Searcharxiv⌕ Search

arXiv subjects

Partha Pratim Roy

Publications and source records attributed to Partha Pratim Roy.

At least 37 records · Page 2Linked to original sources
arXiv2024

Awake at the Wheel: Enhancing Automotive Safety through EEG-Based Fatigue Detection

Driver fatigue detection is increasingly recognized as critical for enhancing road safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net reached an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification performance. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed approach for practical applications. The implications of this research are profound, extending beyond academic realms to inform the development of more sophisticated driver assistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the road, thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential real-world applications of the findings, aiming to contribute significantly to advancements in automotive safety.

cs.HC↗
arXiv2024

DrowzEE-G-Mamba: Leveraging EEG and State Space Models for Driver Drowsiness Detection

Driver drowsiness is identified as a critical factor in road accidents, necessitating robust detection systems to enhance road safety. This study proposes a driver drowsiness detection system, DrowzEE-G-Mamba, that combines Electroencephalography (EEG) with State Space Models (SSMs). EEG data, known for its sensitivity to alertness, is used to model driver state transitions between alert and drowsy. Compared to traditional methods, DrowzEE-G-Mamba achieves significantly improved detection rates and reduced false positives. Notably, it achieves a peak accuracy of 83.24% on the SEED-VIG dataset, surpassing existing techniques. The system maintains high accuracy across varying complexities, making it suitable for real-time applications with limited resources. This robustness is attributed to the combination of channel-split, channel-concatenation, and channel-shuffle operations within the architecture, optimizing information flow from EEG data. Additionally, the integration of convolutional layers and SSMs facilitates comprehensive analysis, capturing both local features and long-range dependencies in the EEG signals. These findings suggest the potential of DrowzEE-G-Mamba for enhancing road safety through accurate drowsiness detection. It also paves the way for developing powerful SSM-based AI algorithms in Brain-Computer Interface applications.

cs.HC↗
arXiv2024

Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions and Skeletal Information

Word-level sign language recognition (WSLR) has attracted attention because it is expected to overcome the communication barrier between people with speech impairment and those who can hear. In the WSLR problem, a method designed for action recognition has achieved the state-of-the-art accuracy. Indeed, it sounds reasonable for an action recognition method to perform well on WSLR because sign language is regarded as an action. However, a careful evaluation of the tasks reveals that the tasks of action recognition and WSLR are inherently different. Hence, in this paper, we propose a novel WSLR method that takes into account information specifically useful for the WSLR problem. We realize it as a multi-stream neural network (MSNN), which consist of three streams: 1) base stream, 2) local image stream, and 3) skeleton stream. Each stream is designed to handle different types of information. The base stream deals with quick and detailed movements of the hands and body, the local image stream focuses on handshapes and facial expressions, and the skeleton stream captures the relative positions of the body and both hands. This approach allows us to combine various types of data for more comprehensive gesture analysis. Experimental results on the WLASL and MS-ASL datasets show the effectiveness of the proposed method; it achieved an improvement of approximately 10\%--15\% in Top-1 accuracy when compared with conventional methods.

cs.CV↗
arXiv2024

Sharp Bohr radius involving Schwarz functions for certain classes of analytic functions

The Bohr radius for an arbitrary class $\mathcal{F}$ of analytic functions of the form $f(z)=\sum_{n=0}^{\infty}a_nz^n$ on the unit disk $\mathbb{D}=\{z\in\mathbb{C} : |z|<1\}$ is the largest radius $R_{\mathcal{F}}$ such that every function $f\in\mathcal{F}$ satisfies the inequality \begin{align*} d\left(\sum_{n=0}^{\infty}|a_nz^n|, |f(0)|\right)=\sum_{n=1}^{\infty}|a_nz^n|\leq d(f(0), \partial f(\mathbb{D})), \end{align*} for all $|z|=r\leq R_{\mathcal{F}}$ , where $d(0, \partial f(\mathbb{D}))$ is the Euclidean distance. In this paper, our aim is to determine the sharp improved Bohr radius for the classes of analytic functions $f$ satisfying differential subordination relation $zf^{\prime}(z)/f(z)\prec h(z)$ and $f(z)+βzf^{\prime}(z)+γz^2f^{\prime\prime}(z)\prec h(z)$, where $h$ is the Janowski function. We show that improved Bohr radius can be obtained for Janowski functions as root of an equation involving Bessel function of first kind. Analogues results are obtained in this paper for $α$-convex functions and typically real functions, respectively. All obtained results in the paper are sharp and are improved version of [{Bull. Malays. Math. Sci. Soc.} (2021) 44:1771-1785].

math.CV↗
arXiv2024

Bohr radius for invariant families of bounded analytic functions and certain Integral transforms

In this paper, we first obtain a refined Bohr radius for invariant families of bounded analytic functions on unit disk $ \mathbb{D} $. Then, we obtain Bohr inequality for certain integral transforms, namely Fourier (discrete) and Laplace (discrete) transforms of bounded analytic functions $ f(z)=\sum_{n=0}^{\infty}a_nz^n $, in a simply connected domain \begin{align*} Ω_γ=\biggl\{z\in\mathbb{C}: \bigg|z+\dfracγ{1-γ}\bigg|<\dfrac{1}{1-γ}\;\mbox{for}\; 0\leq γ<1\biggr\}, \end{align*} where $ Ω_0=\mathbb{D} $. These results generalize some existing results. We also show that a better estimate can be obtained in radius and inequality can be shown sharp for Laplace transform of $ f $.

math.CV↗
arXiv2024

The Bohr inequality on a simply connected domain and its applications

In this article, we first establish a generalized Bohr inequality and examine its sharpness for a class of analytic functions $f$ in a simply connected domain $Ω_γ,$ where $0\leq γ<1$ with a sequence $\{φ_n(r) \}^{\infty}_{n=0}$ of non-negative continuous functions defined on $[0,1)$ such that the series $\sum_{n=0}^{\infty}φ_n(r)$ converges locally uniformly on $[0,1)$. Our results represent twofold generalizations corresponding to those obtained for the classes $\mathcal{B}(\mathbb{D})$ and $\mathcal{B}(Ω_γ)$, where \begin{align*} Ω_γ:=\biggl\{z\in \mathbb{C}: \bigg|z+\dfracγ{1-γ}\bigg|<\dfrac{1}{1-γ}\biggr\}. \end{align*} As a convolution counterpart, we determine the Bohr radius for hypergeometric function on $ Ω_γ $. Lastly, we establish a generalized Bohr inequality and its sharpness for the class of $ K $-quasiconformal, sense-preserving harmonic maps of the form $f=h+\overline{g}$ in $Ω_γ.$

math.CV↗
arXiv2024

Revisiting Bohr Inequalities with Analytic and Harmonic Mappings on unit disk

In this paper, we study some improved and refined versions of the classical Bohr inequality applicable to the class $\mathcal{B}$, which consists of self-analytic mappings defined on the unit disk $\mathbb{D}$. First, we improve the Bohr inequality for the class $\mathcal{B}$ of analytic self-maps, incorporating the area measurements of sub-disks $\mathbb{D}_r$ of $\mathbb{D}$. Secondly, we establish a sharp inequality with suitable setting as an improved version of the classic Bohr inequality. Then we obtain a sharp refined Bohr inequality in which the coefficients $|a_k|$ $(k=0, 1, 2, 3)$ in the majorant series $M_f(r)$ of $f$ are replaced by $|f^{(k)}(z)|/k!$. Finally, for a certain class $\mathcal{P}^0_{\mathcal{H}}(M)$ of harmonic mappings of the form $f=h+\overline{g}$, we generalize the Bohr inequality incorporating a sequence $\{φ_n(r)\}_{n=0}^{\infty}$ of continuous functions of $r$ in $[0, 1)$ and give some applications.

math.CV↗
arXiv2024

Bohr inequalities via proper combinations for a certain class of close-to-convex harmonic mappings

Let $ \mathcal{H}(Ω) $ be the class of complex-valued functions harmonic in $ Ω\subset\mathbb{C} $ and each $f=h+\overline{g}\in \mathcal{H}(Ω)$, where $ h $ and $ g $ are analytic. In the study of Bohr phenomenon for certain class of harmonic mappings, it is to find a constant $ r_f\in (0, 1) $ such that the inequality \begin{align*} M_f(r):=r+\sum_{n=2}^{\infty}\left(|a_n|+|b_n|\right)r^n\leq d\left(f(0), \partialΩ\right) \;\mbox{for}\;|z|=r\leq r_f, \end{align*} where $ d\left(f(0), \partialΩ\right) $ is the Euclidean distance between $ f(0) $ and the boundary of $ Ω:=f(\mathbb{D}) $. The largest such radius $ r_f $ is called the Bohr radius and the inequality $ M_f(r)\leq d\left(f(0), \partialΩ\right) $ is called the Bohr inequality for the class $ \mathcal{H}(Ω) $. In this paper, we study Bohr phenomenon for the class of close-to-convex harmonic mappings establishing several inequalities. All the results are proved to be sharp.

math.CV↗
arXiv2023

A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications

Sentiment analysis (SA) is an emerging field in text mining. It is the process of computationally identifying and categorizing opinions expressed in a piece of text over different social media platforms. Social media plays an essential role in knowing the customer mindset towards a product, services, and the latest market trends. Most organizations depend on the customer's response and feedback to upgrade their offered products and services. SA or opinion mining seems to be a promising research area for various domains. It plays a vital role in analyzing big data generated daily in structured and unstructured formats over the internet. This survey paper defines sentiment and its recent research and development in different domains, including voice, images, videos, and text. The challenges and opportunities of sentiment analysis are also discussed in the paper. \keywords{Sentiment Analysis, Machine Learning, Lexicon-based approach, Deep Learning, Natural Language Processing}

cs.AI↗
arXiv2023

Enhancing Document Information Analysis with Multi-Task Pre-training: A Robust Approach for Information Extraction in Visually-Rich Documents

This paper introduces a deep learning model tailored for document information analysis, emphasizing document classification, entity relation extraction, and document visual question answering. The proposed model leverages transformer-based models to encode all the information present in a document image, including textual, visual, and layout information. The model is pre-trained and subsequently fine-tuned for various document image analysis tasks. The proposed model incorporates three additional tasks during the pre-training phase, including reading order identification of different layout segments in a document image, layout segments categorization as per PubLayNet, and generation of the text sequence within a given layout segment (text block). The model also incorporates a collective pre-training scheme where losses of all the tasks under consideration, including pre-training and fine-tuning tasks with all datasets, are considered. Additional encoder and decoder blocks are added to the RoBERTa network to generate results for all tasks. The proposed model achieved impressive results across all tasks, with an accuracy of 95.87% on the RVL-CDIP dataset for document classification, F1 scores of 0.9306, 0.9804, 0.9794, and 0.8742 on the FUNSD, CORD, SROIE, and Kleister-NDA datasets respectively for entity relation extraction, and an ANLS score of 0.8468 on the DocVQA dataset for visual question answering. The results highlight the effectiveness of the proposed model in understanding and interpreting complex document layouts and content, making it a promising tool for document analysis tasks.

cs.CV↗
arXiv2023

Hankel and Toeplitz determinants of logarithmic coefficients of Inverse functions for certain classes of univalent functions

The Hankel and Toeplitz determinants $H_{2,1}(F_{f^{-1}}/2)$ and $T_{2,1}(F_{f^{-1}}/2)$ are defined as: \begin{align*} H_{2,1}(F_{f^{-1}}/2):= \begin{vmatrix} Γ_1 & Γ_2 Γ_2 & Γ_3 \end{vmatrix} \;\;\mbox{and} \;\; T_{2,1}(F_{f^{-1}}/2):= \begin{vmatrix} Γ_1 & Γ_2 Γ_2 & Γ_1 \end{vmatrix} \end{align*} where $Γ_1, Γ_2,$ and $Γ_3$ are the first, second and third logarithmic coefficients of inverse functions belonging to the class $\mathcal{S}$ of normalized univalent functions. In this article, we establish sharp inequalities $|H_{2,1}(F_{f^{-1}}/2)|\leq 1/4$, $|H_{2,1}(F_{f^{-1}}/2)| \leq 1/36$, $|T_{2,1}(F_{f^{-1}}/2)|\leq 5/16$ and $|T_{2,1}(F_{f^{-1}}/2)|\leq 145/2304$ for the logarithmic coefficients of inverse functions for the classes starlike functions and convex functions with respect to symmetric points. In addition, our findings are substantiated further through the incorporation of illustrative examples, which support the strict inequality and lend credence to our conclusions.

math.CV↗
arXiv2023

Separate Scene Text Detector for Unseen Scripts is Not All You Need

Text detection in the wild is a well-known problem that becomes more challenging while handling multiple scripts. In the last decade, some scripts have gained the attention of the research community and achieved good detection performance. However, many scripts are low-resourced for training deep learning-based scene text detectors. It raises a critical question: Is there a need for separate training for new scripts? It is an unexplored query in the field of scene text detection. This paper acknowledges this problem and proposes a solution to detect scripts not present during training. In this work, the analysis has been performed to understand cross-script text detection, i.e., trained on one and tested on another. We found that the identical nature of text annotation (word-level/line-level) is crucial for better cross-script text detection. The different nature of text annotation between scripts degrades cross-script text detection performance. Additionally, for unseen script detection, the proposed solution utilizes vector embedding to map the stroke information of text corresponding to the script category. The proposed method is validated with a well-known multi-lingual scene text dataset under a zero-shot setting. The results show the potential of the proposed method for unseen script detection in natural images.

cs.CV↗
arXiv2023

Sharp bounds for second Hankel determinant of logarithmic coefficients for certain classes of univalent functions

The Hankel determinant $H_{2,2}(F_{f}/2)$ is defined as: \begin{align*} H_{2,2}(F_{f}/2):= \begin{vmatrix} γ_2 & γ_3 γ_3 & γ_4 \end{vmatrix}, \end{align*} where $γ_2, γ_3,$ and $γ_4$ are the second, third, and fourth logarithmic coefficients of functions belonging to the class $\mathcal{S}$ of normalized univalent functions. In this article, we establish sharp inequalities $|H_{2,2}(F_{f}/2)|\leq (1272 + 113\sqrt{678})/32856$ and $|H_{2,2}(F_{f}/2)| \leq 13/1080$ for the logarithmic coefficients of starlike and convex functions with respect to symmetric points. Moreover, we provide examples that demonstrate the strict inequality holds.

math.CV↗
arXiv2022

A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets

A machine can understand human activities, and the meaning of signs can help overcome the communication barriers between the inaudible and ordinary people. Sign Language Recognition (SLR) is a fascinating research area and a crucial task concerning computer vision and pattern recognition. Recently, SLR usage has increased in many applications, but the environment, background image resolution, modalities, and datasets affect the performance a lot. Many researchers have been striving to carry out generic real-time SLR models. This review paper facilitates a comprehensive overview of SLR and discusses the needs, challenges, and problems associated with SLR. We study related works about manual and non-manual, various modalities, and datasets. Research progress and existing state-of-the-art SLR models over the past decade have been reviewed. Finally, we find the research gap and limitations in this domain and suggest future directions. This review paper will be helpful for readers and researchers to get complete guidance about SLR and the progressive design of the state-of-the-art SLR model

cs.CV↗
arXiv2022

Hybrid Transformer Network for Different Horizons-based Enriched Wind Speed Forecasting

Highly accurate different horizon-based wind speed forecasting facilitates a better modern power system. This paper proposed a novel astute hybrid wind speed forecasting model and applied it to different horizons. The proposed hybrid forecasting model decomposes the original wind speed data into IMFs (Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries from ICEEMDAN to the transformer network. Each transformer network computes the forecast subseries and then passes to the fusion phase. Get the primary wind speed forecasting from the fusion of individual transformer network forecast subseries. Estimate the residual error values and predict errors using a multilayer perceptron neural network. The forecast error is added to the primary forecast wind speed to leverage the high accuracy of wind speed forecasting. Comparative analysis with real-time Kethanur, India wind farm dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's superior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06, MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1 and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08, MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speed forecasting than state-of-the-art methods and reduces the burden on the power system engineer.

eess.SP↗
arXiv2022

Efficacy of Transformer Networks for Classification of Raw EEG Data

With the unprecedented success of transformer networks in natural language processing (NLP), recently, they have been successfully adapted to areas like computer vision, generative adversarial networks (GAN), and reinforcement learning. Classifying electroencephalogram (EEG) data has been challenging and researchers have been overly dependent on pre-processing and hand-crafted feature extraction. Despite having achieved automated feature extraction in several other domains, deep learning has not yet been accomplished for EEG. In this paper, the efficacy of the transformer network for the classification of raw EEG data (cleaned and pre-processed) is explored. The performance of transformer networks was evaluated on a local (age and gender data) and a public dataset (STEW). First, a classifier using a transformer network is built to classify the age and gender of a person with raw resting-state EEG data. Second, the classifier is tuned for mental workload classification with open access raw multi-tasking mental workload EEG data (STEW). The network achieves an accuracy comparable to state-of-the-art accuracy on both the local (Age and Gender dataset; 94.53% (gender) and 87.79% (age)) and the public (STEW dataset; 95.28% (two workload levels) and 88.72% (three workload levels)) dataset. The accuracy values have been achieved using raw EEG data without feature extraction. Results indicate that the transformer-based deep learning models can successfully abate the need for heavy feature-extraction of EEG data for successful classification.

eess.SP↗
arXiv2020

Modeling Extent-of-Texture Information for Ground Terrain Recognition

Ground Terrain Recognition is a difficult task as the context information varies significantly over the regions of a ground terrain image. In this paper, we propose a novel approach towards ground-terrain recognition via modeling the Extent-of-Texture information to establish a balance between the order-less texture component and ordered-spatial information locally. At first, the proposed method uses a CNN backbone feature extractor network to capture meaningful information of a ground terrain image, and model the extent of texture and shape information locally. Then, the order-less texture information and ordered shape information are encoded in a patch-wise manner, which is utilized by intra-domain message passing module to make every patch aware of each other for rich feature learning. Next, the Extent-of-Texture (EoT) Guided Inter-domain Message Passing module combines the extent of texture and shape information with the encoded texture and shape information in a patch-wise fashion for sharing knowledge to balance out the order-less texture information with ordered shape information. Further, Bilinear model generates a pairwise correlation between the order-less texture information and ordered shape information. Finally, the ground-terrain image classification is performed by a fully connected layer. The experimental results indicate superior performance of the proposed model over existing state-of-the-art techniques on publicly available datasets like DTD, MINC and GTOS-mobile.

cs.CV↗