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Federated Learning for Hierarchical Fall Detection and Human Activity Recognition 10

Federated Learning for Hierarchical Fall Detection and Human...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Afandy, Peter Febrianto Ng, Pai Chet Plataniotis, Konstantinos N. University of Glasgow School of Computing Science United Kingdom Singapore Institute of Technology Infocomm Technology Cluster Singapore Singapore University of Toronto The Edward S. Rogers Sr. Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350373011

In healthcare monitoring, precise fall detection (FD) and human activity recognition (HAR) are paramount, especially for the elderly. This paper presents a federated learning (FL) framework that employs a two-stage hierarchical approach to address these needs. The first stage distinguishes between fall and non-fall events, crucial for minimizing false alarms in sensitive environments such as elderly care facilities. Subsequently, if a fall is detected, the system classifies the type of fall to facilitate appropriate medical responses;if no fall is detected, it classifies the specific activity being performed. This approach enables accurate emergency responses and supports personalized healthcare interventions. With FL, all model training are conducted on local devices using wearable data, including inertial measurement unit (IMU) and physiological signals, without the need to share sensitive data centrally, preserving users' privacy. This method ensures that each device contributes to a global model, whilst maintaining the confidentiality of individual data inputs. Our experimental evaluations demonstrate generalization capabilities in binary classification for FD and highlight challenges in multi-class scenarios for HAR, demonstrating the need for advanced strategies in handling complex classifications. The source code and experimental evaluations are accessible at https://***/SIT-FL/FL-Fall-Detection. © 2024 IEEE.

关键词： Data privacy

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MetaPCbin: Plasmid/Chromosome Classification for Metagenomic Contigs using Machine Learning Techniques 8

MetaPCbin: Plasmid/Chromosome Classification for Metagenomic...

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8th International Multidisciplinary Moratuwa engineering Research Conference, MERCon 2022

作者： Nandasiri, Chamika Alahakoon, Sasindu Dassanayake, Gayal Wickramarachchi, Anuradha Perera, Indika University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka Australian National University School of Computing Canberra Australia

ISBN: (数字)9781665487863

ISBN: (纸本)9781665487863

Chromosomes and plasmids are the major carriers of genetic material in microorganisms such as bacteria. Separating chromosomal and plasmid DNA from large datasets is important as plasmids and chromosomes affect functions and other environmental adaptations. Bioinformatics methodologies have been developed for plasmid classification with the advancements in sequencing technologies. The usage of normalized short k-mer counts with machine learning models has been popular in the characterization of plasmids and chromosomes. Furthermore, bio-markers from DNA sequences as features have also been studied in plasmid classification. However, both approaches suffer from the trade-off between precision and recall. MetaPCbin is a plasmid detection tool that combines computational and genetic approaches into a hybrid method of plasmid prediction. MetaPCbin uses an artificial neural network that uses k-mer counts as features and a random forest model that uses biomarkers. MetaPCbin evaluates the precision and the recall of the classification of real-world DNA sequences from the RefSeq database and simulated sequences. The results show that it is capable of performing plasmid classification while maintaining high precision and recall compared to the state of the art. MetaPCbin is freely available at: https://***/MetaGSC/MetaPCbin © 2022 IEEE.

关键词： Bioinformatics

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Investigation of the separation zone for split-domain light field visualization 28

Investigation of the separation zone for split-domain light ...

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2024 28th International Conference on Information Technology, IT 2024

作者： Javidi, Kamran Martini, Maria G. Kara, Peter A. School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science Penrhyn Road Campus Kingston upon Thames LondonKT1 2EE United Kingdom Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary

ISBN: (纸本)9798350369618

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution. © 2024 IEEE.

关键词： Visualization

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MRI image based Ensemble Voting Classifier for Alzheimer's Disease Classification with Explainable AI Technique 8

MRI image based Ensemble Voting Classifier for Alzheimer's D...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Rashmi, Uppin Singh, Tripty Ambesange, Sateesh Bangalore Amrita Vishwa Vidyapeetham Amrita School of Computing CSE Dept. India Bangalore Amrita VishwaVidyapeetham Amrita School of Computing Department of Computer Science & Engineering India Bangalore Pragyan SmartAI Technology LLP Pragyan AI Pragyan AI School India

ISBN: (纸本)9798350334012

Alzheimer's is one of the causes of dementia, which causes memory loss, problem-solving disability, speaking, and a lot more difficulties in day-to-day life. Generally, dementia is a loss of memory, problem-solving ability, language fluency, and other thinking abilities that severely affect day-to-day life. Alzheimer's creates a huge impact on family life, the economy, and finally, the country as a whole is affected. According to statistics every 3 seconds, one person develops dementia in the world and the estimates say that by 2030, 78 million people will be affected, and by 2050 139 million people will have dementia. Estimates say that the economic impact due to dementia by 2030 in the US will be $2.8 Trillion which causes a huge loss and needs to be ***'s can be diagnosed at various stages, with different datasets like Magnetic Resonance Imaging (MRI) Test images, Speech Tests, Symptoms, genes, and other data. Several models are developed to diagnose, but doctors expect proper insights about results apart from diagnosis, so the paper explains the results using various explainable methods like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME).Data Sets used are MRI Features data extracted with generic information, Cross-sectional MRI data, and Longitudinal MRI Data. The step-by-step data processing includes data balancing using SMOTEENN, and then data transferred, using Quantile Transformer and PCA dimension reduction technique for 6 features, and Meta machine learning model, first level six key machine learning methods and finally voting classifier with hyperparameter tuning to get performance, 97.6 %, Precision 95.8%, recall 97.9% and finally F1 Score 96.8%. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Study of Energy-Efficient Virtual Machine Migration with Assurance of Service-Level Agreements

Study of Energy-Efficient Virtual Machine Migration with Ass...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Panwar, Suraj Singh Rauthan, M.M.S. Barthwal, Varun Gaur, Sachin Mehra, Nidhi Department of Computer Science and Engineering Hemvati Nandan Bahuguna Garhwal University Garhwal Uttarakhand Srinagar India Department of Information Technology Hemvati Nandan Bahuguna Garhwal University Garhwal Uttarakhand Srinagar India Department of Computer Science and Engineering B.T. Kumaon Institute of Technology Uttarakhand Dwarahat India Department of School of Computing Graphic Era Hill University Uttarakhand Dehradun India

ISBN: (纸本)9789819706402

With the rising usage of cloud services, data centers (DC) are improving the services to their customers. The substantial energy consumption (EC) of cloud DCs poses significant economic and environmental challenges. To address this issue, server consolidation through virtualization technology has emerged as a widely adopted approach to decrease energy consumption rates, minimize virtual machine (VM) migration, and prevent breaches of service-level agreements (SLAs) within data centers. Cloud DCs are becoming larger, consuming more energy, and capable of delivering quality of service (QoS) with service-level assurance. People all around the world can use cloud computing to have instant access to resources. It provides pay-per-use services via a vast network of data center locations. The data centers that house cloud servers are kept operational to provide a variety of services, which uses a lot of electricity and has an adverse environmental impact. The primary goal of cloud computing is to offer uninterrupted and continuous Internet-based services, while using virtualization technologies to satisfy end users’ QoS requirements. With the balanced EC and service quality, it is challenging to supply cloud services. The rapid expansion of cloud services significantly rises energy and power consumption daily. This paper reviews previous studies on multiple parameters such as EC, SLA violation, and VM migration by different approaches based on statistical techniques, machine learning approaches, heuristic, and metaheuristic methods. Prediction of host CPU, identifying underload or overload hosts, VM consolidation have been applied to manage the resources using the PlanetLab and Bitbrains workload on different performance metrics. This review paper presents a detailed comparative study of different algorithms to analyze the influence of several parameters such as energy consumption, SLAV, virtual machine migration, active hosts, etc. on the performance of cloud resources. A

关键词： Cloud computing

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Vehicle Door Safety System

Vehicle Door Safety System

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International Conference on Computational Systems and Communications (ICCSC)

作者： Abilash Sivasankar Kumaran U Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

Carelessness and absence of mind leading to unseen obstacles or moving objects from passenger or driver inside the vehicle will make way for accidents to happen when they try to open the side door of the vehicle which will damage the door as well as harm any person or animal. The proposed system with the use of a camera and object detection will be able to detect vehicles and stationary objects that might hit the vehicle door when the passenger or driver tries to open the door without being aware of them. The system will alert the person inside the car so they can open the door in safe manner which prevents damage to the door. The object detection YOLO model is custom trained for the proposed system. Then based on this model object detection is performed using raspberry pi and camera setup. The camera is placed on the mirrors to cover both front and back door. The raspberry pi is remotely connected to laptop for visualizing the detection of objects which will damage the door. The system uses the computer vision concept and objects detection for ensuring the safety of vehicle door.

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Design of AED Based Drone to Deliver the Medical Kit on an Emergency Basis 3

Design of AED Based Drone to Deliver the Medical Kit on an E...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Nagarani, P. Ahmad, Mohd Wazih Shalini, R. Singh, Pavitar Parkash Cvr College of Engineering Department of Emerging Technologies Hyderabad India Shri Ram Murti Smarak College of Engineering and Technology Department of Computer Science and Engineering Bareilly India Vels Institute of Science Technology and Advanced Studies Department of Bca & It School of Computing Sciences Chennai India Lovely Professional University Department of Management Phagwara India

ISBN: (纸本)9798350330724

Access to reliable modes of transportation is essential. Postal delivery to multi-million-dollar freight transports, practically everything is linked to its connections. Wheeled motor vehicles and manned aeroplanes are now the only options for transporting medical supplies at times of urgent need, which may be expensive, time-consuming, and often impossible if the emergency site is far. Drones are a game-changer in the medical industry because they can transfer vital supplies like blood and medical equipment. Unfortunately, given the state of the art, the drone's battery life is a constraint that may slow down the delivery process. This article details the research into the mechanisms designed for drones to increase their battery life and efficiently deliver first aid supplies. The drone's system safely delivers and drops medical supplies, reducing the drone's takeoffs and landings. The primary goal of this research is to develop a device that can safely transport medical equipment to its destination and then carefully unload it there. Results showed the system could safely transport and deploy first aid supplies during flight and landing. © 2023 IEEE.

关键词： Drones

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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Attention Based Generative Adversarial Networks to Augment Image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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Residual VGGNet for Unveiling Optimal Process Parameters of Oil Rig Welding Through Mathematical Modeling and Optimization 15

Residual VGGNet for Unveiling Optimal Process Parameters of ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhas, John T. Mesia Saikia, Utpal Arun, K.K. Josphine, C.Vimala Kingslin, M.Theodore Padmavathi, N. School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Silapathar College Department of Mathematics Dhemaji Assam Silapathar India Kumaraguru College of Technology Department of Mechanical Engineering Tamilnadu Coimbatore India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai India R.M.K College of Engineering & Technology Department of Electronics & Communication Engineering Tamilnadu Puduvoyal India R.M.D. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India

ISBN: (纸本)9798350370249

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data. © 2024 IEEE.

关键词： mathematical modeling oil rig welding optimization Residual VGGNet welding parameters

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