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

作者： Khattar, Sonam Kumar, Vishal Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science Chandigarh University Mohali India

ISBN: (纸本)9798331527549

Breast cancer remains one of the leading causes of death in women worldwide, and thus there is a great need for reliable diagnostic tools. This paper proposes an improved ensemble approach for breast cancer classification using mammography images, which integrates the strengths of two advanced transfer learning architectures: ResNet and MobileNetV2. The ensemble model combines the feature extraction capabilities of ResNet with the computational efficiency of MobileNetV2 to achieve higher diagnostic accuracy without compromising operational effectiveness. This approach was applied to an open mammography dataset with the use of preprocessing and data augmentation techniques for the better generalization of models. The metrics such as accuracy, precision, recall, and F1-score depicted that ensemble methods perform better than the single model, hence being a good approach toward the early detection of breast cancer. This work holds promise toward further integration of deep learning frameworks for advancing computer-aided diagnosis systems in the field of medical imaging. © 2025 IEEE.

关键词： Mammography

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Optimized featured swarm convolutional neural network (OFSCNN) model based dialect recognition system for Bagri Rajasthani language

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International Journal of Information Technology (Singapore) 2025年第1期17卷 121-133页

作者： Kukana, Poonam Sharma, Pooja Bhardwaj, Neeru Department of Computer Science and Engineering University School of Engineering & amp Technology Rayat-Bahra University Punjab Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India

The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken language. A dialect recognition system must effectively distinguish between different dialects of a standard language, as they tend to possess many similarities. The dialect of a language is not a distinct characteristic, as it can be impacted by the utterer's birthplace. Researchers in the domain of automatic speech recognition (ASR) face difficulties in identifying the speech patterns unique to each dialect or language. The proposed work recognizes the dialects of the Bagri Rajasthani language from undefined expressions of speech. Rajasthani language is one of the eldest and most famous languages in the Indo-Aryan languages. It comprises the different dialects and for recognizing the dialects, it used dissimilar phases of acoustic and spectral characteristics of the speech signal (SS). The spectral and acoustic features of SSs are measured to design the system. As there is no specific speech dataset for Bagri dialects, the database is built, to verify the Bagri dialects of the Rajasthani language. To improve the accuracy rate, and error rate in recognizing the Bagri dialects, the acoustic and spectral characteristics of speech expressions are joined. To verify several Bagri dialects of the Rajasthani language, different simulations for classification and investigation are carried out i.e., OFSCNN model, GA-NN, etc. The outcomes are important and the accuracy of 96.95% for the OFSCNN model, 80.63% for GA-NN, and 93.45% for the SVM method is an achievement. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN (convolutional neural network Dialects MFCC (Mel frequency co-efficient cepstral) OFSCNN (optimized featured swarm convolutional neural network) PSO (particle swarm optimization) Speech Dialect Recognition

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Smartphone based human activity recognition irrespective of usage behavior using deep learning technique

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International Journal of Information Technology (Singapore) 2025年第1期17卷 69-85页

作者： Kundu, Soumya Mallik, Manjarini Saha, Jayita Chowdhury, Chandreyee Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Human activity recognition (HAR) from sensory data is a crucial task for a wide variety of applications. The in-built inertial sensor facilities of commercial smartphones have made the data collection process easier. However, different smartphone configurations exhibit variations in sensor readings for the same activities. Different smartphone holding positions, like in hand, shirt, or trouser pockets, also lead to variations in signal patterns for the same activity. Some recent works have shown that automated feature extraction using deep learning methods can significantly improve activity recognition, although there is a lack of experimentation considering device heterogeneity and different smartphone holding positions. The proposed work addresses this research gap with a two-fold contribution. First, a CNN-based HAR framework is proposed that forms 2-D frequency domain images to capture temporal patterns in the data along with inter-axis spatial features. Second, an ensemble of conditional classifiers has been designed based on CNN that exhibits generality in terms of device configurations and usage behavior. Real life data have been collected for different activities using different devices for experimentation. The proposed ensemble model is found to recognize activities with 94% accuracy even when the training and test devices are different for real datasets. © The Author(s) 2024.

关键词： Activity image CNN Deep learning Human activity recognition Smartphone

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Blockchain with drug traceability in medical supply chain with Henry gas solubility optimisation algorithm

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 135-148页

作者： Devi, P. Yamini Sriramya, P. Department of Computer Science and Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai India Department of Data Science Saveetha School of Engineering SIMATS Tamil Nadu Chennai India

The drug traceability model is used for ensuring drug quality and its safety for customers in the medical supply chain. The healthcare supply chain is a complex network, which is susceptible to failures and leakage of information because of cyber security attacks. Hence, this research aims to design a blockchain to perform traceability of drugs using Henry Gas Solubility Optimisation (HGSO). The entities include distributors, wholesalers, retailers, blockchain and patients. This model comprises initialisation, registration, key generation, authentication, drug distribution with traceability and key generation phases. Any exchange of medicines needs the approval of both sender and receiver. The drugs are registered in the blockchain, which then generates a secret key that is obtained using HGSO. The verification and authentication of drugs are performed after the authentication process. The implemented model outperformed with the response time, memory, normalised variance, and privacy of 32.678 sec, 84 MB, 0.958 and 0.958. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： authentication blockchain drug traceability key generation medical supply chain

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A deep learning approach using modified Xception net for oral malignancy detection using histopathological images of oral mucosa

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International Journal of Applied Decision sciences 2025年第2期18卷 168-188页

作者： Das, Madhusmita Dash, Rasmita Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

The early detection of oral malignancy by physicians is a strenuous task. The analysis of histopathological oral malignancy images using image processing and deep learning techniques can be an add-on facility for doctors to diagnose oral cancer. In this work, a deep learning model is used, designing a modified Xception net with swish activation function and generalised mean pool for the detection of oral malignancy. To prove the superiority of the model, three stages of comparative analysis are carried out. In the first stage, the model is compared with a few advanced models explicitly Alexnet, Resnet50, Resnet101, VGG16, VGG19, Inception net and original Xception. In the second stage, loss and accuracy graphs analysis is done and in the third stage, the proposed model’s accuracy is compared with other model’s accuracy available in the literature. It is found that the modified Xception net got upgraded performance by an accuracy of 98.97%. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Lung cancer

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AR-Light: Enabling Fast and Lightweight Multi-User Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

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IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology HoustonTX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

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A lightweight approach for intrusion detection in WSNs based on DCGAN

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International Journal of Information Technology (Singapore) 2025年第2期17卷 951-957页

作者： Devi, Manu Nandal, Priyanka Sehrawat, Harkesh Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India

Ensuring strong security procedures is crucial in the rapidly advancing realm of wireless sensor networks (WSNs) in order to protect sensitive data and preserve network integrity. The resource limitations and unpredictable environments that characterize WSNs frequently provide challenges for traditional intrusion detection systems (IDS). To overcome these issues, this research introduces a unique lightweight intrusion detection system that utilizes Deep Convolutional Generative Adversarial Networks (DCGAN). The proposed system employs wireless sensor network blackhole flooding selective forwarding (WSN-BFSF) dataset for training and evaluation. The suggested approach improves the IDS’s detection capabilities in addition to reducing computing overhead. The findings show that the lightweight DCGAN-based IDS provides an improved defense against new security threats, making it a good fit for real-time deployment in WSNs. Here, the performance metrics like receiver operating characteristic (ROC) is evaluated to check the efficiency of the model. The proposed model is assessed with the existing studies based on metrics such as accuracy, precision, recall and f1-score. The results show that the proposed approach attains the highest accuracy (94%) thus outperforming the existing methods and studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep Learning (DL) Generative Adversarial Networks (GAN’s) Intrusion Detection System (IDS) Lightweight DCGAN Wireless Sensor Networks (WSN’s)

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A Framework for Flood Disaster Detection From Remote Sensing Images Using Spatiotemporal Fusion With Digital Twin Technology

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11547-11560页

作者： Lim, Se-Jung Sankaran, K. Sakthidasan Haldorai, Anandakumar Chonnam National University School of Electrical and Computer Engineering Yeosu Campus Yeosu-si59626 Korea Republic of Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Chennai603103 India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore641202 India

Flood is regarded as common disaster which could cause serious devastation in any country. Typically, it is caused due to precipitation & river runoffs, specifically at the time of excessive rainfall season. The technology of sensor network has been used to monitor changes in landcovers and water level fluctuations. Moreover, effective disaster monitoring & notification system in real-time becomes a crucial part which needs to be overcome. For this reason, the proposed methodology is designed aiming at developing natural disaster prediction and monitoring system for alerting that aids in offering right decision at right time. At first, remote sensing image data are collected and preprocessed using Frequency Ratio and Multi-collinearity test (MCT) to ensure noise removal and image augmentation by enhancing their quality. A feature extraction process is carried with the use of Deep Convolution VGGNet-16 from which optimal features are selected using Improved Harris Hawks Optimization algorithm (IHHOA). Then, a Flexible Spatio-temporal image fusion (F-SPTF) approach is employed to fuse images. After this, Deep cascaded RNN classifier is employed for predicting flood occurrence and to map flood susceptibility areas. This, in turn, classifies the normal and abnormal condition of flood occurrence thus giving alerts in case of natural disaster occurrences which could be visualized through digital twin technologies. The suggested scheme offers an accuracy rate of about (99.89%), precision (99.37%), recall (99.82%), and F1-score (99.74%). The error rates estimated like RMSE (0.784), MAE (0.764), and MAPE (0.102) also seems to be lower than other existing models compared. © 2008-2012 IEEE.

关键词： Image enhancement

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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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Concurrent and Learned Data Structures 25

Concurrent and Learned Data Structures

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26th International Conference on Distributed Computing and Networking, ICDCN 2025

作者： Chatterjee, Bapi Peri, Sathya Department of Computer Science and Engineering IIIT Delhi Delhi India Department of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798400710629

We explore the landscape of modern data structures that employ two powerful scaling mechanisms simultaneously: (a) concurrency to harness the power of today's multicore processors, and (b) learned queries - the operations employing machine learning inference by models trained on the underlying data. We focus on advancements, challenges, and potential future directions in this field. We delve into the motivation for incorporating learned queries in concurrent data structures. We discuss the key techniques, applications, and open research questions. On the theoretical side, we discuss the mathematical foundations for building the learned data structures in addition to the correctness of queries in a concurrent setting. On the practical side, we present an overview of techniques used for managing concurrency with learned queries that need training the models with concurrent real-time updates. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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