Machine Learning System For Indolence Perception

Sankar, S.; Kumar, N. Jagadish; Dhinakaran, D; Kamalesh, S S; Abenesh, R

doi:10.1109/icidca56705.2023.10099959

Cited by 20 publications

(3 citation statements)

References 12 publications

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“…Radiomics, another essential component, delves into the quantitative features extracted from medical imaging data. It goes beyond traditional visual assessments, unraveling subtle patterns, textures, and spatial relationships within the images [29]. The marriage of Radiomics with AI algorithms enables the extraction of intricate details that may elude the human eye, enriching the dataset for predictive modeling.…”

Section: ) Contributions Of Each Modalitymentioning

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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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: ) Contributions Of Each Modalitymentioning

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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“…Background and Significance Background Fish aquaculture, the controlled cultivation of fish for food production, has emerged as an indispensable sector within the broader agriculture industry [12]. With the demand for seafood steadily escalating and wild fisheries under increasing pressure, the growth of aquaculture has been essential to meet the world's need for high-quality protein sources [13]. However, fish farming is a multifaceted endeavor characterized by its dynamic dependence on various environmental parameters, including water quality, temperature, and feeding schedules.…”

Section: Amentioning

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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“…When this happens, the gradients can become less informative, slowing the learning process [33]. Batch normalization helps to address this issue by normalizing the activations of each layer so that they have a more consistent distribution throughout the network [34]. This process helps stabilize the gradients and reduce the number of epochs required to perform well.…”

Section: Depth-based Variant Search Algorithmmentioning

confidence: 99%

Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

Kalaiselvi,

Anusuya

2023

CMC

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In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases. The anticipated model is assessed on a Computed Tomography (CT) scan dataset containing both benign and malignant liver tumors. The proposed approach achieved high accuracy in predicting liver tumors, outperforming other state-of-the-art methods. Additionally, advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver tumors. The results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor prediction. It can assist radiologists in their diagnosis and treatment planning. The proposed system achieved a high accuracy of 95.5% in predicting liver tumors, outperforming other state-of-the-art methods.

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Machine Learning System For Indolence Perception

Cited by 20 publications

References 12 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

Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

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