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A Machine Learning Approach for the Land-Type Classification

Deep neural networks (DNNs) have become an important tool in solving various problems in numerous disciplines. However, DNNs are also known for their high resource requirement, weight redundancy, and large-scale parameters.


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“TeachAR” Smart way to Learn

Consistent changes come up every day like online classes, digital books, kindle editions etc. But changes like this rarely thought about how much it’s efficient or the user understands the concept in the book or paper.


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Smart Mirror – Innovation for smart life

A smart mirror is a system that functions as mirror with additionally capability of displaying Weather details, News, Date & Time, and Calendar.


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Recent trends in electroencephalography based biometric systems

With ever increasing cases of various types of spoof attacks, and due to vulnerabilities present in the existing systems different biometric modalities have been explored and EEG is one of them.


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DualPrune: A Dual Purpose Pruning of Convolutional Neural Networks for Resource-Constrained Devices

The use of deep learning models in edge-devices is still limited. Deploying a large model onto small devices for real-time inference requires an adequate amount of resources.


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A Deep Learning-Based Transfer Learning Approach for the Bird Species Classification

Deep neural networks (DNNs) have become an important tool in solving various problems in numerous disciplines. However, DNNs are also known for their high resource requirement, weight redundancy, and large-scale parameters.


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CovidNet: A Light-Weight CNN for the Detection of COVID-19 Using Chest X-Ray Images

Corona virus more popularly known as COVID-19 is an extremely virulent strain from the Corona virus family of viruses and their origin is attributed to bats and civet cats.


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Design of adaptive ensemble classifier for online sentiment analysis and opinion mining

DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift.


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A Heuristic-Driven and Cost Effective Majority/ Minority Logic Synthesis for Post-CMOS Emerging Technology

Due to the physical restriction of current CMOS technology, emerging technologies that have majority logic gate as a base component are being explored. The process of transforming from boolean network to the majority logic network is called majority logic synthesis (MLS).


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A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices

A significant progress has been made in automated medical diagnosis with the advent of deep learning methods in recent years. However, deploying a deep learning model for mobile and small-scale, low-cost devices is a major bottleneck.


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Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification

COVID-19 has become a pandemic for the entire world, and it has significantly affected the world economy. The importance of early detection and treatment of the infection cannot be overstated.


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Inference aware convolutional neural network pruning

Deep neural networks (DNNs) have become an important tool in solving various problems in numerous disciplines. However, DNNs are also known for their high resource requirement, weight redundancy, and large-scale parameters.


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Conformal properties of soft-operators. Part II. Use of null-states -l

Conformal properties of soft-operators. Part 2 – Use of null-states

Representations of the (Lorentz) conformal group with the soft operators as highest weight vectors have two universal properties, which we clearly state in this paper.


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Conformal properties of soft operators Use of null states-l

Conformal properties of soft operators. Part 1 – Use of null states

Soft operators are (roughly speaking) zero energy massless particles which live on the celestial sphere in Minkowski space.


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Modified celestial amplitude in Einstein gravity-L

Modified celestial amplitude in Einstein gravity

In this paper we evaluate the modified celestial amplitude for gravitons and gluons, as defined in [4]. We find that the modified (tree) amplitude is finite for gravitons in Einstein gravity.


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A comprehensive survey on model compression and acceleration-L

A comprehensive survey on model compression and acceleration

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable improvement in computer vision, natural language processing, stock prediction, forecasting, and audio processing to name a few.


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Design of Ensemble Classifier Selection Framework Based on Ant Colony Optimization for Sentiment Analysis and Opinion Mining

Design of Ensemble Classifier Selection Framework Based on Ant Colony Optimization for Sentiment Analysis and Opinion Mining

Ensemble Classifier provides a promising way to improve the accuracy of classification for sentiment analysis and opinion mining.


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Pot-holes detection on Indian Roads using Mobile Sensors-L

Pitfree: Pot-holes detection on Indian Roads using Mobile Sensors

Pot-holes on road will make transportation slower and costly. India has a big network of roads to connect the villages and cities.


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Comparative analysis of ensemble classifiers for sentiment analysis and opinion mining-l

Comparative analysis of ensemble classifiers for sentiment analysis and opinion mining

Ensemble classifiers are showing a promising way to solve various classification and predictive problems.


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