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Occupancy Estimation in Smart Buildings: Impact of data Quality on Feature Selection 23

Occupancy Estimation in Smart Buildings: Impact of Data Qual...

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Proceedings of the 2023 6th international conference on Computational Intelligence and Intelligent Systems

作者： Manar Amayri Yogesh Pawar Concordia University Canada `concordia University Canada

ISBN: (纸本)9798400709067

Feature selection has been widely applied in machine learning applications to reduce computational time, improve learning accuracy, and better understand the data modeling process. One of the vital challenges is the correct selection of the relevant features from the available ones in the training dataset. A training dataset of poor quality may compromise the features selection step, which will decrease the generalization capability of the resulting model. Defining the essential features based on data from sensors in smart building applications can significantly reduce solution complexity and total cost. In this paper, an indicator named Qscore is proposed to assess the quality of the training data as an essential step before deploying the feature selection methods. Moreover, a high Qscore value would confirm the reliability of the selected features. To validate the proposed novel concept, the occupancy estimation problem in an office contest is investigated. the training data consist of the measurements collected from standard sensors, for instance, motion detection, power consumption, and CO2 concentration, and the label (i.e., the number of occupants). Several features selection methods along with the proposed data quality indicator and cross-validation approach are deployed to assure the best choice of features in the occupancy estimation problem. Extensive simulations and experiments show the merits of our framework.

关键词： Smart buildings data quality feature selection. occupancy estimation

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An Application of Co-plot Analysis: A Multidimensional Scaling data Visualization 23

An Application of Co-plot Analysis: A Multidimensional Scali...

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Proceedings of the 2023 6th international conference on machine learning and Natural Language Processing

作者： Priyanka Poosapati Yao Shi Judith Gebauer Yang Song Department of Computer Science University of North Carolina Wilmington USA Congdon School of Supply Chain Business Analytics Information Systems University of North Carolina Wilmington USA

ISBN: (纸本)9798400709241

Exploratory graphs are a crucial element of statistical analysis and models that help us identify and interpret data patterns. As one example of exploratory graphs, multidimensional scaling (MDS) allows the visualization of large and complex datasets in a reduced dimensional space, while preserving pairwise differences and similarities between data points. Co-plot analysis extends the capabilities of traditional MDS and enables a multifaceted view of relationships between variables from different datasets. To develop a co-plot graph, the analyst starts out by standardizing the dataset and by generating a special coefficient (MADCC – median absolute deviation correlation coefficient) that subjects the dataset to MDS-embedding and helps to determine how the dataset fits the surface. In the current paper, we demonstrate how co-plot analysis can be applied to describe and compare the behavioral patterns of ecological communities and species across multiple sites and environmental contexts. We demonstrate the use of non-metric multidimensional scaling (NMDS) and robust multidimensional scaling (RMDS) techniques to visualize dissimilarities and distances of the observations in a reduced dimensional space. We also explain the use of Shepard graphs as a second technique to visualize categorical variables in a reduced-dimensional space, with points closer together indicating higher similarity. Shepard graphs also allow us to assess the goodness of fit, which is indicated by the strength of a positive linear relationship. For our application, the co-plots graphs are useful to visualize a complete set of co-relations between the species of different communities. It also allows us to plot how every factor affects the number of species against the mean and against the median and to determine the best approach: A co-plot graph against the mean may be influenced by the shape of the distribution, especially if the data is skewed, whereas a co-plot graph against the median is typica

关键词： Co-plot data visualization Multidimensional scaling (MDS)

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Optimal Deep Transfer learning-Based Road Extraction for Intelligent Transportation Systems Using High-Resolution Remote Sensing Imagery

Optimal Deep Transfer Learning-Based Road Extraction for Int...

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international conference on Engineering Technology and their Applications (IICETA)

作者： Hafidh I. Al Sadi Hawraa Ali Sabah Waleed Hadi Madhloom Kurdi Nahlah M.A.M. Najm Waleed Khalid Alazzai Laith H. Alzubaidi College of MLT Ahl Al Bayt University karbala Iraq National University of Science and Technology Dhi Qar Iraq Altoosi University College Najaf Iraq Computer Technology Engineering Department Al Rafidain University College Bagdad Iraq Medical Technical College Al-Farahidi University Baghdad Iraq The Islamic university Najaf Iraq

Remote sensing (both satellite and terrestrial) is deemed a suitable technique for collecting data on a large scale effectively and accurately that satisfies the demand for intelligent transportation systems (ITS). In recent times, the research works relevant to the use of remote sensing (RS) technologies towards their execution in transportation systems had raised immensely. Road network extraction will be one of the important projects for intelligent transportation systems, real-time updating road networks, and disaster emergency responses. Road extraction related to high-resolution remote sensing images (RSI) was becoming a hot research topic. At present, many studies are relevant to conventional machine learning methods that can be complex and computational due to impervious surfaces namely buildings and roads that are visible in the images. With this motivation, this study progresses a salp swarm optimization with Deep Transfer learning based Road Extraction (SSODTL-RE) technique for ITS using high-resolution RSIs. the presented SSODTL-RE technique aims to extract the rod utilizing high-resolution RSIs. Further, to generate a collection of deep features from the RS images, the SSODTL-RE approach employs a capsule network (CapsNet). Moreover, the SSODTL-RE technique applies an adaptive neuro-fuzzy inference system (ANFIS) model for the integration of broken roads and improvises efficiency because of road connectivity. To improve the efficacy of the CapsNet model, the SSO is applied for the process of hyperparameter tuning. the stimulation analysis of the SSODTL-RE system was tested on two benchmark datasets. the experimental results stated the betterment of the SSODTL-RE technique over other DL approaches.

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Improved Deep learning with Metaheuristics Driven COVID-19 Diagnosis on Chest X-Ray Images

Improved Deep Learning with Metaheuristics Driven COVID-19 D...

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international conference on Engineering Technology and their Applications (IICETA)

作者： Mahdi Abdulkhudur Alkhafaij A. Sahib Oleiwi Rabei Raad Ali Eyhab Ali Mustafa Al-Tahee Muntather Almusawi College of MLT Ahl Al Bayt University Karbala Iraq Altoosi University College Najaf Iraq National University of Science and Technology Dhi Qar Iraq Al-Zahraa University for Women Karbala Iraq Medical Technical College Al-Farahidi University Baghdad Iraq The Islamic university Najaf Iraq

Recently, Artificial Intelligence (AI) has undergone significant changes in part of medical image processing. Earlier diagnosis using chest X-ray (CXR) images proved that a key solution in fighting COVID19. Several computer-aided diagnostic (CAD) techniques have been used for assisting radiologists and provide a secondary recommendation for the same. CXR has become the first screening method to play a considerable role in the analysis of COVID19 contamination. Formerly, various traditional machine learning (ML) and deep learning (DL) systems were utilized for automatic diagnosis of chest radiography X-ray images. One method widely applied with DL is transfer learning (TL) which re-uses the data learned from trained modules like resolving one problem and applying it to the same problem. this study presents an Improved Deep learning with Metaheuristics based disease recognition and classification (IDLM-D2C) for COVID19 on CXR images. the presented IDLM-D2C approach concentrates on the precise detection and classification of COVID19 on CXR images. Initially, the IDLM-D2C algorithm implements the Weiner filter (WF) as a noise elimination step. In addition, the IDLM-D2C method exploits seagull optimization algorithm (SGO) with an improved Faster SqueezeNet model for feature extraction. For COVID19 detection, deep learning modified neural network (DLMNN) model was used with chimp optimization algorithm as hyperparameter optimizer. the experimental validation of the IDLM-D2C method is tested on benchmark medical image dataset. the comparison analysis highlighted the improvements of the IDLM-D2C method over other approaches.

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Transfer learning for Pneumonia Classification from DICOM Medical Imaging data 6th

Transfer Learning for Pneumonia Classification from DICOM Me...

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Mohan, P. Pattabhi Rama Ramesh, M.S.V.V. Ganiya, Rajendra Kumar Lakshmanarao, A. Department of IT Aditya Engineering College Surampalem India Pragati Engineering College AP Surampalem India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur India

ISBN: (纸本)9783031840616

the X - ray images examinations is frequently used to identify a number of disorders, including pneumonia. Pneumonia is one of the most frequent infectious disease, producing huge morbidity and fatality rates. Roughly 15% of all child deaths occur because of pneumonia. this disease can be discovered by taking an X-ray scan of the chest of a patient. But, evaluating such X-ray scans directly demands a lot of time for processing. To overcome this difficulty, we suggested an algorithmic strategy for detecting sick areas in X-ray images to aid doctors in detecting pneumonia. the X-ray images of the chest are an effective component in the treatment of pneumonia, and many clinician decisions are based on it. As a result of their superiority in providing the finest solutions, healthcare systems are increasingly incorporating machine learning and deep learning into their operations. In this study, we proposed a Deep learning approach for pneumonia detection. As deep learning has the power to address complex issues, we choose deep learning-oriented pneumonia diagnosis. We employed a dataset from Kaggle and applied various transfer learning techniques resnet, efficient net, and VGG19 for pneumonia detection with X-ray chest pictures. PyTorch, a library for Python, is used to implement the proposed approach. the results suggest that our proposed approach exhibited a decent accuracy rate for pneumonia detections. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Improving the Estimation of Coronary Artery Disease by Classification machine learning Algorithm

Improving the Estimation of Coronary Artery Disease by Class...

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international IEEE conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Hamzeh Ghorbani Alla Krasnikova Parvin Ghorbani Simin Ghorbani Harutyun S. Hovhannisyan Arsen Minasyan Natali Minasian Mehdi Ahmadi Alvar Harutyun Stepanyan Mohammazreza Azodinia Faculty of General Medicine University of Traditional Medicine of Armenia (UTMA) Yerevan Armenia Department of Cardiology Faculty of Medicine Yerevan State medical university Yerevan Armenia Department of Cardiology Faculty of Medicine Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Department of Nursing and Midwifery Faculty of Nursing Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Department of Internal Disease Propaedeutics Yerevan State Medical University Yerevan Armenia Faculty of General Medicine Yaroslavl State Medical University Yaroslavl Russia Department of Computer Engineering Faculty of Engineering Shahid Chamran University Ahwaz Iran Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary

this research paper aims to predict coronary artery disease (CAD) using data from 350 patients collected at one of the hospitals in Armenia. CAD is a critical parameter which can have a significant impact on patients' life and survival. the study considers several input variables, including level of cholesterol (LOC), patient's age (PA), type of chest pain (TCP), number of arteries blocked (NAB), sex (S), and family history (FH), to make accurate predictions. To achieve this crucial task of CAD prediction, the researchers employed three powerful classification algorithms: Random Forest (RF), Support Vector machine (SVM), and Logistic Regression (LR). Among these, the Random Forest algorithm stands out for its robustness and numerous advantages, including high accuracy, ability to handle outliers effectively, provision of feature importance insights, and reduced risk of overfitting. the research findings presented in this article demonstrate the impressive performance of the Random Forest algorithm, showcasing an accuracy value of 0.95 and a precision value of 0.94. these results indicate the model's ability to make precise and reliable predictions, essential when dealing with a life-or-death parameter like CAD. By conducting a comparative analysis based on statistical parameters, the researchers establish that Random Forest outperforms both SVM and LR. thus, the conclusion drawn from the study suggests that the ranking of the algorithms based on their performance is as follows: RF > SVM > LR.

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A transferable model for pedestrian intention prediction in crosswalks based on mobile phone sensors 22

A transferable model for pedestrian intention prediction in ...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer Engineering

作者： Xinyu Wang Xikai Wang Sijing Guo School of Automotive Engineering Wuhan University of Technology China

ISBN: (纸本)9781450397148

To accommodate the path planning of autonomous vehicle (AV), many researchers utilize on-board sensors to predict pedestrians’ intention, but the on-board sensors cannot detect occluded pedestrians. Roadside sensors can solve this problem, but with a high cost if deployed at every crosswalk. Hence, this paper aims at utilizing mobile phone sensors to predict the pedestrians’ intentions at crosswalks. A transferable prediction model is built in the paper, with a purpose of saving the expense of hardware and data collection. that is, the model trained by the pedestrians’ data at one crosswalk can provide high accuracy at other crosswalks, via extracting the features associating the geometric structure of the crosswalks and the pedestrians’ motion data. Various algorithms were applied to build the prediction model, and results showed that the prediction accuracy exceeds 90% in non-transfer task, 80% in normal transfer tasks, with a distance of 5 meters ahead of crossing.

关键词： Mobile phone Pedestrian protection machine learning Pattern recognition V2X Pedestrian intention prediction

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machine learning performance comparison of CSI 300 stock price movement prediction after disclosure of corporate annual reports of year 2020 22

Machine learning performance comparison of CSI 300 stock pri...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer Engineering

作者： Fengyu Han Yue Wang School of Information Central University of Finance and Economics China

ISBN: (纸本)9781450397148

In the current stock market, machine learning based investment robots are widely applied to predict stock price movement. this work studies predicting the stock price movement on the next day just after the release of the annual reports of enterprises, which is different from the scenarios of related work. We use five kinds of machine learning methods, including neural network, decision tree, random forest, logistic regression, prototypical network (a few-shot learning method). Different financial indicators (core and extended) are used in the experiments of this work. We obtain these financial data from the EastMoney website, and we get the results indicating that the machine learning models we use do not behave well to predict companies' price trend. Besides, we also filtrate those stocks which have ROE above 0.15 and net profit cash ratio above 0.9 in hope of improving prediction under this good financial criterion. We conclude that the stock price tendency's predictability on the next day just after the release is weak, and we get the highest accuracy about only 59.6% and the highest precision about only 56% using the random forest classifier which is the best, but the corresponding recall is low (only 13%), and the filtering does not help to enhance the performance very much. Among the models we use, random forest is the best classifier and prototypical network performs better than MLP.

关键词： neural network logistic regression stock price prediction random forest machine learning decision tree few-shot learning prototypical networks

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A Hybrid machine learning Approach for Performing Predictive analytics on Road Accidents

A Hybrid Machine Learning Approach for Performing Predictive...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： M. Sobhana Vemulapalli Krishna Rohith Tellakula Avinash N. Malathi Department of CSE VR Siddhartha Engineering College(A) Vijayawada India Department of CIVIL ENGINEERING VR Siddhartha Engineering College(A) Vijayawada India

ISBN: (纸本)9781665457002

Nineteen thousand accidents occurred on the roads of the Indian state of Andhra Pradesh in the year 2020. Every year, traffic inconsistencies are a major cause of fatalities, injuries, and property damage. Road and traffic accidents are uncertain, unpredictable events, and understanding the elements that influence them is necessary for their prevention. Age, gender, location, time, climate, and many more factors are among those linked to traffic accidents. Several variables, most of which are discrete, define road accidents. the proposed framework applies statistical modeling and machine learning to the accident dataset containing details of 18,251 road accidents that took place in the Vijayawada region over the period 2013 to 2022, to find the hidden patterns particular to the severity of accidents. these patterns will exhibit common features of accidents that took place at a black spot. the obtained insights would help reduce further accidents. the varied nature of accident data analysis is the main challenge. As a result, heterogeneity must be considered while analyzing the data, or any relationship between the data may be obscured. the whole accident data is divided into zones and the proposed methodology is applied to each zone individually. To overcome the heterogeneity of data, gaussian mixture modeling is used to perform soft clustering. then making use of results, like the means and variances obtained from each gaussian, a training dataset is created, and accidents are classified based on the severity of consequence using a support vector classifier, decision tree classifier, random forest, K-Nearst Neighbours classifier, and naive bayes classifier. Later accuracies of all algorithms are compared to find the best suitable model for a zone. the proposed framework makes use of python language libraries like pandas, sklearn, matplotlib, and joblib.

关键词： Training Analytical models Road accidents Computational modeling Roads Support vector machine classification Predictive models

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A Cloud-based Distributed Computing Approach for Extracting Molecular Descriptors 22

A Cloud-based Distributed Computing Approach for Extracting ...

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Proceedings of the 6th international conference on Algorithms, Computing and Systems

作者： Christos Didachos Dionisis Panagiotis Kintos Manolis Fousteris Vassilis C. Gerogiannis Le Hoang Son Andreas Kanavos Department of Computer Engineering & Informatics University of Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Greece Department of Digital Systems University of Thessaly Greece VNU Information Technology Institute Vietnam National University Vietnam Department of Informatics Ionian University Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules’ chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab" approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame.

关键词： Coiled Computing Performance Computational Drug Design Dask Molecular Descriptors Chemical Big data

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