检索结果-内蒙古大学图书馆

A New Integrated Approach for Cloud Service Composition and Sharing Using a Hybrid Algorithm

Mathematical Problems in engineering 2024年第1期2024卷

作者： Jayaudhaya, J. Jayaraj, R. Ramash Kumar, K. Department of Electronics and Communication Engineering Rmd Engineering College Chennai India Department of Data Science and Business Systems Srm Institute of Science and Technology Chennai India Department of Electrical and Electronics Engineering Dr. Ngp Institute of Technology Coimbatore India

The concept of a "Smart City"emphasizes the need to employ information and communication technologies to strengthen the quality, connectivity, and efficiency of various municipal services. Cloud computing and the Internet of Things are shaping future tech. Both ideas greatly impact smart city application and solution development. Cloud computing is amazing at managing and storing remote service access. Several companies have switched to cloud leasing to reduce local resource burden. Due to the intricacy and flexibility of cloud-maintained services, selecting jobs that best suit client needs should be optimized. Quality of service criteria for each cloud service are the best tools for choosing and optimizing cloud carriers. Genetic algorithms (GAs) and ant colony optimization (ACO) are combined to make cloud computing. It is discovered that the recommended ACO + GA obtains an accuracy of 82% when compared to existing methods of energy- and reliability-aware multiobjective optimization method and the hybrid cuckoo particles swarm, artificial bee colony optimization (CPS + ABCO) where accuracy is 68% and 75%, respectively. © 2024 Jayaudhaya J. et al.

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

DC Voltage Prediction based on CNN with Physics Information Embedding for MVDC Distribution systems 10

DC Voltage Prediction based on CNN with Physics Information ...

引用

10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Sun, Pingyang Wu, Rongcheng Wang, Hongyi Shen, Zhiwei Li, Gen Khalid, Muhammad Konstantinou, Georgios Unsw School of Electrical Engineering and Telecommunications Sydney Australia University of Technology Data Science Institute Sydney Australia Alborg University Department of Aau Energy Denmark Dtu Department of Engineering Technology and Didactics Electric Energy Denmark King Fahd University of Petroleum and Minerals Electrical Engineering Department & Interdisciplinary Research Center for Sustainable Energy Systems Saudi Arabia

ISBN: (纸本)9798350351330

Conventional neural networks (NNs), though efficient in rapid dc voltage calculations for medium-voltage direct current (MVDC) distribution systems with diverse converter control schemes, face accuracy challenges with untrained system parameter distributions and topology structures. This paper proposes a physics-embedded convolutional NN (PECNN) to address this issue. The PECNN is enhanced with two additional layers, including a multi-channel combination layer and a physics operation layer. The former channel combination layer strengthens the model feature extraction capability by converting all input channels constituted by initial data matrix into dc voltage, current and line conductance matrix channels. The latter physics operation layer reformulates the combined input channels by physical connections in MVDC systems. The new layers enhance the prediction accuracy and allow generalization of the model. Three MVDC distribution networks with different dc voltage levels and network layouts are used to verify the superiority of proposed PECNN compared to other NNs in different topologies. © 2024 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Hybrid Scheduling and Authentication for Legacy Uniprocessor and Multicore System 12

Hybrid Scheduling and Authentication for Legacy Uniprocessor...

引用

12th International Conference on Computer Communication and Informatics, ICCCI 2022

作者： Muneeswari, G. Selvaraj, Prabha Burugari, Vijay Kumar Kanmani, P. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India K L University Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeeswaram Andhra Pradesh India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India

ISBN: (纸本)9781665480352

In the era of advanced internet technology and parallel processing, the identification of the suitable system is more important than the inefficient computation with the non-compatible work stations. Eventually, it is a mass requirement from the run time environment to perform an efficient scheduling with suitable workloads. Ideally, in the heterogeneous system which comprises of advanced multicore system and legacy uniprocessor system, the resource providers in the network requires high level hybrid scheduling where the heavy workload with parallel processing will be allocated to parallel multicore system and light workload generously allocated to legacy uniprocessor system. In this paper, we come out with the solution for the integration of a new system wherein tasks are distributed to parallel and uniprocessor computing nodes. In advent with the novel idea of scheduling, authentication also provided for workload execution. This authentication is incorporated using MAC code calculation. The evaluation results show that this hybrid scheduling increases the performance of the multicore system as well as uniprocessor system to 8.9% compared to the traditional systems. © 2022 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

A 2.86-TOPS/W CMCB based Edge ML and RO-PUF engine for IoT based nano-electronic material applications

引用

Materials Today: Proceedings 2022年 62卷 1780-1784页

作者： Rajasekar, P. Rama prasad Reddy, M. Deepak, Karanam Balamurugan, K. Amudha, S. Vignesh, C.J. Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu India Head of the Department and Professor Department of Electrical and Electronics Engineering G.pullaiah College of Engineering and Technology India Department of EEE G.Pullaiah College of Engineering and Technology Andhra Pradesh Kurnool518452 India Department of Electrical and Electronics Engineering Sri Ramakrishna Engineering College Tamil Nadu Coimbatore India SRM Institute of Science and Technology Tamilnadu India KPR Institute of Engineering And Technology Tamilnadu Coimbatore India

Energy-Effective Machine Learning and Ring Oscillator (RO) Physical Untraceable (PUF) is an IoT care effort that diminishes information move limit and ensures estimations and search interface protection (IoT). Independent contraption. This test gives preparing on Edge machines and PUF machines for IoT errands. This planning utilizes the current glass beam (CMCB), which is the focal circuit vertical to the two capacitors, to diminish the space of the concede line by a variable of48.5. Another element of expansion style has been proposed to broaden the weight matrix factors past a genuine authorized organization, while keeping the expense of clothing and energy low. Paper development and total is performed by CMCB with current discriminative styles and two-venture change. The means proposed give a disappointment rate of6.34 to the MNIST plate acknowledgment issue with an energy attainability of2.86 covers/W. PUF gives a unique harmony disappointment rate (BER) of2.3 in everywhere and the CRP is upgraded with ternary information sources. This mode has developed incomprehensibly, with a space of4.17 × for taking care of all solicitations/reactions (CRP). Extremely low-10–59 m 2/CRP. © 2021

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling for Disease Progression in Chronic Conditions Using Machine Learning 6

Predictive Modeling for Disease Progression in Chronic Condi...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Umamaheswari, T.S. Dhaygude, Amol Dattatray Dewangan, Omprakash Krishnan, T. Yerpude, Poonam Swarnkar, Suman Kumar Jayagovind Harigopal Agarwal Agarsen College Department of Computer Applications Chennai India University of Washington Department of Data Science Seattle United States Kalinga University Department of CS & IT Chhattisgarh Raipur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Indian Institute of Technology Department of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Department of Computer Science and Engineering Raipur India

ISBN: (纸本)9798350304480

Predictive modeling of disease progression in chronic conditions is a crucial task in healthcare, as it enables early identification and personalized intervention for patients at risk of adverse outcomes. In recent times, there has been a notable advancement in the field of machine learning techniques, which has demonstrated considerable potential in accurately forecasting the evolution of diseases. This development has proven to be highly beneficial for healthcare professionals, as it equips them with vital insights that can significantly enhance the management of patients. This research paper provides a thorough examination and comparative evaluation of different machine learning algorithms in the context of forecasting the course of diseases in chronic situations. We explore the use of diverse datasets containing longitudinal patient records, clinical variables, and disease-specific markers to train and evaluate predictive models. The study also investigates the impact of feature selection and data preprocessing techniques on the model's performance. Moreover, we assess the generalizability and robustness of the models across different chronic diseases to determine their potential for broader clinical applications. The results demonstrate that machine learning models can effectively predict disease progression, with certain algorithms outperforming others in specific scenarios. The research findings presented in this study offer significant contributions to the understanding and implementation of machine learning techniques for predicting disease development. The aforementioned realizations have the potential to be of substantial assistance to medical professionals in terms of making well-informed decisions, which will ultimately lead to better outcomes for patients. © 2023 IEEE.

关键词： Chronic conditions Clinical applications Clinical variables data preprocessing Disease progression Feature selection Generalizability Healthcare Longitudinal patient records Machine learning Patient outcomes Predictive modeling

来源：评论

学校读者我要写书评

暂无评论

Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing 2

Advancements in Artificial Intelligence for Better Image Ana...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Saravanan, V. Adaikkammai, A. Sasikala, G. Iyswariya, A. Babisha, A. Latha, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India R.M.D. Engineering College Department of Computer Science and Business Systems India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India R.M.K. Engineering College Department of Ece Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Srm Institute of Science and Technology Department of It India

ISBN: (纸本)9798350388916

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

No Victim Left Behind: Developing a Dual-Use LEO Satellite System for Real-Time Population Mapping and Everyday Connectivity 23

No Victim Left Behind: Developing a Dual-Use LEO Satellite S...

引用

23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Ohashi, Takumi Noda, Futo Tomura, Takashi Laosunthara, Ampan Suzuki, Koudai Tanaka, Yuno Kishimoto, Maki Seto, Rie Sakamoto, Hiraku Tran, Gia Khanh Shirane, Atsushi Tokyo Institute of Technology Department of Transdisciplinary Science and Engineering Tokyo Japan Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Tokyo Institute of Technology Department of Electrical and Electronic Engineering Tokyo Japan Tokyo Institute of Technology Department of Civil and Environmental Engineering Tokyo Japan Tokyo Institute of Technology Department of Architecture and Building Engineering Tokyo Japan Meteorological Research Institute Department of Observation & Data Assimilation Tsukuba Japan Tokyo Institute of Technology Department of Mechanical Engineering Tokyo Japan Institute of Innovative Research Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9798350353952

Disasters often disrupt communication infrastructure, impeding damage assessment and rescue operations. This paper presents a method for real-time population distribution mapping during disasters using the widespread prevalence of smartphones and Low Earth Orbit (LEO) satellites. The system detects signals emitted by smartphones using LEO satellites equipped with directional antennas. By analyzing the received signal strength indicators (RSSI), it generates high-resolution population distribution maps in affected areas, providing critical information for resource allocation and rescue efforts. The system's design allows for functionality during both normal and emergency conditions, offering connectivity and location-based services in daily life while supporting disaster response efforts. A proof-of-concept experiment demonstrates the detection of smartphone signals from tall buildings, confirming the system's ability to distinguish between the presence and absence of smartphones in the observed area based on the detected spectra. This preliminary result suggests the feasibility of estimating population distribution by detecting leaked signals from smartphones during disasters. Further research is needed to address challenges such as precise localization, indoor signal detection, and system scalability. This research explores the integration of satellite technology with mobile devices for effective disaster management, contributing to the development of resilient and efficient emergency response strategies. © 2024 IEEE.

关键词： Directive antennas

来源：评论

学校读者我要写书评

暂无评论

Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

引用

2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

IoT-based trusted wireless communication framework by machine learning approach

引用

Measurement: Sensors 2024年 34卷

作者： Chakaravarthi, S. Saravanan, S. Jagadeesh, M. Nandhini, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computing Technologies SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

The traditional Radio-Frequency systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system. © 2024 The Authors

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Method for the Diagnosis of Retinal Diseases using Convolutional Neural Network

Machine Learning Method for the Diagnosis of Retinal Disease...

引用

2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Kushwaha, Sumit Boga, Jayaram Rao, Bairaboina Sai Sambasiva Taqui, Syed Noeman Vidhya, R.G. Surendiran, J. University Institute of Computing Chandigarh University Department of Computer Applications Punjab India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Data Science Telangana Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Vaddeswaram India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350348910

The early diagnosis of visual problems such as diabetic retinopathy and glaucoma relies on the successful completion of medical image processing tasks such as the segmentation of retinal blood vessels. The process of isolating retinal blood vessels from images of the retinal fundus is a challenging one because of the similarities in intensity that exist between blood vessels and the surrounding tissues. In addition, there are differences in the size, shape, and intensity of the vessels. In recent times, a number of different algorithms based on deep learning have been presented for the segmentation of retinal blood vessels. These techniques have been shown to be more accurate and dependable when compared to the traditional computer vision methods that have been used. In this abstract, we present the U-Net deep learning model as well as a technique for segmenting retinal blood vessels that is based on the programming language Python. A U-Net model is trained using a series of retinal fundus images so that the features of retinal blood vessels may be comprehended. As part of the inference process, the image of the retinal fundus is loaded into a trained version of the U-Net model. This model then generates a probability map that illustrates the chance that each pixel represents an artery in the human body. The final segmentation is produced by applying a threshold to the probability map. When it comes to the process of segmenting retinal blood vessels, our python-based solution outperforms methods that are considered to be state-of-the-art in studies comparing accuracy and speed. The scientific and medical communities have unrestricted access to the software that may be used to put the suggested strategy into action since it is an open-source initiative. In conclusion, the python-based approach for retinal blood vessel segmentation that makes use of U-Net provides a potentially helpful solution for identifying retinal illnesses and has the potential to serve as a stan

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：