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Homogenous Ensemble Learning for Air Quality Index Prediction 15

Homogenous Ensemble Learning for Air Quality Index Predictio...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Medhi, Shrabani Boruah, Rituraj Baruah, Sagar Pratim Das, Hiranya Kumar Girijananda Chowdhury University Dept. of Computer Science and Engineering Guwahati India Girijananda Chowdhury Institute of Management and Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331300579

Air pollution is a global issue of concern. Accurate prediction of the air quality index (AQI) plays a crucial role in understanding pollution patterns and implementing effective mitigation strategies. This research paper presents a comprehensive study on the application of ensemble learning techniques for AQI prediction, aiming to enhance prediction accuracy and provide insights into the driving factors behind pollution levels. The primary objective is to conduct a comparative analysis of homogenous ensemble method, namely, bagging in conjunction with individual base models, such as multiple linear regression, support vector regression, decision trees, and artificial neural networks. In total we have created 4 non-ensembled models and 4 ensembled models. Additionally, the research investigates the importance of different features in AQI prediction models, aiming to identify the key factors influencing air pollution. Hyperparameter tuning is done to optimize the models. © Grenze Scientific Society, 2024.

关键词： Prediction models

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A Dual-Action Approach to Mosquito Control: Harnessing Mosquitoes Affinity for Light with Metofluthrin's Lethality 18

A Dual-Action Approach to Mosquito Control: Harnessing Mosqu...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Mittal, Manisha Singh, Mahender Dewan, Prachi Singh, Prabhmeet Dept. of Electronics and Communication Engineering New Delhi India Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9789380544519

Amidst rapid urbanization and fluctuating climatic patterns, the world witnessed a surge in mosquito-borne diseases, escalating global health concerns. Mosquitoes, often dismissed as mere irritants, are carriers for a spectrum of conditions, including malaria, dengue, and the Zika virus, with WHO reporting large numbers of dengue cases annually. This review delves deep into the observation of mosquito behavior and the challenges in eradicating these pests, emphasizing the inefficacy of singular sensory targeting repellents. This paper introduces a pioneering device, combining the mosquito's affinity towards light with the suffocating effects of metofluthrin, offering a holistic solution. By the combination of advanced engineering with a comprehensive understanding of mosquito behavioral dynamics, this device promises not only to repel but to substantially reduce these vectors, fostering safer indoor spaces. Through a detailed exploration of current methodologies and the introduction of this integrated strategy, this paper provides insights into creating a healthier, mosquito-free environment. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： fluorescent light metofluthrin mosquito control mosquito-borne diseases multifaceted sensory system

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Analytical Approach Towards Radiological Image-based Fracture Assessment Using Artificial Intelligence 1

Analytical Approach Towards Radiological Image-based Fractur...

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

作者： Sylvia, Takhellambam Dubey, Sanjay Kumar Thongam, Khelchandra Amity University Computer Science & Engineering Dept. Uttar Pradesh Noida India National Institute of Technology Manipur Computer Science & Engineering Dept. Imphal India

ISBN: (纸本)9798350353648

The utilization of Artificial Intelligence (AI) techniques has been prevalent in various applications and the gradual increase in implementation towards healthcare sectors has scaled up. computer aided diagnosis has emerged as a major research area directed toward efficient use of resources. Fracture of bone has been a popular health problem associated with risk for mortality. Fractures are frequent in the long bones or the weight-bearing bones. AI techniques to accurately detect and classify fractures are still a challenging task beyond the conventional methods. Our objective is to assess the effectiveness of multiple algorithms in identifying and categorizing ankle fractures using radiological images. This review paper focuses on several available approaches of Deep-Learning (DL) and Machine-Learning (ML) regarding Ankle Fracture Classification and Identification based on Radiological Images for Medical Applications. The goal is to assist physicians and orthopaedics with simple and realistic decision support systems. In addition to a comprehensive analysis of the research, this study looked at several real-world examples of AI applications in the healthcare industry. Consequently, it is critical to have a fundamental grasp of these ML and DL algorithms to be ready for upcoming advancements in AI. We have reviewed and classified scientific conference and journal articles used for various AI techniques and offer future utility as a comparative analysis for further research in healthcare sectors. © 2024 IEEE.

关键词： Deep learning

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A simplified hybrid model for state of charge estimation using 1-D Kalman filter and Coulomb counting 50

A simplified hybrid model for state of charge estimation usi...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Garg, Kritika Sarker, Rahul Ahmad, Aaqib Kaur, Mehakpreet Teja, A.V. Ravi Dept. of Electrical Engineering IIT Ropar Rupnagar India Dept. of Computer Science Engineering PU Patiala India

ISBN: (纸本)9781665464543

This document is a model with an accurate estimation of the battery’s State Of Charge (SOC), which is pivotal for prime performance and optimal lifespan of the rechargeable batteries. Though traditional methods, like Open Circuit Voltage (OCV) and Coulomb Counting (CC), are advantageous, they suffer from several limitations, such as sensitivity to temperature changes, frequent calibration, ageing effects, and measurement inaccuracies. This paper proposes 1-D Kalman Filter (1-D HKF) with an approach that incorporates the OCV and CC results into the state space model for better and more accurate SOC estimation. So, the model is called 1-D HKF or Hybrid Kalman filter that, influenced the long-term stability and estimation accuracy of CC and the accuracy of OCV at specific points to get over the individual limitations. The model trained by 1-D HKF adapts itself and adjusts the gain based on the uncertainties of each measured value to ensure optimal value information. So, We evaluate the performance of the 1-D HKF versus the traditional methods Kalman Filter using examples and exploratory data. The proposed approach illustrates significantly improved accuracy and robustness across various operating conditions, including varying temperatures and discharge rates. © 2024 IEEE.

关键词： State of charge

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Personal Exercise Trainer Using computer Vision 2

Personal Exercise Trainer Using Computer Vision

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Hari Ram, S. Ashok, A. Ramalingam, A. Dheenadhayalan, S. National Engineering College Dept. of Computer Science and Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9798350328202

The Personal Trainer online application tracks users progress and offers realtime feedback to encourage users in their daily workout routines. To identify and analyze human body movements, the program uses the mediapipe library, which integrates opencv and machine learning methods. The Personal Trainer online app provides an engaging and immersive workout experience by incorporating these technologies. The application supports several exercises, including weightlifting, squats, bicep curls, and pull-ups. The software uses the webcam on the computer to record realtime footage of the user completing the workout after they choose it. The user's body motions are then recognized from the recorded video using opencv and mediapipe, and performance-based feedback is given. Users may quickly move through the various training modes, check their activity history, and measure their overall progress thanks to the app's user-friendly and intuitive interface. © 2024 IEEE.

关键词： computer vision

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Utilising Convolutional Neural Networks for the Automated Detection of Diabetic Retinopathy

Utilising Convolutional Neural Networks for the Automated De...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Lakshmi, R Poornima Shopika, S. Yaazhini, A. Hemalatha, M. Saraswathi, V. Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

Diabetic retinopathy has emerged as one of the leading causes of eye diseases among people suffering from long-term diabetes. Indeed, it raises the risk of being blinded without proper detection and treatment. Conventional detection of retinal fundus images by an ophthalmologist is time-consuming and prone to mistakes owing to human intervention. This is particularly a cause for alarm in the wake of the growing incidence of diabetes globally. The need for automated, accurate detection systems for early diagnosis of diabetic retinopathy has never been more relevant. In this work, we develop a deep learning model based on CNN for classifying four stages of diabetic retinopathy, from no DR to proliferative DR. We tested the model in a public dataset to extract features from retinal images and to screen for abnormalities automatically. Our CNN model achieved 98.5% classification and detection accuracy, thus indicating the ability to make a real difference in the early detection and treatment plan, thereby preserving the vision of many diabetic patients. Practical value The present research is very relevant to clinical practice and, therefore, practically useful because of its relevance to healthcare technology. Using accuracy and loss function metrics, the proposed model performs well compared to the latest techniques in DR detection. An approach based on CNNs is expected to ease much of the workload that healthcare professionals bear in diagnosis and improve the precision, potentially resulting in a very high cutback in vision loss in diabetic patients with better patient outcomes. Our study has highlighted the practical utility of the CNN model, which has improved patient outcomes. DR is a serious condition affecting the eyes, which, in case of untimely detection and untreated in diabetic patients, can cause loss of vision. For centuries, the conventional diagnosis for this disease has been through the manual inspection of retinal fundus images taken through a camera

关键词： Clinical research

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Performance Evaluation of Openairinterface's Scheduling Algorithms for 5G Networks 4

Performance Evaluation of Openairinterface's Scheduling Algo...

引用

4th International Conference for Emerging Technology, INCET 2023

作者： Bhavana, D. Chaudhari, Shilpa Dept. of Computer Science and Engineering Bengaluru India Dept. Computer Networks and Engineering Bengaluru India

ISBN: (纸本)9798350335750

The introduction of the fifth generation (5G) of mobile networks (5G) has showed how crucial enhanced scheduling approaches are managing the scarce frequency spectrum whereas meeting 5G transmission needs. Openairinterface (OAI) simulation environment helps the researcher to analyses various components of 4G/5G Networks. In this paper, performance of 5G mobile network is analyzed by comparing the results with 5G scheduler algorithms available in OAI in terms of throughput. offered by openairinterface to check the performance. Simulated OAI environment consists of UE and gNodeB (gNB). The pcap file is generated and analyzed using Wireshark. The results show how schedulers allocated resources to UE from gNodeB (gNB) and throughput is the performance metric used to analyze. Resource allocation using Round Robin at UE gives uniform throughput compare to first in first out based resource allocation. © 2023 IEEE.

关键词： 5G mobile communication systems

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A Review of Disease Detection in Sugarcane Using Machine Learning and Deep Learning Techniques 15

A Review of Disease Detection in Sugarcane Using Machine Lea...

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

作者： Pavan, Undavalli Chandra Pateriya, R.K. Tomar, Deepak Singh Singh, Surendra Maulana Azad National Institute of Technology Dept. of computer science & engineering Bhopal India National Institute of Technology Dept. of computer science & engineering Uttarakhand India

ISBN: (纸本)9798350370249

Agriculture has been an active source of food and economic growth, but it faces significant challenges from diseases and climate change. In Indonesia, sugarcane production is severely impacted by viral infections such as Sugarcane mosaic virus (SCMV) and Sugarcane streak mosaic virus (SCSMV). These infectious diseases pose a threat to the world's sugarcane industry, resulting in significant crop losses in nations like Brazil and India, along with other viruses like the Fiji Disease Virus (FDV), and Sugarcane Yellow Leaf Virus (SCYLV). For such diseases to be managed and performance to be maintained, early detection is essential. Disease detection is accomplished by both conventional and cutting-edge approaches, such as molecular, serological, and imaging methods. However, the advancements in Machine learning, Deep learning and computational image processing algorithms have demonstrated significant potential in the early and precise diagnosis of disease. In this study, different methods used for machine learning (ML) and deep learning (DL) for plant disease diagnosis were reviewed and it highlights the need of high-quality datasets, various pre-processing techniques, robust extraction of features, and efficient classification techniques. It also discussed the issues with data collection, symptom variability, over-fitting, and imbalances in classes, along with potential avenues for further advancement in diagnosing diseases systems. © 2024 IEEE.

关键词： challenges deep learning detection hybrid models machine learning Plant diseases

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Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson’s Disease Through Drawn Image Patterns 2

Exploring the Efficacy of Modified Transfer Learning in Iden...

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2nd International Conference on Information and Communication Technology, ICICT 2024

作者： Daiyan, Nabil Haque, Md Rakibul Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331508227

Parkinson’s disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (CNNs), transfer learning, and attention mechanisms to improve model performance and resilience against overfitting. To enhance the diversity and richness of both spiral and wave categories, the training dataset undergoes augmentation to increase the number of images. The proposed architecture comprises three phases: utilizing pre-trained CNNs, incorporating custom convolutional layers, and ensemble voting. Employing hard voting further enhances performance by aggregating predictions from multiple models. Experimental results show promising accuracy rates. For spiral images, weighted average precision, recall, and F1-score are 90%, and for wave images, they are 96.67%. After combining the predictions through ensemble hard voting, the overall accuracy is 93.3%. These findings underscore the potential of machine learning in early PD diagnosis, offering a non-invasive and cost-effective solution to improve patient outcomes. ©2024 IEEE.

关键词： Convolutional neural networks

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Utilizing the U-Net Network for Segmenting the Left Ventricle from Cine-MRI Scans 4

Utilizing the U-Net Network for Segmenting the Left Ventricl...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Malhotra, Sonal Thapliyal, Siddhant Aggarwal, Priyanshi Kumar, Mukesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350387490

In this research, we studied the U-Net segmentation model for identifying and demonstrating addresses related to cardiac left ventricle contour in cine-MRI images. Efficacy of the model is evaluated across different stages such as preprocessing, training, validation and robustness testing along with comparisons to conventional methods and other deep learning architectures. But the preprocessing includes standardized pixel intensities, and by data augmentation using rotation, flip method or scaling techniques. The training and validation stages clearly show the robust consolidation of our model with little overfitting, guaranteeing decent performance on different datasets. The developed model is robust by passing the test against noise/ artifacts, and providing a very good segmentation in extreme situations so that there are no accuracy outliers represented. Comparative analyses show better sensitivity-specificity trade-offs and precision-recall characteristics versus other methods. In general, a U-Net model developed here works well for cardiac MRI analysis and can be used as an effective tool to help automatic LV segmentation accurate and efficient in clinical applications which will improve diagnosis of heart diseases from imaging points. © 2024 IEEE.

关键词： Deep learning

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