检索结果-内蒙古大学图书馆

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Sharma, Sumit Kumar Gupta, Richa Kumar, Rakesh Sethi, Tarun Information Technology Abes Institute of Technology UP Ghaziabad India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Raj Kumar Goel Institute of Technology Electronics & Communication Engineering UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Department of Computer Applications Iamr Duhai UP Ghaziabad India

ISBN: (纸本)9798350371055

Massive data Algorithms have the potential to revolutionize healthcare with the aid of making scientific diagnoses quicker., greater correct., and greater value efficient. Cloud-primarily based platforms are increasingly more getting used to save and procedure huge portions of scientific facts., and this allows optimized and automatic selection-making. by way of incorporating massive facts Algorithms., healthcare professionals can analyze massive volumes of scientific facts and discover diseases in a more correct and timely manner. computerized sickness diagnosis can reduce misdiagnosis and the need for useless treatments., main to higher high-quality of healthcare and reduced clinical fees. Consequently., its miles essential to optimize massive facts Algorithms on cloud for automatic ailment diagnosis in healthcare structures. Specific techniques hired include data modelling., statistics compression algorithms., and machine getting to know techniques. Facts modelling let in efficient summarization of huge volumes of information., and information compression techniques permit efficient garage., retrieval., and evaluation. Machine studying strategies such as clustering techniques and category algorithms can further beautify the accuracy of prognosis. All of these optimization strategies in the end offer healthcare expert with accelerated accuracy and progressed efficiency of prognosis. The current surge of crypto currencies and initial Coin offerings (ICOs) has created an abundance of big data.. This paper investigates the feasibility of optimizing massive information algorithms at the cloud for computerized disorder prognosis in a healthcare gadget. First., this paper explores the capacity advantages of cloud computing., along with accelerated computational power., scalability., and fee-performance at the cloud. Outcomes advocate that cloud-based totally optimization can growth the accuracy of ailment diagnoses by means of imparting extra information for extra corre

关键词： Analysis Healthcare Incorporating Predictions Selection

来源：评论

学校读者我要写书评

暂无评论

Wireless Liberty: Sensors Measuring Pressure and Temperature

Wireless Liberty: Sensors Measuring Pressure and Temperature

引用

2023 International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Chaudhary, Pavan Vanithalakshmi, M. Raja Praveen, K.N. Maharishi University of Information Technology School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The continuous collection of physiological data, especially temperature and pressure, throughout the human body, using wireless, skin-adherent detectors based on NFC (near-field communication) technology, is presented in this research study. To better understand human health, forecast illnesses, and avoid problems like pressure ulcers, the article stresses the significance of monitoring skin temperature as well as pressure in diverse anatomical locations. This study proposes a novel NFC-based method for continuous physiological monitoring. It provides hitherto unattainable tracking capabilities for pressure and temperature throughout the human body and has the potential to significantly enhance healthcare results. In order to increase the radius and sensitivities of these sensors, future research will examine enlarged clinical investigations and developments in wireless technology. © 2023 IEEE.

关键词： Continuous monitoring and Health outcomes Healthcare NFC technology Physiological monitoring Pressure sensing element Pressure ulcers Skin temperature Temperature measurement Wireless sensors

来源：评论

学校读者我要写书评

暂无评论

Prediction of Cyclone with Metrological Factors using Gradient Boosting Algorithm 4

Prediction of Cyclone with Metrological Factors using Gradie...

引用

4th International Conference on electronics and Sustainable communication Systems, ICESC 2023

作者： Durai Arumugam, S.S.L. Praveenkumar, R. Shreya, P. Selvakumar, J. Easwari Engineering College Dept. of Information Technology Chennai India Easwari Engineering College Dept. of Electronics and Communication Engineering Chennai India Sri Ramakrishna Engineering College Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300093

Innovations in computer vision and satellite imaging technologies have made fully automatic weather forecasting a reality. The cyclone prediction system is developed with the goal of reducing both natural disasters and human casualties. Machine Learning object recognition system is created to pinpoint the cyclone, which contains more information about the storm's activity. This is crucial work because it pinpoints the exact spot where the cyclone's formation. The accuracy of cyclone forecasts from many machine learning and deep learning models is 86%-95% or higher, while the accuracy of cyclone eye detection is 87% or higher from the same models. The impressive findings show that the experiment can surpass both established methods and previous attempts. © 2023 IEEE.

关键词： Weather forecasting

来源：评论

学校读者我要写书评

暂无评论

Detecting Deepfake Images Using Deep Learning Techniques and Explainable AI Methods

引用

Intelligent Automation & Soft Computing 2023年第2期35卷 2151-2169页

作者： Wahidul Hasan Abir Faria Rahman Khanam Kazi Nabiul Alam Myriam Hadjouni Hela Elmannai Sami Bourouis Rajesh Dey Mohammad Monirujjaman Khan Department of Electrical and Computer Engineering North South UniversityBashundharaDhaka1229Bangladesh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communication Engineering Brainware Group of Institutions-SDETBarasatKolkata700124West BengalIndia

Nowadays,deepfake is wreaking havoc on *** content is created with the help of artiﬁcial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded *** visual media manipulations are not new,the introduction of deepfakes has marked a breakthrough in creating fake media and *** manipulated pic-tures and videos will undoubtedly have an enormous societal *** uses the latest technology like Artiﬁcial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL)to construct automated methods for creating fake content that is becoming increasingly difﬁcult to detect with the human ***,automated solutions employed by DL can be an efﬁcient approach for detecting *** the“black-box”nature of the DL system allows for robust predictions,they cannot be completely *** is theﬁrst step toward achieving transparency,but the existing incapacity of DL to explain its own decisions to human users limits the efﬁcacy of these *** Explainable Artiﬁcial Intelligence(XAI)can solve this problem by inter-preting the predictions of these *** work proposes to provide a compre-hensive study of deepfake detection using the DL method and analyze the result of the most effective algorithm with Local Interpretable Model-Agnostic Explana-tions(LIME)to assure its validity and *** study identiﬁes real and deepfake images using different Convolutional Neural Network(CNN)models to get the best *** also explains which part of the image caused the model to make a speciﬁc classiﬁcation using the LIME *** apply the CNN model,the dataset is taken from Kaggle,which includes 70 k real images from the Flickr dataset collected by Nvidia and 70 k fake faces generated by StyleGAN of 256 px in *** experimental results,Jupyter notebook,TensorFlow,Num-Py,and Pandas were used as software,InceptionResnetV2,DenseNet201,Incep

关键词： Deepfake deep learning explainable artificial intelligence(XAI) convolutional neural network(CNN) local interpretable model-agnostic explanations(LIME)

来源：评论

学校读者我要写书评

暂无评论

Molecular Code-Division Multiple-Access for Nano-Sensor Networks: Signaling, Detection, and Performance

引用

IEEE Sensors Journal 2025年第12期25卷 23003-23017页

作者： Gao, Weidong Shi, Lu Yang, Lie-Liang Wuxi University College of Electronic Information Engineering Jiangsu Province Engineering Research Center of Photonic Devices and System Integration for Communication Sensing Convergence Wuxi214105 China Chongqing University of Science and Technology School of Intelligent Technology and Engineering Chongqing401331 China University of Southampton School of Electronics and Computer Science SO17 1BJ United Kingdom

o accomplish relatively complex tasks of, such as, monitoring in vivo, information collected by different nano-sensors (NSs) is usually sent via multiple-access channels to fusion centers (FCs) for further processing.o accomplish relatively complex tasks of, such as, monitoring in vivo, information collected by different nano-sensors (NSs) is usually sent via multiple-access channels to fusion centers (FCs) for further processing.T Relying on two types of molecules, in this paper, a molecular code-division multiple-access (MoCDMA) scheme is designed for multiple NSs to simultaneously send information to an access-point (AP) in a diffusive molecular communications (DMC) environment. We assume that different NSs may have different distances from AP, which generates ‘nearfar’ effect. Correspondingly, the uniform and channel-inverse based molecular emission schemes are proposed for NSs to emit information molecules. To facilitate the design of different signal detection schemes, the received signals by AP are represented in different forms. Specifically, by considering the limited computational power of nano-devices, three low-complexity detectors are designed in the principles of matched-filtering (MF), zero-forcing (ZF), and minimum mean-square error (MMSE). The noise characteristics in MoCDMA systems and the complexity of various detection schemes are analyzed. The error performance of the MoCDMA systems with various molecular emission and detection schemes is demonstrated and compared. Our studies and performance results demonstrate that MoCDMA constitutes a promising scheme for supporting multiple-access transmission in DMC, while the channel-inverse based transmission can ensure the fairness of communication qualities (FoCQ) among different NSs. Furthermore, different detection schemes may be implemented to attain a good trade-off between implementation complexity and communication reliability. © 2001-2012 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Collaborative Research on Customer Electricity Demand Forecasting and Power System Planning on Basis of Fuzzy Algorithms 5

Collaborative Research on Customer Electricity Demand Foreca...

引用

5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Wu, Fangquan Liu, Yichi Li, Xiong North China Electric Power University School of Computer Science and Technology Baoding071066 China Hunan University Information Science and Engineering Changsha410082 China University of Electronic Science and Technology School of Electronics and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350355253

Accurate prediction of consumer demand for electricity has always been a goal pursued by the power industry. Rational planning of power system resources so as to achieve power supply reliability and improve energy utilization efficiency is the purpose of this paper. This study is based on fuzzy algorithm to forecast consumer electricity demand through fuzzy reasoning and fuzzy logic methods, and synergize with power system planning. The article collects and organizes historical electricity demand data and related environmental factors data. Then a fuzzy inference model is established and fuzzy sets and fuzzy rules are applied to demand forecasting. The experimental results show that the root-mean-square error of the fuzzy algorithm is 0.01-0.03. The algorithm is able to capture the vagueness and uncertainty of demand with high prediction accuracy and comprehensiveness. The algorithm in this paper can meet the goals pursued by the power industry and bring contribution to the power industry. © 2024 IEEE.

关键词： Demand response

来源：评论

学校读者我要写书评

暂无评论

Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

引用

2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

来源：评论

学校读者我要写书评

暂无评论

Statistical modeling and Gaussianization procedure based de-speckling algorithm for retinal OCT images

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1125-1138页

作者： Sahu, Sima Singh, Harsh Vikram Kumar, Basant Singh, Amit Kumar Dr. A. P. J. Abdul Kalam Technical University Uttar Pradesh Lucknow India Uttarpradesh Sultanpur India Department of Electronics and Communication Engineering Motilal Nehru NIT Allahabad India Department of Computer Science and Engineering Jaypee University of Information Technology Waknaghat Himachal Pradesh Solan India

This paper presents a de-noising method for speckle noise removal called de-speckling, retinal optical coherence tomography image by combining the features of wavelet transform, statistical modeling, Bayesian estimators and Gaussianization procedure. De-speckling methods based on statistical modeling of wavelet coefficients depend on the correct estimation of probability density function (PDF). The density estimation problem has been solved by using the Gaussianization procedure. The dynamic range capability of Gaussian PDF has been used for sharp estimation of density. A Cauchy PDF is used for modeling wavelet coefficients and finding the cumulative distribution function (CDF) of wavelet coefficients. This CDF is used as the input to the Gaussianization procedure, where it equalizes with the Gaussian CDF to estimate the density function of wavelet coefficients. Finally, the wavelet coefficients are recovered using Bayesian minimum mean square error estimator. Both visual and quantitative comparisons are performed for demonstrating the prominence of the proposed method. From simulation result it is seen that the proposed method outperforms the state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity, correlation coefficient (CoC) and edge preservation index (EPI). The proposed method has been achieved improvement of 5.13% in PSNR, 2.44% in SSIM, 2.11% in CoC and 3.45% in EPI over the well accepted existing method. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Probability density function

来源：评论

学校读者我要写书评

暂无评论

Deployment of neural networks for WLAN thumbprint indoor positioning system

引用

International Journal of information Technology (Singapore) 2025年 1-7页

作者： Kushwaha, Ajay Agarwal, Trapty Singh, Manpreet MP, Sunil Sharma, Amit Jain, Romil Bhuvaneswari, P. Computer Science and Engineering Rungta College of Engineering and Technology Chhatisgarh Bhilai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Centre of Research Impact and Outcome Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Karnataka Bangalore India School of Computer Applications Lovely Professional University Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Chandigarh India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

With the application and development of related technologies based on user location information, location-based services have become essential for daily lives. Despite this fact, Wireless Local area networks (WLAN’s) often face time complexities in streamlining and collection of data. Thus, the given paper introduces a deep-learning model based on convolutional neural network (CNN) to predict user’s indoor location from the received wireless signal strength fingerprint maps. The aim is to enhance WLAN indoor localization by streamlining the data collection process. The proposed indoor positioning model leverages a region of interest (ROI) extraction technique to simulate the wireless received signal strength intensity in WLAN’s. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of WLAN localization. To optimize and evaluate the model’s performance in real-world scenarios, experiments are conducted using simulated datasets obtained from the publicly available database such as UCI repository. The results show that the proposed model attains the highest validation accuracy (97.11%) and training accuracy (97.34%) as compared to the existing deep learning algorithms used for indoor localization. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN Deep learning Indoor localization Region of interest (ROI) Signal strength WLAN

来源：评论

学校读者我要写书评

暂无评论

Combine deep learning and artificial intelligence to optimize the application path of digital image processing technology 24

Combine deep learning and artificial intelligence to optimiz...

引用

2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Pan, Linying Xu, Jingyu Sun, Wenjian Wan, Weixiang Zeng, Qiang Information Studies Trine university PhoenixAZ85258 United States Computer Information Technology Northern Arizona University FlagstaffAZ86011 United States Electronic and information engineering Yantai University Shandong Province Yantai264005 China Electronics & Communication Engineering University of Electronic Science and Technology of China ChengDu611731 China Computer Technology Zhejiang University Zhejiang Hangzhou310058 China

ISBN: (纸本)9798400717147

Artificial intelligence provides a new research concept for digital image processing. However, at present, artificial intelligence is rarely introduced into the teaching of digital image processing in colleges and universities, and there are problems such as obsolete teaching content, single teaching method and simple course experiment, which affect the teaching effect and are not conducive to the cultivation of comprehensive and innovative talents. Digital image processing technology brings more possibilities to communication engineering and makes people's communication more convenient. For example, video calls and photo transmission make people's communication methods in daily life more and more diversified. The limitation of time and space allows people to meet online, creating more communication possibilities. However, there are still many problems and methods worthy of in-depth exploration. Therefore, this paper has a comprehensive understanding and mastery of the traditional methods and deep learning methods of digital image processing, so as to improve the relevant project practice and scientific research exploration ability, and make reference for similar research conclusions. © 2024 ACM.

关键词： Image enhancement

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：