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Application of XGBoost algorithm and grid search hyperparameter tuning to study health effects among individuals in the industrial area

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Multimedia Tools and applications 2025年 1-44页

作者： Johnson, Susymary Perumalsamy, Deepalakshmi Department of Computer Applications Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil626126 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil626126 India

This research focuses on utilizing data-driven machine learning techniques to study the impact of air pollution on the health of residents in industrial regions. The objective is to develop an efficient machine learning model that can accurately identify communities with a higher prevalence of health issues and diseases. A population-based survey was conducted in four geographical areas, collecting data on sociodemographic characteristics, health effects, perceived health status, and annoyance due to industries. The proposed model, XGBoost_BLR_GridCV, achieved high accuracy (ROC_AUC: 0.9215), precision of 92%, recall of 91% and f1-score of 91% in predicting the proximity of residents with chronic diseases. The results highlight the significant association between living in high proximity to industrial regions and increased rates of asthma, respiratory illness, and heart-related health problems. Comparative analysis with other tree-based models further demonstrates the effectiveness of the proposed model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Forestry

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Colossal pattern extraction using optimised length constraints based on differential evolutionary arithmetic optimisation algorithm

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International Journal of Information and Communication Technology 2024年第3期24卷 289-304页

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjunarao Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Computer Science and Engineering Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Computer Applications Annamacharya Institute of Technology and Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be used for large-scale mining with interruptions. In this paper, the problem of mining the colossal patterns (CPs) is solved by using optimised length constraints (LCs). First, we describe the minimum LC and maximum LC problems and connect them to the optimal LC by identifying the optimal threshold values. Here, the differential evolutionary arithmetic optimisation algorithm (DAOA) is used to find the optimal threshold values of the constraints and extract the colossal patterns. The effectiveness of the proposed algorithm is proven by various experiments using different biological datasets. © 2024 Inderscience Enterprises Ltd.

关键词： Database systems

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Classifying Chest X-rays for COVID-19 using Deep Learning

Classifying Chest X-rays for COVID-19 using Deep Learning

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Sreeja, S.P. Asha, V. Saju, Binju Punyashree, K.C. Manasa, P. Vishwanath, C.R. New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9781665462631

A developing virus called COVID-19 infects the lungs and upper layer respiratory system. Medical imaging and PCR assays can be used to identify COVID-19. Medical images are used to identify COVID-19 diseases in the proposed classification model, which works well. A crucial step in the battle against this fatal illness may turn out to be an efficient screening and diagnostic phase in treating infected sufferers. Chest X-ray (CXR) scans could be used to do this. The utilization of chest X-ray imaging for early detection may prove to be a crucial strategy in the fight against COVID-19. Many computer- aided diagnostic (CAD) methods have been developed to help radiologists and provide them with more information for the same. In a training network with many classes, tertiary classification starts to become more accurate as the number of classes increases. © 2023 IEEE.

关键词： COVID-19

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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A composite manifold learning approach with traditional methods for gradient-based and patch-based adversarial attack detection

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Multimedia Tools and applications 2024年第39期83卷 87019-87044页

作者： Agrawal, Khushabu Bhatnagar, Charul Department of Computer engineering & amp Applications GLA University 7km Stone NH-2 Mathura-Delhi Road Uttar Pradesh Mathura281406 India

Face recognition models that utilize deep learning techniques can be easily targeted by adversarial attacks. In order to detect these attacks, the majority of detection methods focus on enhancing the resilience of recognition models against adversarial perturbations. Nevertheless, these methods have limited capabilities in terms of generalization. Consequently, they remain susceptible to adversarial attacks that have not been previously encountered. On the other hand, deep learning models exhibit considerable robustness against typical perturbations. In the paper, we have proposed a composite manifold learning approach with traditional techniques(CMLAT) detector to detect gradient-based and patch-based adversarial attack detection. The geometrical representation differs from the original input images, thus, we introduce a feature-based embedding that relies on measuring geodesic distances using the isomap manifold learning. This embedding enables us to encode the dissimilarities between adversarial and genuine inputs efficiently. Our training involves a support vector machine classifier that examines the sequence of deep features represented in a distance-based space to identify and classify adversarial images. We have achieved a significant milestone by developing an adversarial face detection system that exclusively utilizes original faces and their adversarial faces and can effectively handle previously unseen attack techniques. The experiment has been conducted on the Labeled Faces in the Wild (LFW) dataset with four gradient-based and patch-based attacks. This experiment confirms that the CMLAT detector is effective and generalizes against various adversarial attacks that were not previously encountered. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Face recognition

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Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques

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Multimedia Tools and applications 2024年第16期84卷 1-32页

作者： Dalal, Surjeet Lilhore, Umesh Kumar Faujdar, Neetu Simaiya, Sarita Agrawal, Akshat Rani, Uma Mohan, Anand Department of Computer Science and Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science & amp Engineering Amity University Haryana Gurugram India Department of Computer Science and Engineering World College of Technology & ampManagement Haryana Gurugram India Department of Physics Kunwar Singh College Bihar Darbhanga India

The thyroid gland, a pivotal regulator of essential physiological functions, orchestrates the production and release of thyroid hormones, playing a vital role in metabolism, growth, development, and overall bodily functions. While thyroid nodules are predominantly benign, certain cases may manifest malignancy, leading to thyroid cancer. Despite the generally favorable prognosis, early detection and intervention remain paramount for optimal outcomes. Given the limitations in clinical data, this paper introduces an innovative approach employing a modified XGBoost model that integrates both clinical and molecular properties into a unified predictive model. Utilizing fine-needle aspiration biopsies and gene expression analysis, our model aims to enhance cancer prediction in thyroid nodules. We explore the influence of incorporating high-quality clinical data on the prioritized gene set, employing a nature-inspired method for hyperparameter optimization. The modified XGBoost model emerges as highly recommended for non-parametric feature selection, extraction, and prediction due to its remarkable accuracy. Our proposed model attains an impressive 94.6% accuracy, surpassing other machine learning models such as C5.0 (92.5%), CART (88.7%), CHAID (83.4%), Quest (81.5%), LSVM (77%), and random tree (24.1%). The real-time efficiency of our model is demonstrated in the results section. Experiments showcase the superior performance of combined molecular and clinical data-based prediction models. Emphasis is placed on the importance of scrutinizing the origins of clinical data and maintaining vigilance over variable quality. This study signifies a substantial advancement in thyroid cancer prediction, offering a robust and accurate model that integrates diverse data sources, ultimately contributing to improved patient outcomes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Intelligent Analysis and Identification of Palm Gestures using ML Algorithms

Intelligent Analysis and Identification of Palm Gestures usi...

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2024 International Conference on computer, Electrical and Communication engineering, ICCECE 2024

作者： Roy, Debraj Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar De, Utpal Chandra Tripathy, Abinash School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications Kiit Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350386479

The area of human-computer interaction (HCI) has made significant strides in recent years, thanks in large part to the development of innovative technologies that aim to improve the way in which people and machines work together. One of these areas is the use of hand gestures to control gadgets and apps, which has the potential to create a form of interaction that is more intuitive and natural. The introduction of a palm gesture detection system that is both dependable and effective has the potential to completely alter the ways in which we interact with various electronic gadgets and software programmes. The objective is to develop a palm gesture recognition system that is capable of precise detection and classification of hand gestures in real time. The computer vision methods is applied to collect and interpret the hand movements. Following this, machine learning algorithms will be used to train the system on a huge dataset consisting of hand gestures. The system will be able to identify the hand gestures in real time and will categorise them according to predetermined categories. The experimental results are compared against other gesture recognition systems to study the accuracy of the system. © 2024 IEEE.

关键词： Support vector machines

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Integrative Remote Sensing Approaches Using Generative Adversarial Networks for Urban Heat Island Analysis and Mitigation

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 681-698页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Venkateshwarulu, Balaji Vijayan Kumar, Thangavel Pradeesh Rajamannar, Kesavamoorthy Tripathi, Rajesh Kumar Department of Computer Applications Sindhi College Chennai India Department of Information Science and Engineering HKBK College of Engineering VTU Belagum Bengaluru Nagawara India VIT School of Agricultural Innovations and Advanced Learning (VAIAL) VIT Vellore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India

The phenomenon of urban heat islands (UHI) presents a critical challenge for urban sustainability, exacerbating local temperatures, increasing energy demands, and impairing public health. Traditional methods for addressing UHI are often resource-intensive and slow. This study introduces a novel approach, utilizing a Hybrid CycleGAN-SVM (HCGS) model that leverages the synergy of Generative Adversarial Networks (GANs) and Support Vector Machines (SVMs) to efficiently analyze and mitigate UHI effects through high-resolution satellite imagery and temperature data. The model incorporates Enhanced Vision Transformers (EViTs) for superior feature extraction, adept at capturing intricate spatial and spectral urban patterns. The CycleGAN component of the model generates high-quality synthetic imagery, enhancing the dataset and addressing class imbalances, thereby bolstering the SVM classifier’s ability to precisely pinpoint heat-prone urban areas. Implemented in Google Colab, the HCGS model demonstrated exceptional performance, achieving a classification accuracy of 0.98. This indicates its potential as an effective tool for urban heat mitigation, offering actionable insights for urban planning and policy-making. By integrating advanced machine learning techniques with remote sensing data, the HCGS model paves the way for innovative climate adaptation strategies, fostering more sustainable and resilient urban environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Enhanced Vision Transformers EViTs GANs Generative Adversarial Networks HCGS model Hybrid CycleGAN-SVM Satellite imagery Support Vector Machines SVM UHI effect Urban heat island

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Low-Cost Weather Monitoring System with Intelligent Design and XGBoost Regression

Low-Cost Weather Monitoring System with Intelligent Design a...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Vishnu Vilashini, S. Kishore, S. Nair, Rekha. R. Babu, Tina Bhadani, Anant Thanushree, G.R. Dixit, Anuj Kumar Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Karnataka India Allaince University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Weather monitoring is a critical aspect of weather forecasting and analysis, providing essential data for decision-making in various sectors. However, traditional weather monitoring systems face limitations such as high costs, limited accuracy, and inadequate coverage. The proposed system addresses these challenges by utilizing the Internet of Things (IoT) technology and NodeMCU, a low-cost microcontroller. The novelty of this work lies in the implementation of a cost-effective weather monitoring system that accurately forecasts upcoming weather conditions. Notably, the XGBoost Regression model emerges as the top performer, exhibiting exceptional accuracy with a mean absolute error (MAE) of 0.0213 indicating high accuracy in weather prediction. This innovative approach not only provides an affordable solution but also demonstrates the system's advanced capability to predict future weather patterns. Utilizing a NodeMCU board, real-time sensor data is gathered and transmitted to ThingSpeak's cloud server for analysis. This methodology has the potential for future applications in industries such as agriculture, transportation, and disaster management. © 2023 IEEE.

关键词： NodeMCU Real-time monitoring Recursive feature elimination ThingSpeak Weather prediction XGBoost Regression

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A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images

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Multimedia Tools and applications 2024年第37期83卷 85421-85481页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Iqbal, Md. Department of Computer Engineering and Applications GLA University Mathura India School of Computing Science and Engineering Galgotias University Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t

关键词： Image classification

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