Use of AI Techniques on Photonic Crystal Sensing for the Detection of Tumor

Sharma, Sunil; Tharani, Lokesh

doi:10.35882/jeeemi.v4i2.2

Cited by 8 publications

(5 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is particularly useful in situations where the dataset is imbalanced, and the classes are not evenly distributed. [21][22][23][24]…”

Section: Machine Learning Algorithms For Pathogen Detectionmentioning

confidence: 99%

Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning

Sharma,

Tharani

2024

Science Progress

Self Cite

View full text Add to dashboard Cite

Contaminated fresh produce remains a prominent catalyst for food-borne illnesses, prompting the need for swift and precise pathogen detection to mitigate health risks. This paper introduces an innovative strategy for identifying food-borne pathogens in fresh produce samples from local markets and grocery stores, utilizing optical sensing and machine learning. The core of our approach is a photonics-based sensor system, which instantaneously generates optical signals to detect pathogen presence. Machine learning algorithms process the copious sensor data to predict contamination probabilities in real time. Our study reveals compelling results, affirming the efficacy of our method in identifying prevalent food-borne pathogens, including Escherichia coli ( E. coli) and Salmonella enteric, across diverse fresh produce samples. The outcomes underline our approach's precision, achieving detection accuracies of up to 95%, surpassing traditional, time-consuming, and less accurate methods. Our method's key advantages encompass real-time capabilities, heightened accuracy, and cost-effectiveness, facilitating its adoption by both food industry stakeholders and regulatory bodies for quality assurance and safety oversight. Implementation holds the potential to elevate food safety and reduce wastage. Our research signifies a substantial stride toward the development of a dependable, real-time food safety monitoring system for fresh produce. Future research endeavors will be dedicated to optimizing system performance, crafting portable field sensors, and broadening pathogen detection capabilities. This novel approach promises substantial enhancements in food safety and public health.

show abstract

“…It is particularly useful in situations where the dataset is imbalanced, and the classes are not evenly distributed. [21][22][23][24]…”

Section: Machine Learning Algorithms For Pathogen Detectionmentioning

confidence: 99%

Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning

Sharma,

Tharani

2024

Science Progress

Self Cite

View full text Add to dashboard Cite

show abstract

“…Step approach, its integration into D-PMSG systems holds the promise of advancing the efficacy of wind energy technologies, supporting the global transition towards more sustainable and environmentally friendly energy solutions [17]- [18]. The ongoing research and development of these control strategies are essential for realizing their full potential in the renewable energy landscape [19].…”

Section: Field Testingmentioning

confidence: 99%

“…The wind turbine can only use a portion of the wind energy [17]- [18], and the wind energy utilization coefficient has a theoretical maximum value of 0.593, according to Betz theory.…”

Section: Wind Power System Control a Wind Turbine Characteristics Ana...mentioning

confidence: 99%

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Nawaz,

Jiang,

Usman

et al. 2024

j.electron.electromedical.eng.med.inform

View full text Add to dashboard Cite

This study introduces an enhanced Maximum Power Point Tracking (MPPT) control strategy for Direct Drive Permanent Magnet Synchronous Generators (D-PMSG) utilizing a Fuzzy Gradient Step Approach. By comparing with traditional MPPT methodologies, this approach demonstrates significant improvements in tracking accuracy, efficiency, and response time to fluctuating environmental conditions. The fuzzy logic control method adapts dynamically, optimizing power output under variable wind speeds. The comparative analysis reveals that our method not only surpasses conventional techniques in performance but also offers a cost-effective solution with less complexity. Implications of these advancements suggest potential applications in optimizing wind energy systems, enhancing the viability of renewable energy sources. By examining the relationship between Boost duty cycle changes and wind turbine output characteristics, a fuzzy gradient step hill-climbing search method is proposed to calculate the wind turbine output speed in order to improve the maximum power point tracking control performance of the direct-drive permanent magnet wind power generation system. The fuzzy controller employs the duty cycle of the Boost converter as its output quantity and its input quantity to accomplish the maximum power point tracking control of the wind turbine. A model was developed and verified through simulation for use in system modeling. The results show that the fuzzy gradient step hill-climbing search approach is more effective at regulating the maximum power point tracking control of the direct-drive permanent magnet wind power producing system than the traditional variable step-size hill-climbing search algorithm. This research paves the way for future exploration in smart grid integration and scalability of fuzzy logic-based MPPT controllers, marking a pivotal step towards sustainable energy solutions.

show abstract

“…Decision tree algorithm is another type of machine learning algorithm that is used for the diagnosis of pancreatic cancer using PCF spectral signatures. Decision trees are supervised learning algorithms that can be used for both classification and regression 11,12 tasks. The algorithm learns to identify the most informative features in the spectral signatures that are indicative of cancer, and uses this information to construct a decision tree that can accurately classify new tissue samples as cancerous or non-cancerous.…”

Section: Decision Tree Model For Diagnose Pancreatic Cancermentioning

confidence: 99%

Photonic crystal fiber based automated system to diagnose silent killer

Sharma¹,

Tharani²

2023

MSEIJ

Self Cite

View full text Add to dashboard Cite

Pancreatic cancer (PC) is a lethal disease that is difficult to diagnose in its early stages. This is the reason it is deadly known as “The silent killer”. Traditional diagnostic methods are often invasive and can lead to misdiagnosis. The purpose of this manuscript is to propose photonic crystal fibers (PCFs) based artificial intelligence (AI) systems to materialize it as a promising tool for diagnosing pancreatic cancer. PCFs are optical fibers (OFs) that allow for the detection of light at high resolution and used to analyze the biochemical composition of tissues samples and feed the resulting data into an AI algorithm. The proposed system has the potential to significantly improve the early detection and diagnosis of pancreatic cancer, which lead to better outcomes. The Decision Tree (DT) model achieved an accuracy of 86.8%, a sensitivity of 81.6%, and a specificity of 90.3%. The Support Vector Machine (SVM) model achieved an accuracy of 90.9%, a sensitivity of 95.7%, and a specificity of 86.0%. The K-nearest neighbor (KNN) model achieved an accuracy of 90.8%, a sensitivity of 91.7%, and a specificity of 89.1%.

show abstract

Use of AI Techniques on Photonic Crystal Sensing for the Detection of Tumor

Cited by 8 publications

References 43 publications

Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning

Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning

Improved Maximum Power Point Tracking Control for D-PMSG Systems: Fuzzy Gradient Step Approach

Photonic crystal fiber based automated system to diagnose silent killer

Contact Info

Product

Resources

About