IoT-Based Smart Diagnosis System for HealthCare

Hanumanthappa, J.; Muaad, Abdullah Y.; Benifa, J. V. Bibal; Chola, Channabasava; Hiremath, V. A.; Pramodha, M.

doi:10.1007/978-981-16-6605-6_34

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…xanthomonas, sigatoka) are identified with MFRCNN model composed of modified RCNN, MAGAN model for dataset generation and learning. Ensemble based approach has shown 98% accuracy [16] [17].…”

Section: Review Of Literaturementioning

confidence: 99%

Explainable AI Based framework for Banana Disease Detection

muaad

et al. 2024

Preprint

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Due to widespread usage of banana as a staple food crop and susceptibility to numerous illnesses. Bananas require sophisticated detection techniques to support sustainable agricultural practices. Bananas are particularly susceptible to various stem and leaf spot diseases, resulting in significant economic losses within the banana cultivation sector. In this paper, a new XAI framework for banana disease detection and classification is introduced. Our framework uses state-of-the-art AI methods to analyze photos of banana plants. With great precision, it can detect a variety of illnesses like Cordana, Black Sigatoka, Pestalotiopsis, and fusarium wilt. The outcomes show that the framework performs better than current techniques in precisely identifying and categorizing banana diseases. The research employed a Convolutional Neural Networks (CNNs) to detect diseases in banana plants using RGB images of banana leaves. We used pre-trained model called EfficientnetB0 model to evaluate using two datasets BLSD and BDT. For BLSD, the model achieved an accuracy of 99.22%. Next for BDT, on the other hand, demonstrated improved performance with an accuracy of 99.63%.

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Section: Review Of Literaturementioning

confidence: 99%

Explainable AI Based framework for Banana Disease Detection

muaad

et al. 2024

Preprint

Self Cite

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“…Altini et al [19] conclude that resting heart rate and heart rate variability (HRV) also can effectively be used to quantify individual stress responses across a large range of individual characteristics and stressors. A recent paper, where an algorithm is proposed to determine the current health status of a patient, introduces a COVID-19 patient health management platform that uses the IoT and cloud computing technology [20]. Ambulatory monitoring devices, including wearables, smartphones and other ambulatory sensors, enable a new healthcare paradigm by collecting and analyzing long-term data for a reliable diagnosis.…”

Section: Related Workmentioning

confidence: 99%

A Validation Study to Confirm the Accuracy of Wearable Devices Based on Health Data Analysis

2023

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This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management.

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“…In the measurement of a set, accuracy is defined as the closeness of the measured value to a specific actual value [76].…”

Section: Accuracymentioning

confidence: 99%

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

Muaad

Hanumanthappa

Guru

et al. 2022

Mathematical Problems in Engineering

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With the increasing number of online social posts, review comments, and digital documentations, the Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within the coronavirus pandemics. The variations in the meaning of the same Arabic words could directly affect the performance of any AI-based framework. This work aims to identify the effectiveness of machine learning (ML) algorithms through preprocessing and representation techniques. This effectiveness is measured via different AI-based classification techniques. Basically, the ATC process is influenced by several factors such as stemming in preprocessing, method of feature extraction and selection, nature of datasets, and classification algorithm. To improve the overall classification performance, preprocessing techniques are mainly used to convert each Arabic word into its root and decrease the representation dimension among the datasets. Feature extraction and selection always play crucial roles to represent the Arabic text in a meaningful way and improve the classification accuracy rate. The selected classifiers in this study are performed based on various feature selection algorithms. The overall classification evaluation results are compared using different classifiers such as multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and Linear SVC. All of these AI classifiers are evaluated using five balanced and unbalanced benchmark datasets: BBC Arabic corpus, CNN Arabic corpus, Open-Source Arabic corpus (OSAc), ArCovidVac, and AlKhaleej. The evaluation results show that the classification performance strongly depends on the preprocessing technique, representation methods and classification technique, and the nature of datasets used. For the considered benchmark datasets, the linear SVC has outperformed other classifiers overall when prominent features are selected.

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IoT-Based Smart Diagnosis System for HealthCare

Cited by 10 publications

References 27 publications

Explainable AI Based framework for Banana Disease Detection

Explainable AI Based framework for Banana Disease Detection

A Validation Study to Confirm the Accuracy of Wearable Devices Based on Health Data Analysis

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

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