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检索条件"机构=Computer Engineering and Information Technology"

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Healthcare Data Analysis using Competitive Ensemble Deep Learning Model

Healthcare Data Analysis using Competitive Ensemble Deep Lea...

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2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Gunavathy, K. Pandeya, Megha Sekar, R. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

By utilising novel techniques to boost classification performance, ML is utilised to obtain healthcare knowledge. These classifiers have the potential to boost model decision and hence boost patient care. Recent research has shown that, compared to mixed model classification, the accuracy of single-model classification is worse. This study delves into the value of DL methods for both organised and unorganised healthcare databases. The exponential growth of healthcare data has made it challenging to mine for meaningful trends. Clinical data volumes are manageable for DL, and the results are improved. To enhance classification efficiency for structured data, a fresh competitive ensemble deep learning model has been presented. When it comes to Twitter sentiment analysis, however, the suggested work emphasises a competitive DL model that can handle unstructured data. The report also covers the planned Capsule Network for text data along with the Gravitational Search Algorithm (GSA). In order to assess the efficacy of the proposed model, it is compared to a more conventional model. The algorithm improved upon previous methods of analysing user sentiment on Twitter. The proposed model outperformed industry standards in the vast majority of tests. Furthermore, for multi-label classification problems, the outcomes are superior in terms of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Machine Vision for Measurement Accuracy in Industrial Automation 15

Machine Vision for Measurement Accuracy in Industrial Automa...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ezhilarasan, G. Singh, Vaishali Upadhyay, Ramakant Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Himachal Pradesh India Department of Computer Science Engineering Sanskriti University Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

Commercial automation has been a powerful device for enhancing productivity, decreasing charges, and increasing performance. In particular, system vision systems have enabled the implementation of advanced automation answers, allowing accurate and rapid quality measurements and inspections. This paper examines the cutting-edge nation of the use of machine imaginative and prescient systems in industrial automation, highlighting the opportunities they provide in phrases of increasing dimension accuracy. It examines the specific sorts of device vision structures to be had, their advantages and boundaries, as well as their ability for improving the exceptional of business automation structures. It also addresses the challenges that ought to be overcome to make certain full adoption of such structures, as well as the strategies for fixing these challenges, inclusive of the improvement of progressed algorithms and laptop imaginative and prescient additives, and training applications for industrial engineers. This paper affords a comprehensive evaluation of associated studies and enterprise standards, permitting readers to gain a better expertise of the development of machine vision systems in industrial automation and to become aware of possibilities to improve business dimension accuracy. © 2024 IEEE.

关键词： Office automation

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The Convergence of Modern Digital Solutions with Online Retail Offers a Comprehensive Examination from Seller and Buyer Standpoints

The Convergence of Modern Digital Solutions with Online Reta...

引用

2023 International Conference on Recent Advances in Science and engineering technology, ICRASET 2023

作者： Dutta, Abhishek Yadav, Rakesh Kumar Muthupandi, G. Department of Computer Science and Information Technology Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306927

The convergence of modern digital solutions with online retail has ushered in a transformative era for both sellers and buyers. In this research, we present a comprehensive examination of this convergence from the perspectives of sellers and buyers. Our study encompasses the development of a novel proposed method, which is compared with several traditional methods, to assess the impact on various facets of online retail. The research explores multiple dimensions, including inventory management, marketing strategies, customer satisfaction, conversion rates, return rates, data security, sales revenue, and personalization effectiveness. The proposed method leverages the power of data-driven analysis and machine learning to provide enhanced insights and decision-making capabilities for online retailers. We evaluate its performance against traditional methods in these diverse aspects, seeking to determine whether modern digital solutions can indeed offer a competitive edge in the everevolving online retail landscape. Our findings reveal that the proposed method consistently outperforms traditional methods in several critical metrics. It demonstrates higher accuracy, precision, and recall, indicating its superior predictive power. Additionally, the proposed method exhibits a lower execution time, highlighting its efficiency in processing and analysis. Scalability, too, is a strong suit, making it suitable for handling larger datasets and accommodating the growing needs of online retail. In conclusion, the convergence of modern digital solutions with online retail is a game-changer for the industry, providing an opportunity for sellers to enhance their strategies and for buyers to enjoy a more personalized and satisfying shopping experience. The proposed method is the shining star in this transformation, showcasing better performance across various evaluation criteria. As the digital retail landscape continues to evolve, embracing modern solutions is not just a choice;it i

关键词： Customer satisfaction

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An Effective Approach Using Machine Learning for Fall Prediction 9

An Effective Approach Using Machine Learning for Fall Predic...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Vinora, A. Maheswari, P. Uma Deborah, R.Nancy Ajitha, E. Srinivasan, A. Sivakarthi, G. Velammal College of Engineering and Technology Department of Information Technology Madurai India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350314090

The ability to fall has now increased due to the usage of phones as well as a variety of distractions on the streets that has led to a major public concern. Fall is an activity that can be caused to carelessness or incoordination. It is also attributed to monitoring senior citizens who tend to have a higher fall rate due to age-related illness, diseases, or poor visibility. It can be predicted for also soldiers for an unknown battle field based on their momentum. A dataset of 40000 rows have been obtained from Kaggle which was procured using a three-axis gyroscope that was deployed on a smartphone to collect data on falls during activities such as walking, jogging, standing, and sitting. A set of Machine Learning (ML) algorithms such as Scaled Conjugate Gradient, Bayesian Regularization, Levenberg-Marquardt, BFGS Quasi-Newton Backpropagation, Resilient Backpropagation, Conjugate Gradient Backpropagation, Conjugate Gradient Backpropagation With Polak-Ribiére Updates, One-Step Secant Backpropagation, Gradient Descent With Momentum And Adaptive Learning Rate Backpropagation and Gradient Descent With Momentum Backpropagation were deployed on the built model to check for fall prediction. Among them, Bayesian Regularization provided high accuracy rates considering cross entropy as a performance metric. This technology helps to prevent the fall of a person that can lead to an uneventful situation. © 2023 IEEE.

关键词： Gyroscopes

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Real-Time Atmospheric Duct Height Prediction Framework Based on Spatio-Temporal to Ensure Maritime Communication Security 18th

Real-Time Atmospheric Duct Height Prediction Framework Based...

引用

18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Ben Yan, Ke Hui, Bei Huang, Qian Gao, Hui School of Computer Science and Engineering University of Electronic Science and Technology of China ChengDu China KASH Institute of Electronics and Information Industry Kashi China

ISBN: (纸本)9783031714665

Atmospheric duct (AD) is a natural phenomenon affected by humidity or air pressure diversities, which impacts the propagation of electromagnetic signals. Recent studies have been focused on ADH prediction based on meteorological data which makes the task of real-time ADH prediction challenging and impacts the global representation of ADs leading to maritime communications security issues. We address these issues by discovering a novel way to abstract the spatial and temporal features from the historical data. Thus, we proposed a framework based on DNN models and attention mechanisms to predict the ADH in a real-time form from the abstracted features. Our experimental results demonstrate that our technique improves the performance of real-time prediction and reduces the acquisition cost, professional devices can receive it in real-time through IoT to avoid dangerous areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ducts

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Coordinated Fog-to-Cloud System Managing

Coordinated Fog-to-Cloud System Managing

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Nair, Amrutha V. Nachappa, M.N. Singh, Vaishali Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Recent developments in cloud services information have sparked several improvements in networking, storage, and computing, among other areas of information technology. The Internet of information, the use of clouds, and the newly developed idea of fog computing have all emerged as a result of this transition. The processing, storing, and exchanging of data between edge devices, user clients, and the cloud are all included in fog computing. Although their quick development was first unanticipated, these technologies currently allow for the implementation of what are known as smart scenarios, such as smart homes, smart cities, and smart transportation. This article outlines the way toward a new IoT environment with highly dispersed and dynamic storage, computing, and networking resources. We also describe an architecture, an illustrated use case, and a comparative productivity study. A wide range of heterogeneity and commodity devices at the edge, developing fog-based resources, and traditional cloud services are all present in this ecosystem. © 2023 IEEE.

关键词： cloud computing decentralized operation disaster management edge devices fog computing Fog-to-Cloud system Internet of Things (IoT) technical challenges and cloud market opportunities urban planning

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Effective Bandwidth based Distributed DoS Attack Detection and Source Identification using Maximum Flow Techniques 3

Effective Bandwidth based Distributed DoS Attack Detection a...

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

作者： Subburaj, T. Suthendran, K. Darshan, P. Deeraj, C. Jai Shankar, M. Pushpalatha, G. Department of Master of Computer Applications Rajarajeswari College of Engineering Bangalore India Department of Information Technology Kalasalingam Academy of Research and Education TamilNadu India

ISBN: (纸本)9798331527549

The DDoS attack is one of the most threatening factors affecting the internet services. Now days, most of the common attacks are targeting the gaming industry, online shopping websites followed by the software and media sectors. Many of the service providers are available to detect and prevent the DDoS attacks. But their software’s are not updated enough to detect the recent time attacks. Cyber security challenges include rising ransomware attacks, AI-powered threats, supply chain risks, and frequent data breaches. In the current IoT trend, available DDoS defenses aren’t sufficient. This work is aimed to propose a novel BW- DDoS attack detection approach based on maximum data flow identification in networks. The proposed method is faster and taking less number of iterations to determine the maximum data flow and also trace out the source of the attacks with minimum time compared with existing approaches. © 2025 IEEE.

关键词： Attacks BW-DDoS Entropy Maximum flow Trace out

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MHANDTI: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Multi-Hop Attention Networks

MHANDTI: Drug-Target Interaction Prediction Model Based on H...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Chen Sun, Jiaqi Xing, Linlin Zhang, Longbo Cai, Hongzhen Guo, Maozu Shandong University of Technology School of Computer Science and Technology Zibo China Shandong University of Technology College of Agricultural Engineering and Food Science Zibo China Beijing University of Civil Engineering and Architecture College of Electrical and Information Engineering Beijing China

ISBN: (纸本)9798350386226

Predicting drug-target interactions (DTI) has become an important step in the drug discovery and drug repositioning process. The biological identification of DTI incurs significant financial and temporal costs, and the deployment of computational methodologies for DTI prediction can substantially curtail both the duration and economic expenditure of drug discovery or repositioning. Drawing from disparate data sources can offer a comprehensive overview and diverse perspectives for predicting drug-target interactions. However, existing methods for processing drug and target information are unable to capture long-range dependency across heterogeneous graphs. To capture more comprehensive drug and target features for DTI prediction, this article proposes a drug-target interaction prediction model based on heterogeneous graph multi-hop attention networks, called MHANDTI. Concretely, the word-level deep convolutional neural network is used to obtain the structural features of drugs and targets as attributes of drug and target entity nodes in heterogeneous graphs. For the processing of heterogeneous graphs, MHANDTI designs a multi-hop attention diffusion layer (MHADL) to establish a connection between the non-direct connection nodes, and summarize the characteristics of the adjacent nodes of the center nodes in several hops. A series of results indicate that the performance of this method is superior to the latest existing frameworks. © 2024 IEEE.

关键词： Convolutional neural networks

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Model-driven Time-varying Channel Estimation Method for OFDM System Based on Neural Network 24

Model-driven Time-varying Channel Estimation Method for OFDM...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Zhu, Yue Fan, Dian Mu, Yunping Wang, Gongpu China Academy of Information and Communications Technology Beijing100191 China Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer and Information Technology Beijing100044 China

ISBN: (纸本)9798350363760

Channel estimation plays an important role in orthogonal frequency division multiplexing (OFDM) systems, which directly affects the performance of wireless communication systems in high-speed mobile scenarios. In this paper, we propose a model-driven channel estimation method for OFDM systems with doubly selective channels. Instead of completely abandoning traditional communication modules, we first obtain rough channel estimation using the least square (LS) method and then use a neural network called RefineNet for accuracy improvement. The results show that our proposed method significantly outperforms the linear minimum mean squared error (LMMSE) method. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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