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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian Institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of Computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah Institute of System Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France Institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare 28th

Generating synthetic mixed-type tabular data by decoding sam...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Drapala, Jaroslaw Swiatek, Jerzy Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types-some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented. © 2024 The Authors.

关键词： Data privacy

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A Study on Classification of Mobile Application Network Traffic on MIRAGE-COVID-CCMA-2022 Dataset via Machine/Deep Learning and Feature Selection Methods 17

A Study on Classification of Mobile Application Network Traf...

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17th International Conference on information Security and Cryptology, ISCTurkiye 2024

作者： Yasar, Imren Dasdemir Coskun, Aysun Department of Information Security Engineering Gazi University Ankara Turkey Faculty of Technology Department of Computer Engineering Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

In recent years, the use of mobile internet has become widespread rapidly with the introduction of smartphones. The increasing weight of mobile network traffic in the overall network traffic has made mobile network traffic classification a new and important topic of study. The study utilizes MIRAGE-COVID-CCMA-2022 dataset for the classification of mobile application network traffic. The MIRAGE-COVID-CCMA-2022 dataset contains the network traffic of nine communication and collaboration mobile applications including Discord, GotoMeeting, Meet, Messenger, Skype, Slack, Teams, Webex, and Zoom. Experiments were conducted using four machine learning classifiers (Support Vector Machine, k-Nearest Neighbor, Adaptive Boosting, and Random Forest), four deep learning classifiers (Convolutional Neural Networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Gated Recurrent Units), and three feature selection methods (Minimum Redundancy Maximum Relevance, k-Nearest Neighbor, and Random Forest). The results of the study show that an overall accuracy of over 95% for the MIRAGE-COVID-CCMA-2022 dataset with a nine-class classification. The study concludes that the most appropriate classification structure is to select the features using the Minimum Redundancy Maximum Relevance method and classify the features with the Random Forest classifier. Also, this classification structure stands out as one of the classification structures with the fastest running time. © 2024 IEEE.

关键词： deep learning feature extraction machine learning mobile computer networks

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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Complex Double Integration Enhanced ZNN with Rejection of Linear Noise for Computing Time-Dependent Sylvester Equation 25

Complex Double Integration Enhanced ZNN with Rejection of Li...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Xu, Jinsha Hua, Cheng Li, Shuai Liao, Bolin College Of Computer Science And Engineering Jishou University Jishou China University Of Oulu Faculty Of Information Technology And Electrical Engineering Oulu Finland

ISBN: (纸本)9798350340266

Recurrent neural networks (RNN) are highly effective in solving the inverse problem of time-dependent matrices. However, in real-world engineering applications, noise interference is inevitable. The zeroing neural network (ZNN) is a special class of RNN that exhibit strong noise tolerance in solving the time-dependent Sylvester equation (TDSE). Despite the usefulness of the previously proposed original ZNN (OZNN) model and integral enhanced ZNN (IEZNN) model, they lack the ability to suppress linear and quadratic noise when solving the Sylvester equation. In this paper, a complex double integration enhanced ZNN (CDIEZNN) model is introduced for the first time to address the problem of TDSE. The theoretical basis for the convergence and robustness of the double integral model is described in detail. Two experiments are conducted to verify the convergence and robustness of the CDIEZNN model, and the digital simulation results demonstrate that in solving the TDSE, the residual error of the CDIEZNN model rapidly and stably converges to 3.82 × 10-6in a linear noise environment, and even in complex quadratic noise environments, the residual error can still converge to 1.99 × 10-2• In contrast, the OZNN and IEZNN models have weak suppression of linear noise and the residual errors cannot converge in a quadratic noise environment and the results show divergence. © 2024 IEEE.

关键词： Neural network models

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Modeling and Assessment of RSSI in LoRa WAN Network Using a Neural Network Approach 21

Modeling and Assessment of RSSI in LoRa WAN Network Using a ...

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21st IEEE International Symposium on Intelligent Systems and Informatics, SISY 2023

作者： Spisic, Josip Zrnic, Matko Zagar, Drago Crgic, Kresimir Computer Science and Information Technology Department of Communications Faculty of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350343366

This study employed a l0-fold cross-validation approach to train and validate neural networks to predict the RSSI (received signal strength indicator) in LoRaWAN communication. The model's performance was accurately estimated by averaging results across multiple validation sets and training sequences, and biased outcomes were mitigated. Implementing data normalization techniques, a simple neural network architecture, regularization approaches, and iteration limits ensured robustness within the limitations of a small dataset. The assessment of three neural network models using different optimization algorithms demonstrated their potential for accurate RSSI predictions, particularly for low-power IoT devices like LoRaWAN end devices, where energy efficiency is crucial. Lightweight neural networks on such devices enable efficient data processing while conserving energy, extending their lifespan, and reducing maintenance needs. Experimentation with deeper structures and unconventional activation functions may be necessary for complex problems, further enhancing performance in specific domains like RSSI assessment for LoRaWAN end devices. © 2023 IEEE.

关键词： Iterative methods

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Mutual character dialogue generation with semi-supervised multitask learners and awareness

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International Journal of information technology (Singapore) 2024年第3期16卷 1357-1363页

作者： Ahrari Khalaf, Ayesheh Hassan Abdalla Hashim, Aisha Olowolayemo, Akeem Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Computer Science Faculty of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Consistent efforts have been ongoing to improve the friendliness and reliability of informal dialogue systems. However, most research focuses solely on mimicking human-like answers. Therefore, the interlocutors’ awareness features of the dialogue system are left unexplored. Meanwhile, cognitive science research reveals that awareness is a crucial indicator of an effective, high-quality informal conversation. This research aims to boost the quality of the conversational generation system by factoring in awareness of the interlocutors in the design and training of the dialogue system model. The Generative Pre-Trained Transformer-2 (GPT-2) model was implemented into the Persona Perception (P²) Bot to achieve the objectives of this study. This was to precisely develop model's understanding, P² Bot was implemented using a transmitter–receiver-based structure. The P² Bot leverages mutual persona awareness to improve the quality of customized dialogue generation. GPT-2 is a 1.5B parameter transformer model that produces state-of-the-art accuracy in a zero-shot setting on seven of the eight evaluated language modeling datasets. The observations of the proposed model on a sizable open-source dataset, PERSONA-CHAT, proved successful, with improvement above the state-of-the-art baselines in both automatic measures and human assessments. The model has achieved 82.2% accuracy on Hits@1 performance metrics in the original data and 68.8% on the revised data. On the human evaluation, the model scored an average of 2.66, pointing out that the responses provided were coherent and informative. A dialogue generation model with character and awareness which can communicate like an informative human expert was introduced. This study presents the submerging of GPT-2 model on a mutual persona perception dialogue generating model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cognitive science Conversational agent Dialogue generation Generative Pre-trained Transformer 2 (GPT-2) Natural language understanding (NLU)

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Impact of UV content of illumination on acceptability of colour reproduction 12

Impact of UV content of illumination on acceptability of col...

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12th Colour and Visual Computing Symposium, CVCS 2024

作者： Nussbaum, Peter Gudzenchuk, Anastasiia Green, Phil Faculty of Information Technology and Electrical Engineering Department of Computer Science NTNU Norway Department of Science and Technology Linköping University Sweden

Paper and board for printing commonly contains optical brightening agents that fluoresce in the presence of UV radiation, making the prints appear brighter and bluer. If the relative amount of optical brightening agents (OBAs) is not consistent between proof and print, and the UV content of measurement and the viewing illumination differs, there will be a colorimetric difference between proof and print. Colour management workflows are normally media-relative, and visual adaptation to the media white can conflict with the goals of colorimetric matching. The impact of UV content in measurement and viewing illumination on the visual acceptability of colour reproductions was investigated in a series of psychophysical experiments, using substrates with varying OBA concentrations and a media-relative colour reproduction workflow. The results showed that visual acceptability was largely unaffected by UV content of measurement or viewing illumination, suggesting that visual adaptation to media white discounts the effect of fluorescence. © 2024 Copyright for this paper by its authors.

关键词： Colorimetry

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Adaptive Graph Embedding With Consistency and Specificity for Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2094-2107页

作者： Shaohua Teng Zefeng Zheng Naiqi Wu Luyao Teng Wei Zhang School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE

Domain adaptation(DA) aims to find a subspace,where the discrepancies between the source and target domains are reduced. Based on this subspace, the classifier trained by the labeled source samples can classify unlabeled target samples *** approaches leverage Graph Embedding Learning to explore such a subspace. Unfortunately, due to 1) the interaction of the consistency and specificity between samples, and 2) the joint impact of the degenerated features and incorrect labels in the samples, the existing approaches might assign unsuitable similarity, which restricts their performance. In this paper, we propose an approach called adaptive graph embedding with consistency and specificity(AGE-CS) to cope with these issues. AGE-CS consists of two methods, i.e., graph embedding with consistency and specificity(GECS), and adaptive graph embedding(AGE).GECS jointly learns the similarity of samples under the geometric distance and semantic similarity metrics, while AGE adaptively adjusts the relative importance between the geometric distance and semantic similarity during the iterations. By AGE-CS,the neighborhood samples with the same label are rewarded,while the neighborhood samples with different labels are punished. As a result, compact structures are preserved, and advanced performance is achieved. Extensive experiments on five benchmark datasets demonstrate that the proposed method performs better than other Graph Embedding methods.

关键词： Adaptive adjustment consistency and specificity domain adaptation graph embedding geometrical and semantic metrics

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