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8th International Conference on Communication and Electronics systems, ICCES 2023

作者： Palarimath, Suresh Pyingkodi, M. Thenmozhi, K. Maqsood, Mohammed Salam, Mohammed Abdul Palarimath, Roopa Devi University of Techonology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Governors State University Information Technology Department University Park United States Mansarovar Global University Faculty of Computer Science Bhopal India

ISBN: (纸本)9798350396638

In today's modern world, IoT plays an important and multifaceted function in all industries. 5th Generation Wireless systems - 5G is the primary technical platform on which the Internet of Things plays a major role in smart technologies. Incorporating 5G technology into the architecture of an IoT system is now simple. Using plug-and-play technology, remote access to configuration and control is possible. Smart technology always results in faster data transfer rates, more bandwidth, greater capacity, lower latency, and a quicker output response. Based on this notion, IoT brings about a dramatic shift in heterogeneous networks. So, there is a need to implement 5G-IoT technologies. In the age of the Internet of Things, the fifth-generation (5G) cellular networks give the smart and complex technical environment. 5G Cellular Networks offer great growth potential for IoT technologies. This article gives a thorough study of the role of 5G Cellular Networks in the expansion of IoT technologies across all industries. Since the inclusion of renewable energy emerges as a new trend in the digital world, 5G is very beneficial in the age of the Internet of Things. To achieve the link between 'Things and People,' 'Things and Things,' and 'People and People' in power systems, it is necessary to combine IoT with 5G in power systems to a high degree of success in various power-based industry-based systems. This article demonstrates a smart scenario and provides a thorough analysis of "how 5G drives the IoT System intelligently in every way and how IoT affects as the "most promising technology"with this smart 5G Wireless Communication System."In addition, the IoT era 5G application possibilities were demonstrated. © 2023 IEEE.

关键词： Internet of things

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Towards Federated Learning-Based Forecasting of Renewable Energy Production

Towards Federated Learning-Based Forecasting of Renewable En...

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Federated Learning Technologies and Applications (FLTA), International Conference on

作者： Viktor Walter Fabian Stricker Andreas Wagner Christian Zirpins Faculty of Management Science and Engineering Karlsruhe University of Applied Sciences (HKA) Karlsruhe Germany Faculty of Computer Science and Business Information Systems Karlsruhe University of Applied Sciences (HKA) Karlsruhe Germany Fraunhofer Institute for Industrial Mathematics (ITWM) Kaiserslautern Germany

ISBN: (数字)9798350354812

ISBN: (纸本)9798350354829

The integration of volatile renewable energy sources requires reliable generation forecasts. Traditional forecasting methods that rely on commercial providers impose costs and de-pendencies on renewable energy operators. This paper proposes a literature survey on federated learning (FL) in the context of renewable energy forecasting and an analysis of open challenges in research and practice and possible solution approaches for re-alizing such a framework. Our focus is on short-term forecasts for day-ahead markets, which are critical for trading and operational efficiency. The FL approach preserves data privacy and improves forecast accuracy by leveraging distributed data from multiple operators. We present an analysis of current FL applications in renewable energy forecasting, identify implementation challenges, and propose solutions to overcome these barriers. This study aims to empower market participants to produce independent, accurate forecasts, thereby improving economical outcomes and operational stability.

关键词： Surveys Industries Renewable energy sources Costs Accuracy Federated learning Production Stability analysis Reliability Forecasting

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Deep Learning Approaches for Recognizing Daily Human Activities Using Smart Home Sensors 8

Deep Learning Approaches for Recognizing Daily Human Activit...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Sanpote, Worrakit Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Science University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Nowadays, one of the most important objectives in health-related research is the improvement of the living condition and well-being of people. Smart home systems can provide health protection for residents based on the results of daily activity recognition. Recent advances and developments in sensor technology have increased the need for sensor-compatible goods and services in smart homes. Consequently, the ever-increasing volume of data requires the field of deep learning (DL) for auto-matic human motion recognition. Recent research has modeled spatiotemporal sequences gathered by smart home sensors using long short-term memory networks. In this work, ResNeXt-based models that learn to classify human activities in smart homes were proposed to improve recognition performance. Experiments conducted on Center for Advanced Studies in Adaptive systems (CASAS) data, a publicly available benchmark dataset, shows that the proposed ResNeXt-based techniques are significantly superior to the existing DL methods and provide better results compared to the existing literature. The ResNeXt model achieved the averaged accuracy over the benchmark method to 84.81%, 93.57%, and 90.38% for the CASAS-Cairo, CASAS-Milan and CASAS-Kyoto3 datasets, respectively. © 2023 IEEE.

关键词： Deep learning

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Latency Analysis of Cloud Infrastructure for Time-Critical IoT Use Cases

Latency Analysis of Cloud Infrastructure for Time-Critical I...

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2022 applied Informatics International Conference, AiIC 2022

作者： Zen, Kartinah Mohanan, Saju Tarmizi, Seleviawati Annuar, Noralifah Sama, Najm Us Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Sarawak Kota Samarahan Malaysia University of Technology and Applied Sciences Department of Information Technology Muscat Oman

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

The time-critical Internet of Things (IoT) use cases such as driverless cars and robotic surgical arms need high bandwidth and low latency for real-time intelligent data processing and trained machine learning inference. Latency in real-time processing is influenced by many factors such as artificial intelligence (AI) computing algorithm, device processing capabilities, the frameworks, and also the distance from the cloud infrastructure. However, the geographical distance between the data origin and data processing is one of the major factors contributing to the network latency for time-critical IoT use cases. In this paper, we analyzed the latency from a particular client point based on the live data generated by their cloud data centers. The experiments were done through the big three cloud vendors, which are Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). As a result, a time-critical IoT low latency approach is proposed in this paper. © 2022 IEEE.

关键词： Edge computing

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From User Preferences to Accurate Predictions: Enhancing Movie Recommendation systems with Neural Collaborative Filtering and Sentiment Analysis

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SN computer science 2025年第3期6卷 1-26页

作者： Bsoul, Qusay Zawaideh, Firas Alqadi, Basma S. Almusfar, Latifa Abdullah Khalaf, Osamah Ibrahim Alattas, Ahmed Saleh Alali, Muath AbdElminaam, Diaa Salama Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Information Science Department Faculty of Arts and Humanities King Abdulaziz University Jeddah Saudi Arabia Data Science and Artificial Intelligence Faculty of Information Technology Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid 21110 Jordan MEU Research Unit Middle East University Amman 11831 Jordan

This paper introduces an advanced movie recommendation system that combines neural collaborative filtering (NCF) with sentiment analysis to improve personalization, accuracy, and robustness in recommending movies. By integrating user preferences with the sentiment polarity of reviews, the system refines recommendations, effectively addressing challenges such as the cold start problem, data sparsity, and limited semantic understanding. Unlike traditional collaborative and content-based filtering methods, which often struggle with diversity and accuracy, our approach utilizes deep learning within NCF to reveal hidden patterns in user behavior, enhancing recommendations’ relevance and precision. The proposed system is evaluated using standard performance metrics, including RMSE, MSE, and MAE, demonstrating its superior performance over conventional filtering techniques. Its enhanced scalability and adaptability position it as a promising tool for personalized content delivery in digital entertainment, with considerable potential for large-scale, dynamic recommendation environments. This research contributes to the existing knowledge on recommendation systems and offers new insights into improving content personalization and user satisfaction. The novelty and scientific value of this work lie in applying deep learning to tackle the challenges of accurate content recommendation in the rapidly evolving digital media landscape. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering Content-based filtering Cosine and person similarities Deep learning Evaluation metrics MAE Movie recommendation system NCF RMSE Surprise library

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Lévy flights and Lévy walks under stochastic resetting

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Physical Review E 2024年第4期109卷 044147-044147页

作者： Bartosz Żbik Bartłomiej Dybiec Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Łojasiewicza 11 30-348 Kraków Poland Institute of Theoretical Physics and Mark Kac Center for Complex Systems Research Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Łojasiewicza 11 30-348 Kraków Poland

Stochastic resetting is a protocol of starting anew, which can be used to facilitate the escape kinetics. We demonstrate that restarting can accelerate the escape kinetics from a finite interval restricted by two absorbing boundaries also in the presence of heavy-tailed, Lévy-type, α-stable noise. However, the width of the domain where resetting is beneficial depends on the value of the stability index α determining the power-law decay of the jump length distribution. For heavier (smaller α) distributions, the domain becomes narrower in comparison to lighter tails. Additionally, we explore connections between Lévy flights (LFs) and Lévy walks (LWs) in the presence of stochastic resetting. First of all, we show that for Lévy walks, the stochastic resetting can also be beneficial in the domain where the coefficient of variation is smaller than 1. Moreover, we demonstrate that in the domain where LWs are characterized by a finite mean jump duration (length), with the increasing width of the interval, the LWs start to share similarities with LFs under stochastic resetting.

关键词： Brownian motion Escape problems Levy flights Noise

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Applications of Neural Networks in Engineer-to-order Environment 15

Applications of Neural Networks in Engineer-to-order Environ...

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15th CIRP Conference on Intelligent Computation in Manufacturing Engineering, ICME 2021

作者： Ziegler, Cedric C. Dobhan, Alexander Heusinger, Moritz University of Applied Sciences Würzburg-Schweinfurt Faculty of Business and Engineering Konrad-Zuse-Straße 2 Schweinfurt97421 Germany University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science and Business Information Systems Sanderheinrichsleitenweg 20 Wuerzburg97074 Germany

Machine tool selection and quotation costing have a low level of automation in today's engineer-to-order environments. The decision-making process is based on imprecise human judgment even if all final product characteristics are known. To improve precision and save time, we propose to apply artificial neural networks that are trained on data of already produced products. In particular, we address the selection of several grinding wheels to produce a milling cutter, which is a multiple-criteria decision, using multi-label classification. The quotation costing is a single output regression problem. In both tasks, our results on real-world data show high accuracy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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Improved Intensity-Based Image Registration via Archimedes Optimization Algorithm 6

Improved Intensity-Based Image Registration via Archimedes O...

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6th International Conference on Intelligent systems and computer Vision, ISCV 2024

作者： Kmich, Mohamed Karmouni, Hicham Harrade, Inssaf Ouazzani, Mohammed Jamil Qjidaa, Hassan Sayyouri, Mhamed Sidi Mohamed Ben Abdellah University Engineering Systems and Applications Laboratory National School of Applied Sciences Fez Morocco Cadi Ayyad University Engineering Systems and Applications Laboratory National School of Applied Sciences Marrakech Morocco Fez Morocco Sidi Mohamed Ben Abdellah University CED-ST STIC Laboratory of Electronic Signals and Systems of Information LESSI Dhar El Mahrez Faculty of Science Fez Morocco

ISBN: (纸本)9798350350180

Image registration is the process of superimposing two or more images to align them correctly. The aim is generally to minimize spatial discrepancies between images. In this context, we propose a new method for intensity-based image registration (IBIR) based on the optimization of the parameters of the 2D affine geometric transformation needed to align the images via the Archimedes Optimisation Algorithm (ArOA), and on the normalization of the sum of squared differences (NSSD) between different images taken at different times or from different views and the desired image as an objective function. The proposed method, based on ArOA, is robust to variations and perturbations in the image data. It can avoid local optima and find optimal solutions even when elements such as noise, distortions, or artefacts are present in the images. Simulations show the efficiency of the proposed method in terms of matching aligned images and computation time compared with other methods based on the Sine-Cosine Algorithm (SCA) and the Equilibrium Optimizer (EA). © 2024 IEEE.

关键词： Optimization algorithms

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Lexicon approach for risk analysis in emerging technologies

Lexicon approach for risk analysis in emerging technologies

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2023 International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Elhoseny, Mohamed Darwiesh, Abdelaziz El-Baz, A.H. University of Sharjah Faculty of Computing and Informatics Information Systems Dep. Sharjah United Arab Emirates Damietta University Faculty of Science Mathematics Dep. New Damietta Egypt Damietta University Faculty of Computers and Artificial Intelligence Computer Science Dep. New Damietta Egypt

ISBN: (纸本)9798350322972

This article proposes a novel methodology to analyze risks in digital currencies. It depends on the perceptions of users in social media to recognize and estimate the potential risks. Besides, it includes utilizing high-level computational techniques such as big data analysis techniques and natural language processing. We illustrate the suggested system mathematically and some important expressions for risk analysis, risk recognition and risk estimation are driven in closed forms. Moreover, we present a use case study to extract insights about the potential risks related to digital currencies. Further, some indicators are provided to illustrate the effectiveness of the suggested approach like the mean accuracy for processes of analyzing risks, identifying risks and assessing risks assessment that is 77 %. © 2023 IEEE.

关键词： Risk management

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