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Design and Optimization of High-Performance Health Monitoring Systems Using Advanced Sensor Networks 2

Design and Optimization of High-Performance Health Monitorin...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Mehta, Heena Singla, Mukesh Baba Mastnath University Dept. of Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798331519056

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution. © 2024 IEEE.

关键词： Electronic health record

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Enhancing Privacy Through Time Aggregation of Load Profiles in Energy Management 8

Enhancing Privacy Through Time Aggregation of Load Profiles ...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Varenhorst, Ivo A.M. Hoogsteen, Gerwin Gerards, Marco E.T. Hurink, Johann L. dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

Demand side management (DSM) applications rely on the exchange of load profiles to effectively manage the operation of energy systems. However, sharing detailed energy profile data raises substantial privacy concerns, such as potential misuse of personal information. To mitigate these concerns, we investigate the potential of time aggregation (TA), which involves merging multiple samples of a profile into a single value representing multiple intervals. TA reduces data exchange and computational requirements in energy management and helps preserve user privacy by reducing granularity of user data. We show that, for an effective implementation of TA in energy management, it is important to make the right choice of TA method. We compare and evaluate seven different TA methods. Furthermore, we perform TA across various time frames using an optimization based DSM approach. Our findings reveal that if we aggregate load profiles from 15 minutes to 4 hours, we obtain both enhanced privacy and a 21% decrease in the required number of iterations with the investigated DSM method, albeit at the cost of a 15% decrease in objective value performance. Based on this, we conclude that depending on the application needs, TA with a carefully selected aggregation method has the potential to bring value to energy management, even when aggregating to a considerable extent. ©2024 IEEE.

关键词： Energy management

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An Intelligent Approach to Grape Leaf Disease Diagnosis Through Machine Learning 2

An Intelligent Approach to Grape Leaf Disease Diagnosis Thro...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Uniyal, Sneha Dhoundiyal, Parveen Sharma, Vikrant Vats, Satvik Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

This study underscores the pressing necessity for early and accurate diagnosis of grape leaf diseases, advocating for an automated solution that harnesses the synergistic power of image processing and machine learning. Employing methodologies such as Naïve Bayes, Gradient Boosting, and Random Forest, the primary objective is to proficiently classify grape leaves as either healthy or afflicted by diseases. The model casts its focus on prevalent grape leaf maladies, including Grapes Healthy Leaf, Grape Black Rot,Grape Black Measles, Grape Isariopsis leaf. The process commences with the meticulous preparation of grape leaf images, followed by the extraction of crucial features, which serve as the foundation for the application of machine learning techniques. The viability of the grape crop sector hinges on the efficacy of this innovative approach, which has the potential to significantly curtail the spread of diseases in grape fields while enhancing the precision of disease identification. This study serves as a compelling demonstration of the formidable effectiveness of machine learning algorithms in the detection of grape leaf diseases, thus ushering in the prospects of automation and improved decision-making within the realm of agriculture. © 2024 IEEE.

关键词： Disease Identification Gradient Boosting Grape leaves Machine Learning Naïve Bayes Random Forest

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Visual Analytics of Streaming Data in Concept Drift Detection 11

Visual Analytics of Streaming Data in Concept Drift Detectio...

引用

11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Ilic, Aleksandra Stojnev Stojanovic, Dragan University of Nis Faculty of Electronic engineering Dept. Computer Science Nis Serbia

ISBN: (纸本)9798350386998

Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspects of the data. High dimensionality and volume of the data poses significant statistical challenges creating a need for accurate concept drift detection and prompt responses, a task that can be performed either by an algorithm or by human expert. Visual analytics plays a crucial role in concept drift detection by enabling analysts to visually explore, analyze, and interpret complex data streams, facilitating real-time monitoring, decision-making, and communication with stakeholders. This paper examines the usability of radar graphs for concept drift exploration, as well as their usage in other steps of analysis. For the purpose of demonstration, a pipeline for real-time health data analysis is presented and steps are visualized using radar graphs. © 2024 IEEE.

关键词： Visual analytics

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A Comparative Exploration of Machine Learning and Graph Deep Learning Models for Discriminatory Speech Detection 2

A Comparative Exploration of Machine Learning and Graph Deep...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Kavya, J. Pragathy Reddy, K.N. Maddipatla, Phanindra Kesanapalli, Anjali Arya, Arti PES University Dept. of Computer Science Engineering Karnataka Bengaluru560100 India

ISBN: (纸本)9798350371314

In an era marked by digital expansion, discriminatory speech remains a widespread concern, exerting significant societal implications. This study presents an overview of discriminatory speech detection techniques and explores the complexities involved in identifying discriminatory speech by utilizing a variety of datasets from various fields. The importance of preprocessing methods and feature engineering is next examined, opening the door for more in-dept. study. Furthermore, the paper delves into traditional Machine Learning (ML) algorithms and state-of-the-art Deep Learning (DL) models along with a crucial component of using graph-based structures with Natural Language Processing (NLP) techniques and the use of graphs to extract contextual information. Using both graphs and NLP algorithms enhances the identification process by uncovering contextual connections within textual data. © 2024 IEEE.

关键词： Speech recognition

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Elastic Blockchain Security Solution for Edge Computing Supporting Mobile IoT Devices

Elastic Blockchain Security Solution for Edge Computing Supp...

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2024 International Conference on Electrical and computer engineering Researches, ICECER 2024

作者： Almubark, Hameed Jedidi, Ahmed Al-Raweshidy, Hamed S. Brunel University London Dept. of Computer Science United Kingdom Ahlia University Dept. of Computer Engineering Manama Bahrain Brunel University of London Dept. of Electronic and Electrical Engineering United Kingdom

ISBN: (纸本)9798331539733

When integrated, blockchain technology and cloud computing (EC) may improve security and protect data. However, when data is sent to EC, the Internet of Things (IoT) device layer cannot guarantee data security. As a result, in the IoT ecosystem, end users and IoT devices are susceptible to assaults. Additionally, as the number of IoT devices rises and generates a large quantity of data, especially in mobile IoT devices, security concerns rise dramatically. High security and privacy standards are necessary for industrial Internet of Things (IIoT) applications in order to safeguard data and guarantee that IoT devices may seamlessly switch to another network. By utilising its decentralised structure, consensus process, and encryption features, blockchain technology can offer a solution. However, because IIoT devices have limited resources, the encryption procedure might be complicated. Actually, in order to secure communication between IIoT devices and EC, this research suggests a new security mechanism built on blockchain technology. Additionally, two security tiers will be used by the Elastic Blockchain Security Solution (EBC) to create a dynamic system for mobile IIoT devices. To evaluate the EBC solution's effectiveness, scalability, and performance, as well as its consensus method and encryption capabilities, it was put into practice and tested in a number of scenarios © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Tuberculosis Diagnosis in Chest X-Rays via Transfer Learning Techniques

Tuberculosis Diagnosis in Chest X-Rays via Transfer Learning...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Keerthana, T. Jayakumar, K. Iyyappan, V. Shopika, S. Sowmiya, X.L. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535476

Tuberculosis (TB) remains one of the world's major health problems. Detection with Chest X-rays is a mainstay tool in the early diagnosis of TB. Traditional methods used in the conventional detection of TB are very slow and susceptible to human errors. Thus, there is a need for an automated technique. The paper discusses the possibility of using advanced deep learning techniques, particularly techniques stemming from transfer learning, to enhance the detectability as well as efficiency of TB. Fine-tuned pre-trained convolutional neural networks, such as ResNet and VGG, on a dataset of chest X-ray images extracted high-level features relevant to the detection of TB. Superhuman performance was achieved with limited medical imaging data by transferring learned representations from large-scale image classification tasks. Experimental results illustrate high accuracy, precision, and recall rates, confirming that transfer learning is adequate for automating TB detection. This approach reduces the need for manual intervention and offers a scalable, cost-effective solution to enable rapid TB screening, particularly in low-resource settings. The novelty of the work comes from using transfer learning with fine-tuned pre-trained models (ResNet and VGG) in getting high generalization accuracy for tuberculosis detection from chest X-rays, even with limited data. © 2024 IEEE.

关键词： Convolutional neural networks

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A Brief Survey on Feature Extraction Models for Brain Tumor Detection 3

A Brief Survey on Feature Extraction Models for Brain Tumor ...

引用

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Janapati, Malathi Akhtar, Shaheda Acharya Nagarjuna University Dept. of Computer Science and Engineering Guntur India Guntur and Research Supervisor Dept. of Computer Science and Engineering Guntur India

ISBN: (纸本)9781665462167

Today, tumours are the second leading cause of cancer deaths. Cancer poses a significant threat to a large population of patients. The medical community needs a quick, automated, efficient, and trustworthy method for detecting tumours like brain tumours. Detection is crucial to effective treatment. If doctors are able to catch a tumour in its earliest stages, they have a better chance of preserving the patient's health. To do this, several distinct image processing methods are used. Through this method, doctors have been able to effectively treat tumours and save the lives of many patients. Tumors are simply abnormal growths of cells that cannot be stopped. As brain tumour cells multiply, they eventually deplete the brain's supply of nutrients. Clinicians currently use MR images (MRI) of the patient's brain to manually pinpoint the location and extent of a brain tumour. Brain tumours can develop at any age in both children and adults. However, this is not the case if detection is timely and accurate. This investigation focuses on three subtypes of brain cancer: gliomas, meningiomas, and pituitary tumours. While there have been numerous publications on the topic of brain tumour classification and prediction, very few have focused on the importance of feature extraction. Manual diagnosis and conventional feature extraction methods have their limitations, and new approaches are needed to overcome them. An automated diagnostic system is necessary for extracting features and making an accurate diagnosis of brain cancer. Although advancements are being made, automatic brain tumour diagnosis continues to struggle with issues like low accuracy and a high proportion of false-positive findings. In this research work, a brief survey is provided on feature extraction for brain tumor detection using machine learning and deep learning techniques. © 2023 IEEE.

关键词： Feature extraction

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Design and Implementation of Low Power Circuits Based on 5nm Double Gate MOSFET Technology Using Different Materials 3

Design and Implementation of Low Power Circuits Based on 5nm...

引用

3rd IEEE International Conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Kundu, Shrabanti Mandal, Jyotsna Kumar. University of Kalyani Dept. of Computer Science and Engineering WB Kalyani Nadia India

ISBN: (纸本)9798350374643

This paper presents a comprehensive investigation of 5nm double gate MOSFETs (DG MOSFETs) circuits using different substrate semiconductor materials for low-power devices. A comparative analysis of characteristics of SiGe, InGaAs and GaN as substrate materials is performed. The study reveals that InGaAs offers superior performance due to its higher electron mobility, lower threshold voltage and thermal stability than other materials studied here for low-power applications. The electrical properties of DG MOSFETs on these substrates have been investigated by calculating key parameters such as threshold voltages (Vth), Ion and Ioff currents. The results show that InGaAs-based DG MOSFETs exhibit superior performance with threshold voltage, improved leakage current and Ion/Ioff ratio compared to other substrate materials. Additionally, a lower threshold voltage has been observed for InGaAs-based devices, indicating reduced power consumption. These findings demonstrate the potential of InGaAs as a preferred substrate material for low-power, high-performance DG MOSFET devices. Double gate MOSFET circuits are designed using InGaAs and power dissipation is calculated. The results show a significant reduction in power consumption and improved circuit performance compared to traditional Si-based devices. This research demonstrates the potential of InGaAs-based DG MOSFETs for low-power electronic devices, enabling the development of energy-efficient circuits for various applications. © 2024 IEEE.

关键词： III-V semiconductors

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MSCE-LT: Multi-Label Supervised Contrastive Enhancement for Long-Tailed Retinal Diseases Recognition

MSCE-LT: Multi-Label Supervised Contrastive Enhancement for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Tingyao Sheng, Bin Shanghai Jiao Tong University Dept. Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350386226

Retinal diseases are leading causes of blindness globally. In real-world clinical practice, a patient may suffer from multiple retinal diseases, and these diseases are often under a long-tailed distribution, which poses significant challenges for accurate diagnosis. In this work, we propose a novel contrastive learning(CL)-based framework for multi-label retinal disease recognition. It consists of two parallel branches, a multi-label supervised contrastive learning branch and a classifier branch. The positive sets are created by the extent of proportional label overlap between samples and the anchor in calculating contrastive loss. For minority information enhancement, we design a hybrid-proxy model to generate class-dependent proxies, which are updated alongside the network. We capture rich relations samples, proxies, and labels by introducing the hybrid sample-proxy contrastive loss. Taking both label co-occurrence and data imbalance into consideration, we further utilize Distribution Balanced (DB) binary cross-entropy loss to guide the classifier branch learning. Experimental results on four public retinal disease datasets have demonstrated the superiority and effectiveness of our method. © 2024 IEEE.

关键词： Contrastive Learning

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