Guardians of the Web: Harnessing Machine Learning to Combat Phishing Attacks

Gamification and Augmented Reality., Journal Year: 2025, Volume and Issue: 3, P. 91 - 91

Published: Jan. 16, 2025

Phishing remains one of the most dangerous threats to internet users and organizations today since it utilizes spoofed websites coax into revealing their data. This paper focuses on effectiveness algorithms in detecting such abusive websites. It goes analyze dataset phishing non- URLs providing explanatory attributes as domain registration date, URL length or existence HTTPS. The models studied include Decision Tree, Random Forest, Support Vector Machines. results found that Forest algorithm had best performance 97% terms classification accuracy, Machines performed generalization accuracy with precision recall values 0.92 0.95, respectively. study investigates feature selection determinants structural features which are crucial determining efficiency detection. Also, enhance model assessment stratified 10-fold cross-validation technique was reduce bias variance. These Results show prospect One Layer Neural Networks a tool improve Detection Systems help provide low-cost fast solutions for current future cyberspace struggles. work aims increase confidence online security applications against modern methods.The proposed modifications will strengthen counter measures attacks shifting technological context while also working towards sustaining thus require further inquiry facets applicability sophisticated artificial intelligence techniques use useful yet diverse sets data incorporation explainable intelligent systems

Language: Английский

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Phishing Website Detection Using Machine Learning

Gamification and Augmented Reality., Journal Year: 2025, Volume and Issue: 3, P. 81 - 81

Published: Jan. 16, 2025

Phishing attacks continue to be a danger in our digital world, with users being manipulated via rogue websites that trick them into disclosing confidential details. This article focuses on the use of machine learning techniques process identifying phishing websites. In this case, study was undertaken critical factors such as URL extension, age domain, and presence HTTPS whilst exploring effectiveness Random Forest, Gradient Boosting and, Support Vector Machines algorithms allocating status or non-phishing. study, dataset containing real URLs are employed build model using feature extraction. Following this, various were put test dataset; out all models, Forest performed exceptionally well having achieved an accuracy 97.6%, also found extremely effective possessing strong accuracy. we compared discussed methods detect site. Some features affect detection performance include length, special characters focus even more aspects need further development. The new proposed method improves because applied, recall (true positive) increase, while false positive decrease. results enrich electronic security system, they enable time mode. has demonstrated importance employing cutting-edge deal safeguard against advanced cyber threats, thus laying groundwork for innovation systems future

Language: Английский

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Diabetes Prediction and Management Using Machine Learning Approaches

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 545 - 545

Published: Feb. 18, 2025

Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines need for greater emphasis on early detection and proactive management to avert or mitigate severe complications this disease. Over recent years, machine learning algorithms have shown promising potential predicting diabetes risk are beneficial practitioners. Objective: study highlights prediction capabilities statistical non-statistical methods over classification 768 samples from Pima Indians Database. It consists demographic clinical features age, body mass index (BMI) blood glucose levels that greatly depend vulnerability against Diabetes. The experimentation assesses various types terms accuracy effectiveness regarding prediction. These include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting Neural Network Models. results show algorithm gained highest predictive 78.57%, then Forest had second position 76.30% accuracy. findings techniques not just highly effective. Still, they also can potentially act screening tools within data-driven fashion valuable information who is more likely get affected. In addition, help realize timely intervention longer term, which step towards reducing outcomes disease burden attributable healthcare systems.

Language: Английский

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Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction

Data & Metadata, Journal Year: 2025, Volume and Issue: 3

Published: Jan. 2, 2025

Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements machine learning data mining have revolutionized traditional diagnostic methodologies, providing sophisticated automated tools for differentiating between benign malignant oral lesions. This study comprehensive review of cutting-edge including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), ensemble techniques, specifically applied the cancer. Through rigorous comparative analysis, our findings reveal that Networks surpass other models, achieving an impressive classification accuracy 93.6% predicting Furthermore, we underscore potential benefits integrating feature selection dimensionality reduction techniques model performance. These insights significant promise advanced bolstering detection, optimizing treatment strategies, ultimately improving outcomes realm oncology

Language: Английский

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From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection

LatIA, Journal Year: 2024, Volume and Issue: 3, P. 84 - 84

Published: Nov. 30, 2024

Gene microarray classification is yet a difficult task because of the bigness data and limited number samples available. Thus, need for efficient selection subset genes necessary to cut down on computation costs improve performance. Consistently, this study employs Correlation-based Feature Selection (CFS) algorithm identify informative genes, thereby decreasing dimensions isolating discriminative features. Thereafter, three classifiers, Decision Table, JRip OneR were used assess The strategy was implemented eleven such that reduced compared with complete gene set results. observed results lead conclusion CFS efficiently eliminates irrelevant, redundant, noisy features as well. This method showed great prediction opportunities relevant differentiation datasets. performed best among Table by average accuracy in all mentioned However, approach has many advantages enhances several classes large numbers high time complexity.

Language: Английский

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Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging

Data & Metadata, Journal Year: 2024, Volume and Issue: 4, P. 472 - 472

Published: Nov. 26, 2024

Brain cancer remains one of the most challenging medical conditions due to its intricate nature and critical functions brain. Effective diagnostic treatment strategies are essential, particularly given high stakes involved in early detection. Magnetic Resonance (MR) imaging has emerged as a crucial modality for identification monitoring brain tumors, offering detailed insights into tumor morphology behavior. Recent advancements artificial intelligence (AI) machine learning (ML) have revolutionized analysis imaging, significantly enhancing precision efficiency. This study classifies three primary types—glioma, meningioma, general tumors—utilizing comprehensive dataset comprising 15,000 MR images obtained from Kaggle. We evaluated performance six distinct models: K-Nearest Neighbors (KNN), Neural Networks, Logistic Regression, Support Vector Machine (SVM), Decision Trees, Random Forests. Each model's effectiveness was assessed through multiple metrics, including classification accuracy (CA), Area Under Curve (AUC), F1 score, precision, recall. Our findings reveal that KNN Networks achieved remarkable accuracies 98.5% 98.4%, respectively, surpassing other models. These results underscore promise ML algorithms, improving process imaging. Future research will focus on validating these models with real-world clinical data, aiming refine enhance methodologies, thus contributing development more accurate, efficient, accessible tools diagnosis management.

Language: Английский

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Echoes in the Genome: Smoking’s Epigenetic Fingerprints and Bidirectional Neurobiological Pathways in Addiction and Disease

Deleted Journal, Journal Year: 2024, Volume and Issue: 3

Published: Dec. 30, 2024

Smoking remains a global health crisis, contributing to addiction and diverse diseases through complex biological mechanisms. This study explores the hypothesis that smoking induces epigenetic modifications alters bidirectional neurobiological pathways, perpetuating disease progression. Leveraging dataset of 55,692 individuals with 27 metrics, we analyze associations between status physiological markers (e.g., lipid profiles, blood pressure, liver enzymes) infer potential mediators. Preliminary data reveal significant correlations elevated triglycerides, LDL cholesterol, function markers, suggesting systemic inflammation oxidative stress as plausible intermediaries. We propose methodology integrating bioinformatics systems biology map smoking-associated phenotypic changes loci DNA methylation) neural circuits dopaminergic pathways). work aims bridge clinical observations molecular mechanisms, offering insights into personalized interventions targeting smoking’s "fingerprints" their consequences.

Language: Английский

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From Puffs to Predictions: Leveraging AI, Wearables, and Biomolecular Signatures to Decode Smoking’s Multidimensional Impact on Cardiovascular Health

Deleted Journal, Journal Year: 2024, Volume and Issue: 3

Published: Dec. 30, 2024

Tobacco smoking keeps to exert a profound effect on cardiovascular health, contributing situations including arterial stiffness, hypertension, and microcirculatory disorder. Traditional studies strategies, often siloed into remoted domains like biomarker analysis or behavioral surveys, fail seize the dynamic interplay between behaviors biological disruptions. This take look at integrates AI-driven analytics, wearable sensor networks, deep biomolecular profiling map smoking’s multidimensional effects. By combining actual-time physiological statistics (e.g., PPG, HRV) with epigenetic proteomic markers, research objectives are expecting individual risks enable preemptive interventions. Results reveal efficacy of ensemble models Random Forest (AUC = zero.889) in taking pictures complex interactions among variables consisting γ-GTP, waist circumference, blood stress. The paintings highlight capability AI wearables convert reactive healthcare personalized, preventive strategies.

Language: Английский

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Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Diagnostics, Journal Year: 2025, Volume and Issue: 15(5), P. 562 - 562

Published: Feb. 26, 2025

Background: Typhoid fever remains a significant public health challenge, especially in developing countries where diagnostic resources are limited. Accurate and timely diagnosis is crucial for effective treatment disease containment. Traditional methods, while effective, can be time-consuming resource-intensive. This study aims to develop lightweight machine learning-based tool the early efficient detection of typhoid using clinical data. Methods: A custom dataset comprising 14 demographic parameters-including age, gender, headache, muscle pain, nausea, diarrhea, cough, range (°F), hemoglobin (g/dL), platelet count, urine culture bacteria, calcium (mg/dL), potassium (mg/dL)-was analyzed. learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree classifiers with Light Gradient Boosting (LGBM), was trained evaluated k-fold cross-validation. Performance assessed precision, recall, F1-score, area under receiver operating characteristic curve (AUC). Results: The proposed metamodel demonstrated superior performance, achieving precision 99%, recall 100%, an AUC 1.00. It outperformed traditional methods other standalone algorithms, offering high accuracy generalizability. Conclusions: provides cost-effective, non-invasive, rapid alternative fever, particularly suited resource-limited settings. Its reliance on accessible parameters ensures practical applicability scalability, potentially improving patient outcomes aiding control. Future work will focus broader validation integration into workflows further enhance its utility.

Language: Английский

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Revolutionizing Blood Banks: AI-Driven Fingerprint-Blood Group Correlation for Enhanced Safety

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 894 - 894

Published: April 7, 2025

Identification of a person is central in forensic science, security, and healthcare. Methods such as iris scanning genomic profiling are more accurate but expensive, time-consuming, difficult to implement. This study focuses on the relationship between fingerprint patterns ABO blood group biometric identification tool. A total 200 subjects were included study, types (loops, whorls, arches) groups compared. Associations evaluated with statistical tests, including chi-square Pearson correlation.The found that loops most common pattern O+ was prevalent. Discussion: Even though there some associative pattern, no statistically significant difference different groups. Overall, results indicate data do not significantly improve personal when used conjunction fingerprinting.Although shows weak correlation, it may emphasize efforts multi-modal based systems enhancing current systems. Future studies focus larger diverse samples, possibly machine learning additional biometrics methods. addresses an element ever-changing nature fields science identification, highlighting importance resilient analytical methods for identification.

Language: Английский

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