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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： M.A. Waheed Baswaraj Gadgay Shubhangi DC Vishwanath P Qurat Ul Ain Department of Computer Science and Engineering VTU CPGS Kalaburagi Karnataka India Regional director VTU Regional office Kalaburagi Karnataka India Department of Computer Science and Engineering VTU CPGS Kalaburagi Karnataka India Department of Electronics and Communication Engineering H.K.E. Society's S.L.N College of Engineering Raichur Karnataka India

A malicious URL is one that was made specifically to attack through spam or fraud. Due to the billions of dollars that are compromised, malicious URLs pose a severe threat to security software. Finding secure and phishing links is therefore crucial. Therefore, machine learning is quite helpful for resolving security-related challenges. In this study, we use about 5 lakh URLs that were retrieved from the Kaggle dataset. We are utilizing three NLP approaches, including the count vectorizer, hash vectorizer, and TF-IDF vectorizer. Six machine learning classifiers, including the decision tree, random forest, K-NN, NB, SVM, and logistic regression, were used in conjunction with all these techniques. The highest accuracy results of 98.2 percent are produced by random forest. To determine whether or not the URL supplied is malicious, we built a web app using Flask.

关键词： Uniform resource locators Support vector machines machine learning algorithms Phishing Software Fraud Security

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Application of machine learning algorithm in operator shop intelligent selection data

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2023年第5-6期16卷 343-353页

作者： Liu, Chao Jiangsu Vocat Coll Elect & Informat Business Coll Jiangsu 223003 Peoples R China

In order to improve the accuracy of data analysis, this paper applies machine learning algorithms to the analysis of smart selection data in operator shops. This paper introduces common machine learning algorithms, analyses the data to be analysed for intelligent selection in operator shops, applies machine learning algorithms to intelligent selection data in operator shops, and finally analyses the effect analysis of the application of machine learning algorithms, finally concluding that the analysis of intelligent selection data in operator shops using machine learning algorithms can not only improve calculation speed and calculation accuracy, but also improve generalisation. It can also reduce the omission rate of data, in which the omission rate of smart selection data of shop 5 is reduced to 5.67%. machine learning algorithms will need to be applied in many more ways in future life.

关键词： smart selection data machine learning algorithms operator stores applied science

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On the ability of machine learning methods to discover novel scaffolds

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JOURNAL OF MOLECULAR MODELING 2023年第1期29卷 22-22页

作者： Jagdev, Rishi Madsen, Thomas Bruun Finn, Paul W. Univ Buckingham Sch Comp Sci Hunter St Buckingham MK18 1EG England Univ West London St Marys Rd London W5 5RF England Oxford Drug Design Ltd Oxford Ctr Innovat New Rd Oxford OX1 1BY England

The recent advances in the application of machine learning to drug discovery have made it a 'hot topic' for research, with hundreds of academic groups and companies integrating machine learning into their drug discovery projects. Nevertheless, there remains great uncertainty regarding the most appropriate ways to evaluate the relative performance of these powerful methods against more traditional cheminformatics approaches, and many pitfalls remain for the unwary. In 2020, researchers at MIT (Stokes et al., Cell 180(4), 688-702, 2020) reported the discovery of a new compound with antibacterial activity, halicin, through the use of a neural network machine learning method. A robust ability to identify new active chemotypes through computational methods would be very useful. In this study, we have used the Stokes et al. dataset to compare the performance of this method to two other approaches, Mapping of Activity Through Dichotomic Scores (MADS) by Todeschini et al. (J Chemom 32(4):e2994, 2018) and Random Matrix Theory (RMT) by Lee et al. (Proc Natl Acad Sci 116(9):3373-3378, 2019). Our results demonstrate that all three methods are capable of predicting halicin as an active antibacterial compound, but that this result is dependent on the dataset composition, pre-processing and the molecular fingerprint used. We have further assessed overall performance as determined by several performance metrics. We also investigated the scaffold hopping potential of the methods by modifying the dataset by removal of the beta-lactam and fluoroquinolone chemotypes. MADS and RMT are able to identify actives in the test set that contained these substructures. This ability arises because of high scoring fragments of the withheld chemotypes that are in common with other active antibiotic classes. Interestingly, MADS is relatively better compared to the other two methods based on general predictive performance.

关键词： Ligand-based virtual screening Antibiotics machine learning algorithms Deep neural network

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Multiclass Classification of GvHD and Relapse: A Comparative Analysis Among machine learning and ANN-based algorithms

Multiclass Classification of GvHD and Relapse: A Comparative...

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International Conference on Computer and Information Technology (ICCIT)

作者： Md. Junayed Hossain Jannatul Ferdaus Saifur Rahman Sarmin Akter Santa Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh

Hematopoietic Stem Cell Transplantation (HSCT) is the most effective therapeutic option or the only chance for a full recovery when the immune system or bone marrow is damaged. Many different malignant and nonmalignant disorders, including leukemia, lymphoma, hematologic malignancies, lymphoid tumors, and many more, have been treated with this technique. But the major drawback of this HSCT is Graft versus Host Disease (GvHD) and sometimes the recurrence of the blood disease, which is popularly known as relapse. In addition, a patient may undergo relapse coupled with acute graft versus host disease (aGvHD) or chronic graft versus host disease (cGvHD), which are the two possible kinds of GvHD, and things get more challenging when a patient experiences a relapse along with any types of GvHD. To effectively treat patients and create follow-up care plans, predicting relapse and GvHD is essential before HSCT. In this study, a multiclass target feature is created to predict potential GvHD and relapse pairings using only three and six crucial attributes from the original dataset, and a comparative analysis is performed between them. For the multiclass classification, this study utilizes several machine learning algorithms, including the Multilayer Perceptron (MLP), a prevalent classical form of artificial neural network. This study demonstrates that the Extra Trees Classifier algorithm achieves the highest accuracy of 94.45% and 95.34%, and its area under the ROC curve (AUC) score is approximately 98.06% and 99.21% using three and six features, respectively. This model is more useful as a diagnostic tool since it predicts multiclass target features and has lower feature dependence with excellent accuracy.

关键词： machine learning algorithms Stem cells Predictive models Multilayer perceptrons Prediction algorithms Approximation algorithms Classification algorithms

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Integrated workflow for interpretation of satellite imageries using machine learning to assess and monitor algal blooms in Utah Lake, USA

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ECOLOGICAL INFORMATICS 2023年 75卷

作者： Davis, Robert Panja, Palash McLennan, John Univ Utah Dept Chem Engn 50 S Cent Campus DrRoom 3290 MEB Salt Lake City UT 84112 USA Univ Utah Energy & Geosci Inst 423 Wakara WaySuite 300 Salt Lake City UT 84108 USA

An integrated workflow is developed to estimate the spatial distribution of harmful algal blooms, especially cyanobacteria concentrations in inland water bodies. The methodology comprises satellite data extraction and preprocessing for atmospheric and water surface corrections, identifying feature importance, in-situ sample collection, training and testing of machine learning algorithms, and prediction. Six input bands are selected using feature importance algorithms from 12 original bands of Sentinel-2 satellite imagery. In-situ sample data that are synchronous with Sentinel-2 image capture time were obtained from a public database. These models are evaluated and compared using spider plots of different error calculations. The workflow developed in this study and the predicted spatial concentration of harmful algal blooms across the lake can be used to improve warning and advisory systems for the public and avoid exposure. The incorporation of other parameters such as water temperature, nutrient concentrations, and surface wind speed could improve the machine-learning models.

关键词： Harmful algal blooms (HABs) Satellite imagery Atmospheric correction Feature importance machine learning algorithms Utah lake

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Prediction of Springback using the machine learning Technique in high-tensile strength sheet metal during the V-Bending Process

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JORDAN JOURNAL OF MECHANICAL AND INDUSTRIAL ENGINEERING 2023年第4期17卷 481-488页

作者： Wasif, Muhammad Rababah, Mahmoud Fatima, Anis Baig, Saad Ur Rehman NED Univ Engn & Technol Dept Ind & Mfg Engn IMD Karachi Pakistan Hashemite Univ Fac Engn Dept Mech Engn POB 150459 Zarqa 13115 Jordan

Bending is one of the widely used forming processes for sheet metals. However, due to the metal elasticity, the springback characteristic is unavoidable, leading to deviations from the desired final shapes and causing cumulative fitting problems in the assembly stages. Thus, precise predictions of the springback responses will enhance the sheet metal forming and the overall manufacturing processes. This is achieved by employing tree-based machine learning algorithms. These algorithms are used for their simplicity, preciseness, and consistency. Based on the tree-based algorithms, many prediction models are constructed and evaluated. First, experimental setup is established to measure the springback angles for different manufacturing conditions such as: the bending angle, the sheet metal's width and thickness, the machine settings, etc. Then, these data sets are divided into training and testing groups for the prediction models. This division is carried randomly, where 90 % of the data sets are used for training, and 10 % are left for testing the models' accuracy. The models are evaluated by comparing their predicted springback angles with the experimental values. The deviation errors are measured using the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE).It isrevealed that the LightGBM prediction model is the most accurate model with 0.42 deg., 0.26 deg., and 0.52 deg. for MAE, MSE, and RMSE, respectively. The Gradient boosting comes in the second place with 0.66 deg., 0.760 deg., and 0.80 deg. for MAE, MSE, and RMSE, respectively.

关键词： Sheet metal bending springback high strength steel machine learning algorithms

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Evaluating machine learning and statistical learning techniques for cancer classification and diagnosis

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Iran Journal of Computer Science 2025年第2期8卷 471-490页

作者： Alwazy, Asmaa Salim Hussaien Buyrukoğlu, Gonca Buyrukoğlu, Selim Baker, Mohammed Rashad Department of Electronics and Computer Engineering Çankırı Karatekin University Çankırı 18100 Türkiye Department of Statistics Çankırı Karatekin University Çankırı 18100 Türkiye Department of Computer Engineering Çankırı Karatekin University Çankırı 18100 Türkiye Department of Software Engineering University of Kirkuk Kirkuk Iraq

Accurate cancer diagnosis is critical for effective treatment and positive patient outcomes. This study investigates the robustness of machine learning (ML) and statistical learning (SL) algorithms in classifying and diagnosing lung, prostate, breast, and heart disease. We evaluated and compared the performance of several algorithms, including support vector classifier (SVC), random forest (RF), XGBoost, decision tree (DT), elastic net, Lasso, and ridge, using four medical data sets. Multiple metrics, such as accuracy, precision, recall, F1-score, and area under the curve (AUC), are used to assess the performance. Hyperparameter tuning is conducted using GridSearchCV. The results demonstrated that RF and SVC achieved near-perfect accuracy (up to 98%) and AUC scores (1.00) in distinguishing between benign and malignant lung and breast cancer samples. SL algorithms exhibited robust and consistent performance for prostate cancer, achieving accuracy, precision, recall, and F1-scores of approximately 90%, making them suitable for data sets with smaller sample sizes. However, SVC-based ML algorithms outperformed SL methods in overall accuracy for prostate cancer, although SL methods provided a stable baseline. For heart disease, SVC achieved the highest accuracy (82%), while regularized SL models such as Elastic Net and Lasso performed similarly, with an accuracy of 80%. This study highlights the importance of selecting algorithms tailored to the data set’s characteristics and the specific diagnostic requirements. It also underscores the complementary strengths of ML and SL approaches, suggesting that an integrated strategy may offer the most effective solution. The findings contribute to the growing evidence supporting integrating computational methods into clinical practice to enhance diagnostic accuracy and improve patient care. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cancer classification Data mining Diagnosis machine learning algorithms Statistical learning algorithms

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Localization of Victims in Disaster Area Using RSSI with machine learning Techniques

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SN Computer Science 2025年第4期6卷 1-14页

作者： Langpoklakpam, Bidyarani Murry, Lithungo K. Department of Computer Science and Engineering National Institute of Technology Nagaland Chumukedima Nagaland Dimapur 797103 India

Aftermath of disaster, efficient and accurate localization of victims and resources is crucial for effective rescue operations. Traditional localization algorithms face challenges posed by environmental noise, multipath reflection, and various uncertainties, all of which can compromise range accuracy and therefore, affect the localization precision. This paper addresses the localization problem by leveraging machine learning techniques for a disaster area network. We first generate a set of Received Signal Strength Indicator (RSSI) values collected by Anchor Nodes (ANs) for signals received from Target Nodes (TNs). Using the RSSI data collected from victims, we train machine learning models to accurately estimate the locations of individuals with maximum coverage. In this paper, we investigate the influence of placing the anchor nodes in a grid pattern as well as randomly distributing these nodes throughout the deployment area. We examine the effect on various network parameters with varying number of anchor nodes and target nodes. Through simulation, we investigate the performance of machine learning algorithms including Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbor (k-NN), and Gradient Boosting (GB) with optimization-based techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) using Received Signal Strength Indicator (RSSI) in terms of Root Mean Square Error (RMSE), Mean Localization Error (MLE) and Node Localization Efficiency (NLE). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disaster area network Localization machine learning algorithms Received signal strength indicator (RSSI) Regression models

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Assessing the color status and daylight chromaticity of road signs through machine learning approaches

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IATSS RESEARCH 2023年第3期47卷 305-317页

作者： Saleh, Roxan Fleyeh, Hasan Alam, Moudud Hintze, Arend Dalarna Univ Sch Informat & Engn S-78170 Borlange Sweden Swedish Transport Adm Roda Vagen 1 S-78189 Borlange Sweden

The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs. The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in *** study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity *** study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, *** supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%,

关键词： Road signs Daylight chromaticity Regression Classification Prediction machine learning algorithms

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A machine learning-based framework for seismic vulnerability assessment of reinforced concrete educational facilities

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Asian Journal of Civil Engineering 2025年第6期26卷 2605-2627页

作者： Kumar, Tapan Siddique, Mohammad Al Amin Ahsan, Raquib Mustafy, Tanvir Department of Civil Engineering Bangladesh University of Engineering and Technology Dhaka 1000 Bangladesh Department of Civil and Environmental Engineering North South University Dhaka Bangladesh

This study presents the development of a machine learning (ML) based framework for assessing the seismic vulnerability of existing educational reinforced concrete (RC) buildings under the jurisdiction of the Rajdhani Unnayan Kartripakkha (RAJUK) in Dhaka. The conventional three major stages of assessment methods are often resource-intensive and time-consuming, especially when applied to large building stocks. The primary objective is to assess the seismic vulnerability of existing RC educational buildings in similar contexts using the ML method, focusing in predicting analytical parameters Story Shear Ratio (SSR) as a critical analytical risk indicator, implementing Rapid Visual Assessment (RVA) 8 parameters. The RVA parameters are construction year, building condition, number of stories, typical floor area, redundancy, pounding, plan irregularity and elevation irregularity, and corresponding building’s SSR value in the preliminary Engineering Assessment (PEA) survey. Three well-known ML models, including Support Vector Regression (SVR), Random Forest Regression (RFR), and Artificial Neural Networks (ANN), were employed to predict SSR using RVA parameters. The dataset of 268 RC educational buildings was collected from the RAJUK. Based on the analysis, the SVR model obtained a higher coefficient of determination (R²) of 0.34 than the 0.17 and 0.16 of the RFR, and ANN models and 0.038, 0.04, and 0.04 for the Mean Square Error, respectively, though all models exhibited limited explanatory power for SSR. The findings reveals that the SVR handled comparatively well the complexities and nonlinearities in the dataset. This study proposes a cost-effective ML framework for seismic vulnerability assessment, with potential to support urban resilience efforts following further validation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： machine learning algorithms PEA RVA Seismic vulnerability assessment Story shear ratio

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