JUHCCR-v1: a database for hand-drawn electrical and electronics circuit component recognition Ayush Roy, Saptarshi Pani, Samir Malakar, Erik Cuevas, Marco Pérez-Cisneros, Ram Sarkar Scientific Reports, 2025 Automatic electrical and electronics component/symbol recognition from hand-drawn circuits is a challenging research problem. However, the literature survey reveals that there has been no significant progress in this domain. One possible reason for this might be a lack of publicly available datasets. To this end, in this work, we have developed a dataset, called JUHCCR-v1, which comprises 20 different hand-drawn circuit components that are commonly found in electrical and electronic circuits. Additionally, we have prepared a synthetic dataset having different variations (like orientations, stroke lengths, and distortions) of collected circuit components that may occur while extracting the components from an entire hand-drawn circuit diagram. This augmented dataset with the original ones would help train the deep learning based circuit component recognition algorithms. In order to provide a base result on this dataset, we have designed a weighted ensemble-based hand-drawn circuit component recognition method applied to snapshots of the convolutional block attention module-aided DenseNet-121 architecture. This benchmarking method achieves an accuracy of 91.15% on test set images. All datasets prepared here, along with codes, are made publicly available for the research community at: https://github.com/AyushRoy2001/Circuit-Component-Analysis .
Compact representation for memory-efficient storage of images using genetic algorithm-guided key pixel selection Samir Malakar, Nirwan Banerjee, Dilip K. Prasad Engineering Applications of Artificial Intelligence, 2025 In the past few years, we have observed rapid growth in digital content. Even in the biological domain, the arrival of microscopic and nanoscopic images and videos captured for biological investigations increases the need for space to store them. Hence, storing these data in a storage-efficient manner is a pressing need. In this work, we have introduced a compact image representation technique with an eye on preserving the shape that can shrink the memory requirement to store. The compact image representation is different from image compression since it does not include any encoding mechanism. Rather, the idea is that this mechanism stores the positions of key pixels, and when required, the original image can be regenerated. The genetic algorithm is used to select key pixels, while the Gaussian kernel performs the reconstruction task with the help of the positions of the selected key pixels. The model is tested on four different datasets. The proposed technique shrinks the memory requirement by 87% to 98% while evaluated using the bit reduction rate. However, the reconstructed images’ quality is a bit low when evaluated using metrics like structural similarity index (ranges between 0.81 to 0.94), or root means squared error (ranges between 0.06 to 0.08). To investigate the impact of quality reduction in reconstructed images in real-life applications, we performed image classification using reconstructed samples and found 0.13% to 2.30% classification accuracy reduction compared to when classification is done using original samples. The proposed model’s performance is comparable to state-of-the-art’s similar solutions.
GUNet++: guided-U-Net-based compact image representation with an improved reconstruction mechanism Nirwan Banerjee, Samir Malakar, Alexander Horsch, Dilip K. Prasad Journal of the Optical Society of America A Optics and Image Science and Vision, 2024 The invention of microscopy- and nanoscopy-based imaging technology opened up different research directions in life science. However, these technologies create the need for larger storage space, which has negative impacts on the environment. This scenario creates the need for storing such images in a memory-efficient way. Compact image representation (CIR) can solve the issue as it targets storing images in a memory-efficient way. Thus, in this work, we have designed a deep-learning-based CIR technique that selects key pixels using the guided U-Net (GU-Net) architecture [Asian Conference on Pattern Recognition, p. 317 (2023)], and then near-original images are constructed using a conditional generative adversarial network (GAN)-based architecture. The technique was evaluated on two microscopy- and two scanner-captured-image datasets and obtained good performance in terms of storage requirements and quality of the reconstructed images.
Deepfake detection using deep feature stacking and meta-learning Gourab Naskar, Sk Mohiuddin, Samir Malakar, Erik Cuevas, Ram Sarkar Heliyon, 2024 Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This paper aims to address the problem of deepfake detection by identifying deepfake forgeries in video sequences. In this paper, a solution to the said problem is presented, which at first uses a stacking based ensemble approach, where features obtained from two popular deep learning models, namely Xception and EfficientNet-B7, are combined. Then by selecting a near-optimal subset of features using a ranking based approach, the final classification is performed to classify real and fake videos using a meta-learner, called multi-layer perceptron. In our experimentation, we have achieved an accuracy of 96.33% on Celeb-DF (V2) dataset and 98.00% on the FaceForensics++ dataset using the meta-learning model both of which are higher than the individual base models. Various types of experiments have been conducted to validate the robustness of the current method.
Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study Samir Malakar, Swaraj Sen, Sergei Romanov, Dmitrii Kaplun, Ram Sarkar Journal of King Saud University Computer and Information Sciences, 2023 Particle Swarm Optimization (PSO) is a classic and popularly used meta-heuristic algorithm in many real-life optimization problems due to its less computational complexity and simplicity. The binary version of PSO, known as BPSO, is used to solve binary optimization problems, such as feature selection. Like other meta-heuristic optimization techniques designed on the continuous search space, PSO uses the transfer functions (TFs) to map the candidate solutions to the discrete search space in BPSO, and these TFs play a vital role to get the desired results. Over the years, many forms of TFs have been introduced in the literature, most of which fall under one of the five families - Linear, S-shaped, V-shaped, U-shaped, and Time-varying Mirrored S-shaped TFs. The goal of this study is to determine an appropriate setup constituting a TF and a classifier for feature selection from different types of clinical data. In this study, the impacts of the five TF families have been investigated, considering one from each family for the selection of attributes/features, while predicting disease using diagnosis or medical reports. The classification tasks are carried out using four standard classifiers: Support Vector Machine, Decision Tree, K-Nearest Neighbors, and Gaussian Naive Bayes. For experimental purposes, we have used four publicly available datasets namely, the UCI Heart disease dataset, Wisconsin Breast Cancer dataset, UCI Chronic Kidney Disease dataset, and PIMA Indians Diabetes dataset. After an exhaustive set of experiments, we have obtained 96.72%, 99.82%, 100.00%, and 84.41% disease prediction scores in the best case for Heart disease, Breast cancer, Chronic Kidney disease, and Diabetes, respectively. The obtained results are comparable to several state-of-the-art methods considered here for comparison. The present study helps in selecting a suitable BPSO setup (i.e., a TF and a classifier) to select important diagnostic attributes useful to design a computer-aided decision support system for the said diseases.
Guided U-Net Aided Efficient Image Data Storing with Shape Preservation Nirwan Banerjee, Samir Malakar, Deepak Kumar Gupta, Alexander Horsch, Dilip K. Prasad Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
Handwritten Bangla word recognition using HOG descriptor Showmik Bhowmik, Md. Galib Roushan, Ram Sarkar, Mita Nasipuri, Sanjib Polley, Samir Malakar Proceedings 4th International Conference on Emerging Applications of Information Technology Eait 2014, 2014
An improved offline handwritten character segmentation algorithm for Bangla script Proceedings of the 5th Indian International Conference on Artificial Intelligence Iicai 2011, 2011
JUHCCR-v1: a database for hand-drawn electrical and electronics circuit component recognition A Roy, S Pani, S Malakar, E Cuevas, M Pérez-Cisneros, R Sarkar Scientific Reports 15 (1), 38617 , 2025 2025
A feature selection-aided deep learning based deepfake video detection method S Mohiuddin, A Roy, S Pani, S Malakar, R Sarkar Multimedia Tools and Applications 84 (35), 43499-43522 , 2025 2025 Citations: 1
DNA Fragmentation Estimation Using Light-Weight Deep Learning Model S Rai, S Malakar, DK Prasad International Conference on Pattern Recognition, 335-349 , 2025 2025
Compact representation for memory-efficient storage of images using genetic algorithm-guided key pixel selection S Malakar, N Banerjee, DK Prasad Engineering Applications of Artificial Intelligence 139, 109540 , 2025 2025 Citations: 4
Enhancing Deep Learning based RMT Data Inversion using Gaussian Random Field K Ghosal, A Singh, S Malakar, S Srivastava, D Gupta arXiv preprint arXiv:2410.19858 , 2024 2024
Deepfake Detection using Deep Feature Stacking and Meta-learning G Naskar, S Mohiuddin, S Malakar, E Cuevas, R Sarkar Heliyon 10 (4), e25933 , 2024 2024 Citations: 68
OMRNet: A lightweight deep learning model for optical mark recognition S Mondal, P De, S Malakar, R Sarkar Multimedia Tools and Applications 83 (5), 14011-14045 , 2024 2024 Citations: 13
Compact Image Representation by Detecting Distortion in Reflected Acoustic Waves N Banerjee, S Malakar, A Horsch, DK Prasad Nordic AI Meet 2024 , 2024 2024
GUNet++: Guided U-Net based Compact Image Representation with Improved Reconstruction Mechanism N Banerjee, S Malakar, A Horsch, D Prasad Journal of the Optical Society of America A, 1-8 , 2024 2024 Citations: 1
Deep Learning based Joint Inversion of Electrical Resistivity Tomography and Radio Magnetotelluric Data K Ghosal, A Singh, S Malakar, D Gupta AGU23 , 2023 2023
Guided U-Net Aided Efficient Image Data Storing with Shape Preservation N Banerjee, S Malakar, DK Gupta, A Horsch, DK Prasad Asian Conference on Pattern Recognition, 317-330 , 2023 2023 Citations: 3
Role of transfer functions in PSO to select diagnostic attributes for chronic disease prediction: An experimental study S Malakar, S Sen, S Romanov, D Kaplun, R Sarkar Journal of King Saud University-Computer and Information Sciences 35 (9), 101757 , 2023 2023 Citations: 9
Image contrast improvement through a metaheuristic scheme S Mukhopadhyay, S Hossain, S Malakar, E Cuevas, R Sarkar Soft Computing 27 (18), 13657-13676 , 2023 2023 Citations: 34
Handwritten Arabic and Roman word recognition using holistic approach S Malakar, S Sahoo, A Chakraborty, R Sarkar, M Nasipuri The Visual Computer 39 (7), 2909-2932 , 2023 2023 Citations: 19
A modified GNN architecture with enhanced aggregator and Message Passing Functions D Sarkar, S Roy, S Malakar, R Sarkar Engineering Applications of Artificial Intelligence 122, 106077 , 2023 2023 Citations: 18
A hierarchical feature selection strategy for deepfake video detection S Mohiuddin, KH Sheikh, S Malakar, JD Velásquez, R Sarkar Neural Computing and Applications 35 (13), 9363-9380 , 2023 2023 Citations: 28
A comprehensive survey on state-of-the-art video forgery detection techniques S Mohiuddin, S Malakar, M Kumar, R Sarkar Multimedia Tools and Applications 82, 33499–33539 , 2023 2023 Citations: 30
An ensemble approach to detect copy-move forgery in videos S Mohiuddin, S Malakar, R Sarkar Multimedia Tools and Applications 82, 24269–24288 , 2023 2023 Citations: 10
Copy-move forgery detection using local tetra pattern based texture descriptor S Ganguly, S Mandal, S Malakar, R Sarkar Multimedia Tools and Applications 82, 19621–19642 , 2023 2023 Citations: 17
Offline signature verification system: a graph neural network based approach S Roy, D Sarkar, S Malakar, R Sarkar Journal of Ambient Intelligence and Humanized Computing 14, 8219–8229 , 2023 2023 Citations: 30
MOST CITED SCHOLAR PUBLICATIONS
A GA based hierarchical feature selection approach for handwritten word recognition S Malakar, M Ghosh, S Bhowmik, R Sarkar, M Nasipuri Neural Computing and Applications 32 (7), 2533-2552 , 2020 2020 Citations: 205
ViXNet: Vision Transformer with Xception Network for deepfakes based video and image forgery detection S Ganguly, A Ganguly, S Mohiuddin, S Malakar, R Sarkar Expert Systems with Applications 210, 118423 , 2022 2022 Citations: 98
Screening of breast cancer from thermogram images by edge detection aided deep transfer learning model S Dey, R Roychoudhury, S Malakar, R Sarkar Multimedia Tools and Applications 81 (7), 9331-9349 , 2022 2022 Citations: 79
Handwritten English word recognition using a deep learning based object detection architecture R Mondal, S Malakar, EH Barney Smith, R Sarkar Multimedia Tools and Applications 81 (1), 975–1000 , 2022 2022 Citations: 69
Deepfake Detection using Deep Feature Stacking and Meta-learning G Naskar, S Mohiuddin, S Malakar, E Cuevas, R Sarkar Heliyon 10 (4), e25933 , 2024 2024 Citations: 68
Off-line Bangla handwritten word recognition: a holistic approach S Bhowmik, S Malakar, R Sarkar, S Basu, M Kundu, M Nasipuri Neural Computing and Applications 31 (10), 5783-5798 , 2019 2019 Citations: 67
An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from Chest X-ray images S Dey, R Roychoudhury, S Malakar, R Sarkar Applied Soft Computing 114, 108094 , 2022 2022 Citations: 61
Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection S Dey, R Bhattacharya, S Malakar, S Mirjalili, R Sarkar Computers in Biology and Medicine 135, 104585 , 2021 2021 Citations: 61
An ensemble of deep transfer learning models for handwritten music symbol recognition A Paul, R Pramanik, S Malakar, R Sarkar Neural Computing and Applications 34 (13), 10409-10427 , 2022 2022 Citations: 55
A new wrapper feature selection method for language-invariant offline signature verification D Banerjee, B Chatterjee, P Bhowal, T Bhattacharyya, S Malakar, ... Expert Systems with Applications 186, 115756 , 2021 2021 Citations: 53
Visual attention-based deepfake video forgery detection S Ganguly, S Mohiuddin, S Malakar, E Cuevas, R Sarkar Pattern Analysis and Applications 25 (4), 981-992 , 2022 2022 Citations: 51
A two-stage CNN-based hand-drawn electrical and electronic circuit component recognition system M Dey, SM Mia, N Sarkar, A Bhattacharya, S Roy, S Malakar, R Sarkar Neural Computing and Applications 33 (20), 13367-13390 , 2021 2021 Citations: 49
Text line extraction from handwritten document pages using spiral run length smearing algorithm S Malakar, S Halder, R Sarkar, N Das, S Basu, M Nasipuri 2012 international conference on communications, devices and intelligent … , 2012 2012 Citations: 44
TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images R Pramanik, S Dey, S Malakar, S Mirjalili, R Sarkar Scientific Reports 12 (1), 15409 , 2022 2022 Citations: 41
Feature selection for handwritten word recognition using memetic algorithm M Ghosh, S Malakar, S Bhowmik, R Sarkar, M Nasipuri Advances in intelligent computing, 103-124 , 2018 2018 Citations: 40
A two-tier ensemble approach for writer dependent online signature verification P Bhowal, D Banerjee, S Malakar, R Sarkar Journal of Ambient Intelligence and Humanized Computing 13, 21-40 , 2022 2022 Citations: 39
CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays S Dey, R Bhattacharya, S Malakar, F Schwenker, R Sarkar Expert Systems with Applications 206, 117812 , 2022 2022 Citations: 37
Handwritten bangla word recognition using hog descriptor S Bhowmik, MG Roushan, R Sarkar, M Nasipuri, S Polley, S Malakar 2014 Fourth International Conference of Emerging Applications of Information … , 2014 2014 Citations: 37
A holistic approach for handwritten hindi word recognition S Malakar, P Sharma, PK Singh, M Das, R Sarkar, M Nasipuri International Journal of Computer Vision and Image Processing (IJCVIP) 7 (1 … , 2017 2017 Citations: 35
Image contrast improvement through a metaheuristic scheme S Mukhopadhyay, S Hossain, S Malakar, E Cuevas, R Sarkar Soft Computing 27 (18), 13657-13676 , 2023 2023 Citations: 34
