Neural Information Processing
29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part I
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Article
A real-time approach for smart building operations prediction using rule-based complex event processing and SPARQL query
Due to the intelligent, adaptive nature of various operations and their ability to provide maximum comfort to the occupants residing in them, smart buildings are becoming a pioneering area of research. Since t...
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Article
Multimodal image fusion on ECG signals for congestive heart failure classification
The electrocardiogram, or ECG, records electrical signals from the heart to detect various heart conditions. It helps in the diagnosis of important cardiovascular diseases such as myocardial infarction (MI), c...
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A model fusion approach for severity prediction of diabetes with respect to binary and multiclass classification
Diabetes Mellitus has impacted millions of people across the globe and continues with the same. It is caused due to increased blood sugar levels, as the pancreas fails to produce enough insulin and if produced...
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Article
Open AccessDiversity and Representation Among United States Participants in Amgen Clinical Trials
Describe the demographic profile of US participants in Amgen clinical trials over a 10-year period and variations across therapeutic areas, indications, and geographies.
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Article
Target-class guided sample length reduction and training set selection of univariate time-series
The novelty/anomaly detection in time-series (TS) data is an admired research domain, which is specifically a one-class classification (OCC) task, where only target-class samples are present during training an...
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Chapter and Conference Paper
An Optimized Hybrid Solution for IoT Based Lifestyle Disease Classification Using Stress Data
Stress, anxiety, and nervousness are all high-risk health states in everyday life. Previously, stress levels were determined by speaking with people and gaining insight into what they had experienced recently ...
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Chapter and Conference Paper
Anomaly Detection in Surveillance Videos Using Transformer Based Attention Model
Surveillance footage can catch a wide range of realistic anomalies. This research suggests using a weakly supervised strategy to avoid annotating anomalous segments in training videos, which is time consuming....
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Book and Conference Proceedings
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Book and Conference Proceedings
Neural Information Processing
29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part III
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Book and Conference Proceedings
Neural Information Processing
29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part II
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Book and Conference Proceedings
Neural Information Processing
29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part VI
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Book and Conference Proceedings
Neural Information Processing
29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part V
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Book and Conference Proceedings
Neural Information Processing
29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part IV
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Chapter and Conference Paper
Impact of the Composition of Feature Extraction and Class Sampling in Medicare Fraud Detection
With healthcare being critical aspect, health insurance has become an important scheme in minimizing medical expenses. Medicare is an example of such a healthcare insurance initiative in the United States. Fol...
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Book and Conference Proceedings
Neural Information Processing
29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part VII
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Article
BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models
Deep learning has brought the most profound contribution towards biomedical image segmentation to automate the process of delineation in medical imaging. To accomplish such task, the models are required to be ...
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CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images
The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as computed tomograph...
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Modality specific U-Net variants for biomedical image segmentation: a survey
With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical im...
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Article
RCA-IUnet: a residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging
The advancements in deep learning technologies have produced immense contributions to biomedical image analysis applications. With breast cancer being the common deadliest disease among women, early detection ...
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Article
Astronomical big data processing using machine learning: A comprehensive review
Astronomy, being one of the oldest observational sciences, has collected a lot of data over the ages. In recent times, it is experiencing a huge data surge due to advancements in telescopic technologies with a...
