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Investigating the impact of sensor axis combinations on activity recognition and fall detection: an empirical study

Multimedia Tools and Applications 2024年 1-40页

作者： Kavuncuoğlu, Erhan Özdemir, Ahmet Turan Uzunhisarcıklı, Esma Department of Computer Technology Gemerek Vocational School Cumhuriyet University SivasTR-58840 Turkey Department of Electrical and Electronics Engineering Engineering Faculty Erciyes University KayseriTR-38039 Turkey Department of Electrical and Electronics Engineering Yozgat Bozok University Engineering Faculty YozgatTR-66900 Turkey

Activity recognition is a fundamental concept widely embraced within the realm of healthcare. Leveraging sensor fusion techniques, particularly involving accelerometers (A), gyroscopes (G), and magnetometers (M), this technology has undergone extensive development to effectively distinguish between various activity types, improve tracking systems, and attain high classification accuracy. This research is dedicated to augmenting the effectiveness of activity recognition by investigating diverse sensor axis combinations while underscoring the advantages of this approach. In pursuit of this objective, we gathered data from two distinct sources: 20 instances of falls and 16 daily life activities, recorded through the utilization of the Motion Tracker Wireless (MTw), a commercial product. In this particular experiment, we meticulously assembled a comprehensive dataset comprising 2520 tests, leveraging the voluntary participation of 14 individuals (comprising 7 females and 7 males). Additionally, data pertaining to 7 cases of falls and 8 daily life activities were captured using a cost-effective, environment-independent Activity Tracking Device (ATD). This alternative dataset encompassed a total of 1350 tests, with the participation of 30 volunteers, equally divided between 15 females and 15 males. Within the framework of this research, we conducted meticulous comparative analyses utilizing the complete dataset, which encompassed 3870 tests in total. The findings obtained from these analyses convincingly establish the efficacy of recognizing both fall incidents and routine daily activities. This investigation underscores the potential of leveraging affordable IoT technologies to enhance the quality of everyday life and their practical utility in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wearable sensors

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An efficient machine learning based CPU scheduler for heterogeneous multicore processors

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International Journal of Information technology (Singapore) 2025年第4期17卷 2493-2501页

作者： Allaqband, Sugariya Firdous Nazish, Mir Allaqband, Saltanat Firdous Bashir, Janibul Banday, M. Tariq Department of Electronics & amp Instrumentation Technology University of Kashmir Srinagar India Department of Computer Science & amp Engineering Indian Institute of Technology Madras India Department of Information Technology National Institute of Technology Srinagar India

While machine learning has excelled in various domains, its impact on computer architecture has been limited. Despite powerful, flexible multicore CPUs, efficient thread scheduling remains challenging due to mapping overhead. This study introduces a novel approach that leverages machine learning to enhance CPU scheduling in heterogeneous multicore systems by developing a mapping methodology that improves system throughput. The proposed scheduling approach uses Long Short-Term Memory, Artificial Neural Networks, and Linear Regression to predict thread parameters and Instructions Per Cycle for threads running on different cores. The predicted values are then utilized to schedule threads using the mapping that maximizes Instructions per Cycle count. The results demonstrate that the artificial neural network outperforms Linear Regression for predicting Instructions Per Cycle values. In addition, compared to traditional heterogeneous scheduling, the proposed machine learning-based scheduling approach enhances the performance of the heterogeneous system by about 1.2 times and increases throughput by 20%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： ANN CPU scheduling Linear regression LSTM Machine learning

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental Science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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On Applying Preprocessing Techniques on Gallbladder Ultrasound Images 8

On Applying Preprocessing Techniques on Gallbladder Ultrasou...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Suji, R. Jenkin Godfrey, W. Wilfred Madhav Institute of Technology and Science Department of Electronics Engineering Gwalior India ABV-Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798331531539

Ultrasound imaging is a common and non-invasive method for diagnosing gallbladder diseases, including gallbladder cancer (GBC). However, the inherent challenges of ultrasound images-such as noise, low contrast, and variable image quality-necessitate the use of preprocessing techniques to enhance image clarity and improve diagnostic accuracy. In this study, we apply a series of preprocessing methods, including median filtering, Gaussian filtering, bilateral filtering, adaptive histogram equalization, and wavelet transforms, to gallbladder ultrasound images. We evaluate the effectiveness of these techniques using a variety of quality metrics such as sharpness, signal-to-noise ratio (SNR), entropy, structural similarity index (SSIM), and perceptual image quality evaluator (PIQE). A detailed analysis of the preprocessing methods was conducted, where the performance of each technique was systematically compared. Additionally, we highlight the statistical variations in these metrics across different preprocessing approaches to identify the most suitable methods for enhancing GBC detection. The results show that specific preprocessing methods improve image quality and feature clarity, aiding accurate gallbladder disease detection models. This work is an initial groundwork for enhancing the diagnostic value of ultrasound images for GBC. It suggests future directions for integrating these methods into machine-learning pipelines for automated disease detection. © 2024 IEEE.

关键词： Wavelet transforms

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5G Advanced: A Step Towards 6G

5G Advanced: A Step Towards 6G

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Routray, Sudhir K. Singh, Monika Samal, Laxmipriya Samal, Debashisa Sarangi, Susanta K. Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India SoA University Department of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350390759

This article explores the features and proximity of 5G advanced (well known as 5.5G) to 6G cellular communication system, emphasizing its speed, connectivity, and intelligence. Expected benefits of 5.5G include an enhanced user experience, industrial revolution, and support for the Internet of Things (IoT). The motivation behind 5.5G arises from addressing 5G limitations, accommodating IoT growth, enabling real-time applications, providing personalized experiences, and driving economic growth. Anticipated applications span from enhanced mobile broadband to ultra-reliable low latency communication and IoT-based massive machine-type communication, extending to personalized experiences, next-gen gaming, connected healthcare, and advanced education. Despite being in development, 5.5G holds immense significance for bridging the physical and digital world, fostering innovation, and unlocking the potential of the digital age. © 2024 IEEE.

关键词： 5G mobile communication systems

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Surface Plasmon Resonance Sensor based on Ag Layer Coated PCF for Dry Sandy Soil Detection with Deep Learning Algorithm 5

Surface Plasmon Resonance Sensor based on Ag Layer Coated PC...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Ahammed, Md. Tabil Rahman, Md Ashikur Ghosh, Sudipto Khulna University of Engineering and Technology Dept of Electronics & Communication Engineering Bangladesh Bangladesh University of Business and Technology Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

This paper suggests an LSTM deep learning algorithm that can improve the sensing accuracy of a Photonic Crystal Fiber (PCF) based Surface Plasmon Resonance (SPR) sensor used in dry sandy soil detection. Optimized coupling efficiency between the plasmonic and core layers improves the sensor's refractive index sensitivity and detectable range. Training an LSTM model to predict resonance wavelength improves SPR sensor accuracy and real-time monitoring. The functionality of the sensor is simulated and analyzed using the Finite Element Method. In order to prevent oxidation, titanium oxide was applied to the visually appealing Ag plasmonic material. The sensor's 8 nm TiO2 and 55 nm Ag improve resolution from 1.35 to 1.40 RIU. This sensor has high sensitivity at 1890 RIU-1 amplitude sensitivity and 19000 RIU-1 wavelength sensitivity, and experiments have shown that the sensor is sensitive to refractive index changes, has a large detectable range, and can accurately predict the resonance wavelength using deep learning. The proposed sensor design could reliably and cheaply detect dry sandy soil in agriculture and environmental monitoring. © 2023 IEEE.

关键词： Titanium dioxide

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Rice Variety Classification Based on Optimized Near-Infrared Spectral Classification Model

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Rice science 2024年第1期31卷 6-9,I0009-I0012页

作者： YANG Sen WANG Zhenmin ZHANG Houqing SONG Wenlong College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040China The 49th Research Institute of China Electronics Technology Group Corporation Harbin 150000China

Accurate classification of rice variety is essential to ensure the brand value of high-quality rice *** the impact of sample state on modeling optimization algorithms,rice samples after grinding and sealing were *** enhance the accuracy of rice variety classification,we introduced a spectral characteristic wavelength selection method based on adaptive sliding window permutation entropy(ASW-PE).

关键词： sealing Infrared grinding

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Comparative Analysis of ML Algorithms for Forecasting Time Series Stock Returns 4

Comparative Analysis of ML Algorithms for Forecasting Time S...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Bondili, Bala Karthikeya Singh Bolagani, Sai Pavan Thatikonda, Rakshith Vnr Vignana Jyothi Institute of Engineering and Technology Computer Science and Engineering Telangana India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andra Pradesh India Cmr Technical Campus Electronics and Communication Engineering Telangana India

ISBN: (纸本)9798331523923

Forecasting stock returns is a challenging task due to high data variability, noise, and the inherent unpredictability of financial markets. Traditional methods like ARIMA and regression lack scalability and fail to capture complex data patterns. Recent advancements in machine learning, including ensemble methods such as Random Forest and Boosting, have shown promise in improving predictive accuracy. However, these methods face challenges such as overfitting, computational complexity, and data dependency. This paper evaluates the performance of various machine learning algorithms and their ability to address these challenges to improve forecast reliability. By using techniques like dimensionality reduction and hyperparameter tuning, the study identifies strategies to enhance prediction accuracy while reducing computational complexity and costs. © 2025 IEEE.

关键词： Dimensionality reduction

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Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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Science China(Information Sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN Gwanggil JEON Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

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Predicting Lung Cancer Disease Using Optimized Weighting-Based Enhanced Neural Network Classification 3

Predicting Lung Cancer Disease Using Optimized Weighting-Bas...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chitra, T. Hemanth, S.V. Karthikeyan, S. Reddy, Vallem Ranadheer Banupriya, K.M. Amirthayogam, G. Christian College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Oddanchatram624619 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624622 India Vaagdevi Engineering College Department of Computer Science and Engineering Telangana Bollikunta506005 India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai606603 India Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

Lung cancer is a highly lethal disease, claiming the lives of approximately 1 million individuals annually. Cancer is characterized by abnormal and rapid cell growth, making it difficult to control, though early diagnosis through Computed Tomography (CT) scan images can significantly improve chances of survival. However, detecting lung cancer early is challenging and can increase the risk of complications such as infection, inflammation, and tumor growth in the lungs. Moreover, the current methods for analyzing lung cancer prognosis have low accuracy. To solve this problem, we proposed an Optimized Weighting-Based Enhanced Neural Network (OWENN) method to classify patients based on attributes accurately. Furthermore, they utilize the Adaptive Median Filter (AMF) technique during image pre-processing to calculate the mean value of each pixel and remove noise. Moreover, Improved Particle Swarm Optimization (IPSO) can be implemented to extract tumors from lung images efficiently. Finally, the OWENN method improves the accuracy of cancer or non-cancer detection by classifying patients based on their selected attributes. Afterward, the experimental results indicate that the suggested OWENN approach can attain lung cancer prediction by evaluating sensitivity, precision, accuracy, and time delay. © 2024 IEEE.

关键词： Median filters

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