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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Prasanth, Ashwin Annie Micheal, A. Department of Computer Science & Engineering Sathyabama Institute of Science & Technology Chennai India

ISBN: (纸本)9798331527549

Face recognition under occlusion presents a persistent challenge in computer vision, primarily due to difficulties in capturing and effectively integrating visible and obscured facial features. This paper introduces a novel device-edge collaborative framework that advances occluded face recognition through an innovative cross-domain feature fusion technique. Our method employs a software-generated, occlusion-aware heatmap created from standard visible-light images, which emphasizes key facial landmarks even when regions of the face are partially covered. This heatmap provides a complementary data layer that enhances traditional visible facial features, allowing the cross-domain feature fusion process to synthesize a more comprehensive set of features for reliable recognition under occlusion. The combined feature set leverages both visible and simulated heatmap information to improve model robustness, even in complex occlusion scenarios. Additionally, a delay-optimized task scheduling method is applied on the edge, ensuring efficient resource allocation, faster processing, and minimal latency. Although this project is ongoing, preliminary evaluations indicate the potential effectiveness of this approach, with expected outcomes to include high recognition accuracy and reduced latency. This framework provides a scalable solution for occluded face recognition by integrating complementary data sources without requiring specialized hardware. © 2025 IEEE.

关键词： Face recognition

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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TrackNCount - Intelligent Vehicle Tracking, Counting, and Speed Estimation using YOLOv8 and DeepSORT Algorithms 6

TrackNCount - Intelligent Vehicle Tracking, Counting, and Sp...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Paluru Ambareesh Ram Venkatesh, Digumalla Manisri Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the TrackNCount project combines DeepSORT with YOLOv8 for object identification to create a smart system for tracking, counting, and speed estimation of vehicles. The idea behind this design is to use sophisticated computer vision techniques in conjunction with efficient algorithms to handle real-world scenarios. Setting up a reliable procedure that recognizes cars, gives them distinct IDs, and records their movements through video frames is our first priority. The system computes vehicle speeds using the Euclidean distance formula, manages item trajectories using double-ended queues, and detects entering and outgoing vehicles based on directional thresholds. Motion trails and bounding boxes are two further visual aids that improve the results' readability. With the added benefit of simple speed estimation feature integration, the model provides exceptional accuracy in real-time vehicle monitoring and counting. Experiments conducted on a variety of traffic scenarios consistently showed that this system can deliver precise vehicle counts in designated directions and speed calculations with a small margin of error. The findings show that TrackNCount is a viable and scalable solution for data-driven city planning and intelligent traffic management. © 2025 IEEE.

关键词： Street traffic control

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Blockchain-based layered architecture for traceability in dairy supply chain management

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International Journal of Information technology (Singapore) 2025年 1-8页

作者： Sharma, Deepti Singh, Gurpreet Kaur, Amanpreet Department of Computer Science & Engineering Giani Zail Singh Campus College of Engineering and Technology Maharaja Ranjit Singh Punjab Technical University (MRSPTU) Punjab Bathinda India Department of Computer Science & Engineering Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Punjab Rajpura India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

Securing the availability of high-quality and safe food for people is the primary goal of food supply chain systems. Traditional food supply systems have inherent difficulties such as the possibility of data loss, consistency problems with the product, quality compromises, and single points of failure. Blockchain technology exhibits significant potential because of its auditability, immutability, and decentralization. Thus, blockchain-based layered architecture is proposed in the given paper to guarantee traceability of dairy products and information sharing across supply chain participants. The proposed architecture is based on the concept of sharding, which is based on the idea of parallel processing. Other components of the dairy supply chain, including quality assurance, automated payment settlements, sales analysis, and consumption monitoring, are also supported by the proposed architecture. For overall implementation, Hyperledger Fabric is used, and the algorithms for all the chain codes used for traceability of dairy items are described. A comparative analysis of Hyperledger-based systems and other existing approaches is presented, which shows the suitability of the platform for supply chain networks. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Blockchain Chain codes Dairy supply chain management Smart Contracts Traceability

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Neural network-guided motion blur estimation and patch representation method for face hallucination

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Neural Computing and Applications 2025年 1-28页

作者： Kumar, Banti Rajput, Shyam Singh Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Face hallucination (or super-resolution) is a technique of generating high-resolution face images from low-resolution (LR) inputs. The current face hallucination algorithms struggle with motion blur, a common issue in captured images due to various factors, including camera defocusing, objects in motion, etc. To address this problem, a new motion blur robust face hallucination algorithm via neural network-guided motion blur estimation and patch representation (NNMEPR) is proposed in this paper. The NNMEPR algorithm first estimates the motion blur kernels i.e., length (λ) and angle (α) from a motion-blurred LR test faces using a neural network fitting approach with scale conjugate gradient (SCG) learning algorithm. Then, the estimated factor is embedded in the LR dictionary face images to make them compatible with the test images and mitigate the impact of motion blur from the reconstruction process. Moreover, the proposed algorithm employs a neighboring position patch representation in the correlation coefficient calculation process to sustain the sharp edges and texture details in the resulting HR face images. Experiments on standard datasets and locally captured real-world faces demonstrate the superior performance of the proposed NNMEPR algorithm over the existing state-of-the-art techniques. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion estimation

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Performance Analysis of Denoising Filters for Ultrasound Fetal Head Images

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SN computer science 2025年第2期6卷 1-13页

作者： Prasad, Kauleshwar Patnaik, Pawan Kumar Agrawal, Anupam Department of Computer Science and Engineering Bhilai Institute of Technology Chhattisgarh Durg India Department of Electrical and Electronics Engineering Bhilai Institute of Technology Chhattisgarh Durg India

Deep learning has significantly advanced image processing in the medical domain, including the analysis of ultrasound (US) fetal images for prediction of fetal growth retardation. Before analysis of fetal images there is need of correction since they are usually adulterated by noise during acquisition of image. In the recent years, there are various deep learning based denoising methods in which U-Net have attracted the attention of many researchers. This paper describes the comparative study of different filters such as mean filter, gaussian filter, anisotropic diffusion, non-local means (NLM) filter and U-Net based on their performance on denoising fetal images prior to the prediction of fetal growth retardation (FGR). In this article peak signal to noise ratio (PSNR), mean square error (MSE), similarity index measure (SSIM), peak signal to interference ratio (PSIM) and multi scale structural similarity index measure (MSIM) values are analyzed for the performance of these filters. This paper reveals that U-Net performs better in image quality evaluation when compared to other filtering algorithms, based on testing and analytical findings. We have also discussed the significance of MSE, PSNR, SSIM, PSIM, MSIM and U-Net in this article. This article also covers the advantages, disadvantages, and characteristics of different filtering methods, including U-Net. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Denoising techniques Fetal growth retardation U-Net

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Exploration of Semantic Priming Studies in Native Language of Children:Study on Bengali L1 and English L2 Bilingual Children

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SN computer science 2025年第2期6卷 1-18页

作者： Banerjee, Jaya Nithin, Kota Sai Sai, Uppalapati Hari Krishna Chakraborty, Baisakhi Basu, Anupam Department of Computer Science & Engineering National Institute of Technology West Bengal Durgapur 713209 India National Institute of Technology West Bengal Durgapur India Department of Computer Science & Engineering Indian Institute of Technology (IIT) Kharagpur 721302 India

Word Priming is an event in which prior subjection to a particular word impacts the reply of the individual to the upcoming correlated word. The response time is inferred to be less in identifying the target word preceded by an associated word compared to the word in seclusion or isolation as per the experiments undertaken on an Indian language where research incorporates Bengali-speaking children. Bengali is an important language of consideration on national and international platforms. A set of twenty-one school-going children with ages in the range of 7 to 10 years are subjected to a word priming assessment. Analysis established a positive impact of word prime in the reaction of a set of children aged seven to ten to certain listed words Due to the presence of word prime, the average error measure of the lower progressing answerers reduced by a significant 19.8%. With reference to the Bengali script, the average error measure is 10 percent. With the involvement of the word prime, the mean error measure is 1.6 percent. This paper proposes first of its kind of research studies and analysis with regard to the positive effect of word priming on the response time of target word identification on Bengali speaking bi-lingual dyslexic children to yield positive results. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bengali Children English Learning impairment Priming Psycholinguistics Second language Word identification Word priming

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Enhancing Security in Logic-Locked Systems Through UART-Based Key Authentication

Enhancing Security in Logic-Locked Systems Through UART-Base...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Jaisachin, B. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

The security of integrated circuits is crucial due to their wide range of applications in modern gadgets. An unauthorized access (i.e., third-party access) or a security breach in the logic-locked system can pose a great threat to user privacy, thus leading to data theft and misusing the user data. Several logic locking methods have been proposed, but with the advancements of technology, the latest attack techniques can easily crack those existing locking techniques. Thus, in this research paper we have proposed a sensor-based methodology to detect the unauthorized access with microswitch and UART. In a globalized world where the security of logic-locked systems is really important, this paper empathizes with UART-based mechanisms as a practical solution. By focusing on the existing logic locking methodologies, this paper contributes to the ongoing security efforts for a secured logic locked system. © 2025 IEEE.

关键词： computer circuits

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Enhancing Accuracy of Handwritten Character Segmentation Using Region-Based Convolutional Neural Networks (R-CNN) 1

Enhancing Accuracy of Handwritten Character Segmentation Usi...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Varpe, Kanchan Sakhare, Sachin Joshi, Rahul Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Handwritten character segmentation plays a pivotal role in the performance of Optical Character Recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-Based Convolutional Neural Networks (R-CNN). Traditional methods often struggle with issues such as varying character sizes, overlapping, and inconsistent writing styles, leading to suboptimal recognition results. The proposed R-CNN model addresses these challenges by effectively localizing and segmenting individual characters from handwritten text images. The methodology involves training the R-CNN on a comprehensive dataset of handwritten characters, annotated to capture diverse styles and complexities. The model leverages deep learning's feature extraction capabilities and region-based localization to accurately segment characters, even in challenging scenarios like connected or touching characters. Comparative experiments show that the proposed approach outperforms conventional techniques in terms of segmentation accuracy and robustness. Results demonstrate significant improvements, with the R-CNN model achieving higher precision and recall rates across multiple handwritten datasets, including different languages and scripts. This advancement holds promise for enhancing OCR systems used in applications such as automated document processing and digital archiving. Future work will focus on integrating this segmentation method with advanced recognition models to further boost overall handwritten text recognition performance. © 2025 IEEE.

关键词： Convolutional neural networks

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Attention-based neural machine translation with quality analysis on low-resource Digaru-English Pairs

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International Journal of Information technology (Singapore) 2025年第1期17卷 575-589页

作者： Rushanti, Kri Kakum, Nabam Sambyo, Koj Department of Computer science and Engineering National Institute of Technology Jote Arunachal Pradesh India

Attention-based neural machine translation (NMT) has significantly improved translation quality for well-resourced language pairs. However, enhancing translation accuracy for low-resource languages remains a challenge due to limited datasets and technical constraints. Despite these challenges, the latest NMT method has notably improved translation precision for language combinations with limited data by employing techniques aimed at boosting the training models. This study focuses on improving translations for the low-resource North-East Indian language, Digaru. We utilize attention-based NMT and the Transformer model with hyperparameter tuning, experimenting with datasets of 5k, 10k, and 50k sentences. Our optimized model shows a notable improvement in BLEU scores over the base models, resulting in significantly better translation accuracy. Additionally, human evaluations and multidimensional quality metrics (MQM) confirm the enhanced translation quality. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： BLEU Global attention Hyper-parameter optimization MQM Transformer

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