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Soil Nutrient Detection and recommendation Using IoT and Fuzzy Logic

Computer Systems Science & Engineering 2022年第11期43卷 455-469页

作者： R.Madhumathi T.Arumuganathan r.Shruthi Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatore641022India ICAr-Sugarcane Breeding Institute Coimbatore641007India

Precision agriculture is a modern farming practice that involves the usage of Internet of Things(IoT)to provide an intelligent farm management system.One of the important aspects in agriculture is the analysis of soil nutrients and balancing these inputs are essential for proper crop growth.The crop productivity and the soil fertility can be improved with effective nutrient management and precise application of fertilizers.This can be done by identifying the deficient nutrients with the help of an IoT system.As traditional approach is time consuming,an IoT-enabled system is developed using the colorimetry principle which analyzes the amount of nutrients present in the soil and a fuzzy expert system is designed to recommend the quantity of fertilizers to be added in the soil.A set of 27 IF-THEN rules are framed using the Mamdani inference system by relating the input and output membership functions based on the linguistic variable for fertilizer recommendation.The experiments are conducted using MATLAB for different ranges of Nitrogen(N),Phosphorous(P)and Potassium(K).The NPK data retrieved by the system is sent to the ThingSpeak cloud and displayed on a mobile application that assists the farmers to know the nutrient information of their field.This system delivers the required nutrient information to farmers which results in efficient green farming.

关键词： Precision agriculture nutrient detection fertilizer recommendation internet of things fuzzy logic

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Assessment of Sentiment Analysis Using Information Gain Based Feature Selection Approach

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Computer Systems Science & Engineering 2022年第11期43卷 849-860页

作者： R.Madhumathi A.Meena Kowshalya r.Shruthi Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatoreIndia Government College of Technology CoimbatoreIndia

Sentiment analysis is the process of determining the intention or emotion behind an article.The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion.The data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual polarity.In social behavior,sentiment can be thought of as a latent variable.Measuring and comprehending this behavior could help us to better understand the social issues.Because sentiments are domain specific,sentimental analysis in a specific context is critical in any real-world scenario.Textual sentiment analysis is done in sentence,document level and feature levels.This work introduces a new Information Gain based Feature Selection(IGbFS)algorithm for selecting highly correlated features eliminating irrelevant and redundant ones.Extensive textual sentiment analysis on sentence,document and feature levels are performed by exploiting the proposed Information Gain based Feature Selection algorithm.The analysis is done based on the datasets from Cornell and Kaggle repositories.When compared to existing baseline classifiers,the suggested Information Gain based classifier resulted in an increased accuracy of 96%for document,97.4%for sentence and 98.5%for feature levels respectively.Also,the proposed method is tested with IMDB,Yelp 2013 and Yelp 2014 datasets.Experimental results for these high dimensional datasets give increased accuracy of 95%,96%and 98%for the proposed Information Gain based classifier for document,sentence and feature levels respectively compared to existing baseline classifiers.

关键词： Sentiment analysis sentence level document level feature level information gain

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robotic Assisted Surgery for Endometriosis—“Is the Way Forward?”

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Open Journal of Obstetrics and Gynecology 2016年第2期6卷 93-102页

作者： rooma Sinha madhumathi Sanjay rupa Bana Fozia Jeelani Samita Kumari Department of Gynecology & Minimally Access Surgery Apollo Health City Hyderabad India

Endometriosis is a chronic and progressive gynecologic disorder that affects 10% - 50% of women of reproductive age worldwide. Chronic pain and infertility are the most debilitating problems associated with it requiring both medical and surgical treatment. Laparoscopy is considered the gold standard for diagnosis and treatment. However, a 10% rate of conversion to laparotomy has been reported when performed by skilled laparoscopic surgeons and much higher in low volume less skilled surgeons. To improve surgical outcomes, robotic assistance is the logical next step in performing minimally invasive gynecological surgeries, especially in complex endometriosis cases. Enhanced 3D visualization and 10× magnification along with Endowrist instruments with seven degrees of freedom facilitates precise and careful dissection. Firefly technology using ICG green dye can improve detection of small and invisible lesions. robotics is useful in deep infiltrating disease manifesting as lesions deeper than the superficial tissues of rectovaginal septum, vaginal fornix, pelvic sidewalls, parametrium, bowel or ureter and bladder. Trials show no increase in surgical time, blood loss, or intra- or postoperative complications and similar clinical outcome when robotics is compared with laparoscopy. At present, it is more appropriate to compare it with laparotomy rather than laparoscopy. robotics can be used to manage recurrence of endometriosis after hysterectomy. Surgeons experienced in conventional laparoscopy can utilize robotic platform for deep infiltrating endometriosis for performing complex surgical dissection and achieving the surgical goals in mind and reduce conversions to open surgery. robotic assistance can bridge the gap in performance of laparoscopic surgery in advanced endometriosis.

关键词： Endometriosis Advanced robotic Surgery Laparoscopy Deep Infiltrating Endometriosis Hysterectomy recurrence

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