Article
Computer Science, Information Systems
M. F. Mridha, Abu Quwsar Ohi, Jungpil Shin, Muhammad Mohsin Kabir, Muhammad Mostafa Monowar, Md. Abdul Hamid
Summary: Writer identification is the process of identifying individuals through handwriting, and this study introduces a supervised Indic script writer identification system using a lightweight CNN architecture, which outperforms various deep CNN architectures in the experiment.
Article
Computer Science, Artificial Intelligence
Amar Jindal, Rajib Ghosh
Summary: This article proposes a novel method to segment any textline of ancient handwritten Devanagari and Maithili documents into different words and each word into different characters. The proposed method has achieved high accuracies for both word segmentation and character segmentation in the ancient documents, outperforming the state-of-the-art methods in this regard.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ritam Guha, Manosij Ghosh, Pawan Kumar Singh, Ram Sarkar, Mita Nasipuri
Summary: In this study, a new feature selection algorithm, HSGFS, is introduced to reduce dimensionality and improve accuracy of handwritten script classification. Experimental results demonstrate an average improvement in classification accuracy of 2-5% when using 75-80% of the original feature vectors. The proposed method also outperforms some popular FS models in terms of performance.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Reya Sharma, Baijnath Kaushik
Summary: This research proposes an approach based on adaptive particle swarm optimization to automatically design the architecture of a convolutional neural network for the recognition of handwritten characters and digits in Indic scripts. Experimental results demonstrate that this method outperforms state-of-the-art approaches on three popular Indic scripts.
SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES
(2022)
Article
Business
Sangkil Moon, Nima Jalali, Reo Song
Summary: In the pre-production stage of a movie, movie studios often have limited information about the movie, making it difficult to select and edit scripts that can be commercially successful. To address this issue, researchers propose a method of predicting movie revenues based on the textual scripts. They theorize that market's collective consumption experiences with prior movies that share similar content features can be used to predict the revenue. They hypothesize that the market desires both repeated enjoyment of certain content features and different content features in the new movie. To achieve this, they integrate two components - LIWC (a text-mining tool) and the auto-Gaussian spatial model - into their prediction procedure. Their empirical application shows that their procedure surpasses select benchmark models in predictive accuracy.
JOURNAL OF BUSINESS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Shivangi Nigam, Adarsh Prasad Behera, Manas Gogoi, Shekhar Verma, P. Nagabhushan
Summary: Strike-off text in handwritten documents poses challenges, especially for Indic scripts. Existing deep learning approaches for strike-off removal are mostly focused on Roman scripts. In this study, we address the problem of strike-off removal in Indic scripts by leveraging transfer learning and few-shot learning techniques.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Amar Jindal, Rajib Ghosh
Summary: This article proposes a robust method for textline segmentation in ancient handwritten documents using a faster region-convolution neural network (R-CNN). The proposed system outperforms existing methods in experimental evaluation.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Rajib Ghosh
Summary: This paper introduces a novel method for online handwritten text and non-text stroke classification using artificial Recurrent Neural Network (RNN) with Devanagari script. The system extracts features from basic strokes and outperforms CNN and existing studies in this field.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Ruchika Malhotra, Maru Tesfaye Addis
Summary: This study uses a deep learning approach to recognize historical handwritten Ethiopic texts. The recognition model employs an end-to-end strategy and shows promising results in improving the recognition of historical handwritten Ethiopic text.
Article
Business
Rubing Bai, Baolong Ma, Zhichen Hu, Hong Wang
Summary: This paper investigates the negative effects of products branded with handwritten scripts during product-harm crises. Through five experimental studies, it is found that consumers react more negatively towards brands using handwritten scripts compared to those using machine typefaces. This negative effect is explained by a serial mediation process: typeface → perceived humanization → brand responsibility → brand attitude. The negative effect decreases when the crisis is perceived to be an accident.
JOURNAL OF PRODUCT AND BRAND MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Sofiene Haboubi, Tawfik Guesmi, Badr M. Alshammari, Khalid Alqunun, Ahmed S. Alshammari, Haitham Alsaif, Hamid Amiri
Summary: Handwriting recognition is a challenging task, especially for complex Arabic script. This study proposes an improvement in recognizing Arabic text using bidirectional gated recurrent units (BGRUs), which have faster execution time compared to other methods. The experimental results demonstrate the effectiveness of BGRUs in recognizing handwritten Arabic script.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Information Systems
Iti Sharma, Abhay Sharma, Rekha Chaturvedi, Jitendra Rajpurohit, Manoj Kumar
Summary: Text clustering is an overlooked field in text mining that requires more attention. Conventional methods to accelerate k-means may not apply to spherical k-means, so this study proposes an iterative feature filtering technique to reduce data size during clustering, resulting in more relevant feature clusters in less time.
JOURNAL OF INFORMATION SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Pawan Kumar Singh, Ram Sarkar, Ajith Abraham, Mita Nasipuri
Summary: This study analyzes the performance of handwritten script classification methods for Indian scripts at different granularity levels, comparing results using various feature sets and classifiers to suggest an optimal classification level and improve outcomes. Conducted on different Indic script databases, the research provides a foundation for establishing an official Indian handwritten script classification method.
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Santhoshini Gongidi, C. Jawahar
Summary: The study introduces a large-scale handwritten dataset for Indic scripts, providing new directions for HTR in Indian languages. By combining different datasets, it establishes a high baseline for text recognition and explores the utility of pre-training models.
DOCUMENT ANALYSIS AND RECOGNITION, ICDAR 2021, PT IV
(2021)
Article
Multidisciplinary Sciences
Ishfaq Ali, Atiq Ur Rehman, Dost Muhammad Khan, Zardad Khan, Muhammad Shafiq, Jin-Ghoo Choi
Summary: The importance of unsupervised clustering methods is well established, and in this paper, a novel method for finding the optimum number of clusters using a data-driven approach is proposed. By considering the cluster symmetry property, the performance of the algorithm is evaluated on both simulated and real datasets, showing better accuracy and minimum root mean square error compared to existing methods.
Article
Computer Science, Artificial Intelligence
Bitanu Chatterjee, Trinav Bhattacharyya, Kushal Kanti Ghosh, Agneet Chatterjee, Ram Sarkar
Summary: This article presents a framework for maximizing influence propagation in a social network, which includes community detection and the utilization of the Shuffled Frog Leaping algorithm. Experimental results show that our method performs well compared to other algorithms.
Article
Computer Science, Information Systems
Soham Chattopadhyay, Arijit Dey, Pawan Kumar Singh, Ali Ahmadian, Ram Sarkar
Summary: Speech is crucial in human communication and human-computer interaction. In the field of AI and ML, it has been extensively studied to recognize human emotions from speech signals. To address the challenge of large feature dimension, a hybrid feature selection algorithm called CEOAS is proposed. By extracting LPC and LPCC features, the proposed model reduces feature dimension and improves classification accuracy. Impressive recognition accuracies have been achieved on four benchmark datasets, surpassing state-of-the-art algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ashis Paul, Arpan Basu, Mufti Mahmud, M. Shamim Kaiser, Ram Sarkar
Summary: This article discusses the use of deep learning models and an inverted bell-curve weighted ensemble method to assist in the detection of COVID-19 in CXR images. By using transfer learning and retraining models pretrained on the ImageNet dataset, as well as performing weighted average predictions, the accuracy of COVID-19 identification in CXR images can be improved.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Samir Malakar, Samanway Sahoo, Anuran Chakraborty, Ram Sarkar, Mita Nasipuri
Summary: Handwritten word recognition is an open research problem due to variations in writing style and degraded images. This paper proposes a holistic approach combined with distance calculation and feature descriptors to address the problem. The experimental results demonstrate the effectiveness of the proposed method on standard databases compared to deep learning models.
Article
Computer Science, Artificial Intelligence
Souradeep Mukhopadhyay, Sabbir Hossain, Samir Malakar, Erik Cuevas, Ram Sarkar
Summary: This paper introduces a new gray-scale contrast enhancement algorithm, which improves image quality by calculating near-optimal values using the Artificial Electric Field Algorithm (AEFA). Through comparisons with other techniques using standard metrics, simulation results show that the proposed method increases image contrast and enriches image information.
Article
Computer Science, Information Systems
Anubhab Das, Arka Choudhuri, Arpan Basu, Ram Sarkar
Summary: This study proposes a GAN-based method for generating handwritten Bengali compound characters to address data scarcity. The model's performance is evaluated by assessing the quality of generated samples, showing that it outperforms basic AC-GAN architecture and some other existing GAN architectures.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Apu Sarkar, S. K. Sabbir Hossain, Ram Sarkar
Summary: This paper proposes a method for human activity recognition (HAR) from wearable sensor data. It utilizes Continuous Wavelet Transform and a Spatial Attention-aided Convolutional Neural Network (CNN) to extract features, and employs feature selection and a modified version of Genetic Algorithm (GA) for activity recognition. Experimental results show that the proposed method outperforms existing models in terms of classification performance and improves overall recognition accuracy by reducing the number of features.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Rishav Pramanik, Payel Pramanik, Ram Sarkar
Summary: Breast cancer is a leading cause of premature death among women globally, but early detection and diagnosis can save lives. Hence, computer scientists are working to develop reliable models to tackle this disease. A proposed lightweight model combines transfer learning-based deep learning (DL) with feature selection to detect abnormalities in breast thermograms. This model performs well in detecting and differentiating malignant and healthy breasts.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Sagnik Ganguly, Sanmit Mandal, Samir Malakar, Ram Sarkar
Summary: This paper introduces a new copy-move image forgery detection technique which relies on a texture feature descriptor called Local Tetra Pattern (LTrP) for block level image comparison used to localize tampered region(s). Experimental results demonstrate that the proposed technique has been able to detect the forged regions with higher accuracy as compared to many state-of-the-art copy-move forgery detection methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Payel Pramanik, Souradeep Mukhopadhyay, Seyedali Mirjalili, Ram Sarkar
Summary: Breast cancer is a common malignancy in women, and early detection is crucial. In this research, a method for classifying breast masses using mammograms is proposed. Deep features are extracted using the VGG16 model with an attention mechanism, and an optimal features subset is obtained using a meta-heuristic algorithm. The proposed model shows successful identification and differentiation of malignant and healthy breasts.
NEURAL COMPUTING & APPLICATIONS
(2023)
Correction
Computer Science, Artificial Intelligence
Apu Sarkar, S. K. Sabbir Hossain, Ram Sarkar
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Sk Mohiuddin, Khalid Hassan Sheikh, Samir Malakar, Juan D. Velasquez, Ram Sarkar
Summary: Digital face manipulation has become a significant concern recently due to its harmful effects on society, particularly for high-profile celebrities who can easily be targeted using apps like FaceSwap and FaceApp. Detecting deepfake images or videos is challenging, and existing models often fail to check for irrelevant or redundant features. In this study, a hierarchical feature selection (HFS) method using a hybrid population-based meta-heuristic model and a single solution-based meta-heuristic model was proposed. The model achieved high AUC scores on three publicly available datasets and outperformed most state-of-the-art methods.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Medicine, General & Internal
Arnab Bagchi, Payel Pramanik, Ram Sarkar
Summary: Breast cancer is a deadly disease that affects women worldwide. Early diagnosis and proper treatment can save lives. Breast image analysis, including histopathological image analysis, and computer-aided diagnosis, can help improve efficiency and accuracy in breast cancer detection. In this study, a deep learning-based method was developed to classify breast cancer using histopathological images, achieving high classification accuracy.
Article
Computer Science, Information Systems
Avirup Bhattacharyya, Avigyan Bhattacharya, Sourajit Maity, Pawan Kumar Singh, Ram Sarkar
Summary: Designing an automatic vehicle detection system that caters to the requirements of the traffic management system is important. This research develops a still image database, JUVDsi v1, for designing an automated traffic management system in India. The database addresses the shortcomings of existing databases and is evaluated using state-of-the-art deep learning architectures.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Ritam Guha, Kushal Kanti Ghosh, Suman Kumar Bera, Ram Sarkar, Seyedali Mirjalili
Summary: This paper proposes a binary adaptation of Equilibrium Optimizer (EO) called Discrete EO (DEO) for solving binary optimization problems. DEOSA algorithm, combining DEO with Simulated Annealing (SA) as a local search procedure, is applied to various datasets and outperforms other algorithms. The scalability and robustness of DEOSA are also tested on high-dimensional Microarray datasets and Knapsack problems, showing its superiority.
JOURNAL OF COMPUTATIONAL SCIENCE
(2023)