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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sunaina Kaur, Sukhpreet Kaur, Sukhdeep Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Fungal infections at the skin of puppies, including dermatophytosis, are commonplace and pose great health dangers to each the animals and their proprietors. Although these infections do no longer affect human skin immediately, rapid detection remains important for well-timed remedy and spreading prevention. The currently established diagnosis process frequently includes the visual confirmation of the infection by medical professionals and fungal culture and is often referred to as time-intensive and highly dependent on medical professionals’ qualifications. Over the past few years, deep learning has become quite a promising area of research, R&D, and deployment in multiple fields, including medical imaging. The current research develops a deep learning model for fungal infection detection on the skin of dogs based on a variety of architectures and variations of Convolutional Neural Networks to classify image patches as healthy and infected. Transfer learning for the model structuring is reviewed to determine the impact of pre-trained models on the model’s learning efficiency. © 2025 the Author(s).

关键词： Convolutional neural networks

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News Sentiment Analysis in Financial Markets: A Survey 8

News Sentiment Analysis in Financial Markets: A Survey

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Thakur, Akshat Shaw, Prajwal Das, Soma Institute of Engineering and Management University of Engineering and Management Department of Computer Science and Engineering India

ISBN: (纸本)9798331520762

In recent years, the integration of sentiment analysis into financial markets has gained considerable attention, particularly through the use of machine learning and deep learning techniques. This survey explores the progress and challenges in applying these models to analyze news sentiment and its impact on market behavior. The work describes a comparison of recent deep learning architectures, such as Long Short-Term Memory, Convolutional Neural Networks, and hybrids in comparison to more traditional approaches of machine learning such as Support Vector Machines and Random Forest. The experiments indicated that the deep learning technique generally performed better than those conventional techniques, particularly where the model used a pre-trained language model. This work offers a comprehensive overview of the current state of development in financial news sentiment analysis and points out areas for further research in making models more robust and accurate. © 2025 IEEE.

关键词： Financial markets

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Projection Image Synthesis Using Adversarial Learning Based Spatial Transformer Network For Sparse Angle Sampling CT 46

Projection Image Synthesis Using Adversarial Learning Based ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zhou, Huanyi Reeves, Stanley Liu, Jueting Auburn University Electrical and Computer Engineering Department AuburnAL36830 United States China University of Mining and Technology Computer Science and Technology 221000 China

ISBN: (纸本)9798350371499

In sparse-angle X-ray tomography, CT images possess significant noise and artifacts in reconstruction. An important image reconstruction research effort in this area aims to remove these artifacts while preserving image features. Approaches to the problem can be divided into two main categories, post-processing on reconstructed images and pre-processing on the sinogram. Following the rise of deep learning methods, many data-driven models have been proposed in both categories. However, those methods require a large amount of fully sampled data for training. In this paper, inspired by the development of the video frame synthesis technique, we propose an adversarial learning-based spatial transformer network for projection image synthesis, which aims to improve the quality of reconstructed images by reasonably increasing the number of projection images based on the existing projection data. Simulation and experiments have shown that this data-driven model can give rise to competitive results compared with conventional algorithms. © 2024 IEEE.

关键词： Spatio-temporal data

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Heart Disease Risk Analysis and Assessment: A Data Driven Approach

Heart Disease Risk Analysis and Assessment: A Data Driven Ap...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Kumar, B.H. Manjunatha Kumar, Abhinav Iyer, Aditya Soumika, M.L. Mayuri, S. SJC Institute of Technology Computer Science & Engineering Chikkaballapur India SJC Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

The heart is indispensable for body. Diagnosis of heart disease must be complete and accurate. Heart disease is a dangerous disease. This disease is caused by many problems such as excessive stress, diabetes, high blood pressure, cholesterol and other factors in the human body. This article reviews and predicts heart disease using Python and machine learning. We can anticipate the occurrence of this disease by analyzing various features within the dataset to analyze the patient's performance, we have compiled a dataset containing 13 scores and 38 individual values. The principal focus of this article is to enhance the exactness of cardiac disorder assesment through employing machine learning algorithms. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Cloud Infrastructure Security with GraphSAGE-Based Intrusion Detection Systems 1

Enhancing Cloud Infrastructure Security with GraphSAGE-Based...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, G. Nanda Kishor Syed, Shakir Mogal, Amit Karbhari Abid, Tahera Perada, Anvesh Suneetha, Sampathirao University of South Florida United States Malla Reddy University Department of Computer Science and Engineering Hyderabad India Bargersville IN United States Mvp Samaj's Cmcs College Department of Computer Science and Application Nashik India Nawab Shah Alam Khan College of Engineering and Technology Department of Information Technology Hyderabad India Drexel University Department of Electrical and Computer Engineering Philadelphia United States Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798331529246

Cloud computing provides scalable and adaptable infrastructure however is significantly susceptible to cyber-attacks. This work presents a hybrid methodology that integrates the GOA and GA for feature selection, alongside the Residual GraphSAGE model for intrusion detection. The hybrid GOA-GA method enhances feature selection by optimizing the balance between exploration and exploitation, mitigating overfitting, and augmenting classification performance. The Residual GraphSAGE model alleviates over smoothing while effectively capturing node and edge interactions for precise intrusion classification. Assessment on a benchmark dataset reveals exceptional performance, attaining an accuracy of 91.74%, surpassing established models such as GCN and ResNet. This study emphasizes the need of hybrid optimization and sophisticated graph-based learning in improving cloud infrastructure security. © 2025 IEEE.

关键词： Network intrusion

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A Survey on Wireless Sensor Network Routing Performance Optimizing and Security Techniques 4th

A Survey on Wireless Sensor Network Routing Performance Opti...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Nag, Anindya Mandal, Dishari Roy, Nilanjana Firoz Ahmed Fahim, S.M. Chand, Nisarga Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science and Engineering Adamas University Kolkata700126 India Dhaka1229 Bangladesh Department of Electrical and Communication Engineering Adamas University Kolkata700126 India

ISBN: (纸本)9789819726134

Wireless Sensor Network (WSN) is a contemporary topic of research that has proven to be an advantageous technology for a wide variety of applications, including those dealing with the environment, the military, and health, as well as uses at home and in the workplace. There are two different kinds of WSNs: mobile WSN and static WSN. Distribution, location, and tracking are three of the many new conceptual and optimization challenges presented by WSNs. These challenges are particularly important given the fact that a great number of applications are dependent on WSNs for information. There are a number of challenges associated with this type of WSN, including high bandwidth requirements, high energy consumption, providing Quality of Service (QoS), data processing, compression, and cross-layer design. Authors of this review have conducted a survey of the relevant literature and draw the conclusion that routing protocols for WSNs make use of a variety of approaches, such as Hierarchical Based Routing Protocols (HBRP) and Location-Based Routing Protocols (LBRP), in order to accomplish their goals. Each of these approaches is capable of meeting the necessary transmission specifications. The fundamental goal of the analysis is to ensure that both power consumption and security are taken into account while conducting the analysis of performance matrices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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RECRIoT-MAC: A Reliable and Energy Efficient Resource Management for Internet of Things Based Cognitive Radio Multi- Channel MAC Protocol

RECRIoT-MAC: A Reliable and Energy Efficient Resource Manage...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Agrawal, Navin Kumar Kumar, Sandeep Bisht, Girish Singh Srivastav, Akanksha Bansla, Vandna Jain, Arpit Gla University Department Of Computer Engineering And Applications U.P. Mathura India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

Currently, the overcrowding of unlicensed spectrum bands is causing severe interference to IoT devices embedded with sensors. CR-IoT networks apply cognitive radio capabilities to the internet of things to solve the spectrum scarcity issue in conventional IoT networks. An CR radio detects available spectrum channels by dynamically modifying its programming and configuration. IoT, smart cities, and 5G all benefit from cognitive radio applications. A cognitive radio application is useful for IoT, smart cities, and 5G. Compared to benchmark Cognitive Radio MAC (CR- MAC) protocols, the proposed protocol is more reliable, intelligent, and smart. CR technology for IoT applications can be advanced by us in order to make IoT more affordable and applicable. It is demonstrated by comparing the proposed RECRIoT-MAC protocol with benchmark CR-MAC protocols that is more energy efficient and consumes less communication time. This paper examines RECRIoT-MAC's energy consumption and throughput. © 2024 IEEE.

关键词： Cognitive radio

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On the Convergence of Finite Element Domain Decomposition Methods for Discontinuous Subdomain Interfaces

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 4122-4127页

作者： Xin, Xi-Min Gao, Hong-Wei Wang, Shu Lim, Qi Jian Peng, Zhen Beijing Institute of Technology Beijing100081 China University of New Mexico Applied Electromagnetics Group Department of Electrical and Computer Engineering AlbuquerqueNM87131 United States NVIDIA Corporation CA95050 United States University of Illinois at Urbana-Champaign Electrical and Computer Engineering Department UrbanaIL61801 United States

In this paper, we propose a finite element domain decomposition method with Robin-type transmission conditions at discontinuous subdomain interfaces. By definition, these discontinuous interfaces refer to impedance surfaces where electromagnetic fields exhibit discontinuities, which are common in applications of metamaterials/metasurfaces, frequency-selective surfaces, or thin dielectric layer approximation. Through a detailed convergence study, we propose an optimized set of parameters in the transmission condition (TC). The proposed TC provides convergence for both propagating and evanescent electromagnetic waves across subdomain interfaces and is valid for all resistivity values. Numerical experiments are supplied to validate our theoretical analysis, demonstrating the convergence and accuracy of our proposed work. © 1963-2012 IEEE.

关键词： Electromagnetic wave transmission

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A Machine Learning Approach for Predicting Physical Activity Intensity from Wearable Sensor Data 7

A Machine Learning Approach for Predicting Physical Activity...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Venkata Lavanya, T. Sivaraman, K. Department of Computer Science and Engineering Tamil Nadu Chennai India Dept of Computer Science and Engineering Tamil Nadu Chennai600073 India

ISBN: (纸本)9798350372816

The paper aims to establish a robust machine learning approach for accurately categorizing the level of physical activity based on data collected from wearable sensors. Data from more than 10,000 participants was gathered through the utilization of sensors embedded in smartphones and smartwatches. These sensors include accelerometers, gyroscopes, and heart rate monitors. The acquisition of critical attributes, such as heart rate metrics, spectral entropy, and variance, involves meticulous feature engineering and thorough preprocessing. These measures effectively capture the temporal and spectral aspects of physical activity. An ensemble model is constructed using Gradient Boosting Machines (GBM) to achieve precise predictions of activity intensity categories. The GBM model consistently maintains accuracy levels above 99% for all types of physical activity, ranging from moderate to vigorous, sedentary, and mild. In order to ensure the model's robustness and prevent overfitting, hyperparameter optimization is conducted through k-fold cross-validation. The findings demonstrate the impressive precision with which machine learning algorithms classify activities. The implications can have far-reaching effects on the promotion of physical activity and the improvement of health outcomes. © 2024 IEEE.

关键词： Gyroscopes

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Secured Entry Control: An IoT and Deep Learning based Mask Detection System for COVID-19 Suspected Patient Screening and Access Regulation 3

Secured Entry Control: An IoT and Deep Learning based Mask D...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Sultana, Abida Peyal, Hasibul Islam Islam, Md. Injamul Abdal, Md. Nazmul Miraz, Shafiun Kontho, Md. Mahin Rahaman Leion, Zannatul Mawa Mamun, Abdul Al Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Tejgaon College Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350388282

The new coronavirus SARS-CoV-2, which triggered the COVID-19 pandemic, has had an unparalleled effect on economies, cultures, and world health. In response to the critical need for strict COVID-19 screening systems in public areas, this study presents a creative Secured Entry Control system. Detecting and controlling possible COVID-19 carriers attempting to enter the country is made possible by this system, which makes use of deep learning algorithms and IoT technology. The mask detection algorithm, MobileNetV2 model, has an exceptional validation accuracy of 98.96\%. The model's reliability is supported by performance evaluations using ROC curves, confusion matrix analysis, and an AUC value of 98.96\%, which is close to the optimal AUC score of 100\%. Because MobileNetV2 is well-suited for low-processing devices, it is easy to deploy it on Raspberry Pi, which helps to create an affordable system. Furthermore, by spotting increased body temperatures, a contactless temperature sensor improves the system's ability to identify possible COVID-19 carriers. The functioning of the system is confirmed by the working prototype that is presented in the Experimental Results section. The main goal of this research is to create an autonomous system that is affordable and selectively allows access to people who are less likely to transmit COVID-19 in public areas. To achieve the overall objective of reducing the spread of COVID-19 in public spaces, this research highlights the successful integration of mask identification algorithms and IoT. © 2024 IEEE.

关键词： COVID-19

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