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DOR: a novel dual-observation-based approach for recommendation systems

Applied Intelligence 2023年第23期53卷 29109-29127页

作者： Wang, Mengyan Li, Weihua Shi, Jingli Wu, Shiqing Bai, Quan School of Engineering Computer & Mathematical Sciences Auckland University of Technology 55 Wellesley Street East Auckland1010 New Zealand School of Computer Science University of Technology Sydney 15 Broadway Rd Ultimo SydneyNSW2007 Australia School of Information and Communication Technology University of Tasmania Churchill Ave HobartTAS7005 Australia

As online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, often overlooking the intricate connection between the reading content and the user’s prior knowledge and interest. This disconnect can consequently lead to a paucity of diverse and personalized recommendations. In this paper, we propose a novel approach to tackle the multifaceted issue of recommendation. We introduce the Dual-Observation-based approach for the Recommendation (DOR) system, a novel model leveraging dual observation mechanisms integrated into a deep neural network. Our approach is designed to identify both the core theme of an article and the user’s unique engagement with the article, considering the user’s belief network, i.e., a reflection of their personal interests and biases. Extensive experiments have been conducted using real-world datasets, in which the DOR model was compared against a number of state-of-the-art baselines. The experimental results explicitly demonstrate the reliability and effectiveness of the DOR model, highlighting its superior performance in news recommendation tasks. © 2023, The Author(s).

关键词： Deep neural networks

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URLLC in 5G & Beyond: Specifications and Technologies

URLLC in 5G & Beyond: Specifications and Technologies

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Sudhamsu, Gadug Mishra, Awakash Kale, Kiran Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

With the ability to communicate between machines and people, the Tactile Internet (TI) represents a substantial advancement over the Internet of Things (IoT). TI wants to make it easier to develop real-time interactive applications in a variety of fields, including as engineering, social engagement, and business. The future 5th Generation (5G) technology is focused on offering Ultra-Reliable Low Latency communication (URLLC) services to satisfy the demanding needs of TI applications. According to 3GPP specifications, URLLC may transmit a single 32-byte packet with a latency of less than one millisecond and achieve 99.99% dependability. In order to accomplish these objectives, the 3GPP presents 5G New Radio (5G NR), a flexible orthogonal frequency division multiplexing (OFDM) approach, as a new radio access technology (RAT). With the introduction of this new RAT, it is clear that we need next-generation solutions, especially ones that include network intelligence. To fulfil the URLLC needs of 5G NR, machine learning (ML) methods are anticipated to be crucial in building intelligent network resource distribution algorithms. This review analyses the possible use of Federated reinforcement learned, an ML approach, to satisfy the needs of 5G NR URLLC and lists the pertinent URLLC accomplishments. Plane channel entry techniques that support URLLC in 5G NR for TI workloads are covered in great detail in this study. It also pinpoints seven crucial upcoming situations where FRL might function as a possible facilitator for URLLC in 5G NR. © 2023 IEEE.

关键词： 3GPP specifications 5G Federated reinforcement learning latency and reliability machine learning network architecture New Radio Tactile Internet URLLC

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Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

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computers, Materials & Continua 2022年第2期70卷 4123-4145页

作者： Shahab Ul Islam Saifullah Jan Abdul Waheed Gulzar Mehmood Mahdi Zareei Faisal Alanazi Department of Computer Science IQRA National UniversityPeshawar25124Pakistan Department of Computer Science City University of Science&Information TechnologyPeshawar25124Pakistan Department of Information Technology Hazara UniversityMansehra21120Pakistan School of Electrical and Computer Engineering Seoul National UniversitySeoul08826Korea Department of Computer Science and Information Technology University of Malakand23021Pakistan Tecnologico de Monterrey School of Engineering and SciencesZapopan45201Mexico Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management ***,a change in infrastructure has a direct impact on other land-cover and climatic *** study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and *** this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were ***,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic information System)were ***,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were *** Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under *** composition of red,green,and near infra-red bands was used for supervised *** images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to *** has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

关键词： Remote sensing temperature extraction urbanization satellite image classification artificial neural network support vector machine LU/LC land surface temperature

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On an estimate on Götzky's domain

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Indagationes Mathematicae 2025年第4期36卷 1096-1111页

作者： Tóth, Dávid HUN-REN Alfréd Rényi Institute of Mathematics Budapest Hungary Department of Computer Science and Information Theory Budapest University of Technology and Economics Budapest Hungary

A fundamental domain F⊂H2 for the Hilbert modular group belonging to the quadratic number field Q(5) was constructed by Götzky almost a hundred years ago. He also gave a lower bound for the height y1y2 of the poi... 详细信息

A fundamental domain F⊂H² for the Hilbert modular group belonging to the quadratic number field Q(5) was constructed by Götzky almost a hundred years ago. He also gave a lower bound for the height y₁y₂ of the points (z₁,z₂)=(x₁+iy₁,x₂+iy₂)∈F. Later Gundlach used analogous domains and estimates for other fields as well to give a complete list of totally elliptic conjugacy classes in some Hilbert modular groups, while not long ago Deutsch analyzed two of these domains by numerical computations and stated some conjectures about them. We prove one of these by giving a sharp lower bound for the height of the points of Götzky's domain. © 2024 Royal Dutch Mathematical Society (KWG)

关键词： Fundamental domain Hilbert modular groups Quadratic fields

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Design of Optical Reversible Hybrid Adder-Subtractor Device Using Mach-Zehnder Interferometers for WDM Applications

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Journal of Optical communications 2022年第4期43卷 539-548页

作者： Chauhan, Chanderkanta Kaushik, Brajesh Kumar Kumar, Santosh Department of Electrical and Electronics & Communication Engineering Dit University Uttarakhand Dehradun248009 India Shandong Key Laboratory of Optical Communication Science and Technology School of Physics Science and Information Technology Liaocheng University Liaocheng Shandong252059 China Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India

As energy efficiency is one of the prominent issues in the today's era. Reversible computing may serve as a critical step toward this paramount issue. Optical reversible computing is one of the tactics to serve this aspiration. In this paper, an optical reversible hybrid adder-subtractor device delineated with the help of electro-optic effect inside lithium-niobate based Mach-Zehnder interferometer (MZI) for WDM applications. The proposed device in this work is combinational circuits and can be used as an adder as well as subtractor in advance applications. The perception is established with the help of simulation results of beam propagation method (BPM) and mathematically proven with MATLAB simulation results. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Mach-Zehnder interferometers

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Wavelet-Based Hybrid Ensemble Forecasting for Accurate Web Traffic Prediction 1

Wavelet-Based Hybrid Ensemble Forecasting for Accurate Web T...

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1st International Conference on Innovations in communications, Electrical and computer Engineering, ICICEC 2024

作者： Manoj Kumar, M.V. Puneetha, B.H. Prashanth, B.S. Sneha, H.R. Patil, N.S. Ranjana, B.J. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Information Science and Engineering Karnataka Davanagere577004 India Karnataka Davanagere577004 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

This paper presents a novel approach for accurate web traffic prediction on the websites using a Wavelet-Based Hybrid Ensemble Forecasting method. The proposed method decomposes the web traffic time series into time-shifted discrete wavelets, enabling individual forecasting on each decomposed series. Four models - SARIMA, ARIMA, LSTM, and KNN - were evaluated, and their forecasts were combined in a hybrid ensemble model. Evaluation metrics such as accuracy percentage, MAE, RMSE, and MAPE were employed. The hybrid model exhibited superior performance, achieving an accuracy of 99.39%, along with the lowest MAE and RMSE values. Combining forecasts from multiple models significantly improved accuracy and reduced error rates in web traffic prediction. © 2024 IEEE.

关键词： Prediction models

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Resource Allocation in Cloud Computing Using Genetic Algorithm and Neural Network 8

Resource Allocation in Cloud Computing Using Genetic Algorit...

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8th IEEE International Conference on Smart Cloud, SmartCloud 2023

作者： Manavi, Mahdi Zhang, Yunpeng Chen, Guoning Department of Computer Science University of Houston Houston United States Department of Information Science Technology University of Houston Houston United States

ISBN: (纸本)9798350313505

Cloud computing is one of the most used distributed systems for data processing and data storage. Due to the continuous increase in the size of the data processed by cloud computing, scheduling multiple tasks to maintain efficiency while reducing idle becomes more and more challenging. Efficient cloud-based scheduling is also highly sought by modern transportation systems to improve their security. In this paper, we propose a hybrid algorithm that leverages genetic algorithms and neural networks to improve scheduling. Our method classifies tasks with the Neural Network Task Classification (N2TC) and sends the selected tasks to the Genetic Algorithm Task Assignment (GATA) to allocate resources. It is fairness aware to prevent starvation and considers the execution time, response time, cost, and system efficiency. Evaluations show that our approach outperforms the state-of-the-art method by 3.2% at execution time, 13.3% in costs, and 12.1% at response time. ©2023 IEEE.

关键词： Cloud computing

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Semantics Preserving Dimensionality Reduction of Citation Network Using Topic Models

Semantics Preserving Dimensionality Reduction of Citation Ne...

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2024 IEEE Conference on Engineering Informatics, ICEI 2024

作者： Mahalakshmi, G.S. Sendhilkumar, S. Rajeshkumar, Deepak Jaysankar, Pranav Anna University Department of Computer Science and Engineering College of Engineering Guindy Chennai India Anna University Department of Information Science and Technology College of Engineering Guindy Chennai India

ISBN: (纸本)9798331505776

This research focuses on constructing a more compact network from a large citation network using Latent Dirichlet Allocation (LDA) and evaluating the efficiency and structural characteristics of the resulting network. A comparison is made between the compact LDA-derived network and the original citation network across various graph-based metrics. The results demonstrate that, despite its reduced size, the LDA-derived network retains or even improves key structural properties, enhancing efficiency while preserving critical information. © 2024 IEEE.

关键词： Centrality Citation network Space Complexity Topic Modelling

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Webcam-Based OCR System with Text-to-Speech Conversion 5

Webcam-Based OCR System with Text-to-Speech Conversion

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5th International Conference on Sustainable communication Networks and Application, ICSCNA 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 IEEE.

关键词： Optical character recognition

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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

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International Journal of computers and Applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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