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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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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 ...

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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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 Model for Suicidal Behavior Disorder Risk Analysis by Correlating Cyber and Real World Data 4

Prediction Model for Suicidal Behavior Disorder Risk Analysi...

引用

4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Neelakantam, Gone Ecole Centrale School of Engineering Mahindra University Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350387063

In the era of internet of things (IoT), people are attentive to express their non verbal behavior on cyber world such as social media by using smart phone, laptop or tablet. People are more active in sharing their daily activities status such as current activity the person is into and personal life situations such as achievement, problem, stress and even any hazardous desire (i.e. suicide). In contrast with real world such as smart home environment, people generally spent their non verbal behavior in term of Activity Daily Livings (ADLs) in associating with smart home sensors. Based on non verbal and verbal behaviors, data are generated from both Cyber and Real world, which are big in volume and variety. However, there is lack of investigating Cyber and Real world data especially in analyzing the risk of committing suicide, considering suicide is a big issues and threats in the society. Therefore, it motivate us to propose prediction model to determine high and low risk of committing suicide by combining two separate approaches such as Activity Recognition (AR) for real world and Sentiment Analysis (SA) for cyber world. © 2024 IEEE.

关键词： Risk assessment

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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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Optimal Deep Learning Models for Post-Flood House Detection in Drone Imagery

Optimal Deep Learning Models for Post-Flood House Detection ...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Jain, Swati Sushmitha, M. Einstein, A Manish Mahapatra, Ansuman National Institute of Technology Puducherry Dept. of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Floods remain a persistent threat to human lives and infrastructure. Identifying flood-impacted houses in drone imageries will support the rescue teams to prioritize rescue operations in highly affected areas. This study investigates the performance of various deep-learning detection models for post-flood house detection using drone imagery. Our research aims to identify the most suitable model and optimized parameters for accurately and efficiently identifying flood-affected houses, thereby facilitating timely disaster response and recovery efforts. The paper explores various state-of-the-art architectures and provides a thorough performance evaluation. © 2024 IEEE.

关键词： Flood damage

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Analyzing the Impact of Air Quality on UV Index using Real-Time Weather Data from Major Cities Across the World 2

Analyzing the Impact of Air Quality on UV Index using Real-T...

引用

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

作者： Middha, Parth Basim Muitaba Andrabi, Syed Gupta, Tanushree Singhal, Abhishek Amity University Uttar Pradesh Dept. of Computer Science & Engineering Noida India

ISBN: (纸本)9798331519056

Ultraviolet radiation from sunlight has both beneficial and harmful effects on human health, with the UV index serving as a crucial metric for assessing potential harm. Excessive exposure can lead to adverse effects like skin cancers, cataracts, and immune suppression. Air quality influences the UV index by affecting the scattering and absorption of UV radiation, with particulate matter and pollutants altering the amount of radiation reaching the Earth's surface. Recognizing the dynamic relationship between air quality and the UV index is vital for understanding their combined impact on human health and the environment. This work utilizes the real-time weather information from prominent cities across the world, capturing diverse climatic conditions and air quality variations. With a particular emphasis on UV index prediction, the study focuses on employing a comparative analysis of various regression models, including linear regression, decision trees, support vector machines, logistic regression, random forest, XGBoost, LightGBM, gradient boosting and neural networks. The comparative analysis aims to identify the most proficient model for capturing the intricate relationships between chosen meteorological variables, including temperature, wind degree, precipitation, and others, in relation to the UV index. This paper explores the impact of air quality, temperature, wind degree, precipitation, and other relevant factors on UV index variations, offering insights into the complex interplay between meteorological parameters and ultraviolet radiation. The findings of this research hold potential implications for public health, as understanding the factors influencing UV index can contribute to enhanced sun exposure risk assessments. Furthermore, the comparative analysis of regression models provides valuable guidance for future studies aiming to predict UV index variations based on real-time weather data, thereby contributing to the development of more accurate and regi

关键词： Air quality

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Minimization of data centers in survivable dynamic SDM-EONs 14

Minimization of data centers in survivable dynamic SDM-EONs

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chandra, Sourabh Mondal, Khokan Calcutta Institute of Technology Dept. of Computer Science & Engineering Howrah India Techno India University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350335095

Energy consumption due to massive use of data center (DC) is increasing rapidly. The need to improve the energy efficiency of such DC aims to develop an effective DC-placement algorithm. Again, a sudden disaster in the network can affect both fiber path as well as the DC nodes resulting in fiber cuts and huge data loss. Space division multiplexing-based elastic optical network (SDM-EON), being more efficient than the traditional elastic optical network (EON) due to the inclusion of multiple cores in a single fiber link, tries to maximize the utilization of resources by reducing blocking probability. This paper presents a novel DC-placement algorithm in dynamic DC-based SDM-EONs which focuses on enhancing energy efficiency in the network, thereby ensuring the survivability of the network against any sudden disaster. Rigorous simulation analysis proves the efficacy of the proposed algorithm over other existing algorithms. © 2023 IEEE.

关键词： Energy efficiency

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Cardiac Disease Diagnosis from Echocardiogram using Random Forest Classifier 5

Cardiac Disease Diagnosis from Echocardiogram using Random F...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shruthi, T. Srimathi, P. Janani, S. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Cardiovascular diseases (CVDs) represent a leading global health issue, necessitating early detection and accurate diagnosis for effective treatment. Electrocardiography (ECG) serves as an essential tool that provides valuable insights into the heart's electrical activity, aiding in the identification of potential abnormalities. This project leverages machine learning techniques to enhance the diagnostic potential of ECG data, paving the way for improved cardiovascular care through precision medicine. The methodology focuses on training machine learning models to analyze ECG signals and classify heartbeats into categories that reflect various cardiac conditions. A com- prehensive dataset of approximately 20,000 ECG recordings, encompassing a wide range of heart rhythms and anomalies, establishes a robust foundation for model training. The Decision Tree classifier achieves an outstanding accuracy of 97%, demonstrating the ability to detect subtle patterns in ECG data. Furthermore, the Random Forest classifier showcases strong performance in distinguishing between different cardiac rhythms. Key metrics, such as precision, recall, and F1-score, provide a deeper understanding of the classifier's diagnostic effectiveness. The confusion matrix serves as a visualization tool to pinpoint areas where model adjustments are necessary, further enhancing accuracy. These promising results highlight the potential of machine learning in transforming cardiac health monitoring and improving diagnostic processes, ultimately contributing to better patient care and outcomes. By harnessing advanced algorithms, this project illustrates the immense possibilities for integrating machine learning into modern healthcare systems. © 2024 IEEE.

关键词： Electrocardiography

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