A Way for Smart Home Technology for Disabled and Elderly People

Sankar, S.; Dhinakaran, D.; Kavya, T.; Priyanka, S.; Oviya, P. Pugal

doi:10.1109/icidca56705.2023.10099817

Cited by 12 publications

(3 citation statements)

References 24 publications

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“…The integration of AI, particularly leveraging convolutional autoencoders for spatial analysis and genomic feature extraction for molecular insights, emerges as a compelling avenue to address the challenges posed by liver tumors [14]. Convolutional autoencoders, such as HistoCovAE, hold promise in enhancing the precision of tumor segmentation by deciphering intricate spatial patterns in medical images.…”

Section: B Motivationmentioning

confidence: 99%

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AI-Enhanced Comprehensive Liver Tumor Prediction using Convolutional Autoencoder and Genomic Signatures

Prabaharan,

Dhinakaran,

Raghavan

et al. 2024

IJACSA

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Liver tumor prediction plays a pivotal role in optimizing treatment strategies and improving patient outcomes. In our proposed work, we present an innovative AI-driven framework for liver tumor prediction, uniting cutting-edge techniques to enhance precision and depth of analysis. The framework integrates a Histological Convolutional Autoencoder (HistoCovAE) for meticulous tumor segmentation in medical imaging, and Genomic Feature Extraction (MIRSLiC) for a nuanced understanding of molecular markers. Additionally, a Multidimensional Feature Extraction module amalgamates videomics, radiomics, acoustics, and clinical data, creating a comprehensive dataset. These dimensions synergize in a unified model, offering detailed predictions encompassing tumor characteristics, subtypes, and prognosis. Model evaluation and continuous improvement, guided by real-world outcomes, underscore reliability. This integrative approach transcends conventional boundaries, providing clinicians' actionable insights for personalized treatment strategies and heralding a new era in liver tumor prediction. Our model undergoes rigorous evaluation against diverse datasets, and the performance metrics underscore its reliability and accuracy. With precision exceeding 87%, recall rates above 92%, and a Dice coefficient surpassing 0.89 in tumor segmentation, our model showcases exceptional accuracy and robustness. In prognostic modeling, survival prediction accuracy consistently surpasses 84%, highlighting the model's ability to provide valuable insights into the future trajectory of liver cancer.

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Section: B Motivationmentioning

confidence: 99%

“…Updating the model parameters using gradient descent and backpropagation: (14) where, α is the learning rate. Liver cancer exhibits a remarkable degree of heterogeneity, both at the genetic and clinical levels.…”

Section: ) Significance Of Prognostic Model Developmentmentioning

confidence: 99%

AI-Enhanced Comprehensive Liver Tumor Prediction using Convolutional Autoencoder and Genomic Signatures

Prabaharan,

Dhinakaran,

Raghavan

et al. 2024

IJACSA

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“…Successful fish farm management hinges on maintaining optimal conditions for aquatic life, with key factors such as water quality, temperature, and feeding schedules playing pivotal roles. However, the intricacies and interdependencies of these parameters necessitate a sophisticated approach to their management [2][3][4]. In response to these challenges, we introduce a pioneering Internet of Things (IoT)-based environmental control system that combines sensor integration with advanced machine learning decision support.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

Dhinakaran

2023

IJRITCC

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In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.

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A Lightweight (Recurrent Neural Network) RNN Architecture for IoT Based Baby Monitoring System to Provide Real Time Alerts and Notification to Parents or Care Takers

Kumar,

Kumar

et al. 2023

2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS)

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A Way for Smart Home Technology for Disabled and Elderly People

Cited by 12 publications

References 24 publications

AI-Enhanced Comprehensive Liver Tumor Prediction using Convolutional Autoencoder and Genomic Signatures

AI-Enhanced Comprehensive Liver Tumor Prediction using Convolutional Autoencoder and Genomic Signatures

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

A Lightweight (Recurrent Neural Network) RNN Architecture for IoT Based Baby Monitoring System to Provide Real Time Alerts and Notification to Parents or Care Takers

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