检索结果-内蒙古大学图书馆

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Solainayagi, P. Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

Businesses must optimize service delivery to compete in today's fast-paced environment. Gas stations and fuel hubs are under pressure to improve efficiency and customer service. This study suggests integrating the Internet of Things (IoT) with XGBoost modeling to simplify gas station operations and speed up service. First, IoT sensors are strategically placed across the gas station infrastructure to monitor fuel levels, traffic movement, and customer lines in real time. This thorough data gathering creates a dynamic operating environment for proactive decision-making. Second, XGBoost, a strong machine learning algorithm, analyzes IoT data to forecast consumer demand and traffic trends. XGBoost predicts accurately using past data and various factors, enabling resource allocation and service planning. Gas station operators profit from IoT and XGBoost modeling. Predictive maintenance speeds up fix and reduces downtime. It optimizes staffing plans to ensure enough workers during peak hours to speed up service. It also allows consumer behavior analysis-based, tailored marketing. The suggested method allows gas stations to adjust to shifting demand patterns, improve operational efficiency, and provide quicker, better service. Gas stations may lead in service and innovation by integrating IoT and sophisticated analytics. © 2024 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Incremental Weighted Ensemble for Data Streams With Concept Drift

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第1期5卷 92-103页

作者： Jiao, Botao Guo, Yinan Yang, Cuie Pu, Jiayang Zheng, Zhiji Gong, Dunwei School of Electromechanical and Information Engineering Beijing100083 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Victoria University of Wellington School of Engineering and Computer Wellington6140 New Zealand Electromechanical and Information Engineering Beijing100083 China Qingdao University of Science and Technology School of Information Science and Technology Qingdao266061 China

As a popular strategy to tackle concept drift, chunk-based ensemble method adapts a new concept by adjusting the weights of historical classifiers. However, most previous approaches normally evaluate the historical classifier based on an entire chunk newly arrived, which may cause delayed adaptation. To address the issue, two novel ensemble models, named incremental weighted ensemble (IWE) and incremental weighted ensemble for multi-classification (IWE-M), are proposed. At each time step, all base classifiers are incrementally updated on a newly arrived instance. Following that, the instance is collected into a cache array. Once a data chunk is formed, a new base classifier is created. More specially, a forgetting mechanism based on variable-size window is designed to adjust the weight of each base classifier in IWE in terms of its classification accuracy on the latest instances in an online manner. IWE-M, an extension of IWE, aims to solve multiclass problems with local concept drifts. In IWE-M, the weight of a base classifier is expanded to a weight vector. In this way, this ensemble model can retain specific historical information about nondrift regions from a local drift. Experimental results show that the proposed ensemble frameworks outperform six competitive approaches on accuracy and G-mean. © 2020 IEEE.

关键词： Classifiers

来源：评论

学校读者我要写书评

暂无评论

Task Offloading and Resource Management for IIoT with Satellite-Terrestrial Integrated Computing Power Network Based on D3QN

引用

IEEE Internet of Things Journal 2025年第12期12卷 21770-21783页

作者： Li, Meng Li, Meihui Wang, Kan Yu, F. Richard Wang, Zhuwei Si, Pengbo Beijing University of Technology School of Information Science and Technology Beijing100124 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The management of computing resources through the computing power network (CPN) has gradually become a focal point of research. With the development of the 6th generation (6G) mobile networks, some promising technologies such as satellite-terrestrial integrated network (STIN) and smart endogenous network driven by artificial intelligence (AI) are increasingly being applied in Industrial Internet of Things (IIoT). However, several issues in current studies are worthy of attention: 1) the large number of devices powered by battery in IIoT, 2) the complex communication environments, 3) the finite computing resources for task data processing. To cope with these challenges, a satellite-terrestrial integrated computing power network (STICPN) framework is introduced in this article. Within this framework, a task offloading link selection scheme is proposed, which minimizes the delay and the consumption of energy. The task offloading optimization problem is modeled as a Markov Decision Process (MDP). Meanwhile, deep reinforcement learning (DRL) algorithm is employed to adapt to the dynamic states of environment. Specifically, a Dueling Double Deep Q Network (D3QN) is used to make optimal decisions and delay as well as energy consumption can be reduced significantly. Moreover, the D3QN-based scheme extends the usage time of IIoT devices. The simulation results indicate that the proposed scheme outperforms the comparison schemes significantly. © 2025 IEEE.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Medical Plant Detection using Transfer learning Coupled with K-Nearest Neighbour Algorithm 3

Medical Plant Detection using Transfer learning Coupled with...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Srambickal, Sai Subramanian Srivastava, Aman Somasundaram, S.K. School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Information Security Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Medical Plants are being used for more than a thousand year, with evidences dating before the Mauryain Era (around 322 BCE), they are widely used in the Ayurvedic school of medicine for therapeutic as well as medicinal purposes, it requires an expert to identify the species as well as the medical property associated with a given plant. This paper aims to use deep learning in order to identify a given plant using the leaves of the plant by using a pretrained Convolutional Neural Network (CNN) model namely ResNet50 and MobileNet to extract the latent vector which will then be passed to K-Nearest Neighbour (KNN) algorithm in order to identify the plant class, the best accuracy of the proposed models comes out to be 97.65%. © 2024 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

引用

computer Systems science & engineering 2023年第4期45卷 701-714页

作者： Helen Josephine V L Ramchand Vedaiyan V.M.Arul Xavier Joy Winston J A.Jegatheesan D.Lakshmi Joshua Samuel Raj Business Analytics School of Business and ManagementChrist UniversityBangalore560029India Department of Computer Science and IT School of SciencesJain Deemed to be UniversityBangalore560027India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatore641114India College of Computer Studies University of Technology Bahrain18041Bahrain Institute of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Institute of TechnologyChennai600123India Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India

The Internet of Things(IoT)technologies has gained significant interest in the design of smart grids(SGs).The increasing amount of distributed generations,maturity of existing grid infrastructures,and demand network transformation have received maximum *** essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further *** dynamic electrical energy stored model using Electric Vehicles(EVs)is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the *** paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder(HBFOA-SAE)model for IoT Enabled energy *** proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge(SOC)values in the IoT based energy *** accomplish this,the SAE technique was executed to proper determination of the SOC values in the energy ***,for improving the performance of the SOC estimation process,the HBFOA is *** addition,the HBFOA technique is derived by the integration of the hill climbing(HC)concepts with the BFOA to improve the overall *** ensuring better outcomes for the HBFOA-SAE model,a comprehensive set of simulations were performed and the outcomes are inspected under several *** experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.

关键词： Internet of things energy systems state of charge estimation machine learning deep learning metaheuristics

来源：评论

学校读者我要写书评

暂无评论

High Dynamic Range Imaging for Dynamic Scenes Based on Multi-Level Spike Camera

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5394-5406页

作者： Zhu, Zhenkun Xiong, Ruiqin Zhao, Jing Zhao, Rui Fan, Xiaopeng Zhu, Shuyuan Huang, Tiejun Peking University School of Computer Science Beijing100871 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China

Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes of high-speed motion by firing a continuous stream of spikes at an extremely high temporal resolution. The limitation in the current design is that each spike only represents the arrival of a fixed amount of photons. It can not deal with strong light areas in which the amount of accumulated photons reaches the pre-specified threshold multiple times within a single readout interval. In this paper, we propose a new spike camera model of high-speed imaging for high dynamic range scenarios. In this scheme, each pixel accumulates the incoming photons persistently and generates a new type of spike stream in which each spike symbol may be associated with different levels, indicating the arrival of different amounts of photons since the last readout. This enables the camera to support dynamic scenes with wider dynamic range. To achieve this, we propose a two-level buffer mechanism, one for photon accumulation and one for spike-firing encoding. We use a register to hold the number of spike-firings which has not been read out yet. At each readout time, the major part in the counter is read out via a carefully designed exponential encoding and the counter is updated. Such encoding and readout strategy enables a very efficient expansion of the dynamic range using a small number of encoding bits. Furthermore, we propose an image reconstruction scheme for the proposed camera, utilizing both spike intervals and spike levels to recover the light intensity. We incorporate Mamba and propose a temporal-spatial selective scan mechanism to extract temporal-spatial correlation within spike streams. We employ a pyramid adaptive filtering and alignment module to achieve coarse-to-fine feature alignment. Experimental results show that the proposed scheme can achieve better imaging quality and outperform the existing spike camera in high dynamic range scenarios. © 1991-2012 IEEE.

关键词： Adaptive filtering

来源：评论

学校读者我要写书评

暂无评论

Gait Pattern Recognition through Force Sensor Platform based on XGBoost Model and Harris’ Hawks Optimization

引用

IAENG International Journal of Applied Mathematics 2025年第1期55卷 118-125页

作者： Yu, Lie Mei, Pengzhi Ding, Lei School of Electronic and Electrical Engineering Wuhan Textile University Wuhan China School of Electronic and Electrical Engineering Wuhan Textile University Wuhan China School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China

This study developed a gait pattern classification system based on ground contact forces measured by six force sensors embedded inside the shoe sole. The data transmission is facilitated via the Bluetooth module integrated into an STM32 microcontroller. The extreme gradient boosting (XGBoost) algorithm is used to identify the gait patterns, and the basic idea of XGBoost is to use second-order derivatives to make the loss function more precise, incorporate regularization to prevent tree overfitting, and enable block storage for parallel computation. By optimizing the XGBoost algorithm with four algorithms, the exploration capabilities of these algorithms are effectively incorporated into the fusion model. Experimental results indicate that the XGBoost algrithm optimized by Harris’ hawks optimization (HHO) outperforms the other optimization algorithms. Specifically, the HHO-XGBoost achieved high values of 97.41%, 97.03%, and 97.22% severally in the metrics of precision, recall, and F1 score. This research illustrates the HHO-XGBoost method's superiority in gait phase recognition. © (2025), (International Association of Engineers). All rights reserved.

关键词： Gait analysis

来源：评论

学校读者我要写书评

暂无评论

Cough Sounds, Symptoms, CXR, and CT Images for COVID-19 Detection 1

引用

3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Pandey, Priyanka Tyagi, Nidhi Pattnaik, Prasant Kumar Department of Computer Science and Engineering School of Engineering and Technology SIET Meerut Meerut India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Since the coronavirus epidemic has reached every corner of the globe, we need to develop machine-based solutions to detect it. The global spread of novel coronaviruses has claimed millions of lives and prompted the development of various low-cost diagnostic assays for the presence of COVID-19 infection. The identification of chronic and communicable diseases is only one example of how current technology and transfer learning (TL) methodologies have improved human health and wellness. In order to combat the spread of this terrible infection, intelligence-based models and preventive measures and much research are required. This review presents a comprehensive analysis of the literature on TL and fuzzy ensemble techniques related to COVID-19 detection procedures. For the purpose of identifying COVID-19, researchers have used cough sounds, CT scans, X-ray pictures, and a symptom dataset. In this study, we explore the use of DL/ML, TL, fuzzy ensemble, and fuzzy inference methods for identifying COVID-19. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Blockchain Breakthrough: A Review of its Role in Modern Technology 2

Blockchain Breakthrough: A Review of its Role in Modern Tech...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Asthana, Sameer Chaturvedi, Ravi Prakash Mishra, Annu Nayyar, Poorva Shaliyar, Md. Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350368109

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains. © 2024 IEEE.

关键词： artificial intelligence blockchain ledger security

来源：评论

学校读者我要写书评

暂无评论

A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform

引用

Intelligent Automation & Soft Computing 2023年第1期35卷 537-552页

作者： Amishi Mahesh Kapadia P.Nithyanandam School of Computer Science and Engineering Vellore Institute of TechnologyChennai600012TamilnaduIndia

Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret *** suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold *** notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in *** scrambling the cover image,Arnold transform adds security to the information that gets embedded and also allows embedding more information in each *** hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure *** proposed method employs a set of keys to ensure that information cannot be decoded by an *** experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats,including medical *** performance metrics proves that the retrieved cover image and hidden image are both *** System is proven to withstand rotation attack as well.

关键词： Reversible data hiding(RDH) integer wavelet transforms(IWT) arnold transform payload embedding and extraction

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：