检索结果-内蒙古大学图书馆

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Shivam Khurana Latha B Manisha P. Mali Mala Mathur Preeti Naval Naveen Kumar Rajendran Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Department of Civil Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Aerospace Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

This study investigates the optimization of bitrate for underwater optical fiber networks using bandwidth evaluation. The research seeks to discover viable strategies to obtain higher bit-price throughputs with increased reliability and lower latency in such networks. The community performance parameters, including latency, throughput, and jitter, might be analyzed and appropriate answers will be proposed. Bandwidth optimization models, together with the SPF version, could be used for the analysis. An empirical approach will also be taken to analyze the consequences of varying transmission characteristics in those networks. The effects of this look at will offer insights to set up high-pace and dependable transmission that may be applied in underwater optical fiber networks. The intention is to provide an extra comprehensive understanding of those networks and so as to optimize the bit-charge for green use.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Expression of Concern for: Adaptive Modeling for Real-Time Data Analysis with Machine Learning

Expression of Concern for: Adaptive Modeling for Real-Time D...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Mahesh TR Shweta Singh Vaibhav Srivastav Hitesh Kalra T. Surulivel rajan Gitanjali B. Yadav Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

Adaptive Modeling for actual-Time information evaluation with system studying is a critical studies area in the modern-day technology of information-in depth evaluation. Conventional gadget studying strategies are static, meaning that the models do no longer evolve as information scientist interactions occur or because the data changes over time. As a result, models can grow to be obsolete and further downstream machine overall performance will become constrained. Consequently, the want for adaptive modeling algorithms has arisen with the intention to permit dynamic, system-stage getting to know from statistics. Such strategies look to provide affordable predictions for real-time inputs, while also looking forward to enough destiny reaction behavior to make more accurate predictions for destiny, unseen facts. The purpose of this approach is to create a solution which optimally balances accuracy and flexibility for an ever converting environment. One of these system could be carried out for predictive upkeep, diagnostic analysis, cyber security detection or self-sustaining vehicle decision making.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Improving Real-Time Data Analysis Accuracy with Machine Learning Tools

Improving Real-Time Data Analysis Accuracy with Machine Lear...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Abhishek Singla Divya K Manohar K. Ganesh Kumar Kantak Akhilendra Pratap Singh A Rajesh Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Mechanical Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

Gadget studying can be used to enhance the accuracy of actual-time records evaluation. System-gaining knowledge can discover patterns and tendencies in information extra quickly and accurately while not having to manually analyze the data, allowing for decisions to be made with extra self-belief and accuracy. By means of incorporating capabilities that tune user interaction, machines gaining knowledge can use supervised and unsupervised studying to regulate the models to beautify accuracy. This paper explores the use of machine learning tools to improve the accuracy of real-time data analysis. With the constant influx of data in various industries, accurate and timely analysis is crucial for making informed decisions. However, traditional data analysis methods may fall short in handling the complexity and volume of real-time data. Machine learning algorithms offer the potential to overcome these challenges by autonomously identifying patterns and trends in real-time data. This paper discusses the benefits of incorporating machine learning in real-time data analysis and provides a comprehensive review of existing tools and techniques. Through case studies and experiments, we demonstrate the effectiveness of machine learning in improving the accuracy of real-time data analysis. The results of our study highlight the potential of machine learning tools in enhancing decision-making processes and driving better outcomes in various industries. Furthermore, we discuss the challenges and limitations of using machine learning in real-time data analysis and provide recommendations for future research in this area. This method of the usage of system gaining knowledge of tools will increase the efficiency and accuracy of real-time records evaluation and allows to make knowledgeable choices which can result in better effects.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Expression of Concern for: Design Strategies for Machine Learning Algorithms for Text Classification

Expression of Concern for: Design Strategies for Machine Lea...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Aparna A. Junnarkar Sunil Kumar Jakhar Manish Nagpal Rakesh Kumar Yadav Vinod Moger M. Sindhu Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Department of Mechanical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

The text type is a vital undertaking within the realm of gadget-gaining knowledge of algorithms and can contain classifying text files into predefined classes. Text category is used in many herbal language processing obligations, sentiment evaluation, topic modeling, and record summarization. Design techniques for device mastering algorithms for textual content categories contain information preprocessing, feature engineering, model choice, and evaluation. Records preprocessing requires expertise in text information and includes the stairs of tokenizing the text records, removing stopwords, acting stemming and lemmatization, part-of-speech tagging, and vectorization. Feature engineering, which is a step that requires domain expertise, applies strategies consisting of feature selection, function extraction, and feature transformation. Version choice and optimization techniques, including hyperparameter tuning and version choice, are used to choose the most appropriate studying algorithm for the task. Lastly, assessment metrics evaluate the model's effectiveness, including accuracy, precision, remembering, and f-ratings. On average, these layout techniques are critical components while growing a successful text-type model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An approach for crop recommendation with uncertainty quantification based on machine learning for sustainable agricultural decision-making

Results in Engineering

引用

Results in engineering 2025年 26卷

作者： Alam, Md. Sakib Bin Esichaikul, Vatcharaporn Lameesa, Aiman Ahmed, Shams Forruque Gandomi, Amir H. Dhaka 1212 Bangladesh Computer Science and Information Management Program School of Engineering and Technology Asian Institute of Technology Pathum Thani12120 Thailand Department of Computer Science American International University-Bangladesh Dhaka 1229 Bangladesh School of Mathematical Sciences Sunway University Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Mathematics & Physics North South University Dhaka 1229 Bangladesh Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Óbuda University Budapest 1034 Hungary Department of Computer Science Khazar University Mahsati 41 Baku Azerbaijan

While machine learning (ML) models for crop recommendation have demonstrated high predictive accuracy, a critical gap persists in their practical reliability: the omission of uncertainty quantification. Existing studies predominantly deliver deterministic recommendations, neglecting inherent uncertainties arising from data noise. This raises concerns about the reliability of the decision support systems for crop recommendation. To address this, we propose an ensemble ML framework incorporating entropy-based uncertainty quantification. Trained on a publicly available Indian agricultural dataset with 2200 samples across seven agro-climatic features (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and 22 crop classes, the model achieves a predictive accuracy of 99.54 %. By estimating prediction confidence using entropy, the framework offers probabilistic recommendations that support environmentally informed decision-making under uncertainty. These findings suggest that integrating uncertainty measures into ML-driven crop recommendation systems can enhance reliability and promote sustainable agricultural practices. © 2025

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Estimating Blur Parameters to Reconstruct the Motion Blurred Images 6th

Estimating Blur Parameters to Reconstruct the Motion Blurred...

引用

6th International Conference on New Trends in information and Communications technology Applications, NTICT 2022

作者： El Abbadi, Nidhal K. Abdulkhaleq, Ali Hussein Al Hassani, Safaa Alwan Computer Techniques Engineering Department College of Engineering & Technology Al-Mustaqbal University Babylon Iraq Department of Information Technology Technical College of Management Al-Furat Al-Awsat Technical University Najaf Iraq Computer Science Department Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq

ISBN: (纸本)9783031354410

One of the important topics of image restoration is the restoration of a motion-blurred image. The restored blurred images are based on estimating the two parameters of a point spread function (PSF) angle and length of the blur kernel. In this article, we suggested checking the quality of the input images before going further in processing when the image is not blurred, estimating the PSF parameters, and reconstructing the image based on the periodicity of motion-blurred in the frequency domain. Fourier transform is used to detect the blur in the input images, and the Hough transform is used to estimate the blurred image angle. Also, the cepstrum domain is used for blur length estimation by finding the lowest peak. After blur parameters are estimated, we determine a point spread function (PSF) to deblur the image using a wiener filter of non-blind deconvolution. The visual, as well as the peak signal-to-noise ratio (PSNR) of restored images, are compared with competent recent schemes. The experimental results of the suggested algorithm show good accuracy, robustness, and time efficiency for blur detection, estimation of the PSF parameters, and reconstruction of the image when compared with other works and methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical transfer function

来源：评论

学校读者我要写书评

暂无评论

Expression of Concern for: Enabling Autonomous Deep Learning Strategies for Real-Time Data Analysis in Machine Learning

Expression of Concern for: Enabling Autonomous Deep Learning...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Girija Shankar Sahoo Ramesh Sekaran G. Renuka Dharmesh Dhabliya Pramod Kumar Faujdar Deepak Minhas Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Mechanical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

This studies focuses on leveraging self-sustaining deep getting to know techniques for real-time records analysis in device getting to know. We propose a machine this is enabled by means of artificial intelligence to streamline the technique of extracting significant insights from sensor-based records. Our approach considers two middle components: interpreting data with recurrent neural networks and deploying a mastering agent to routinely whole the statistics analysis process. The getting to know agent's training is based on supervised mastering, enabling it to discover and traverse styles inside the information. The recurrent neural community (RNN) offers the functionality to seize temporal styles within the information, with its capacity to make predictions based on previous input. As a result, our system can produce fairly correct insights with minimal human intervention. This makes it suitable for time-sensitive information analysis situations in the industrial area in which decisions have to be taken in actual-time.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Neural Network-based System Identification: A Comprehensive FPGA Design and Implementation

Neural Network-based System Identification: A Comprehensive ...

引用

Artificial Intelligence and Mechatronics Systems (AIMS), International Conference on

作者： Saif Hasan Abdulnabi Yousif Samer Mudhafar Ali Abdulhassan Kadhim Mohammad Baqer Mahdi Hassan Hadi Sojar Department of Electronic and Communications Engineering Faculty of Engineering University of Kufa Najaf Iraq Department of Electrical Engineering College of Engineering University of Baghdad Al-Jadriya Baghdad Iraq Department of Computer Techniques Engineering Information Technology College Imam Ja'afar Al-Sadiq University Najaf Iraq Computer Techniques Engineering Department College of Technical Engineering The Islamic University Najaf Iraq

Neural networks are more commonly used to identify systems for system diagnostics without fully implementing and building the system. The project aims to design and implement the neural network identification system and implement it based on a Field Programmable Gate Array (FPGA) by interfacing between MATLAB / Simulink and Xilinx programs. The work was done thanks to God through two methods: the first through the simulation method through the coupling between MATLAB and Xilinx. While the second method was practically done by loading the simulation designs onto the FPGA. Work performance is measured by subtracting the value resulting from the actual system operation of the identification signal system and the simplified system and the difference between the simulation result and practical result. The use of FPGA with system identification in this project gives us a big advantage that simulation results are equal to practical results (difference is zero).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Potential Benefits of Data Mining for Customer Relationship Management

Analyzing the Potential Benefits of Data Mining for Customer...

引用

Emerging Research in Computational Science (ICERCS), International Conference on

作者： Aravind A R Shilpa M. Katikar Kalyan Acharjya G. Ezhilarasan Surendra Yadav Abhinav Mishra Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Data mining for client dating control (CRM) is figuring out patterns and traits in customer records to inform selections about product imparting, pricing, sales, advertising and marketing, and different consumer techniques. Companies can use automated records mining strategies to outline client profiles, section clients, and better recognize purchasing conduct. Through knowledge of client conduct, corporations can target their customers greater correctly, grow revenue, and enhance customer satisfaction and loyalty. The advantages of fact mining for CRM are several, which include spotting purchaser needs and options, improving marketing effectiveness, increasing client acquisition, and boosting sales. Additionally, records mining can enable an employer to perceive lost sales possibilities, lessen operational costs, and optimize customer retention efforts. Essential data mining allows CRM-targeted organizations to recognize clients higher, optimize operations, and broaden strategically centered initiatives to improve consumer loyalty.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multipath Routing Strategies for End-to-End Traffic Management in Underwater High Speed Networks

Multipath Routing Strategies for End-to-End Traffic Manageme...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Akhilendra Pratap Singh Srisathirapathy S Dharmesh Dhabliya Ashmeet Kaur Mamatha G N Jyoti Seth Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

Underwater high-speed networks (HSN s) use several environmental, operational, and technical demanding situations, making their layout and efficient operation challenging. The underwater medium introduces variabilities on the acoustic channels that, in turn, increase the complexity of the routing and site visitors' control. Multipath routing has been proposed to manage agile, nice-grain, and actual-time cease-to-end visitors in these networks. This painting provides and evaluates several multipath routing techniques for excessive velocity Underwater Networks (HSUN s), The proposed algorithms originate from direction Vector protocols and think about link metrics, hop to be counted, put-off jitter, packet loss fee, and other applicable parameters. We examine the performance of the proposed multipath routing strategies, based totally on the standard packet put-off, packet loss fee, and jitter average in a large-scale natural experiment carried out using acoustic modems underneath controlled channel conditions in an underwater tunnel. The experiment results show vast enhancements in the performance of the network when compared to single-route routing. Furthermore, the effects display the strong effect of the multipath routing techniques on the community's overall performance. In step with the acquired outcomes, the offered multipath routing techniques may be increased and used to enhance the throughput and first-rate service of excessive-velocity underwater networks.

关键词：

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：