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Deep Reinforcement Learning in Healthcare and Bio-Medical Applications 5

Deep Reinforcement Learning in Healthcare and Bio-Medical Ap...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thakur, Payal Talwandi, Navjot Singh Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Deep reinforcement learning (DRL), which learns a set of behaviors that maximize the projected reward, combines the representational power of deep neural networks with the reinforcement learning paradigm. DRL holds great promise for the future of healthcare and medicine, according to recent *** overview of the research on DRL in medical imaging is provided in this article. We start with a comprehensive DRL course that covers both the most recent model-based and model-free approaches. The tasks covered in the next section of this article are loosely divided into three main categories: (i) parametric medical image analysis tasks like landmark detection,object/lesion detection, registration, and view plane localization;optimization tasks like hyperparameter tuning, augmentation strategy selection, and neural architecture search;and (iii) other applications like surgical gesture segmentation, person tracking, and perso The study finishes with thoughts of potential future directions. © 2024 IEEE.

关键词： Deep reinforcement learning (DRL) Healthcare Hyper-parameter Tuning Surgical gesture segmentation

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Triple-Node-Upset Recoverable Radiation-Hardened by Design Latch

Triple-Node-Upset Recoverable Radiation-Hardened by Design L...

引用

2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Paparsenos, Evangelos Tsiatouhas, Yiorgos Lab Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350318845

CMOS technology evolution enhances integrated circuits (ICs) performance characteristics at the cost of their increased susceptibility to radiation and thus to the occurrence of single-event upsets (SEUs) that may lead to soft errors generation. SEUs may affect multiple nodes in a circuit (multiple node upsets - MNUs). Fortunately, these disturbances do not cause permanent damage to the circuits. Various techniques have been proposed to deal with SEUs that concurrently affect one, two or three nodes. In this paper, we propose the design of a latch that offers tolerance up to the level of triple-node upsets (TNUs). This is achieved by using redundancy in the hardware which provides the ability to store a logic value in multiple nodes within the latch as well as by using multiple feedback paths which allow it to recover its correct state in the event of an SEU. Compared to other techniques dealing with the same problem, our proposed method provides faster recovery time (higher than 16.87%) after an SEU, and at the same time reduced power consumption and higher speed performance. © 2024 IEEE.

关键词： Radiation hardening

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SipSmart: Elevating Palates with Machine Learning for Wine Quality Prediction 6

SipSmart: Elevating Palates with Machine Learning for Wine Q...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Talwandi, Navjot Singh Khare, Shanu Yadav, Anup Lal Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350306613

This study explores the request of Mechanism Education (ML) techniques for predicting wine quality, aiming to enhance the understanding and precision in assessing the characteristics of different wine varieties. The dataset utilized encompasses diverse oenological parameters, such as acidity, residual sugar, alcohol content, and more. Various ML algorithms, including regression and classification models, were employed to analyze and predict wine quality based on these features. The results demonstrate the effectiveness of ML in discerning patterns and providing accurate predictions, offering valuable insights for both connoisseurs and the wine *** classification poses a formidable challenge, exacerbated by the elusive criteria that define a good-tasting wine. Certification processes currently rely on subjective sensory analysis conducted by human tasters. To address this, our study aims to predict wine quality, offering a more objective approach. We leveraged a dataset featuring various physicochemical properties of red and white variantsof Portuguese 'Vinho Verde' wine, sourced from Kaggle, and utilized Google Colab for analysis. Machine learning algorithms were employed to discern ex- ceptional or subpar wine qualities. Data preprocessing involved identifying and handling missing values, while One-Hot encoding transformed categorical values into numerical ones. Feature Scaling normalized the range of independent variables. Our predictive models, including Logistic Regression, Decision Tree Classifier, Random Forest Classifier, and Extra Trees Classifier, underwent training and evaluation. Comparisons of accuracyand precision guided the selection of the most effective machine learning algorithm. This process enables us to predict wine quality on a scale of 0-10 based on a given set of features. The results contribute to the refinement of wine quality assessment, offering a more standardized and data-driven approach than traditional sensory analysis © 2024

关键词： Classification Models Data Mining Feature Analysis Machine Learning Oenological Parameters Predictive Analytics Regression Models Wine Quality

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Synergizing Insights for Precise Rice Leaf Disease Diagnosis Via Multi-Modal Fusion 10

Synergizing Insights for Precise Rice Leaf Disease Diagnosis...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pally, Shelly Chelsy Jenefa, J. Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350384369

Rice holds a significant position in India, especially in the southern part of the country, where people tend to eat some rice at least once a day. Farmers are facing a huge loss due to diseases in leaf, which is the main problem of agriculture. By using techniques like machine learning, main problems detection can be done. This review, discusses common plant diseases that affect the leaf. Some include Leaf Spots, Rusts, Fusarium Wilt, Early Blight, Powdery Mildew and Downey Mildew. Our research found that machine learning techniques on rice plants make finding diseases on leaves easier. Finally, we concluded that the most accurate method is the Enhanced VGG16, with an accuracy of 99.60% because it is really good at spotting diseases on rice leaves because it's great at recognizing the small details and patterns in leaf pictures. This helps it to tell the diseases apart more accurately and make fewer mistakes in identifying them. © 2024 IEEE.

关键词： Plant diseases

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Enhancing Road Safety: The Role of Intelligent Driver Drowsiness Detection Systems 3

Enhancing Road Safety: The Role of Intelligent Driver Drowsi...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Khare, Shanu Talwandi, Navjot Singh Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350354348

The analysis of this research paper is touching the road safety concerns by focusing on developing a system which catches the drowsy state of the person who is driving which emerges as a big reason that causes up to 30due to this. Throughout the paper, the study discusses about literature review and various methodologies that include physiological, behavioural, and multimodal fusion approaches showing the evolution and history of drowsiness detection technologies. The methodology includes using of various sensor technologies and algorithms to collect data from the driver and process it to analyse data in real time. Eye movement and facial expressions are extracted which helps the system to identify signs of drowsiness with greater accuracy. When the driver is detected as drowsy a mechanism is triggered to alert the driver, averting accidents. Even with some challenges, the system is a big step towards increasing road safety utilizing advanced technologies to reduce the risk of driver fatigueless. © 2024 IEEE.

关键词： Highway accidents

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Cloud-Based Agricultural Monitoring System for Precision Farming 11

Cloud-Based Agricultural Monitoring System for Precision Far...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Yadav, Anup Lal Khare, Shanu Talwandi, Navjot Singh Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350350357

Precision agriculture is responsible for the agricultural industry's recent boom;this paper discusses the creation of the Cloud Based Agricultural Monitoring System Precision Farming (CAMS-PF). To fully use of cloud computing, loT devices, data analytics, remote sensing technologies for optimized farming practices. The goal here is to enhance crop yield, lessen how much resources are used up, and make everything more sustainable. Throughout this paper you'll find an analysis of the CAMS- PF architecture along with any challenges faced developing it, the benefits it brings to the table, and its potential in future developments © 2024 IEEE.

关键词： Remote sensing

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Share-a-Meal: A Mobile App-based Food Sharing System 6

Share-a-Meal: A Mobile App-based Food Sharing System

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Uddin, Mohammad Salah Ansary, Fozlay Rabby Dept. of Computer Science and Engineering East West University Bangladesh

ISBN: (纸本)9798331522667

Many problems have been solved using the internet of things (IoT). Waste management, resource management, traffic management, financial services (e.g. stock rate prediction), logistics services, agriculture, etc. are the major sectors where IoT has played a fruitful role. The use of IoT is increasing day by day due to its immense advantages. This paper presented an IoT-based system for sharing leftover food with others or hungry people. The system consists of a mobile application and an embedded system. Cloud service is used as middle-ware between the mobile application and embedded system. Mobile application is used for sending and receiving notifications. The rectangular-shaped box type embedded system is responsible for food collecting and distribution. The development and prototype implementation of our proposed system are discussed throughout this paper. The system helps to share leftovers (food) with others which can reduce food wastage. © 2025 IEEE.

关键词： Cloud-Food Food waste Food-sharing internet of things Leftover food

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Implementation of Fuzzy Logic Model to Solve Traffic Congestion Problem at Road Intersections

Implementation of Fuzzy Logic Model to Solve Traffic Congest...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Negi, Vinay Jha, Sundaram Kumar Behl, Rachna Mriirs Dept. of Computer Science & Engineering Faridabad India

ISBN: (纸本)9798350382723

This research aims to tackle traffic congestion at intersections, primarily caused by static traffic light timings that don't adapt to changing traffic conditions. The proposed solution involves using adaptive fuzzy controllers, which can adjust their decisions based on fuzzy IF-THEN rules. These controllers employ both Mamdani and Sugeno fuzzy logic. The Mamdani method considers factors like the number of vehicles, queue length, emergency vehicle access, and road width to optimize traffic light timings and reduce unnecessary green signals. The Sugeno-type fuzzy logic enhances controller efficiency and decreases waiting times. The study also explores the application of direct adaptive fuzzy controllers to manage unstable systems, which is relevant in optimizing traffic control. Ultimately, the goal is to develop a simulator capable of predicting the right traffic light timings in real-time. This involves extending green light timings for lanes with high vehicle volumes and also for the emergency vehicles to alleviate congestion at intersections. © 2024 IEEE.

关键词： Traffic congestion

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An Automatic Navigation System for New Technical Advanced Drones for Different Alpplications 5

An Automatic Navigation System for New Technical Advanced Dr...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Talwandi, Navjot Singh Thakur, Payal Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Autonomous pilot technology integrated into drones has ushered in a new era of unmanned aircraft systems, creating a multitude of applications This abstract examines the importance of the importance and potential of autonomous pilot aircraft, underscoring its transformative impact on industries ranging from agriculture to surveillance and beyond Navigation requires intelligent a unmanned aerial vehicles (UAVs) will be equipped with sophisticated artificial and sensor systems, enabling them to operate autonomously without constant human intervention A rapidly growing field in robotics and aerospace technology Tires It is showing to do so, the paper provides a comprehensive overview, including key concepts, challenges, and recent developments in autonomous automation enter the box. It also discusses, and educates, the critical importance of real-time operations, the critical role of obstacle detection and avoidance systems, and the use of GPS and optical-based navigation delves into how machine learning and artificial intelligence enhance the autonomy of drones. In addition, the paper explores the many potential applications and benefits of autonomous drone communications, from search and rescue missions to precision agricultural delivery Growing interest and investment in this technology in 2013 highlights its potential to transform industries, promising a future marked by improved safety and performance. © 2024 IEEE.

关键词： and applications artificial intelligence Autonomous drones GPS machine learning navigation obstacle avoidance real-time data processing vision-based navigation

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Predicting Landslides with Machine Learning: A Data-Driven Approach

Predicting Landslides with Machine Learning: A Data-Driven A...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Sarawagi, Karan Virdi, Khushwant Talwandi, Nvajot Singh Chandigarh University Dept Of Computer Science Engineering Punjab India

ISBN: (纸本)9798350306446

Routs are usual tragedies that can cause significant damage to the environment and pose a serious danger to humanoid lives and infrastructure. Early detection and identification of potential landslide-prone areas are crucial for disaster mitigation and preparedness efforts. This abstract out-lines a comprehensive approach to landslide identification utilizing machine learning techniques. In recent years, machine learning has emerged as a powerful tool for analyzing geospatial data and predicting geological hazards such as landslides. This research leverages a diverse range of data sources, including remote sensing imagery, topographical maps, rainfall records, and geological data, to develop a robust landslide identification model. The key components of the proposed methodology involve data preprocessing, feature engineering, and the application of various machine learning algorithms. Remote sensing data, such as satellite imagery and LiDAR data, are used to extract valuable terrain features and land cover information. Rainfall data are incorporated to assess the influence of precipitation on landslide occurrence. Geological data contribute to the understanding of local geological conditions. Several machines learning algorithms, including but not limited to decision trees, support vector machines, and neural networks, are employed to create predictive models. These models are trained on historical landslide data and validated against real-world cases. Cross-validation techniques are applied to ensure the model's robustness and generalization capabilities. © 2024 IEEE.

关键词： Landslides

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