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International Conference on Advancements in Computational Sciences (ICACS)

作者： Rubab Mehdi Jawad Usman Arshed Department of Computer Scienc University of Baltistan Skardu Skardu Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Facial paralysis refers to the impairment or a condition where the facial muscles on one or both sides lose the ability to function, significantly affecting a person’s ability to perform essential functions such as talking, closing the eyes, swallowing, having food, and expressing emotions naturally through facial movements. The primary method for diagnosing facial paralysis is to observe the patient’s face for facial spatial information, such asymmetry. The clinical subjective assessments of therapists are still used to treat this condition. In the current era of technology, the automation for this activity is therefore highly significant and is the subject of much research in both academic and industry domains. In this paper we have used the traditional facial landmarks identification using machine learning approaches to test the accuracy of different machine learning algorithms. We used 11 selected key-points and regions from the facial image crucial for identifying facial paralysis. The detection process comprises of three stages in which the initial stage is the detection of the face, followed by the extraction of landmarks and Region of Interests (ROIs), the computing of the facial measure, and the categorization of facial paralysis. Three different data sets are used for the experiments. The experiments show that the Random Forest has the maximum accuracy of 92% as compared to the Decision Tree with 85%, Gaussian Naïve Bayes with 82% and Support Vector machine with 76% respectively.

关键词： Support vector machines Accuracy machine learning algorithms Static VAr compensators Feature extraction Paralysis Bayes methods Decision trees Faces Random forests

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using machine learning algorithms

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Patan Feridoz Khan C Sasikala Mavalluru Swathi M Prema Kumar Ajay Kumar Yadav Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of ECE Shri Vishnu Engineering College for Women (A) Bhimavaram Andhra Pradesh India Department of Computer Application United Institute of Management Prayagraj India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time.

关键词： Cloud computing machine learning algorithms Automation Accuracy Linear regression Energy efficiency Virtual machines Time factors Industrial Internet of Things Smart manufacturing

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Prediction of Land Use and Land Cover Using Google Earth Engine (GEE) and machine learning algorithms: A Review

Prediction of Land Use and Land Cover Using Google Earth Eng...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Promtucs Garnagee Jackson Paurav Goel Sartajvir Singh University Institute of Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Reliability is a critical requirement for classifiers that generate maps of land use and land cover (LULC), and these classifiers are highly sought after. Particularly, machine learning classifiers yield accurate classification outcomes even with complicated, high-dimensional data. Numerous parameters, including training sample size, quality, thematic correctness, classifier selection, research region size, and others, influence the classified maps’ accuracy. Understanding these aspects will help in attaining the highest possible classification accuracies for a particular requirement. Large-scale satellite picture and feature classification jobs become computationally demanding, giving rise to Big Data issues. Lately, cloud-based platforms that are free to use, like Google Earth Engine (GEE), have made environments for parallel processing available to handle picture categorization tasks. Both land use and land cover (LULC) changes are crucial for disciplines like monitoring natural disasters and assessing the effects of environmental change. Assessing the state and progress of relevant machine learning is the aim of this study to LULC by analyzing several publications published in the last ten years. Trend analysis of the quantity and yearly citations of published works, identification of top institutions, nations/regions, and publication sources, investigation of scientific partnerships between major institutions and nations/regions, and analysis of important research topics and their trends of development are the main areas of focus. Additionally, the paper clarifies how ML is used in LULC, offering insights that may lead future research paths to hasten advancements in this field. This review assesses various Geospatial machine learning algorithms. Among the algorithms are Naïve Bayes (NB), Random Forest (RF), Support Vector machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Gradient Boosting (GTB) as well as Extreme Gradien

关键词： Support vector machines Earth machine learning algorithms Accuracy Reviews Land surface Artificial neural networks Boosting Random forests Regression tree analysis

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Perceptual learning Technology Based on Computer Vision and machine learning algorithms

Perceptual Learning Technology Based on Computer Vision and ...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. The mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. This may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. The mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. The perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision Image segmentation machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

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Exploration of the Application of Big Data and machine learning algorithms in Electric Shock Prevention Warning

Exploration of the Application of Big Data and Machine Learn...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Kan Zhang Yong Li Yichen Cui Ke Zheng Peiyi Zhang Qian Yang Marketing Service Center of State Grid Henan Electric Power Company Zhengzhou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In view of the high incidence of electric shock accidents and the imperfect early warning system, big data analysis and machine learning algorithms are applied to precisely predict potential electric shock risks and give early warnings, thereby reducing the occurrence of electric shock accidents. First, various environmental, equipment, and personnel behavior data related to electric shock are collected and integrated, including indicators such as temperature and humidity, current fluctuations, and equipment usage frequency. Then, data preprocessing technology is used to clean the noise data to ensure data quality. Next, the support vector machine (SVM) machine learning algorithm is used to build a prediction model and train it through historical electric shock cases. Finally, the electric shock risk is predicted by inputting the model into the real-time monitoring data, and automatic warning is realized by combining the warning threshold. Studies have shown that the electric shock prevention warning system based on big data and machine learning has significantly improved in accuracy and response speed. Compared with traditional methods, the model can respond within 30 seconds after the electric shock event occurs, and the recall rate is close to 92%. The application of big data and machine learning algorithms in electric shock prevention warning has high practical value and potential. It not only improves the accuracy and real-time nature of warning, but also provides data support for preventive measures and reduces the risk of electric shock accidents.

关键词： Support vector machines machine learning algorithms Accuracy Electric shock Prevention and mitigation Big Data Predictive models Real-time systems Personnel Accidents

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Application of Evolutionary algorithms to Enhance the Efficiency of Neural Networks and machine learning algorithms

Application of Evolutionary Algorithms to Enhance the Effici...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Mekhriddin Nurmamatov Shokhrukh Sariyev Bunyod Eshonkulov Department of Artificial intelligence and Information systems Samarkand state university Samarkand Uzbekistan Head of the Department of Affairs of the Jizzakh branch of the National University of Uzbekistan named after M. Ulugbek Jizzakh Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This study examines approaches to hyperparameter optimization in machine learning and deep learning systems using genetic algorithms. Today, the success of neural networks and machine learning algorithms largely depends on the correct tuning of their hyper parameters. Traditional approaches, such as search networks and Bayesian optimization, can sometimes be inefficient for high-dimensional and complex models. This study analyzes the possibility of improving machine learning results by selecting hyper parameters based on the principles of natural evolution of genetic algorithms. The proposed methods are tested on practical problems related to neural networks, and the results of the research on performance indicators reveal opportunities for improving the accuracy and efficiency of predictions in machine learning and neural networks.

关键词： Industries Deep learning machine learning algorithms Accuracy Neural networks Metaheuristics Evolutionary computation Hyperparameter optimization Tuning Genetic algorithms

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Exploring machine learning algorithms for Early Diagnosis of Plant Disease

Exploring Machine Learning Algorithms for Early Diagnosis of...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Rev Ojha Saru Dhir Raghav Betty Paulraj Sumita Gupta Amity University Noida Uttar Pradesh

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Plant leaf diseases threaten agricultural productivity and food security, affecting global crop production. Traditional detection methods are often time-consuming and error-prone. This paper explores using machine learning to automate plant leaf disease detection, improving speed and accuracy. Paper elaborates the execution of a comprehensive database of plant leaf diseases, detailing symptoms, environmental conditions, and affected species, to train. The experimental results generated through the implementation were tested with the state-of-the-art methodologies and found to be over 90% accurate. The proposed method outperformed the existing methods. The performance measures like the accuracy and other evaluation metric proved the efficiency of the methodology presented with improved accuracy. The outcome of the paper served as a socio-beneficial so as the reformation of agricultural practices have been emerged out. The new revolution in the modern agricultural practices had emerged owing to the inclusion of machine learning (ML) and the Deep learning models in the early disease prediction in plant.

关键词： Support vector machines Productivity Measurement Plant diseases Accuracy machine learning algorithms Plants (biology) Food security Predictive models Nearest neighbor methods

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Comparative Analysis of Software Reliability Prediction and Optimization using machine learning algorithms

Comparative Analysis of Software Reliability Prediction and ...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Prashanth Kumar Bolisetty Midhunchakkaravarthy Faculty of AI AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia Faculty of AI Computing and Multimedia Lincoln University College Selangor Darul Eshan Malaysia

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Software reliability plays a crucial role in assessing the overall quality of software systems. Developing highly reliable software presents challenges, including ensuring usability, maintaining consistent performance, offering reliable service, and facilitating smooth maintenance. To overcome these limitations, researchers have turned to intelligent prediction and optimization algorithms. This paper presents a comprehensive review of software reliability prediction, emphasizing the application of machine learning techniques. It also provides an analytical evaluation of existing methods to identify areas for improvement and future research opportunities in software reliability prediction.

关键词： machine learning algorithms Reviews Software algorithms machine learning Software systems Prediction algorithms Software reliability Maintenance Usability Optimization

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Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms

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Emerging Trends in Drugs, Addictions, and Health 2025年 5卷 100165-100165页

作者： Chandrashekarappa, Shivashankar Basapura Assi, Sulaf Jayabalan, Manoj Al-Hamid, Abdullah Al-Jumeily, Dhiya Oracle India Pvt. Ltd. Bangalore India Computer Sciences and Mathematics Liverpool John Moores University Liverpool United Kingdom Pharmacy and Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Department of Pharmacy Practice College of Clinical Pharmacy King Faisal University Al-Ahsa Saudi Arabia

Background and aims: The opioid pandemic has contributed to deaths globally, and prescription opioids have played a crucial role in these deaths. Addressing overdose requires understanding the reasons behind prescription, especially in cases of chronic diseases. Several factors play a role in the increased prescription of opioids, relating to patients’ lifestyle, characteristics, and disease. As these factors are complex in nature, understanding them requires machine learning approach. This study explored overprescribing opioids among nephrologists in the US using unsupervised machine learning algorithms. Design: Two types of unsupervised clustering were applied to the Medicare Provider Utilisation and Payment Data Part-D Prescriber Summary. Setting: The dataset had 50,134 records with 85 features relating to opioids prescription per US state. Univariate and bivariate analysis were applied first to gain understanding of the data followed by K-mean clustering and Gaussian Mixture Models. Findings: Unsupervised clustering showed that prescription issued to males were three times higher than those issued to females. Moreover, male nephrologists were higher prescribers than female nephrologists, and a third of male nephrologists were high prescribers of opioids. The highest rates of prescriptions were seen in California. Conclusions: Unsupervised machine learning algorithms enabled understanding of high opioid prescription across gender and US state by analysing multiple features. Both K-mean clustering and Gaussian Mixture Models achieved the same outcomes. Future work will benefit from applying deep learning in order to understand in-depth patterns in prescription and contributing factors related to over-prescribing. © 2024 The Authors

关键词： Epidemic Gaussian mixture models K-mean clustering machine learning algorithms Nephrologists Opioids

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Prediction of the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids using different machine learning algorithms

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Graish, Mohammed Shorbaz Ali, Ali B.M. Al-Zahiwat, Murtadha M. Alardhi, Saja Mohsen Baghoolizadeh, Mohammadreza Salahshour, Soheil Pirmoradian, Mostafa Department of Chemical Engineering University of Technology- Iraq Baghdad 10066 Iraq Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Chemical Engineering College of Engineering University of Misan Amarah Iraq Nanotechnology and Advanced Materials Research Center University of Technology – Iraq Baghdad Iraq Department of Mechanical Engineering Shahrekord University Shahrekord 88186-34141 Iran Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Department of Mechanical Engineering Khomeinishahr branch Islamic Azad University Khomeinishahr Iran

Viscosity is a crucial parameter for heat transfer systems, governing pumping power, Rayleigh number, and Reynolds number;thus, viscosity prediction for hybrid nanofluids is important. Although some studies have employed ML algorithms for predicting viscosity, limited ML algorithms or specific nanofluid types were examined in previous studies, disregarding the complexities involved in the rheological behavior of a complex nanofluid system such as non-Newtonian hybrid nanofluids. To overcome this limitation, this study offers a practical contribution by utilizing 20 different machine-learning models to predict the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids. The influences of the input variables: solid volume fraction (SVF), temperature, and shear rate on viscosity prediction are systematically assessed. We evaluate the prediction accuracy and reliability of algorithms using ten performance metrics including RMSE, MAE, R² and NSE. Multivariate Polynomial Regression (MPR) outperforms the other algorithms, which is evident in the highest correlation coefficient (R² = 0.992) and lowest error metrics. At the other end, is the Extreme learning machine (ELM), which turns out to be the worst performer. A unique contribution of this paper is that we extract a mathematical equation from the MPR model that allows for straightforward calculation of viscosity, avoiding non-trivial ML computations. This simplicity aids in practical applications and increases usefulness for engineers and researchers alike. Using advanced data visualization techniques (heatmaps, box plots, KDE plots and Taylor diagrams), the relationships between input variables and viscosity as well as the model performance are explored. These results give a better understanding of the non-Newtonian hybrid nanofluid behavior and a solid predictor of design-efficient heat transfer systems. © 2025 The Authors

关键词： machine learning algorithms Non-Newtonian hybrid nano- antifreeze Viscosity

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