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2023 IEEE International Conference on Big Data, BigData 2023

作者： Biernacki, Piotr Ziebinski, Adam Syu, Jia-Hao Lin, Jerry Chun-Wei Silesian University of Technology Institute of Theoretical and Applied Informatics Polish Academy of Sciences Institute of Informatics Gliwice Poland Silesian University of Technology Institute of Informatics Gliwice Poland National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324457

Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors. © 2023 IEEE.

关键词： machine learning sensors calibration single-beam distance sensors

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Deep Learning Networks for Complex Activity Recognition Based on Wrist-Worn Sensor 38

Deep Learning Networks for Complex Activity Recognition Base...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350302196

Wearable smart devices, such as smartphones and smartwatches, offer great potential as platforms for automated human action identification. However, accurately monitoring complex human actions on these devices poses a challenge due to the presence of similarities in patterns across different actions. This occurs when distinct human actions exhibit comparable signal patterns or characteristics. The placement of motion sensors on the body plays a crucial role in detecting human behavior. Typically, wearable sensors placed at the trouser pocket or a similar location are used for this purpose. However, this positioning is not suitable for identifying actions involving manual gestures. To address this, wrist-worn motion sensors are employed to detect these specific behaviors. This study aims to investigate the effectiveness of deep learning models in accurately categorizing complex human actions using sensor data from wrist-worn devices. Nine deep learning models utilizing convolutional neural networks and recurrent neural networks were examined for their identification capabilities. The models were evaluated using the WHARF dataset, a publicly available benchmark dataset for human activity recognition. The investigation revealed that the proposed CNN-BiGRU model outperformed other deep learning models, achieving an accuracy rate of 87.20% and an Fl-score of 84.46%. © 2023 IEEE.

关键词： Wearable sensors

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Estimating the Probability of Falling Share Price Below the Minimum Level 9

Estimating the Probability of Falling Share Price Below the ...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Semakov, Sergei L. Moscow Institute of Physics and Technology Faculty of Control and Applied Mathematics Russia Moscow Automobile and Road Construction Institute Department of Mathematics Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Russia

ISBN: (纸本)9798350311402

A technology is proposed for estimating the probability that a share price will fall below the minimum level. The exact expression is obtained for the required probability under the assumption that the behavior of the share price is described by the well-known model of Samuelson, according to which the relative change in price is the sum of the non-random trend and the Wiener process. The obtained result is qualitatively analyzed for various relations of the problem parameters. © 2023 IEEE.

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A Hybrid Deep Model for Brain Tumor Classification

A Hybrid Deep Model for Brain Tumor Classification

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International Conference on Medical Imaging and computer-Aided Diagnosis, MICAD 2021

作者： Ayaz, Hamail Ahmad, Muhammad Tormey, David McLoughlin, Ian Unnikrishnan, Saritha Faculty of Engineering and Design and Centre for Precision Engineering Materials and Manufacturing Research Institute of Technology Sligo Sligo91 YW50 Ireland Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Department of Computer Science and Applied Physics Galway-Mayo Institute of Technology Galway Ireland

ISBN: (纸本)9789811638794

Classification of brain tumors from Magnetic Resonance Images (MRIs) using computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI’s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94%), Precision (92%), Recall (95%), and F1 score (93%) and outperformed many state of the art hybrid methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance

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Towards Explainable Renewable Energy Communities Operations Using Generative AI

Towards Explainable Renewable Energy Communities Operations ...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Amal Nammouchi Cyrine Chaabani Andreas Theocharis Andreas Kassler Computer Science Department Karlstad University Karlstad Sweden Center for Digital Technology and Innovation Munich Germany Engineering and Physics Department Karlstad University Karlstad Sweden Institute of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Renewable Energy Communities (RECs), characterized by localized energy generation and consumption, are a key enabler for enhancing renewable energy utilization, cost-efficient planning and clean energy transition. However, optimizing RECs operations is challenging due to the complex interplay of different stakeholders with conflicting requirements. The complexity of managing such systems often leads to a lack of transparent and reliable decision-making, creating barriers for actors within the community. This paper explores the integration of Generative AI into Renewable Energy Communities (RECs) to enhance the transparency, explainability and accessibility of energy management systems (EMSs) that depend on solving optimization problems. We propose a novel framework, Chat-SGP, which uses generative AI to synthesize optimization modeling code to provide actionable, explainable insights for managing the REC operations. Our approach allows us to interact with the EMS through natural language queries, enhancing the system’s accessibility and user-friendliness. Our evaluation shows that using GPT-4 with in-context learning performs 96.72% accuracy on average in generating correct answers.

关键词： Green energy Generative AI Natural languages Europe Planning Smart grids Stakeholders Reliability Energy management systems Optimization

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Forecasting of Groundwater Quality Utilizing an Efficient K-Means with Principal Component Analysis Approach 2

Forecasting of Groundwater Quality Utilizing an Efficient K-...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Adnan, Myasar Mundher Manjula, B.M. Mishra, Antim Dev Ganti, Swathi Bala Dhandayuthapani, V. The Islamic University of Babylon Babylon Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Engineering and Technology Sushant College Gurgaon India Department of Nano Technology Andhra University Visakhapatnam India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman

ISBN: (纸本)9798331504960

Groundwater stands as a pivotal freshwater resource in numerous regions worldwide, but the burgeoning population and diverse economic activities have adversely impacted its quality. However, the K-means algorithm is sensitive to the initial placement of centroids, where outliers can significantly influence the position of centroids resulting in suboptimal clusters. To address this, it is recommended to remove or clip outliers before applying K-means scales well to large datasets making it suitable for handling substantial amounts of data. In this proposed method, attributes are first calculated using Principal Component Analysis (PCA) determines the weight of each attribute based on eigenvalues. The PCA, by determining attribute weights and Rapid Centroid Estimation (RCE), by establishing the initial cluster centre, contribute to the overall effectiveness of the method. The classification step utilizes a Linear Support Vector Machine (SVM), suitable for linear hyper line data when the dataset can be classified into two classes using a single straight line. The results obtained showcase the superiority of the proposed method, achieving a Root Mean Squared Error (RMSE) of 1.23, R-squared (R2) of 0.90, Mean Absolute Deviation (MAD) of 0.28 and E of 0.97 on the Indian groundwater dataset. These outcomes affirm better detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Mean square error

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Improving Lower Limb Activity Recognition Based on Inertial Sensors using CNN-LSTM Network 4

Improving Lower Limb Activity Recognition Based on Inertial ...

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4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350330618

The utilization of wearable sensors has gained significant popularity across various domains. This widespread adoption has driven the need for advancements in sensor technology, focusing on attributes such as lightweight design, dependability, and integration capabilities. One specific area where wearable sensor networks have faced challenges is in human activity recognition, particularly in accurately inferring movement data. The effectiveness of this process depends on several factors, such as the quality of sensor-derived features, the discriminatory power of selected features, and the chosen classification methodology. To address these challenges, this research introduces a novel hybrid deep learning architecture called CNN-LSTM. The primary goal of this architecture is to enhance the accuracy of recognizing human lower limb movement. To evaluate the effectiveness of the proposed model, a series of investigations were conducted using the publicly available HuGaDB dataset. This dataset consists of data collected from a body sensor network comprising six wearable inertial sensors, including accelerometers and gyroscopes. These sensors were positioned on both the right and left thighs, shins, and feet. The outcomes of the investigation demonstrated that the suggested model achieved an impressive precision of 97.81% and a maximum F1-score of 95.50%. These results highlight the effectiveness of the CNN-LSTM model in accurately recognizing human lower limb movement. ©2023 IEEE.

关键词： Wearable sensors

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Improving EEG-Based Epileptic Seizures Detection Using Self-Normalizing Neural Network 4

Improving EEG-Based Epileptic Seizures Detection Using Self-...

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4th International Conference on Big Data Analytics and Practices, IBDAP 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao 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)9798350300192

Epilepsy, a neurological disorder, is typically identified by analyzing electroencephalogram (EEG) signals. However, manually inspecting epileptic seizures is a time-consuming and labor-intensive task. Previous automatic detection algorithms utilizing traditional methods have demonstrated good accuracy in specific EEG classification scenarios but have shown poor performance in others. To tackle this challenge, our study introduces a new deep learning model called the self-normalizing neural network. This model leverages convolutional neural networks and recurrent neural networks to enhance the detection of epileptic seizures using EEG signals. To evaluate our approach, we designed and assessed the proposed detection model using a publicly available Epileptic Seizures Recognition Dataset (ESRD). The experimental findings indicate that the self-normalizing neural network achieves a performance improvement of up to 1.03% accuracy and 1.78% F1-score in epileptic seizure detection, surpassing traditional convolutional neural network approaches. © 2023 IEEE.

关键词： Electroencephalography

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PARALLEL SCALABILITY OF THREE-LEVEL FROSch PRECONDITIONERS TO 220000 CORES USING THE THETA SUPERcomputer

PARALLEL SCALABILITY OF THREE-LEVEL FROSch PRECONDITIONERS T...

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作者： Heinlein, Alexander Rheinbach, Oliver Rover, Friederike Delft University of Technology Faculty of Electrical Engineering Mathematics & Computer Science Delft Institute of Applied Mathematics Mekelweg 4 Delft2628 CD Netherlands Technische Universität Bergakademie Freiberg Freiberg09596 Germany

The parallel performance of the three-level fast and robust overlapping Schwarz (FROSch) preconditioners is investigated for linear elasticity. The FROSch framework is part of the Trilinos software library and contains a parallel implementation of different preconditioners with energy minimizing coarse spaces of generalized Dryja-Smith-Widlund type. The three-level extension is constructed by a recursive application of the FROSch preconditioner to the coarse problem. In this paper, the additional steps in the implementation in order to apply the FROSch preconditioner recursively are described in detail. Furthermore, it is shown that no explicit geometric information is needed in the recursive application of the preconditioner. In particular, the rigid body modes, including the rotations, can be interpolated on the coarse level without additional geometric information. Parallel results for a three-dimensional linear elasticity problem obtained on the Theta supercomputer (Argonne Leadership Computing Facility, Argonne, IL) using up to 220 000 cores are discussed and compared to results obtained on the SuperMUC-NG supercomputer (Leibniz Supercomputing Centre, Garching, Germany). Notably, it can be observed that a hierarchical communication operation in FROSch related to the coarse operator starts to dominate the computing time on Theta, which has a dragonfly interconnect, for 100 000 message passing interface (MPI) ranks or more. The same operation, however, scales well and stays within the order of a second in all experiments performed on SuperMUC-NG, which uses a fat tree network. Using hybrid MPI/OpenMP parallelization, the onset of the MPI communication problem on Theta can be delayed. Further analysis of the performance of FROSch on large supercomputers with dragonfly interconnects will be necessary. © 2022 Alexander Heinlein, Oliver Rheinbach, and Friederike Röver.

关键词： Supercomputers

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Energy efficient indoor localisation for narrowband internet of things

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1150-1163页

作者： Ismail Keshta Mukesh Soni Mohammed Wasim Bhatt Azeem Irshad Ali Rizwan Shakir Khan Renato RMaaliw III Arsalan Muhammad Soomar Mohammad Shabaz Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia Department of CSE University Centre for Research and Development Chandigarh UniversityMohaliPunjabIndia Department of Computer Science and Engineering National Institute of TechnologySrinagarIndia Asghar Mall College Rawalpindi Higher Education Department(HED)Govt.of the PunjabRawalpindiPakistan Department of Industrial Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia College of Engineering Southern Luzon State UniversityLucbanQuezonPhilippines Department of Automation Electronics and Electrical EngineeringGdansk University of TechnologyGdańskPoland Arba Minch University Arba MinchEthiopia

There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops *** high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these *** maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a *** on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot *** a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is *** investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT *** current work primarily considers the NOMA Narrow Band IoT network under a single EH ***,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance *** simulation,the proposed approach is successfully *** improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.

关键词： artificial inteligence detection of moving objects internet of things

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