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10th International Workshop 3D-ARCH "3D Virtual Reconstruction and Visualization of Complex Architectures"

作者： Codiglione, Matteo Mazzacca, Gabriele Remondino, Fabio University of Trento Italy Trento Italy Dept. Mathematics Computer Science and Physics University of Udine Italy

3D point clouds feature valuable geometric and, often, radiometric and semantic information to support studies, analyses and understanding of the surveyed scene. Due to their generally large size, the use and interpretation of point clouds could be problematic. User-friendly and quick approaches for querying these valuable datasets and retrieving information could surely support end-users, in particular in the heritage sector. This work presents an ontology-based approach to facilitate the query and use of 3D heritage point clouds by means of sets of rules in order to infer properties and characteristics of the surveyed scene. Our approach is focused on linking together 3D spatial data and expert knowledge, in a way that the ontology can elaborate, represent, enrich and query a given point cloud. Results show how different queries can be set-up and how the procedure can be replicated to various queries and datasets. © 2024 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Ontology

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A review on 3D image reconstruction on specific and generic objects

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Materials Today: Proceedings 2023年 80卷 2400-2405页

作者： Shalma, H. Selvaraj, P. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Information Technology SRM Institute of Science and Technology Kattankulathur Chennai India

With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D Data Network Architectures, dept. Estimation methods, Multi View Representations and the Data Representation Techniques. © 2021

关键词： Object recognition

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Leveraging Permissioned Blockchain for securing controlled drug prescriptions in India 6

Leveraging Permissioned Blockchain for securing controlled d...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Sunku, Sai Sudhir Varuni, H.K. Vinodha, K. Dept. of Computer Science PES University Bengaluru India

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351538

The pharmaceutical industry in India grapples with challenges related to the unauthorized procurement of controlled drugs listed in Schedule H and beyond, often due to the absence of genuine prescriptions or the use of invalid ones. In response, this paper proposes a targeted permissioned blockchain-based solution to ensure the authenticity and validity of prescriptions for such controlled medication. By integrating blockchain technology, the system provides a secure and transparent platform for issuing prescriptions by authentic doctors and verifying them by authorized pharmacists. The inclusion of nodal authorities, responsible for implementing the Drug and Cosmetic Act and its rules, as validators in the Proof of Authority (PoA) consensus incentivizes them to adopt this model. By employing PoA, the benefits of blockchain as a decentralized ledger can be utilized, along with fast transaction approvals and high scalability. Our approach not only mitigates the risks associated with privacy concerns of patient information but also enhances accountability and trust within the healthcare ecosystem. The paper surveys the present research regarding application of blockchain based prescription systems across the world, and discusses the feasibility of such a system in the Indian context. © 2024 IEEE.

关键词： Smart contract

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Unlocking Textual Relationships: Deep Learning for Recognizing Entailment 9

Unlocking Textual Relationships: Deep Learning for Recognizi...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Pranata, Franco Antonio Tjondropurnomo, Matthew Christopher Setiawan, Nicholas Lorenzo Nur Majiid, Muhammad Rizki Dwi Wijanarko, Bambang Binus University Dept. of Computer Science Semarang Indonesia

ISBN: (纸本)9798331510732

In the field of Natural Language Processing (NLP), understanding implicit meanings in texts is a complex and significant task. This study delves into the use of deep learning models to address these challenges, specifically by focusing on the implementation and evaluation of RoBERTa models. The research compares the performance of RoBERTa with other variations of BERT such as DistilBERT and DeBERTa using evaluation metrics such as accuracy, precision, recall, F1 score, BLEU score and Cohen's Kappa. Findings revealed that RoBERTa outperformed other models achieving an accuracy rate of 88.05%, a precision level of 88.02 %, recall rate at 88.01 % and F1-score score of 88.01 %. Furthermore, with BLEU score of 0.783 and Cohen's Kappa at 0.778, RoBERTa demonstrates its ability to make predictions that are in line with expert annotations and reflect human judgments. This research highlights the potential for advanced BERT models to improve the accuracy and reliability of textual implication identification thus contributing towards more sophisticated and adaptable NLP applications. © 2024 IEEE.

关键词： Contrastive Learning

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Advancing Medical Imaging: A Comparative Exploration of Generative Adversarial Networks for Chest X-ray Synthesis

Advancing Medical Imaging: A Comparative Exploration of Gene...

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

作者： Keerthi, R. Kiran, Kuval Kiran, S. Likitha, P. Pes University Dept. of Computer Science Bangalore India

ISBN: (纸本)9798350376876

Chest X-ray imaging is essential for diagnosing and treatment planning of individuals with diseases like COVID-19, Tuberculosis, and Pneumonia. However, creating efficient machine-learning algorithms for early detection is made difficult by a lack of labeled medical images. The study investigates the possibility of using Generative Adversarial Networks (GANs) to create synthetic chest X-ray images for various lung diseases in order to address this issue. This study compares three distinct GAN architectures: Progressive Growing of Generative Adversarial Network (ProGAN), Deep Convolutional Generative Adversarial Network (DCGAN), and Auxiliary Classifier Generative Adversarial Network (ACGAN). Acknowledging constraints within existing GAN architectures, the study employs a hybrid methodology. To improve training stability and image quality, this method combines the progressive generation of ProGAN with the Gradient Penalty loss of Wasserstein GAN (WGAN-GP). An InceptionV3 classifier was used to assess the quality of the generated images. On the original dataset, this classifier recorded an accuracy of 91%. The classifier achieved 72.08 %, 68.62 %, and 45.50% accuracy on images generated by ProGAN, DCGAN, and ACGAN, respectively. The study demonstrates GANs potential to assist in alleviating data scarcity, hence improving the precision and effectiveness of machine learning algorithm training. © 2024 IEEE.

关键词： Generative adversarial networks

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Triple-Node-Upset Recoverable Radiation-Hardened by Design Latch

Triple-Node-Upset Recoverable Radiation-Hardened by Design L...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Paparsenos, Evangelos Tsiatouhas, Yiorgos Lab Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350318845

CMOS technology evolution enhances integrated circuits (ICs) performance characteristics at the cost of their increased susceptibility to radiation and thus to the occurrence of single-event upsets (SEUs) that may lead to soft errors generation. SEUs may affect multiple nodes in a circuit (multiple node upsets - MNUs). Fortunately, these disturbances do not cause permanent damage to the circuits. Various techniques have been proposed to deal with SEUs that concurrently affect one, two or three nodes. In this paper, we propose the design of a latch that offers tolerance up to the level of triple-node upsets (TNUs). This is achieved by using redundancy in the hardware which provides the ability to store a logic value in multiple nodes within the latch as well as by using multiple feedback paths which allow it to recover its correct state in the event of an SEU. Compared to other techniques dealing with the same problem, our proposed method provides faster recovery time (higher than 16.87%) after an SEU, and at the same time reduced power consumption and higher speed performance. © 2024 IEEE.

关键词： Radiation hardening

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Data Driven Approach to Leaf Recognition: Logistic Regression for Smart Agriculture 5

Data Driven Approach to Leaf Recognition: Logistic Regressio...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Abbasi, Altamash Ahmad Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Artificial Intelligence has been involved in the restructuring of farming practices. Compared to conventional methods that involved using pesticides to remove weeds, smart and precision agriculture has increased productivity and produced healthy crops. Plant leaves with varying behaviors should be separated as one of the measures in preserving yield quality. We suggest using logistic regression to implement a four-step AI-based process to classify different leaves. Pre-processing, segmentation and feature extraction, have been used for images classification. Various grapevine and swedish leaves have been used in the work by applying the suggested technique to two different datasets. We used image of size $256\times 256$. During pre-processing, the median filter was used, k-means was used for segmentation, Kaze and Blob were selected for feature extraction. Logistic regression was used for classification. Our findings demonstrate that the suggested strategy beats alternative approaches, yielding an accuracy of 88% on the swedish leave dataset and 83% on the datasets pertaining to grapevine leaves. © 2024 IEEE.

关键词： Feature extraction

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Maternal Risk Factors and Infant Sleep Difficulties: A Multivariate Regression Analysis and Comprehensive Review of Empirical Studies 8

Maternal Risk Factors and Infant Sleep Difficulties: A Multi...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Mala, Shehzad Bhingarkar, Sukhada Prasad Mit Wpu Dept. of Computer Science Pune India

ISBN: (纸本)9798350391770

The inability to get an infant to sleep can be a major stressor for everyone involved since it can affect the family's health and development. Understanding the impact of maternal characteristics, especially those considered "at risk,"is crucial for creating successful interventions. The paper employs multivariate regression analysis to examine the correlation between maternal risk factors and baby sleep problems. Compile empirical study results and utilize literature evaluations to comprehend the complex interaction between maternal features and infant sleep patterns... These findings stress the need to consider a wide range of variables when trying to forecast when a baby will sleep, including the mother's socioeconomic position, her mental health, the stress she experiences as a parent, and the bond between her and her child. To guide future studies, review several methodological approaches and point out where the literature is lacking. Researchers, physicians, and legislators aiming to promote good baby sleep health in vulnerable populations can ultimately benefit from the insights provided by this analysis. © 2024 IEEE.

关键词： Sleep research

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Dynamic Real-Time Scheduling on Distributed Hierarchical Fog Networks 31

Dynamic Real-Time Scheduling on Distributed Hierarchical Fog...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Amit Auluck, Nitin Dept. of Computer Science & Engineering IIT Ropar India

ISBN: (纸本)9798331509118

We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (ML) techniques, our proposed solution autonomously allocates tasks across fog nodes, dynamically adapting to changing workload patterns and system conditions. Our novel methodology integrates supervised learning algorithms to predict workload patterns and resource availability, enabling intelligent decision-making in task assignment. Our findings contribute to advancing the state-of-the-art in fog computing by offering a scalable and adaptive solution for dynamic task scheduling in hierarchical heterogeneous environments. © 2024 IEEE

关键词： Fog computing

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Stuxnet Unveiled: The Blueprint for Modern Cyber Conflict 3

Stuxnet Unveiled: The Blueprint for Modern Cyber Conflict

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rai, Preeti Nain, Vishal Dept. of Computer Science and Applications New Delhi India

ISBN: (纸本)9798331518592

The 2010 discovery of the Stuxnet malware signaled a sea change in the field of cyberwarfare, and the 21st century has seen an unparalleled spread of digital technology and their incorporation into vital infrastructure systems. Given this, Stuxnet is a sobering reminder of the weaknesses present in our globalized society. This paper aims to investigate the intricacies of the Stuxnet attack, including its origins, development, objectives, and significant implications for cybersecurity and international relations. By carefully analyzing this sophisticated cyberweapon, this study sheds light on how cyberthreats are evolving in the twenty-first century. © 2024 IEEE.

关键词： Critical infrastructures

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