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17,230 results on '"Ghosh, Ashish"'

2. Autoencoder and Extreme Learning Machine Based Deep Multi-label Classifier

Author

Law, Anwesha, Ray, Ratula, Ghosh, Ashish, Hartmanis, Juris, Founding Editor, Goos, Gerhard, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ghosh, Ashish, editor, King, Irwin, editor, Bhattacharyya, Malay, editor, Sankar Ray, Shubhra, editor, and K. Pal, Sankar, editor

Published
2024
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4. Autoencoder based Hybrid Multi-Task Predictor Network for Daily Open-High-Low-Close Prices Prediction of Indian Stocks

Author

Chakraborty, Debasrita, Ghosh, Susmita, and Ghosh, Ashish

Subjects
Computer Science - Machine Learning
Abstract

Stock prices are highly volatile and sudden changes in trends are often very problematic for traditional forecasting models to handle. The standard Long Short Term Memory (LSTM) networks are regarded as the state-of-the-art models for such predictions. But, these models fail to handle sudden and drastic changes in the price trend. Moreover, there are some inherent constraints with the open, high, low and close (OHLC) prices of the stocks. Literature lacks the study on the inherent property of OHLC prices. We argue that predicting the OHLC prices for the next day is much more informative than predicting the trends of the stocks as the trend is mostly calculated using these OHLC prices only. The problem mainly is focused on Buy-Today Sell-Tomorrow (BTST) trading. In this regard, AEs when pre-trained with the stock prices, may be beneficial. A novel framework is proposed where a pre-trained encoder is cascaded in front of the multi-task predictor network. This hybrid network can leverage the power of a combination of networks and can both handle the OHLC constraints as well as capture any sudden drastic changes in the prices. It is seen that such a network is much more efficient at predicting stock prices. The experiments have been extended to recommend the most profitable and most overbought stocks on the next day. The model has been tested for multiple Indian companies and it is found that the recommendations from the proposed model have not resulted in a single loss for a test period of 300 days., Comment: 29 pages

Published
2022

5. Improving the Robustness of Federated Learning for Severely Imbalanced Datasets

Author

Chakraborty, Debasrita and Ghosh, Ashish

Subjects
Computer Science - Machine Learning
Abstract

With the ever increasing data deluge and the success of deep neural networks, the research of distributed deep learning has become pronounced. Two common approaches to achieve this distributed learning is synchronous and asynchronous weight update. In this manuscript, we have explored very simplistic synchronous weight update mechanisms. It has been seen that with an increasing number of worker nodes, the performance degrades drastically. This effect has been studied in the context of extreme imbalanced classification (e.g. outlier detection). In practical cases, the assumed conditions of i.i.d. may not be fulfilled. There may also arise global class imbalance situations like that of outlier detection where the local servers receive severely imbalanced data and may not get any samples from the minority class. In that case, the DNNs in the local servers will get completely biased towards the majority class that they receive. This would highly impact the learning at the parameter server (which practically does not see any data). It has been observed that in a parallel setting if one uses the existing federated weight update mechanisms at the parameter server, the performance degrades drastically with the increasing number of worker nodes. This is mainly because, with the increasing number of nodes, there is a high chance that one worker node gets a very small portion of the data, either not enough to train the model without overfitting or having a highly imbalanced class distribution. The chapter, hence, proposes a workaround to this problem by introducing the concept of adaptive cost-sensitive momentum averaging. It is seen that for the proposed system, there was no to minimal degradation in performance while most of the other methods hit their bottom performance before that., Comment: 23 pages

Published
2022

6. Change Detection in Hyperspectral Images Using Deep Feature Extraction and Active Learning

Author

Chakraborty, Debasrita, Ghosh, Susmita, Ghosh, Ashish, Ientilucci, Emmett J., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tanveer, Mohammad, editor, Agarwal, Sonali, editor, Ozawa, Seiichi, editor, Ekbal, Asif, editor, and Jatowt, Adam, editor

Published
2023
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8. Improved Multi-label Classification with Frequent Label-set Mining and Association

Author

Law, Anwesha and Ghosh, Ashish

Subjects
Computer Science - Machine Learning
Abstract

Multi-label (ML) data deals with multiple classes associated with individual samples at the same time. This leads to the co-occurrence of several classes repeatedly, which indicates some existing correlation among them. In this article, the correlation among classes has been explored to improve the classification performance of existing ML classifiers. A novel approach of frequent label-set mining has been proposed to extract these correlated classes from the label-sets of the data. Both co-presence (CP) and co-absence (CA) of classes have been taken into consideration. The rules mined from the ML data has been further used to incorporate class correlation information into existing ML classifiers. The soft scores generated by an ML classifier are modified through a novel approach using the CP-CA rules. A concept of certain and uncertain scores has been defined here, where the proposed method aims to improve the uncertain scores with the help of the certain scores and their corresponding CP-CA rules. This has been experimentally analysed on ten ML datasets for three ML existing classifiers which shows substantial improvement in their overall performance.

Published
2021

9. Unsupervised Change Detection in Hyperspectral Images using Feature Fusion Deep Convolutional Autoencoders

Author

Chakraborty, Debasrita and Ghosh, Ashish

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Binary change detection in bi-temporal co-registered hyperspectral images is a challenging task due to a large number of spectral bands present in the data. Researchers, therefore, try to handle it by reducing dimensions. The proposed work aims to build a novel feature extraction system using a feature fusion deep convolutional autoencoder for detecting changes between a pair of such bi-temporal co-registered hyperspectral images. The feature fusion considers features across successive levels and multiple receptive fields and therefore adds a competitive edge over the existing feature extraction methods. The change detection technique described is completely unsupervised and is much more elegant than other supervised or semi-supervised methods which require some amount of label information. Different methods have been applied to the extracted features to find the changes in the two images and it is found that the proposed method clearly outperformed the state of the art methods in unsupervised change detection for all the datasets., Comment: 19 pages

Published
2021

21. Registration of CT and MR image in multi-resolution framework using embedded entropy and feature fusion

Author

Samant, Sunita, Nanda, Pradipta Kumar, Ghosh, Ashish, Sahoo, Subhaluxmi, and Panda, Adya Kinkar

Abstract

In this paper, a new scheme for the registration of brain CT and noisy MR images is proposed in a multi-resolution framework based on the notions of embedded entropy and nonlinear combination of the mutual information (MI) corresponding to Renyi's and Tsallis entropy. Gabor and Sobel's features are fused probabilistically and the registration is carried out in fused feature space. The weights for the fusion of the two distributions are obtained using the Bhattacharyya distance as the similarity measure. Registration parameter is obtained at different resolutions by maximising the combined mutual information obtained at different resolutions. The proposed algorithm is tested with the real patient data obtained from Retrospective Image Registration Evaluation (RIRE) database. It is found that the optimum registration parameter obtained at a low resolution of (64 × 64) has high accuracy. The proposed scheme exhibits improved performance as compared to other existing algorithms.

Published
2024
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33. Spontaneous Combustion in Relation to Drying of Low Rank Coal.

Author

Ghosh, Ashish Kumar and Choudhury, Debapriya

Subjects
*SPONTANEOUS combustion, *LIGNITE, *COAL, *COAL combustion, *IGNITION temperature, *CARBOXYL group, *FUNCTIONAL groups
Abstract

The water molecules associated with coal play a significant role in spontaneous ignition and combustion of coal. This paper deals with studies to evaluate the impact of moisture content on crossing point temperature (CPT) and ignition point temperature (IPT) of some low-rank coal and lignite after various stages of drying. Experimental data suggest (i) existence of critical moisture content value determining the propensity of a particular coal toward self-heating and (ii) content of moisture and nature of oxygen-bearing functional groups to be the most influencing factors in self-heating. The auto-oxidation cum self-heating characteristics of coal become more conducive with increase in the ratio of quantity of hydroxyl to carboxyl functional groups. The FTIR studies further corroborate that the initial stages of auto-oxidation cum self-heating occur presumably at the surface of the coal. [ABSTRACT FROM AUTHOR]

Published
2022
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36. Semantic-Degrade Learning Framework for Open World Object Detection

Author

He, Siqi, Yu, Cancan, Li, Hainan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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37. MADP: Multi-modal Sequence Learning for Alzheimer’s Disease Prediction with Missing Data

Author

Wang, Yudie, Wang, Zirui, Gong, Huiyun, Wang, Sanwang, Li, Mingzhe, Dong, Jian, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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39. Multi-modal Spatiotemporal Forecasting via Cross-Scale Operator Learning and Spatial Representation Aggregation

Author

Gao, Yajun, Ma, Tianrui, Xu, Chujie, Wang, Miao, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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40. Towards Point Cloud Compression for Machine Perception: A Simple and Strong Baseline by Learning the Octree Depth Level Predictor

Author

Liu, Lei, Hu, Zhihao, Chen, Zhenghao, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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41. Robust Autonomous Unmanned Aerial Vehicle System for Efficient Tracking of Moving Objects

Author

Dong, Zhiwei, Yong, Yang, Wang, Zining, Chen, Song-Lu, Yin, Xu-Cheng, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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42. Integrating Text-to-Image and Vision Language Models for Synergistic Dataset Generation: The Creation of Synergy-General-Multimodal Pairs

Author

Huang, Mao Xun, Huang, Hen-Hsen, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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43. Adapter-Based Contextualized Meta Embeddings

Author

O’Neill, James, Dutta, Sourav, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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44. Bridging the Language Gap: Domain-Specific Dataset Construction for Medical LLMs

Author

Kim, Chae Yeon, Kim, Song Yeon, Cho, Seung Hwan, Kim, Young-Min, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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46. How to Assess Job Satisfaction and Self-efficacy in Teachers’ Professional Training with 'Best' Questionnaire: A Perspective Article

Author

Lavanga, Andreana, Baldini, Roberta, Guarini, Piergiorgio, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Casalino, Gabriella, editor, Di Fuccio, Raffaele, editor, Fulantelli, Giovanni, editor, Raviolo, Paolo, editor, Rivoltella, Pier Cesare, editor, Taibi, Davide, editor, and Toto, Giusi Antonia, editor

Published
2024
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47. Towards Efficient Fault Detection of Ultra-High Voltage Direct Current Circuit Breakers

Author

Wang, Jiayi, Peng, Dong, Chen, Shaoqing, Zhou, Dianbo, Long, Zhenze, Cheng, Botao, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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48. Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap

Author

Amalanshu, Avi, Nagaswamy, Viswesh, Prudhvi, G. V. S. S., Sirvi, Yash, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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49. Evaluating Perspective Teachers’ Reaction to Student-Driven Cognitive Activation with Kahoot!

Author

Tinterri, Andrea, di Padova, Marilena, Dicataldo, Maria Clara, Dipace, Anna, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Casalino, Gabriella, editor, Di Fuccio, Raffaele, editor, Fulantelli, Giovanni, editor, Raviolo, Paolo, editor, Rivoltella, Pier Cesare, editor, Taibi, Davide, editor, and Toto, Giusi Antonia, editor

Published
2024
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50. Toward Efficient Deep Spiking Neuron Networks: A Survey on Compression

Author

Xie, Hui, Yang, Ge, Gao, Wenjuan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Guo, Jinyang, editor, Ma, Yuqing, editor, Ding, Yifu, editor, Gong, Ruihao, editor, Zheng, Xingyu, editor, He, Changyi, editor, Lu, Yantao, editor, and Liu, Xianglong, editor

Published
2024
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