检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Qiu, Zhengzhong Bi, Wei Xu, Dong Guo, Hua Ge, Hongwei Liang, Yanchun Lee, Heow Pueh Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun130012 China YGSOFT INC Guangdong Zhuhai519085 China Department of Electrical Engineering and Computer Science Informatics Institute Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States College of Computer Science and Technology Dalian University of Technology Liaoning Dalian116081 China School of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai519041 China Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore

The evolutionary algorithm has become a major method for neural architecture search recently. However, the fixed probability distribution adopted by the traditional evolutionary algorithm makes the algorithm unable to control the increase or decrease of the individual architecture size and thus cannot guarantee the lightweight and inference efficiency of the candidate architecture. In addition, the existing approaches cannot learn the optimal sampling probability distribution relevant to the specific problem from the empirical information accumulated through the search process. What’s more, the algorithm needs to evaluate the performance of all the individual architectures, which requires huge computing resources and time overhead. To overcome these problems and challenges, an Efficient Self-learning Evolutionary Neural Architecture Search method, called ESE-NAS, is presented in this paper. Firstly, we propose a Model Size Control module, which generates the probability distribution for sampling the type of mutation operators according to the current model size, so as to control both the node number of network architecture and the sparsity of links, therefore ensuring that neural architectures can remain compact and efficient as they evolve. Thereafter, a Mutation Candidate Credit Assignment method is proposed, which enables the algorithm to dynamically adjust the probability distribution for available types of node operations and directed links according to the empirical information from individuals’ performance evaluation, which can guide the evolutionary direction of neural architecture and shorten the first hitting time of the optimal architecture. Finally, a neural architecture performance predictor is formulated, and the accuracy of each individual architecture is predicted by using the simplified input features, to further improve the efficiency of neural architecture search. Experiments show that the ESE-NAS method proposed in this paper can learn a highly i

关键词： Evolutionary algorithms

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Adaptive Multi-Resolution Framework for Scalable and Efficient Big Data Processing

Adaptive Multi-Resolution Framework for Scalable and Efficie...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V. Asha Yagateela Pandu Rangaiah Ginni Nijhawan Ali Albawi K. Pushpa Rani Susmita Kalyani Master of Computer Application New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The increasing applicability of the big data in different domains require the formulation of effective data collection and analysis framework that can accommodate complex and large datasets. This paper proposes the new DRAF that enhance both data aggregation and mining for large datasets. The Dynamic Resolution Adaptive Framework (DRAF) takes advantage of using multiple levels of representation of data, which makes it easy to expand the workload or scale down depending on the needs of the user. This proposed framework employs methods like hierarchical clustering, branching multi-level indexing and Adaptive Resolution Switching in order to optimize the trade-off of data accuracy and computational cost. Moreover, high level of detail is maintained in the DRAF, which supports mining necessary data and model details at multiple resolutions, making the DRAF specifically appropriate for operational environments where data come from numerous different sources and types. Substantial experiments in the book prove the efficiency of the DRAF-based algorithms in terms of speed and accuracy in comparison with other existing methods. The study establishes potentiality of the DRAF toward the application of big data problems in areas of high level of resolution and immediacy of decision making.

关键词： Accuracy Computational modeling Weapons Switches Big Data Data models Data mining Informatics Indexing Load modeling

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Multi-objective Optimization for Multi-UAV-assisted Mobile Edge Computing

arXiv

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arXiv 2024年

作者： Sun, Geng Wang, Yixian Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China School of Automation Guangdong University of Technology Guangzhou510006 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we present a multi-objective optimization approach for multi-UAV-assisted MEC systems. First, we formulate a multi-objective optimization problem aiming at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks by jointly optimizing task offloading, computation resource allocation, and UAV trajectory control. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem which is challenging, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach to solve the problem. However, since the decision variables of task offloading, computation resource allocation, and UAV trajectory control are coupled with each other, the original problem is split into three sub-problems, i.e., task offloading, computation resource allocation, and UAV trajectory control, which are solved individually to obtain the corresponding decisions. Moreover, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method. Simulation results show that the proposed JTORATC has superior performance compared to the other benchmark methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Exploring Pre-Trained Neural Audio Representations for Audio Topic Segmentation

Exploring Pre-Trained Neural Audio Representations for Audio...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Iacopo Ghinassi Matthew Purver Huy Phan Chris Newell Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London UK Centre for Digital Music School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Amazon Alexa Cambridge MA USA BBC R&D UK

Recent works have shown that audio embeddings can improve automatic topic segmentation of formats such as radio shows. In this work we expand the work in that direction by showing how and which publicly available, pre-trained neural audio embeddings can perform the task, without the need of any further fine-tuning of the audio encoders. The ranking of the encoders suggest that neural encoders pre-trained for speaker diarization and general purpose audio classification are the best suited to be used as features, beating non-neural baselines. We show that we can obtain perfect results on a newly created random dataset similar to the one used in previous work. We also show for the first time results on real-world data, proving that our method can be applied to actual radio shows with good results, but the choice of audio encoders is extremely important in order to achieve those. Finally, by releasing the datasets we used we make the contribution of providing the first (to our knowledge) publicly available, free of charge datasets for audio topic segmentation of media products.

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Smart Blind Stick for Visually Impaired People using IoT

Smart Blind Stick for Visually Impaired People using IoT

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Rajanish Kumar Kaushal K. Tamilarasi P. Babu T. A. Mohanaprakash S. E. Murthy M Jogendra Kumar Department of Electrical Engineering Chandigarh University Mohali Punjab India Department of Computer Science and Engineering VIT University Chennai Tamil Nadu India Department of Information Technology PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Knowledge Institute of Technology Salem Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (纸本)9781665460859

The existing assistive device used by the visually impaired, a stick, is insufficient for allowing them to live independently. Getting from one place to another, whether indoors or outside, is a huge issue for persons who are visually impaired. And the traffic conditions make their everyday journeys more onerous. Consequences such as falling over stationary objects, relocating barriers, or missing a traffic light can only be avoided if they maintain a constant state of vigilance. For the visually challenged, technology can solve these problems. The proposed method employs the Internet of Things (IoT) paradigm to serve as a link between the visually handicapped and their surroundings. An ultrasonic sensor may be used to scan the surroundings for possible obstacles, while an ESP32 camera collects the traffic signal image. An Artificial Neural Network (ANN) model is particularly tailored for the task to detects the traffic signal. The speaker is permanently attached to the controller's output and can alert the user about obstacles and traffic signals. The next step is to confirm whether the person is roaming within the nearer area or not. If the person is navigating a long distance away from the house, the system will send a text message to the registered mobile number. The suggested smart blind stick is both simple to use and cost-effective due to the incorporation of several types of sensors and modules with IoT.

关键词： Productivity Visualization Renewable energy sources Navigation Atmospheric modeling Sociology Artificial neural networks

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Efficient Data Sampling and Reduction Methods in Large-Scale Forensic Analysis

Efficient Data Sampling and Reduction Methods in Large-Scale...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ala Harika Praveen Sharma K Aravinda Amandeep Nagpal Praveen Ali Albawi Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal M. P India Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

In this era of enormous, continuously changing datasets, forensic analysis needs innovative approaches that can adapt to changing data characteristics while maintaining data security. To satisfy this demand, our recommended system includes all algorithms for large forensic investigations. The adaptive stratified sampling algorithm sets the scene. It adjusts strata and sample quantities based on data attributes. The Feature Weighting with Machine Learning Algorithm improves selection by weighting features using machine learning. Based on them, the dynamic cluster-based sampling algorithm organizes data points dynamically. By determining how much information each data point has, the entropy-guided data reduction algorithm improves the framework. The Temporal Correlation Analysis Algorithm finally allows you to view changing data. Tables3 and Tables4 indicate that our approach outperforms others in several key areas. The recommended technique outperforms others in accuracy, memory, F1 score, usability, stability, legal acceptability, ease of application, computing complexity, and freedom. The findings in Figures.6–10 indicate that the proposed framework can balance accuracy and memory, decrease data, and be highly usable, stable, and versatile. Finally, our technology represents a novel technique to conduct large-scale research. It is fast, adaptable, and data-safe. Machine learning, dynamic changes, and temporal variables make our platform robust and comprehensive for current forensic investigations.

关键词： Technological innovation Machine learning algorithms Accuracy Correlation Forensics Heuristic algorithms Clustering algorithms

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Ensemble Modeling for the Classification of Birth Data

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Intelligent Automation & Soft Computing 2024年第4期39卷 765-781页

作者： Fiaz Majeed Abdul Razzaq Ahmad Shakir Maqbool Ahmad Shahzada Khurram Muhammad Qaiser Saleem Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Osama E.Sheta Department of Information Technology University of GujratGujrat50700Pakistan School of Digital Convergence Business University of Central PunjabRawalpindi46000Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan College of Computer Science and Information Technology Al Baha UniversityAl Baha1988Saudi Arabia School of Computer Science and Engineering Yeungnam UniversityGyeongsan38541Republic of Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaLibreville1989Gabon Department of Electrical and Electronic Engineering Science School of Electrical EngineeringUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia College of Computer Science and Engineering University of Ha’ilHa’il55476Saudi Arabia College of Science Zagazig UniversityZagazig44511Egypt

Machine learning(ML)and data mining are used in various fields such as data analysis,prediction,image processing and especially in *** in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the *** ML algorithms,researchers have developed applications for decision support,analyzed clinical aspects,extracted informative information from historical data,predicted the outcomes and categorized diseases which help physicians make better *** is observed that there is a huge difference between women depending on the region and their social *** to these differences,scholars have been encouraged to conduct studies at a local level in order to better understand those factors that affect maternal health and the expected *** this study,the ensemble modeling technique is applied to classify birth outcomes based on either cesarean section(C-Section)or normal delivery.A voting ensemble model for the classification of a birth dataset was made by using a Random Forest(RF),Gradient Boosting Classifier,Extra Trees Classifier and Bagging Classifier as base *** is observed that the voting ensemble modal of proposed classifiers provides the best accuracy,i.e.,94.78%,as compared to the individual *** algorithms are more accurate due to ensemble models,which reduce variance and classification *** is reported that when a suitable classification model has been developed for birth classification,decision support systems can be created to enable clinicians to gain in-depth insights into the patterns in the *** such a system will not only allow health organizations to improve maternal health assessment processes,but also open doors for interdisciplinary research in two different fields in the region.

关键词： Birth data classification ensemble model machine learning

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Advancing Image Segmentation, Analysis, and Feature Extraction Techniques

Advancing Image Segmentation, Analysis, and Feature Extracti...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V. Asha Yagateela Pandu Rangaiah Ginni Nijhawan Susmita Kalyani K. Pushpa Rani Ali Albawi Master of Computer Application New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study centers on review, feature extraction, and image segmentation, integrated through the Dual-Path Convolutional Neural Network (DP-CNN). The DP-CNN is designed to accurately identify objects in images, even in the presence of intricate lines and discontinuities. The MAMAM technique efficiently prioritizes critical image regions, expediting the review process and enhancing accuracy in image analysis. Additionally, feature extraction is refined using the Adaptive Feature Selection (AFS) method, facilitating precise and adaptable image processing. Each component of this approach is systematically developed to meet the research objectives. The combined methodology demonstrates robustness and versatility, proving effective across diverse and challenging data scenarios, thereby presenting a promising solution for rapid and accurate complex image processing tasks.

关键词： Image segmentation Image analysis Accuracy Reviews Feature extraction Solids Robustness Object recognition Convolutional neural networks Informatics

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Sophisticated Neural Network Architectures for the Holistic Simulation and Performance Enhancement of Convective Boiling Processes in Industrial Applications

Sophisticated Neural Network Architectures for the Holistic ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： V Asha Shaik Anjimoon Atul Singla Irfan Khan Haider Asaad Farhoud Alsharifi Ramya Maranan Department of Computer Applications New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Collage of Dentist National University of Science and Technology Dhi Qar Iraq Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

In this research, neural network approaches for industrial convective boiling simulation are fully examined. Five strategies address feature extraction, temporal dynamics, memory retention, data enrichment, and knowledge sharing issues. Recurrent neural networks (RNNs) discover temporal changes, whereas CNNs find spatial patterns. Long ShortTerm Memory (LSTM) Networks assist in recalling things; Generative Adversarial Networks (GANs) add data; and Transfer Learning conveys information using taught models. These methods accurately simulate convective boiling $96.0 \%$ of the time. Compared to tried-and-true approaches, it’s faster, more extensible, and more resilient. Visualizations indicate how superior the technique is at accuracy, stability distribution, and multi-metric scores. Here’s a detailed design for modeling convective boiling, including how space and time change, how to protect memories, add data, and exchange information. Industrial usage is possible since the recommended technique mimics convective cooking processes more complexly and cost-effectively.

关键词： Training Technological innovation Accuracy Recurrent neural networks Transfer learning MIMICs Data models

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IoT Based Smart Energy Forecasting and Data Logging System 5

IoT Based Smart Energy Forecasting and Data Logging System

引用

5th International Conference on Computing Methodologies and Communication, ICCMC 2021

作者： Sivakumar, S A Kumar, R. Ayshwarya, B. Hariharan, S. Ponnusamy, Muruganantham Ganesh, R Senthil Ashoka Women's Engineering College Department of Ece Kurnool India Department of Computer Science Bangalore India Knowledge Institute of Technology Department of Ece Salem India Indian Institute of Information Technology Kalyani India Sri Krishna College of Engineering and Technology Department of Ece Coimbatore India

ISBN: (纸本)9781665403603

The energy crisis is one of the prime challenges being faced by many countries today. To an enormous extent of the demand for energy in industrial development has increased tremendously. The shrewd meter as a critical component for the keen framework is relied upon to give monetary, social, and ecological advantages for different partners. There has been a lot of discussion over the genuine estimations of savvy meters. The solution aspect that will decide the accomplishment of shrewd meters is savvy meter information examinations which manage information obtaining, transmission, preparing, and understanding that carry advantages to all partners. A lot of techniques are suggested like an Energy monitoring and prediction system which is an efficient technique to watch the devices present inside a house or industries and provide notification about their abnormal behavior. The implementation of an IoT-based system for skilled energy administration in Industries and home usage is analyzed. The users can view their status through the IoT-based android application and the web server. © 2021 IEEE.

关键词： Internet of things

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