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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Pandey, Gaurav Kumar Raj, Vansh Agarwal, Ayush Dixit, Mayank Chauhan, Sachin Singh Srivastava, Sumit Birla Institute of Technology Mesra Computer Science and Engineering Department Ranchi India Hindustan College of Science and Technology Computer Science and Engineering Department Mathura India Hindustan College of Science and Technology Cse Department Mathura India Birla Institute of Technology Mesra Cse Department Ranchi India

ISBN: (纸本)9798331523923

Offline signature verification is essential for identity authentication and fraud mitigation in many applications, such as banking and legal documentation. Conventional approaches to signature verification predominantly depended on manually designed features and statistical methodologies, although they frequently encountered difficulties with intricate intra-class variances and inter-class similarities. In recent years, deep learning has arisen as a potent instrument to address these difficulties through autonomous feature extraction and resilient categorization. This study offers an extensive examination of cutting-edge deep learning methodologies for offline signature verification. We examine fundamental designs like Convolutional Neural Networks (CNNs), hybrid models, and transfer learning methodologies, and evaluate their efficacy on renowned signature datasets such as CEDAR and GPDS. Furthermore, we examine the difficulties related to imbalanced datasets, counterfeit detection, and the choice of suitable assessment metrics, including accuracy, False Acceptance Rate (FAR), and False Rejection Rate (FRR). This paper identifies existing limits in DL methodologies and proposes future research routes to tackle challenges including dataset scarcity, model generalization, and computational efficiency. Our findings provide studies with insights into the advantages and disadvantages of diverse deep learning methodologies, thereby aiding in advancing more precise and efficient signature verification systems. © 2025 IEEE.

关键词： Convolutional neural networks

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Multi-Species Classification of Malaria Parasites using Ensemble Transfer Learning Technique 1

Multi-Species Classification of Malaria Parasites using Ense...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Ohdar, Kanak Nigam, Akriti Birla Institute of Technology Department of Computer Science & Engineering Mesra Ranchi India

ISBN: (纸本)9798350353648

Malaria, a prevalent global health concern, necessitates accurate and prompt diagnosis for the treatment and control of disease. Microscopic examination of thin blood smear images is the definitive method for diagnosing Malaria. However, this method relies on skilled experts and is prone to human error. This paper introduces an innovative method for categorizing thin blood smear images of Plasmodium parasite species into their four types through an ensemble transfer learning technique. By utilizing the information gathered from pre-trained models on sizable datasets, our approach uses a deep learning framework built on transfer learning. A diverse ensemble of models, such as Xception, EfficientNet, MobileNet and DenseNet 121, was utilized to collectively capture intricate patterns and representative features of different Malaria parasite species. To develop and evaluate our method, we collected and preprocessed the MP-IDB dataset, which includes blood slide images of all four parasite types. The literature reveals that no research has explicitly focused on identifying and categorizing all four Plasmodium species in blood smear images. It signifies a gap in the current body of knowledge, emphasizing the need for comprehensive investigations in this specific domain. Through rigorous experimentation and evaluation, our proposed weighted ensemble transfer learning technique achieves an accuracy 98.9 in classifying Malaria parasite species into its four types. Our work contributes to advancing computer-aided diagnosis of Malaria, paving the way for more effective and efficient disease management strategies. © 2024 IEEE.

关键词： Federated learning

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Deep Learning Based Techniques to Develop & Enhance Assistive Gear for Visually Impaired

Deep Learning Based Techniques to Develop & Enhance Assistiv...

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2024 IEEE International Conference on Computer Vision and Machine Intelligence, CVMI 2024

作者： Shadangi, Shuswabhit Rout, B.K. Birla Institute of Technology and Science Pilani Department of Mechanical Engineering Pilani India

ISBN: (纸本)9798350376876

This work investigates existing solutions tailored for the visually impaired, focusing on economically viable options for non-first-world communities. The exploration involves developing a real-time obstacle-tracking model using the YOLO (You Only Look Once) algorithm and Text-to-Speech synthesis to provide auditory cues. This effort yields improvements in assistive technology, though it still faces limitations in algorithmic precision and user feedback integration. The research paves the way for refining this technology and envisions its seamless integration into the daily lives of the visually impaired. The findings enhance the performance of assistive technologies, especially for distances less than 1.5 meters. The results show an inaccuracy of less than 10 %, translating to a margin of 10-15 ~cm for objects located one meter away. This work thus provides increased independence and confidence for individuals with visual impairments in navigating and interacting with their surroundings. © 2024 IEEE.

关键词： Assistive technology

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OptiFog: A Framework for Acquiring State Information and Predicting Resource Availability for Task Offloading in Cooperative Fog-Networks

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IEEE Transactions on Services Computing 2024年 1-13页

作者： Alam, Mehbub Ahmed, Nurzaman Ghosh, Shyamal Matam, Rakesh Barbhuiya, Ferdous Ahmed Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science Dartmouth College Hanover USA School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram India

The primary objective of fog computing is to minimize the reliance of IoT devices on the cloud by leveraging the resources of fog network. Typically, IoT devices offload computation tasks to fog to meet different task requirements such as latency in task execution, computation costs, etc. So, selecting such a fog node that meets task requirements is a crucial challenge. To choose an optimal fog node, access to each node's resource availability information is essential. Existing approaches often assume state availability or depend on a subset of state information to design mechanisms tailored to different task requirements. In this paper, OptiFog: a cluster-based fog computing architecture for acquiring the state information followed by optimal fog node selection and task offloading mechanism is proposed. Additionally, a continuous time Markov chain based stochastic model for predicting the resource availability on fog nodes is proposed. This model prevents the need to frequently synchronize the resource availability status of fog nodes, and allows to maintain an updated state information. Extensive simulation results show that OptiFog lowers task execution latency considerably, and schedules almost all the tasks at the fog layer compared to the existing state-of-the-art. IEEE

关键词： Computer architecture

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A transfer learning approach for continuous speech recognition system in Indian language sadri

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International Journal of Information technology (Singapore) 2025年 1-10页

作者： Paul, Shubhojeet Bhattacharjee, Vandana Saha, Sujan Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi 835215 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur 713209 India

Sadri is the most widely used language of the Chotanagpur Plateau region of India. This is primarily a spoken language and developing an automatic speech recognition (ASR) system in Sadri is extremely important. When we searched the literature, we found no open ASR system or relevant resources in Sadri. So, we worked on developing an ASR system in Sadri. We created a Sadri speech corpus of around 20 h, employing 53 native speakers. Then, we implemented the baseline model using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). As the size of the training data was not sufficient, we investigated a transfer learning technique to improve performance. We found that ASR data are openly available in other related languages such as Hindi and Bengali. We used Bengali and Hindi data of 100 h each in a transfer learning framework along with the Sadri data. In our experiments, we found that the transfer learning-based model outperforms the baseline model. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： ASR Deep learning Indian language Sadri Speech recognition

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Influence of Structural Tailoring on the Dynamics of a Tapered Anisotropic Box Beam 2nd

Influence of Structural Tailoring on the Dynamics of a Taper...

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2nd International Conference on Mechanical engineering, INCOM 2024

作者： Deepak Kumar, G. Panigrahi, B. Department of Mechanical Engineering Birla Institute of Technology and Science Hyderabad Campus Pilani India

ISBN: (纸本)9789819766666

Structural tailoring is a crucial step toward the design of any composite structure, as it decides the structural couplings to be developed. In the present work, the influence of couplings arising due to structural tailoring, on the dynamics of the thin-walled tapered anisotropic box beam, is presented. The present model is derived by considering all the effects like primary and secondary warping, transverse shear. The governing equations those derived are solved using Ritz approximation. Two structural configurations those mentioned in the literature are considered for the present work. The results procured from the present model have been verified against those available in literature and proved to be accurate. The influence of bending–transverse shear and bending–torsional couplings on the first few frequencies is present for different ply angles and tapered ratios (C). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anisotropy

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Deep Learning Model for UAV aided Traffic Analysis and Vehicle Classification 5

Deep Learning Model for UAV aided Traffic Analysis and Vehic...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kumar, Mayank Anwar, Shamama Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi India

ISBN: (纸本)9798350389609

Intelligent transportation systems (ITS) depend upon a range of communication that allows for data exchange between vehicles and traffic information data centers to create applications for providing information like traffic alert in view of lane closure, traffic congestion, track unsafe driving behaviour, optimize routes and emergency management, etc. An important component in this setup are video clips of highways or cross-section areas which needs to be monitored. Traditionally, a closed circuit television (CCTV) is the most often used method of monitoring traffic information, but have limitations like fixed positions, small monitoring region leading to numerous CCTV installations which are costly. A UAV, unlike CCTV, is capable of recording an extensive area of roadways by elevating its altitude or altering its location, as well as travel to a specific spot to monitor unforeseen circumstances such as crashes. Additionally, a UAV observes highways perpendicularly, thus the automobiles in the captured footage do not overlap. This paper explores the issue of vehicle identification on highways using UAV images through deep learning models. Deep learning approaches can automatically learn problem-specific features, eliminating the need for standard feature extraction procedures. Three pre-trained deep learning based model VGG16, ResNet-18 and EfficientNetB3 models have been used for the vehicle classification problem, which is essentially a five class classification problem. The highest accuracy of 97% is reported by the EfficientNet model. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Facial Expression Recognition Method Integrating Uncertainty Estimation and Active Learning

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Computers, Materials & Continua 2024年第10期81卷 533-548页

作者： Yujian Wang Jianxun Zhang Renhao Sun School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China Data Space Research Institute of Hefei Comprehensive National Science Center Hefei230088China

The effectiveness of facial expression recognition(FER)algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression ***,labeling large datasets demands significant human,time,and financial *** active learning methods have mitigated the dependency on extensive labeled data,a cold-start problem persists in small to medium-sized expression recognition *** issue arises because the initial labeled data often fails to represent the full spectrum of facial expression *** paper introduces an active learning approach that integrates uncertainty estimation,aiming to improve the precision of facial expression recognition regardless of dataset scale *** method is divided into two primary ***,the model undergoes self-supervised pre-training using contrastive learning and uncertainty estimation to bolster its feature extraction ***,the model is fine-tuned using the prior knowledge obtained from the pre-training phase to significantly improve recognition *** the pretraining phase,the model employs contrastive learning to extract fundamental feature representations from the complete unlabeled *** features are then weighted through a self-attention mechanism with rank ***,data from the low-weighted set is relabeled to further refine the model’s feature extraction *** pre-trained model is then utilized in active learning to select and label information-rich samples more *** results demonstrate that the proposed method significantly outperforms existing approaches,achieving an improvement in recognition accuracy of 5.09%and 3.82%over the best existing active learning methods,Margin,and Least Confidence methods,respectively,and a 1.61%improvement compared to the conventional segmented active learning method.

关键词： Expression recognition active learning self-supervised learning uncertainty estimation

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Digital Twin Simulation and Comparative study to understand the Microgrid Operation and Control 10

Digital Twin Simulation and Comparative study to understand ...

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10th IEEE International Conference on Power and Energy, PECon 2024

作者： Saini, Krishna Kumar Yadav, Chinmay Mathur, Hitesh Datt Gautam, Aditya R. Department of Electrical & Electronics Engineering Birla Institute of Technology & Science Pilani India

ISBN: (纸本)9798350361971

Solar photovoltaic (PV) microgrid (MG) systems are the future of the growing electrical industry. However, these systems have a dependency on the environment. To mitigate this dependency, better control and technologies are required. The digital twin (DT) technology can be utilized to make these MGs more sustainable and promising. In this paper, the DT of the installed MG has been developed on the Simulink platform. The irradiance data, and load power consumption data have been used as input for the DT of the MG. Moreover, it is found that the DT of the MG is working stably with the given data. On the output side, the grid power data of the physical MG has been compared with the output grid power of DT. Further, it has been found that the solar power generation, load power consumption data, and grid power data have matched the results of the physical MG. ©2024 IEEE.

关键词： control algorithm Digital twin Grid power Microgrid

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