IoT-Based Environmental Parameter Monitoring Using Machine Learning Approach

Kashid, M. M.; Karande, K. J.; Mulani, Altaf O.

doi:10.1007/978-981-19-2350-0_5

Cited by 20 publications

(4 citation statements)

References 12 publications

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“…Accuracy is referred as the distance relating the calculated value of the developed form and the [73] correct value. Sensitivity: Sensitivity is a necessary component for accurately identifying patients with [74] brain abnormalities. Specificity: Specificity is measured by its accuracy in identify brain abnormality of the proposed method.…”

Section: Methodsmentioning

confidence: 99%

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Basawaraj Birajadar,

Osman Mulani,

Ibrahim Khalaf

et al. 2024

IJCDS

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The Electroencephalogram (EEG) stands as a burgeoning frontier in the study of neuronal activity, offering a rich tapestry of information crucial for identifying abnormalities and addressing cognitive disorders and irregularities. This paper delves into the examination of EEG from subjects exhibiting abnormalities, contrasting them with those from normal subjects. Various topographical features such as Mean, Entropy, and Wavelet bands are meticulously evaluated and compared.Inspired by the adaptive hunting strategies observed in coyotes, this study introduces a novel hybrid computational model that integrates deep learning architectures, aiming to amplify diagnostic accuracy. The methodology hinges upon the development of a unique computational algorithm inspired by the intricate hunting behaviors of coyotes, seamlessly fused with the potent data-driven capabilities of deep neural networks. This hybrid model is meticulously applied to scrutinize EEG data for the detection of brain disorders, capitalizing on both the biologically-inspired algorithm and the data-centric strengths of deep learning. The results obtained from this innovative approach are highly promising. The proposed scheme exhibits a remarkable diagnostic accuracy, achieving an impressive rate of 98.65 per for training (True Positive -TP) and 98.82 per utilizing k-fold validation. These preliminary findings underscore the potential efficacy of the hybrid methodology in accurately discerning brain disorders from EEG signals. However, it is essential to acknowledge that these results represent an initial success and form just a fragment of the extensive evaluation process.This study marks a significant stride towards leveraging interdisciplinary insights, blending principles from ethology with advanced computational techniques to tackle complex neurological challenges. By harnessing the sophisticated strategies observed in nature alongside cutting-edge technological advancements, this research endeavors to carve a path towards more nuanced and precise diagnostic tools for understanding and addressing brain disorders. Further exploration and refinement of this hybrid model hold promise for revolutionizing the landscape of neurodiagnostics, offering hope for more effective interventions and treatments in the realm of cognitive health.

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Section: Methodsmentioning

confidence: 99%

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Basawaraj Birajadar,

Osman Mulani,

Ibrahim Khalaf

et al. 2024

IJCDS

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“…Kashid et al [18] explains that Nowadays, environmental preservation is critical for humans to ensure secure and prosperous living. Tracking requirements vary greatly, depending on geography and expanding to specialized uses that require flexibility.…”

Section: IImentioning

confidence: 99%

Integrating AI and IoT in Advanced Optical Systems for Sustainable Energy and Environment Monitoring

Khan,

Kalifullah,

Ibragimova

et al. 2024

ijacsa

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The increasing demand for sustainable energy solutions and environmental monitoring necessitates advanced technologies. This work combines the capabilities of AI, in the form of a GRU-Auto encoder, with IoT-connected Advanced Optical Systems to create a comprehensive monitoring system. Current monitoring systems often face limitations in real-time analysis and adaptability. Conventional methods struggle to provide timely insights for sustainable energy and environmental management due to the complexity of data patterns and the lack of dynamic adaptability. Our proposed methodology introduces an optimized GRU-Auto encoder, which excels in learning complex temporal patterns, making it well-suited for dynamic environmental and energy data. The integration with Advanced Optical Systems ensures a continuous influx of high-quality realtime data through IoT, enabling more accurate and adaptive analysis. The study involves optimizing the GRU-Auto encoder through hyper parameter tuning and gradient clipping. The model is integrated into an IoT platform that connects with Advanced Optical Systems for seamless data flow. Real-time data from environmental and energy sensors are processed through the AI model, providing immediate insights. Performance is evaluated based on the system's ability to accurately predict environmental trends, optimize energy consumption, and adapt to dynamic changes. Comparative analyses with traditional methods show advantages of the suggested strategy in terms of efficiency and accuracy. This research presents a significant development in the field of study of sustainable energy and environment monitoring, offering a robust solution for real-time data analysis and adaptive decision-making. The integration of an optimized GRU-Auto encoder with IoT-connected Advanced Optical Systems showcases promising results in improving overall system performance and sustainability.

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“…Mulani and Mane [30] suggested implementation of fast and area efficient discrete wavelet transform (DWT) oriented invisible image watermarking system integrated with cryptography on reconfigurable platform. Kashid et al [31] proposed a device which is beneficial for measuring temperature, and humidity.…”

Section: Literature Surveymentioning

confidence: 99%

An algorithm for enhancement of audio content classification

Bang¹,

Purandare²,

Ratnaparkhi³

2023

Bulletin EEI

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Presently, fast proliferation of information enforces novel challenges on content management. Further, computerized audio classification along-with content description is considered as valuable method to manage audio contents. In general, classification involves two steps. First, is the processing of accessible data in economical ways to deliver explanatory features. Second is how accurate features of undetermined tests is evaluated to choose classifier. In this paper, k-neighbor algorithm with machine learning is proposed for feature extraction as well as content classification/description. This algorithm enhances Quality of Service parameters of classifiers. Here, development of training as well as testing data set is developed to increase the classifier accuracy. A test engine set-up bed using simulation tool MATLAB is designed to estimate the implementation performance of the algorithm. A range of features are studied to evaluate effectiveness in terms of accuracy, zero crossing rate (ZCR) and spectral roll frequency. From the experimentation results, it is observed that the proposed algorithm can achieve accuracy of 95.8% for 2 sec window length as compare with k-neighbor algorithm. A total enhancement of 11% is achieved with cross validation error of 29.6. A superior assortment of training fabric to extract few additional useful features can enhance accuracy further.

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IoT-Based Environmental Parameter Monitoring Using Machine Learning Approach

Cited by 20 publications

References 12 publications

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Epilepsy Identification using Hybrid CoPrO-DCNN Classifier

Integrating AI and IoT in Advanced Optical Systems for Sustainable Energy and Environment Monitoring

An algorithm for enhancement of audio content classification

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