Latest Publication

Smart Landscapes: ML for Accurate Land 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.


Read More >>

TeachAR: A Smart Twist to Learning for the Modern Age

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.


Read More >>

Reflecting Tomorrow: Unleashing the Magic of Smart Mirrors

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


Read More >>

From Brain to Biometrics: Unraveling the Newest in EEG Technology

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.


Read More >>

DualPrune: Streamlined CNNs for Resource-Efficient 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.


Read More >>

Bird Species Classification with Deep Transfer Learning

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.


Read More >>

CovidNet Unleashed: Lightweight CNN Detects COVID from X-Rays

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.


Read More >>

Opinion Evolution: The Adaptive Ensemble’s Journey in Sentiment Analysis

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.


Read More >>

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).


Read More >>

Smart Diagnosis: Enhancing Breast Cancer Classification with Filter Pruning

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.


Read More >>

Deep Learning Transfer: COVID-19 CT-scan Classification Insights

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.


Read More >>

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.


Read More >>

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.


Read More >>

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.


Read More >>

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.


Read More >>

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.


Read More >>

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.


Read More >>

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.


Read More >>

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.


Read More >>

Stay In the Know

Get Latest updates and industry insights every month.