Automatic pancreas segmentation using ResNet-18 deep learning approach

Kakarwal, S. N.; Paithane, Pradip

doi:10.20535/srit.2308-8893.2022.2.08

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…The study explores the potential of deep learning-driven methods in healthcare, particularly for predicting PED visits [6]. The authors examined seven deep learning models (including DBN, RBM, GRUCNN, LSTM-CNN, GAN-RNN, LSTM, and GRU) and two baseline models (SVR and RR) to forecast patient flow [7]. To prepare the data, multivariate PED visits data from various departments were preprocessed by smoothing and normalization [8].…”

Section: Related Workmentioning

confidence: 99%

Effective Forecasting of Key Features in Hospital Emergency Department

2024

ART

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Efficiently forecasting the demands within a hospital’s Emergency Department (ED) is critical for optimal resource allocation and patient care management. This study focuses on leveraging deep learning techniques to predict various types of ED patient flows, facilitating informed decision-making by ED managers. The rising success of deep learning networks in modeling timeseries data makes them a compelling choice for patient flow forecasting. In this context, we investigate and compare seven deep learning models-Deep Belief Network (DBN), Restricted Boltzmann Machines (RBM), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), combined GRU and Convolutional Neural Networks (CNN-GRU), LSTM-CNN, and Generative Adversarial Network based on Recurrent Neural Networks (GAN-RNN)—to accurately forecast patient flow within a hospital’s emergency department. To enable traffic flow forecasting, a forecaster layer is introduced for each model. Real-world patient flow data spanning different ED services (biology, radiology, scanner, and echography) at Lille regional hospital in France serve as a case study to evaluate these models. Four effectiveness metrics are employed to assess and compare the forecasting methods. The outcomes demonstrate the superior performance of deep learning models in predicting ED patient flows compared to conventional shallow approaches like ridge regression and support vector regression. Significantly, the Deep Belief Network (DBN) stands out, achieving an averaged mean absolute percentage error of approximately 4.097.

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Section: Related Workmentioning

confidence: 99%

Effective Forecasting of Key Features in Hospital Emergency Department

2024

ART

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“…Artificial intelligence (AI)-based computing techniques, such as computerized mechanism that enable specialist to practice approximate perceptive with partial and imprecise information. The subject-matter professionals are increasingly being used to address the essential doubts, prejudice, and engineering experiments in environmental complications [15]. This study describes the creation of a novel fuzzy logic-based WQI known as the "fuzzy water quality index" (FWQI) in order to evaluate the usefulness of this instrument.…”

Section: Research Contributionmentioning

confidence: 99%

Computation of Groundwater Quality of Baramati with the help of Fuzzy Water Quality Index (FWQI)

Wagh,

Paithane,

Sangale

et al. 2023

Int. Res. J. multidiscip. Technovation

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One of the natural resources having the latent for home, agrarian, and engineering use is surface and groundwater. Due to humanoid and certain natural reasons, the eminence of the groundwater serving Baramati City and Tehsil has deteriorated. Agriculture is using more pesticides and fertilizer, therefore this supply is being affected. Municipal water pollution can be caused by “septic boilers”, “bathe sewage”, “polluted aquatic”, “improper discarded management”, “public excretion”, “improper waste structure”, “public sewage discharges”, and “unorganized solid waste disposal”. The current study will improve the accuracy of the water quality index for areas in Baramati Tehsil that are affected by industry and drinking water supplies. The groundwater zones were created using a weighted index overlay analysis, which assigned weights based on several classes of individual water quality metrics and drinking water standards. Based on few observations, fuzzy logic offers an effective and practical tool for categorizing drinking water quality. This study's objective is to provide a fuzzy logic-based water quality indicator for basin-wide reservoirs. For a weight-based fuzzy quality index, a minimum of 6 physico-chemicals are needed.

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“…Crop yield prediction includes predicting a crop's yield based on historical information such as temperature, humidity, pH, rainfall, and the crop's name. It provides information about the best crop that may be expected to be grown in a field [13]. These predictions can be made using the machine learning method Random Forest.…”

Section: I1 Key Contributionsmentioning

confidence: 99%

Random forest algorithm use for crop recommendation

Paithane

2023

ITEGAM

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The proposed method seeks to assist Indian pleasant in selecting the optimum crop to produce based on the characteristics of the soil as well as external factors like temperature and rainfall by using an intelligent system called Crop Recommender. The Indian economy is significantly impacted by the agricultural sector. Whether publicly or covertly, the bulk of Indians are relying on agriculture for their living. As a result, it is undeniable that agriculture is significant to the country. The majority of Indian farmers believe that they should trust their intuition when deciding on a crop to grow in a particular season or they simply employ the methods they have been doing from the beginning of time. They are more at ease just adhering to conventional agricultural practices and standards than truly appreciating how crop yield is influenced by the present weather and soil conditions. The farmer can unintentionally lose money if he makes one bad decision, which would hurt both him and the surrounding agricultural industry. As the agriculture business is the foundation of the entire lateral system. Using the machine learning algorithm, this problem can be resolved. A crucial perspective for identifying a practical and workable solution to the crop production issue is machine learning (ML). Machine learning (ML) may predict a target or outcome from a set of predictors using supervised learning. A recommendation system is implemented using decision trees. The major goals of this system are to provide farmers with recommendations regarding the best crops to sow based on their soil and local rainfall patterns. We have employed the Random Forest Machine Learning technique to forecast the crop. Crop prediction is assessing the crop based on historical data from the past that includes elements like temperature, humidity, ph, and rainfall. It gives us a broad picture of the best crop that can be raised in light of the current field weather conditions. These predictions can be made by Random Forest, a machine learning technique. The highest level of accuracy, up to 90%, will be possible for crop predictions. The random forest algorithm achieved the accuracy about 99.03%.

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Automatic pancreas segmentation using ResNet-18 deep learning approach

Cited by 7 publications

References 12 publications

Effective Forecasting of Key Features in Hospital Emergency Department

Effective Forecasting of Key Features in Hospital Emergency Department

Computation of Groundwater Quality of Baramati with the help of Fuzzy Water Quality Index (FWQI)

Random forest algorithm use for crop recommendation

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