检索结果-内蒙古大学图书馆

Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

来源：评论

学校读者我要写书评

暂无评论

Tracking and Monitoring of Medical Equipments using UWB for Smart Healthcare 6

Tracking and Monitoring of Medical Equipments using UWB for ...

引用

6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Shyam, Shilpa Juliet, Sujitha Ezra, Kirubakaran Karunya Institute of Technology and Sciences Computer Science and Engineering Department Coimbatore India

ISBN: (纸本)9781665410281

Healthcare sector has been witnessing prodigious developments across the decade. The developments in the sector has been brought about by the amalgamation of several technologies, from IoT to AI. The vast changes in this arena has brought about a change is the quality of life. With the expansion of such systems in a hospital, the tracking and monitoring of medical equipments have become a tedious task. The equipments in the hospital needs to be located in the most efficient manner in terms of cost, accuracy and reliability. Considering the hurdles faced, the authors introduce a system which would trace the medical equipments and monitor them using the technology of Ultra wide band (UWB). TDoA (time difference of arrival) method and 3 D triangulation techniques are implemented along with. The authors have put forth a hardware and software architecture of the system along with a complete component design. The implementation at the first instance is executed inside a hospital room with dimensions of 13.5m X 17.5m and will be later carried forward in the entire hospital premises. The equipments will be further watched upon continually using a proposed novel software application. © 2022 IEEE.

关键词： Triangulation

来源：评论

学校读者我要写书评

暂无评论

Exploring the Dynamics of Exchange Rates: An Empirical Analysis and Predictive Modelling 2

Exploring the Dynamics of Exchange Rates: An Empirical Analy...

引用

2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, R.N. Pati, Peeta Basa Deepa, K. Konidhala, Jothibabu K Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering India Amrita School of Engineering Department of Electrical & Electronics Engineering Bangalore India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350397635

In India, globalization and reforms in the financial sector have brought about a major overhaul of the financial landscape. The connection between financial markets and the tangible economy is now highly valued in the current scenario. Exchange rate affects a country's economy in several ways. It has a huge effect on international trade, economic growth, capital flows, interest rates and also inflation. Here, the aim is to understand the underlying factors that affects the exchange rate of US dollar with respect to Indian rupee. To accomplish this, a Vector Auto Regression (VAR) model, as well as other machine learning techniques such as multi-variable linear regression, polynomial regression, and regression tree, are employed to forecast exchange rates. Our findings indicate that Trade Balance is a significant factor which cause fluctuations in exchange rate of USD and regression tree model performs better from the side of all machine learning model applied for predicting the same. © 2023 IEEE.

关键词： Multiple linear regression

来源：评论

学校读者我要写书评

暂无评论

Highly Efficient Dual-Buck Structured Buck-Boost AC-AC Converter With Versatile Identical Inverting/Noninverting Operations

引用

IEEE Transactions on Industrial Informatics 2023年第12期19卷 11403-11417页

作者： Ahmed, Hafiz Furqan Alzaabi, Omar El Moursi, Mohamed Shawky Al Hosani, Khalifa National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804201 Taiwan Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates Mansoura University Faculty of Engineering Mansoura35516 Egypt

Single-phase dual-buck ac-ac (DBAC) converters are gaining attention due to their intrinsic protection from shoot-through and open-circuit problems of conventional ac-ac converters. However, research works on DBAC converters are mainly concentrated around unipolar topologies. In a few developed bipolar topologies to date, the inverting buck-boost operations (for series voltage injection and step-variable frequency outputs) are inefficient and burdened by large voltage and current stresses (v in + vo and i in + i o) of switching devices and ripples of passive elements. In this article, an efficient dual-buck structured buck-boost ac-ac converter is proposed, with the following features: no voltage source shoot-through and inductor open-circuit problems, natural attainment of safe commutation without additional protection circuitry or complex control, no need for pulsewidth modulation dead times, and elimination of high-frequency conduction of mosfet's body diodes and related slow reverse recovery issues. The proposed converter provides distinct types of efficient inverting and noninverting buck and boost operations, with smaller switch voltage/current stresses (v in/vo and i in/i o) and passive component ripples. Combined inverting and noninverting buck-boost operations are also proposed with separate tuning of buck and boost voltage transfer ratios. A simple adaptable switching strategy provides the switch control pulses for all circuit operations by modulating the buck and boost control reference signals. The proposed converter provides sustained input/output currents and performs well with nonresistive loads. Extensive theoretical analysis is presented followed by practical verifications on a 400-VA laboratory circuit. © 2005-2012 IEEE.

关键词： Buck-Boost converter

来源：评论

学校读者我要写书评

暂无评论

Testing the Structural Equivalence of Pre-processed Chronic Pain MRI Data 6

Testing the Structural Equivalence of Pre-processed Chronic ...

引用

6th International Conference on Devices, Circuits and Systems, ICDCS 2022

作者： Rejula, V. Anitha, J. Belfin, R.V. Karunya Institute of Technology and Sciences Department of Computer Science of Engineering Coimbatore India

ISBN: (纸本)9781665480949

The use of functional magnetic resonance imaging (fMRI) to investigate the brain correlates of cognition has grown more important in recent years. fMRI is a non-invasive way of detecting patterns in brain activity induced by tasks done during scanning. Despite the technique's lengthy history, the distinct properties of each dataset have forced the adoption of custom preprocessing techniques for each sample. This approach is inefficient, prone to errors, and unfit for combining data from many sources. Preprocessing the functional magnetic resonance imaging (fMRI) data requires various processes to clean and standardise the data before any further research. The fMRIPrep (http://***) specification describes a thorough approach for preprocessing human fMRI data for data analysis, which has been standardised as a pipeline. The Chronic Pain data was preprocessed using this fMRIPrep. We compare the chronic pain MRI data to the healthy control data after preprocessing the anatomical and functional data received from the MRI. The structural similarity index (SSIN) is used to find structural differences between chronic pain patients and healthy controls. The findings show that the structures of healthy and chronic pain regulation are quite similar. © 2022 IEEE.

关键词： Brain Pain Pipelines Machine learning Functional magnetic resonance imaging Regulation Physiology

来源：评论

学校读者我要写书评

暂无评论

Mindful Breathing Coaches Using IoT-Integrated AI Systems for Personalized Anxiety Relief 1

Mindful Breathing Coaches Using IoT-Integrated AI Systems fo...

引用

1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Dhal, P.K. Sivakumar, T. Vasudevan, B. Lakshmi, V.T. Muthaiyan, Baskar Murugan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tami Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Tamil Nadu Chennai India Department of Mathematics Tamil Nadu Salem India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

The proposed system focuses on personal anxiety alleviation through mindful breathing coaching, which investigates the use of Artificial Intelligence (AI) systems combined with the Internet of Things (IoT). Treatments for anxiety disorders should be easily available because of how common and crippling they are. The proposed solution integrates IoT devices with AI algorithms to provide personalized instructions for mindful breathing exercises and real-time tracking. Using a mixed-methods strategy, quantitative and qualitative user experiences and anxiety levels are analyzed. Anxiety symptoms were significantly reduced, and users were very satisfied with the tailored coaching, according to preliminary studies. There are scalable solutions to individual anxiety requirements that can be provided by integrating IoT and AI technology. It emphasizes the potential of technology to promote well-being and helps advance individualized therapies for mental health. It shows that it is possible to provide effective personalized anxiety reduction. By providing individualized assistance to those dealing with anxiety problems, this method has the potential to completely transform the way mental health treatment is provided. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

引用

Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

来源：评论

学校读者我要写书评

暂无评论

Impact of PON network range and laser power on GPON and XGSPON coexistence system 8

Impact of PON network range and laser power on GPON and XGSP...

引用

8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Kherici, Cheikh Kandouci, Malika Bechikh, Amel Benhamidi, Aicha Laboratory of Electromagnetism Photonics and Optronics L.E.P.O Faculty of Mathematics and Computer Science Computer Science Department Sidi Bel Abbes Algeria Ibn Khaldoun University of Tiaret Tiaret Algeria Laboratory of Electromagnetism Photonics and Optronics L.E.P.O Faculty of Electrical Engineering Electronics Department Sidi Bel Abbes Algeria Djillali Liabes University of Sidi Bel Abbes Sidi Bel Abbes Algeria Ibn Khaldoun University of Tiaret Faculty of Mathematics and Computer Science Computer Science Department Tiaret Algeria

ISBN: (纸本)9798350309249

This article discusses the effect of laser emission power and PON network range when GPON and XGSPON systems coexist. The analysis aims to understand how these two factors influence the overall performance and stability of the coexistence between the two optical network technologies. In order to also meet the increasing demand for data rates and new services such as videoconferencing and very high-quality television (4K, 6K,...etc). the coexistence system is preferred, allowing effective collaboration between GPON and XGSPON *** work deals with the performance evaluation of GPON and XGSPON systems, focusing on reconciliations and adjustments related to BER error rates and Q factor. The study explores different scenarios by adjusting the laser power from -12 dBm to 12 dBm and varying the array range from 10 km to 120 km. The main objective is to determine the best combination of laser power and network range to ensure optimal performance for coexistence systems. © 2024 IEEE.

关键词： Q factor measurement

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence and emotional intelligence in the workplace: Their roles and effects for industry 4.O 2

Artificial intelligence and emotional intelligence in the wo...

引用

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

作者： Bhatia, Sonakshi Rawat, Babita Thapliyal, Nitin Tiwari, Rohak Kumar, Santosh Joshi, Kapil Kumar, Arun Uttaranchal University UIM Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India DPS RK Puram Delhi India Sharda University Greater Sharda School of Basic Sciences and Research Department of Mathematics UP Noida201310 India Uttaranchal University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350368109

Automation and digitization are the key components of Industry 4.0, which is also considered to as the fourth industrial based revolution, but more significantly, it is a turbulent, unpredictable, and unsettling time. People are always reluctant and resistant when industry adopts new technology or automation because they are afraid of change. Change is inevitable, but what matters is how well we can adapt to it. Only those businesses who can foresee changes in the future and plan accordingly will endure and develop throughout time. Although every division of an organization will be affected by industry 4.0, the influence on human resources will be the greatest. Massive benefits of the fourth industrial revolution include a more individualized job-searching experience for prospects. The ability to identify, understand, regulate, and utilize one's own emotions as well as those of those around one can be defined as Emotional Intelligence (EI). EI is an important interpersonal communication ability that has gained interest in a variety of fields, including the workplace. Artificial intelligence is a field that combines large datasets and computer science to make problem solving easier. As we enter the industry 4.0 of automation through robotics, artificial intelligence (AI) will immediately have an impact on jobs in several industries. The purpose of this paper is to investigate the potential impact that artificial intelligence (AI) and emotional intelligence (EI) may have on workers' experiences at work., as well as the influence of EI on improving the workforce for Industry 4.0. Finally, we discuss the effects of AI and EI in the workplace of Industry 4.0. © 2024 IEEE.

关键词： Resource valuation

来源：评论

学校读者我要写书评

暂无评论

Ecosystem of Technologies for Smart Agriculture to Improve the Efficiency and Profitability of Indian Farmers 10

Ecosystem of Technologies for Smart Agriculture to Improve t...

引用

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sugandh, Urvashi Nigam, Swati Khari, Manju Banasthali Vidyapith Faculty of Mathematics and Computing Department of Computer Science Banasthali India School of Computing Science Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computer and System Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9789380544472

An increasing population demands more food, but the available resources are shrinking. The agriculture sector in India is facing several challenges such as climate change, water scarcity, soil degradation, and declining farm profitability. To address these challenges, there is a growing need for the adoption of smart agriculture practices that leverage technology. This paper presents an overview of the ecosystem of technologies for smart agriculture and their potential to improve the efficiency and profitability of Indian farmers. The paper explores the role of various technologies such as precision agriculture, IoT, artificial intelligence, drones, and robotics in improving crop yields, reducing input costs, and enhancing overall farm management. Additionally, the paper discusses the challenges that need to be overcome to ensure the successful adoption of smart agriculture technologies in India. These include the lack of awareness and access to technology, inadequate infrastructure, and limited availability of financing. The paper concludes that the ecosystem of technologies for smart agriculture holds significant promise for Indian farmers to overcome the challenges and improve the efficiency and profitability of their farms. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Profitability

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：