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S3AM++: Integrating Differential Strategy into Spectral-Similarity-Based Spatial Attention Module for Hyperspectral Remote Sensing Image Classification 23

S3AM++: Integrating Differential Strategy into Spectral-Simi...

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15th International Conference on Digital Image Processing, ICDIP 2023

作者： Li, Ningyang Wang, Zhaohui Cheikh, Faouzi Alaya Ullah, Mohib Faculty of Computer Science and Technology Hainan University China Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology China

ISBN: (纸本)9798400708237

Algorithms based on deep learning, especially the attention mechanism, have been the preference for hyperspectral remote sensing image classification. Recently, a spectral similarity based spatial attention module (S3AM) was designed to solve the deficiency that the existing attention modules cannot take the center pixel into full account for the deduction of the spatial attention. However, two crucial processes, including the calculation and conversion of spectral similarity, of this module may be affected by its single policy, which is harmful to alleviate the spectral variability and the accurate spatial attention. In this article, an improved version called S3AM++ which integrates the differential strategy into the original attention module is proposed for hyperspectral image classification. The S3AM++ divides each neighborhood of the center pixel into the relevant pixel and the interfering pixel implicitly to make the individual optimization of the network for the two kinds of pixels possible. Benefit from this specific, the spectral variability can be relieved effectively for the similarity calculation and the relevant pixels and the interfering pixels can be preserved and restrained appropriately during the similarity conversion, respectively. Experimental results reveal that the spatial attention derived by the S3AM++ is more representative and the classification performance of the model adopting the new attention module is superior. © 2023 ACM.

关键词： Pixels

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A Review of Key Elements for Underwater Wireless Sensor Networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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Electricity Consumption Classification using Various Machine Learning Models

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-6页

作者： Paikaray, Bijay Kumar Jena, Swarna Prabha Mondal, Jayanta Van Thuan, Nguyen Tung, Nguyen Trong Mallick, Chandrakant Bhubaneswar India Department of Electronics and Communication Engineering Centurion of University of Technology and Management Odisha India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Faculty of Engineering Technology Hung Vuong University Viet Nam Quality Assurance Dong A University Viet Nam Department of Computer Science & Information Technology GITA Autonomous College Odisha Bhubaneswar India

INTRODUCTION: As population has increased over successive generations, human dependency on electricity has increased to the point where it has become a norm and indispensable, and the idea of living without it has become unthinkable. OBJECTIVES: Machine learning is emerging as a fundamental method for performing tasks autonomously without human intervention. Forecasting electricity consumption is challenging due to the many factors that influence it;embracing modern technology with its heavy focus on machine learning and artificial intelligence is a potential solution. METHODS: This study employs various machine learning algorithms to forecast power usage and determine which method performs best in predicting the dataset based on different variables. RESULTS: Eight models were tested, including Linear Regression, DT Classifier, RF Classifier, KNN, DT Regression, SVM, Logistic Regression, and GNB Classifier. The Decision Tree model had the greatest accuracy of 98.3%. CONCLUSION: The Decision Tree model’s accuracy can facilitate efficient use of electricity, leading to both conservation of electricity and cost savings, and be a guiding light in future planning. Copyright © 2024 Paikaray, B.K. et al., licensed to EAI. This open-access article is distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transforming, and building upon the material in any medium so long as the original work is properly cited.

关键词： Forecasting

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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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Skin Cancer Detection using Machine Learning Framework with Mobile Application 7

Skin Cancer Detection using Machine Learning Framework with ...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Emam Ananna, Mariam Nayeem, Jannatul Jahangir Alam, Mohammad Islam, Saiful Daffodil International University Computer Science and Engineering Faculty of Science and Information Technology Bangladesh

ISBN: (纸本)9798350397284

A notable increase in skin cancer mortality, one of the most lethal kinds of cancer, has been caused by a lack of awareness of warning signals and preventative measures. The need for early skin cancer diagnosis has increased because of the fast development rate of melanoma skin cancer, its high cost of treatment, and its mortality risk. Treatment of cancer cells usually requires perseverance and manual identification. This study recommends using image synthesis and machine learning techniques to develop a system for diagnosing skin cancer. Thermoscopic pictures are the input for the pre-processing phase. Following the segmentation of the thermoscopic images, the attributes of the injured skin cells are obtained using a feature extraction approach. Utilizing a convolutional neural network classifier with deep learning, the collected characteristics are stratified. An accuracy of 89% has been discovered using the publicly available dataset. © 2023 IEEE.

关键词： Feature extraction

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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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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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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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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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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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