Breast Cancer Classification using XGBoost

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(2), P. 1985 - 1994

Published: Feb. 28, 2024

Breast cancer continues to be one of the foremost illnesses that results in deaths numerous women each year. Among female population, approximately 8% are diagnosed with (BC), following Lung Cancer. The alarming rise fatality rates can attributed breast being second leading cause. manifests through genetic transformations, persistent pain, alterations size, color (redness), and texture breast's skin. Pathologists rely on classification identify a specific targeted prognosis, achieved binary (normal/abnormal). Artificial intelligence (AI) has been employed diagnose tumors swiftly accurately at an early stage. This study employs Extreme Gradient Boosting (XGBoost) machine learning technique for detection analysis cancer. XGBoost provides accuracy 94.74% recall 95.24% Wisconsin (diagnostic) dataset.

Language: Английский

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Skin cancer classification using NASNet

International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(1), P. 775 - 785

Published: Jan. 30, 2024

The importance of making an early diagnosis in both the prevention and treatment skin cancer cannot be overstated. A very effective medical decision support system that can classify lesions based on dermoscopic pictures is essential instrument for determining prognosis cancer. In spite fine-grained variation way different types appear, Deep Convolutional Neural Networks (DCNN) have made great strides recent years toward improving ability to detect using images. It has been claimed there are a few machine learning techniques accurate photos. good number these methods predicated convolutional neural networks (CNNs) already trained, which makes it possible train models only small quantity available training data. However, because so sample images malignant tumors available, classification accuracy still typically severely restricted. primary purpose this study construct DCNN-based model capable automatically classifying as either melanoma or non-melanoma with high level accuracy. We propose optimized NASNet architecture, enhanced additional data basic layer employed CNN added. strategy proposed enhances model's capacity deal incomplete inconsistent dataset 2637 used demonstrate benefits technique proposed. analyze performance suggested method by looking at its precision, sensitivity, specificity, F1-score, area under ROC curve. Optimized Mobile Large provides 85.62% 83.98%, respectively Adam optimizer.

Language: Английский

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Skin cancer classification using Inception Network

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(2), P. 839 - 849

Published: Feb. 15, 2024

Since skin disease is a universally recognized condition among humans, there has been growing interest in utilizing intelligent systems to classify various ailments. This line of research deep learning holds immense significance for dermatologists. However, accurately determining the presence formidable task due intricate nature texture and visual similarities between different diseases. To address this challenge, images undergo filtration eliminate unwanted noise further processing enhance overall quality image. The primary purpose study construct neural network-based model that capable automatically classifying several types cancer as either melanoma or non-melanoma with prominent level accuracy. We propose an optimized Inception architecture, which InceptionNet enhanced data augmentation basic layers. strategy proposed enhances model's capacity deal incomplete inconsistent data. A dataset 2637 are used demonstrate benefits technique proposed. analyze performance suggested method by looking at its precision, sensitivity, specificity, F1-score, area under ROC curve. Proposed provides accuracy 84.39% 85.94%, respectively Adam Nadam optimizer. training process each subsequent layer exhibits notable enhancement effectiveness. An examination inquiry can assist experts making early diagnoses, thereby providing them insight into infection enabling initiate necessary treatment, if deemed necessary.

Language: Английский

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Heart Disease Prediction using SVM

International Journal of Science and Research Archive, Journal Year: 2024, Volume and Issue: 11(2), P. 412 - 420

Published: March 18, 2024

Diagnosing and predicting the outcome of cardiovascular disease are essential tasks in medicine that help ensure patients receive accurate classification treatment from cardiologists. The use machine learning healthcare sector has grown due to its ability identify patterns data. By applying techniques classify presence diseases, it's possible decrease rate misdiagnosis. This study aims create a model capable accurately forecasting diseases minimize deaths associated with these conditions. In this paper, two types SVM such as linear polynomial is used. Accuracy, precision, recall F1 score been evaluated for comparing SVM. Polynomial provides better accuracy than

Language: Английский

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Mechanical characterization of materials using advanced microscopy techniques

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(3), P. 274 - 283

Published: March 6, 2024

This review explores the synergistic relationship between advanced microscopy techniques and mechanical engineering, outlining their profound impact on materials science system design. We delve into multifaceted applications of electron microscopy, X-ray diffraction, spectroscopic methods in understanding microstructural dynamics, properties, failure mechanisms integral to engineering. Through a comprehensive synthesis recent research, we emphasize pivotal role these play optimizing material performance, bolstering structural integrity, driving innovation By elucidating intricate details behavior at microscale, contributes informed decision-making selection design processes. Furthermore, address emerging trends prospects, underscoring continued synergy collaboration remains forefront technology, promising ongoing advancements that will shape future landscape innovation.

Language: Английский

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Empowering blockchain with SmartNIC: Enhancing performance, security, and scalability

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 22(1), P. 151 - 162

Published: April 7, 2024

This paper introduces BlockNIC, an innovative blockchain infrastructure designed to operate exclusively on SmartNICs. Unlike traditional implementations, BlockNIC leverages the unique capabilities of SmartNICs execute relatively simple computations directly network path, eliminating need for additional hardware and reducing reliance host CPUs. By harnessing idle resources within network, significantly reduces energy consumption requirements, addressing environmental concerns associated with conventional architectures. Through comprehensive performance comparisons between bare-metal servers, this study demonstrates promising potential in achieving scalability, security, sustainability networks. The findings highlight BlockNIC's ability enhance overall reliability while minimizing resource limitations, thereby unlocking new possibilities various applications use cases previously hindered by constraints. emergence aligns global agenda, offering a timely solution challenges posed technologies. promoting adoption SmartNIC-based infrastructures, research contributes greener more secure digital future. It emphasizes importance exploring approaches address impact technological innovations, urging researchers, industry professionals, policymakers recognize transformative solutions advancing efficiency ecosystems.

Language: Английский

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Decoding COVID-19 Conversations with Visualization: Twitter Analytics and Emerging Trends

Journal of Computer Science Engineering and Software Testing, Journal Year: 2024, Volume and Issue: 10(1), P. 21 - 31

Published: Jan. 1, 2024

This study delves into the vast landscape of COVID-19 discussions on Twitter, aiming to unveil pertinent insights and emerging trends within this dynamic social media platform. Analyzing a substantial volume Twitter data related pandemic, our research scrutinizes content, sentiments, patterns conversations among users. By employing advanced analytics, we discern key themes, prevalent evolution discourse over time. investigation not only provides comprehensive overview diverse topics encompassed but also sheds light shaping public opinion awareness. The abstract influencers amplifiers virtual discourse, identifying pivotal accounts trending hashtags that significantly contribute dissemination information. Moreover, investigates geographical temporal variations in discussions, offering nuanced understanding how these evolve across different regions timeframes. As plays an increasingly central role perceptions, aims valuable for policymakers, health organizations, comprehend dynamics communication Twitter. Ultimately, by uncovering endeavours enhance surrounding pandemic its implications strategies.

Language: Английский

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Exploratory approaches for improved cost effectiveness and profitability: Utilizing mathematical analysis and value stream mapping on production floors

World Journal of Advanced Engineering Technology and Sciences, Journal Year: 2024, Volume and Issue: 11(1), P. 076 - 085

Published: Jan. 30, 2024

This paper focuses on the application of Value Stream Mapping (VSM) within context electronics manufacturing industry, aiming to improve its operational efficiency and financial performance. The study thoroughly analyzes costs, integrating VSM justify economic benefits. Data was collected directly from floor create a current state map, enabling identification non-value-added activities sources waste. Areas for potential improvement were pinpointed reduce or eliminate these inefficiencies. By implementing proposed enhancements, outlines future map process presents results applying Mapping. Comparing maps, reveals that embracing Lean principles, in conjunction with Mapping, can significantly benefit industry. Specifically, it production lead times by 67.84% decrease costs 8.69%. research underscores implications adopting illustrating combining principles VSM, industries offer rapid customer responses at lower crucial factor improving competitive performance existing market landscape.

Language: Английский

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Enhancing Operations Quality Improvement through Advanced Data Analytics

Journal of Computer Science Engineering and Software Testing, Journal Year: 2024, Volume and Issue: 10(1), P. 1 - 14

Published: Jan. 1, 2024

This study focuses on the application of data analytics algorithms for real-time monitoring in additive manufacturing processes. The utilization advanced plays a pivotal role enhancing quality control and efficiency these techniques. research explores how data-driven insights can be harnessed to identify, analyze, rectify deviations process, ensuring optimal performance product quality. By integrating sophisticated algorithms, aims create robust framework that continuously analyzes various parameters during manufacturing. includes factors such as temperature, pressure, material properties real-time. collected is processed through tools detect anomalies or from expected standards. implementation machine learning further facilitates predictive maintenance proactive adjustments, contributing overall reliability effectiveness outcomes this hold significant implications industries relying technologies, providing foundation improved process contributes growing field Industry 4.0 by showcasing integration key enabler efficient reliable

Language: Английский

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Advanced analytics for predicting traffic collision severity assessment

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(2), P. 2007 - 2018

Published: Feb. 28, 2024

Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving risk by harnessing unique data sources extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to lack diversity scope the available data, which hinders their predictive capabilities. In response this challenge, our study integrates broad spectrum heterogeneous encompassing traffic flow, weather conditions, road infrastructure details, historical records. To overcome difficulties associated with we employ advanced science techniques such as feature engineering, imputation, machine learning. The novel dataset that amalgamates various types, establishing robust foundation for model. Through meticulous analysis, derive valuable these sources, significantly enhancing ability assess risks. proposed offers numerous advantages, including capacity predict accidents areas were previously underrepresented under varying conditions. We rigorously evaluate model's performance through extensive experimentation validate its accuracy using real-world data. Our results indicate substantial improvements compared conventional models. research contributes field highlighting potential benefits integrating leveraging techniques. underscores importance tapping into concealed patterns promote optimize resource allocation accident-prone regions, fostering more secure environments.

Language: Английский

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