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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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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...

引用

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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A Wireless Intrusion Detection System using Feature Selection with Random Forest 8

A Wireless Intrusion Detection System using Feature Selectio...

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Warhekar, Abhijit Sharad Kshirsagar, Deepak D. Coep Technological University Dept. of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350391770

Wireless networks have become integral to modern communication infrastructure, facilitating seamless connectivity in various domains. However, their presence everywhere also makes them vulnerable to various attacks. Those can cause failure in network services. Indeed, a robust Wireless Intrusion Detection System (WIDS) is essential for identifying and mitigating attacks on wireless networks. The key objective of WIDS is to detect wireless traffic and classify it as an attack or normal. In this situation, machine learning (ML) algorithms are used to detect attacks or intrusions in wireless systems. However, these algorithms need to accurately detect attacks early. Feature selection (FS) techniques are used to reduce this detection time and increase accuracy. These allow our system to identify attacks against local nodes. The proposed system is implemented, experimented and tested on AWID3 dataset. The performance of the system is determined by using a random forest (RF) algorithm and FS selection techniques. The experimentation conducted on AWID3 shows that the proposed methodology achieved an accuracy of 99.9990% for the binary model using 143 significant features and an accuracy of 99.9985% for multilabel classification using 144 significant features © 2024 IEEE.

关键词： Network intrusion

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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...

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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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An Integrated System for Potato Leaf Disease Recognition 2

An Integrated System for Potato Leaf Disease Recognition

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rathore, Robin Dhoundiyal, Praveen Sharma, Vikrant Vats, Satvik Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

Since potatoes are a staple crop in India, there has been a noticeable growth in their cultivation. Given the significant nutritional and economic value of potatoes, this study addresses the significant issue of leaf diseases. The study enhances farmer lives and supports agricultural success in India by using machine learning techniques to categorize and detect diseases in real time. The major areas of study are image processing, feature extraction, and machine learning models like Random Forest, Naive Bayes, and K-Nearest Neighbors (KNN). The paper proposes a systematic framework that comprises steps for data collecting, picture preprocessing, feature extraction, training, and classification in order to reliably diagnose diseases. It also demonstrates how these procedures can be combined. The datasets used include approximately 5,000 labeled pictures of potato leaves that show early blight, late blight, and healthy leaves. Among the preprocessing methods are data augmentation, contrast enhancement, and image normalization. When it came to classifying diseases, the trained models fared extremely well;Naive Bayes, Random Forest, and KNN were especially accurate. The study demonstrates how integrating image processing and machine learning could aid in addressing potato leaf disease issues in agriculture.. © 2024 IEEE.

关键词： feature extraction image normalization Image processing K-Nearest Neighbors Machine Learning Naive Bayes Random Forest

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Active Control for Resonance Mitigation in Electric Drive Applications 15

Active Control for Resonance Mitigation in Electric Drive Ap...

引用

15th International Symposium on Industrial Electronics and Applications, INDEL 2024

作者： Berglund, Emelie Borg, Johan Johansson, Jonny Luleå University of Technology Dept. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350352320

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance. © 2024 IEEE.

关键词： Control nonlinearities

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Esteso: Interactive AI Music Duet Based on Player-Idiosyncratic Extended Double Bass Techniques

Esteso: Interactive AI Music Duet Based on Player-Idiosyncra...

引用

International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Stefani, Domenico Tomasetti, Matteo Angeloni, Filippo Turchet, Luca Dept. of Information Engineering and Computer Science University of Trento Trento Italy Italy

Extended playing techniques are a crucial characteristic of contemporary double bass practice. Players find their voice by developing a personal vocabulary of techniques through practice and experimentation. These player-idiosyncratic techniques are used in composition, performance, and improvisation. Today’s AI methods offer the opportunity to recognize such techniques and repurpose them in real-time, leading to new forms of interactions between musicians and machines. This paper is the result of a collaboration between a composer/double-bass player and researchers, born from the musician’s desire for an interactive improvisational experience with AI centered around the practice of his extended techniques. With this aim, we developed Esteso: an interactive improvisational system based on extended technique recognition, live electronics, and a timbre-transfer double-bass model. We evaluated our system with the musician with three duet improvisational sessions, each using different mapping strategies between the techniques and the sound of the virtual double bass counterpart. We collected qualitative data from the musician to gather insights about the three configurations and the corresponding improvisational duets, as well as investigate the resulting interactions. We provide a discussion about the outcomes of our analysis and draw more general design considerations. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： computer music

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Prediction of Diabetes Using Machine Learning Approach 1

Prediction of Diabetes Using Machine Learning Approach

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Sinha, Niraj Kumar Pundir, Meena Dhiman, Tanu Chandigarh University Dept. of Computer Science & Engineering Gharuan Punjab Mohali India

ISBN: (纸本)9798350353648

Diabetes is a chronic disease posing a significant global health challenge, with the number of affected individuals projected to nearly double by 2045. This disease, which is characterized by high blood sugar, can cause severe complications such as kidney failure, blindness, and heart problems. To address this pressing concern this study target to enhanced machine learning technology-based system for the early understanding of diabetes with high accuracy. The suggested method makes use of the output of different technique, such as Decision Tree, Random Forest, K-Nearest Neighbor and Naive Bayes, to maximize their benefits and enhance overall prediction performance. The study evaluates the accuracy of each algorithm individually and selects the techniques with the best result as the final predictive system. The results of this study support ongoing initiatives to apply innovative data science methods for diabetes early diagnosis and treatment, ultimately assisting in the avoidance of the disease's severe consequences and enhancing patient outcomes. Based on the Random Forest classifier model, this model generated an accuracy of 96.83%. © 2024 IEEE.

关键词： Adversarial machine learning

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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 ...

引用

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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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...

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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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