Amit Konar

Jadavpur University

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Atulya K. Nagar

Liverpool Hope University

146

Pratyusha Rakshit

Jadavpur University

R. Janarthanan

Shri BM Patil Medical College

Aruna Chakraborty

Maulana Abul Kalam Azad Institute of Asian Studies

Sriparna Saha

Indian Institute of Technology Patna

Lidia Ghosh

Jadavpur University

D. N. Tibarewala

Jadavpur University

Swagatam Das

Indian Statistical Institute

Anwesha Khasnobish

Tata Consultancy Services (India)

Saugat Bhattacharyya

University of Ulster

Cooperative Institutions

Jadavpur University

203

Indian Institute of Technology Kharagpur

Institute of Electrical and Electronics Engineers

Canadian Standards Association

Tata Consultancy Services (India)

Indian Institute of Engineering Science and Technology, Shibpur

University of Calcutta

University of Engineering & Management

Indian Statistical Institute

Indian Institute of Science Bangalore

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Prediction of protein–protein interaction network using a multi-objective optimization approach

Journal of Bioinformatics and Computational Biology (2015)

Archana Chowdhury Pratyusha Rakshit Amit Konar

Protein-Protein Interactions (PPIs) are very important as they coordinate almost all cellular processes. This paper attempts to formulate PPI prediction problem in a multi-objective optimization framework. The scoring functions for the trial solution deal with simultaneous maximization of functional similarity, strength of the domain interaction profiles, and the number of common neighbors of the proteins predicted to be interacting. The above optimization problem is solved using the proposed Firefly Algorithm with Nondominated Sorting. Experiments undertaken reveal that the proposed PPI prediction technique outperforms existing methods, including gene ontology-based Relative Specific Similarity, multi-domain-based Domain Cohesion Coupling method, domain-based Random Decision Forest method, Bagging with REP Tree, and evolutionary/swarm algorithm-based approaches, with respect to sensitivity, specificity, and F1 score.

10.1142/s0219720016500086

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Citations (3)

DEMO-TDQL: An adaptive multi-objective optimization algorithm

Pratyusha Rakshit Amit Konar Eun‐jin Kim Atulya K. Nagar

An adaptive memetic algorithm incorporates an adaptive selection of memes (units of cultural transmission) from a meme-pool to improve the cultural characteristics of the individual member of a population-based search algorithm. The paper proposes an extension of Multi-objective Optimization realized with Differential Evolution algorithm by utilizing the composite benefits of Differential Evolution for Multi-objective Optimization (DEMO) for global search and Templocal refinementoral Difference Q-Learning (TDQL) for local refinement. Computer simulations performed on a well known set of 23 benchmark functions reveal that the proposed algorithm outperforms its competitors with respect to inverted generational distance, spacing and error ratio.

Benchmark (surveying)

Differential Evolution

Memetics

Memetic algorithm

10.1109/cec.2013.6557947

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Conclusions and Future Research

Studies in computational intelligence (2009)

Swagatam Das Ajith Abraham Amit Konar

Scope (computer science)

Differential Evolution

Feature (linguistics)

10.1007/978-3-540-93964-1_7

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Citations (2)

N400検出のための新しい特徴抽出法【Powered by NICT】

IEEE Conference Proceedings (2016)

Reshma Kar Amit Konar Chakraborty Aruna Ghosh Sanchita

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Hemodynamic Analysis for Assessing Creative Skill of Subjects Using Convolutional Neural Network

Sayantani Ghosh Amit Konar Mousumi Laha

The paper introduces an innovative method for evaluating the scientific creative potential of subjects from their analogical reasoning ability using functional Near Infrared Spectroscopy (fNIRS). The fNIRS signals acquired from the subjects during analogical problem solving are transmitted to a novel Convolutional Neural Network (CNN) after pre-processing to identify two class labels: creative and non-creative. Executed experiments reveal the proposed model's effectiveness compared to its traditional counterparts, substantiating its superiority.

10.1109/codec60112.2023.10466048

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Decoding of EEG Signals Using Deep Long Short-Term Memory Network in Face Recognition Task

2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2018)

Lidia Ghosh Sayantani Ghosh Amit Konar Pratyusha Rakshit Atulya K. Nagar

The paper proposes a novel approach to classify the human memory response involved in the face recognition task by the utilization of event related potentials. Electroencephalographic signals are acquired when a subject engages himself/herself in familiar or unfamiliar face recognition tasks. The signals are analyzed through source Iocalization using eLORETA and artifact removal by ICA from a set of channels corresponding to those selected sources, with an ultimate aim to classify the EEG responses of familiar and unfamiliar faces. The EEG responses of the two different classes (familiar and unfamiliar face recognition)are distinguished by analyzing the Event Related Potential signals that reveal the existence of large N250 and P600 signals during familiar face recognition.The paper introduces a novel LSTM classifier network which is designed to classify the ERP signals to fulfill the prime objective of this work. The first layer of the novel LSTM network evaluates the spatial and local temporal correlations between the obtained samples of local EEG time-windows. The second layer of this network models the temporal correlations between the time-windows. An attention mechanism has been introduced in each layer of the proposed model to compute the contribution of each EEG time-window in face recognition task. Performance analysis reveals that the proposed LSTM classifier with attention mechanism outperforms the efficiency of the conventional LSTM and other classifiers with a significantly large margin. Moreover, source Iocalization using eLORETA shows the involvement of inferior temporal and frontal lobes during familiar face recognition and pre-frontal lobe during unfamiliar face recognition. Thus, the present research outcome can be used in criminal investigation, where meticulous differentiation of familiar and unfamiliar face detection by criminals can be performed from their acquired brain responses.

10.1109/ssci.2018.8628757

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Citations (7)

Fuzzy Vector Quantization for Classification of Olfactory Stimuli from the Acquired Brain Signals

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2019)

Lidia Ghosh Dipanjan Konar Amit Konar

Quantization offers a simple means to reduce unwanted data variations into quantized levels of fixed amplitudes. A simple Normalization of data in the scale [0, 1] without attributing special importance to specific sub-intervals in [0, 1] results in significant loss in information, which cannot be retrieved otherwise to identify the proposer class of a given data point. This paper attempts to represent n-dimensional data point patterns into quantized fuzzy vectors of 3n dimensions. As the class information for all the data points of the training instances are known, a majority voting over (the individual quantized fuzzy) components of the data points in a class effectively returns an approximate centroidal measure of the data points lying in the same class. The concatenation of such majority-voted components of the data points in a class represents the class centroid of the respective class. The class centroids thus obtained are saved to determine the class of an unknown data point. The classification of an unknown data point here is performed in two steps. First the unknown data point is quantized in fuzzy space similarly as for the training data, and the resulting quantized fuzzy vector is projected along all the unit class centroid vectors of all known classes. In case the projection along the k-th unit class centroid vector is the largest, then the class of the unknown data point is regarded as k. The proposed principle has successfully been applied in classification of olfactory stimuli from the acquired EEG signals of the subjects and the classification performance is found to be superior with respect to the existing state-of-the art algorithms.

Centroid

Data point

Normalization

Unit vector

10.1109/icccnt45670.2019.8944662

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EEG-induced Probabilistic Prediction of the Color-Pathways in the Brain using Dempster-Shafer Theory

Mousumi Laha Priti Hazra Amit Konar Pratyusha Rakshit

The paper proposes a novel approach to predict the signaling pathways in the brain in response to color stimuli by the following four steps. First, the EEG response of different brain lobes to a (monochromatic) colored light stimulus is recorded, and the acquired EEG signals are filtered using an active band pass filter to eliminate the effect of artifacts. Second, short-term Fourier transform is taken on a moving window placed over the EEG signals to extract the power in each window with an ultimate aim to determine the time-point (centre of one specific window) on the EEG signal having the maximum power. Each selected brain lobe is labeled with a time-point corresponding to the maximum power in the EEG signal acquired from that lobe. Third, a brain signaling pathway of an individual subject in response to a color stimulus is determined from multiple experiments on the same subject with the same stimulus, and a color-pathway graph is developed where nodes and edges respectively indicate brain lobes and transition pathway from one lobe to the next. A probability estimate of each transition (edge) is determined from the results of multiple experiments. Finally, the color-pathway graphs obtained from different individuals for the same color stimuli are fused using Dempster-Shafer theory to have a generic consensus about the signaling pathway in the brain due to retinal response to monochromatic color stimulus. The predicted pathways for a specific color (red/green/blue) are found to be unique and time-invariant across 30 experimental subjects. This method is useful for the diagnosis in abnormal color pathways for people with psychological disorder.

Monochromatic color

Stimulus (psychology)

10.1109/wispnet45539.2019.9032841

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Citations (3)

Emotion Recognition: A Pattern Analysis Approach

Amit Konar Aruna Chakraborty

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Citations (90)

A hierarchical algorithm for fuzzy template matching in emotional facial images

Journal of Intelligent & Fuzzy Systems (2013)

Anisha Halder Rajshree Mandal Amit Konar

The paper aims at developing a hierarchical algorithm for matching a given template of m × n on an image of M × N pixels partitioned into equal sized blocks of m × n pixels. The algorithm employs a fuzzy metric to measure the dispersion of individual

Template matching

Template

10.3233/ifs-2012-0547

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Citations (3)