that can identify patterns of irregularities in a company's financial reports. To this end, popular machine learning classification algorithms are employed in a novel multi‐label classification setting that not only identifies fraudulent cases but also considers the nature of the auditors' comments. The results indicate that the proposed multi‐label approach provides enhanced results compared to binary classification algorithms, avoiding inconsistent outputs with respect to the existence of different forms of manipulation of financial statements.

Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction

Machine learning-based software defect prediction (SDP) approaches have been commonly proposed to help to deliver high-quality software. Unfortunately, all the previous research conducted without effective feature reduction suffers from high-dimensional data, leading to unsatisfactory prediction performance measures. Moreover, without proper feature reduction, the interpretability and generalization ability of machine learning models in SDP may be compromised, hindering their practical utility in diverse software development environments. In this paper, an SDP approach using deep Q-learning network (DQN)-based feature extraction is proposed to eliminate irrelevant, redundant, and noisy features and improve the classification performance. In the data preprocessing phase, the undersampling method of BalanceCascade is applied to divide the original datasets. As the first step of feature extraction, the weight ranking of all the metric elements is calculated according to the expected cross-entropy. Then, the relation matrix is constructed by applying random matrix theory. After that, the reward principle is defined for computing the Q value of Q-learning based on weight ranking, relation matrix, and the number of errors, according to which a convolutional neural network model is trained on datasets until the sequences of metric pairs are generated for all datasets acting as the revised feature set. Various experiments have been conducted on 11 NASA and 11 PROMISE repository datasets. Sensitive analysis experiments show that binary classification algorithms based on SDP approaches using the DQN-based feature extraction outperform those without using it. We also conducted experiments to compare our approach with four state-of-the-art approaches on common datasets, which show that our approach is superior to these methods in precision, F-measure, area under receiver operating characteristics curve, and Matthews correlation coefficient values.

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.