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A Security Aware Lighting Control System for Public Open Spaces: An IoT Powered Approach

A Security Aware Lighting Control System for Public Open Spa...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Firdhous, Mohamed Fazil Mohamed Elbreiki, Walid Abdullahi, Ibrahim Briki, Wesam Arlimatti, Shivaleela University of Moratuwa Dept. of Information Technology Moratuwa Sri Lanka College of Computer Technology Dept. of Computer Science Benghazi Libya Ibrahim Badamasi Babangida University Dept. of Computer Science Niger State Lapai Nigeria Higher Institute of Engineering Technologies Dept. of Computer Science Benghazi Libya Dr. D y Patil Prathisthan's College of Engineering Dept. of Computer Science and Engineering Maharashtra Kolhapur India

ISBN: (纸本)9798350313796

Cities world over are becoming more congested as people moving into them for better living conditions. Such fast migration towards cities puts heavy demands on city resources. One of the resources that suffers an impact almost immediately is the open spaces in cities. Open spaces are the public spaces that are accessed by people freely for different purposes. Open spaces help citizens maintain a healthy life by relieving stresses. The open spaces visited by people at night must provide adequate lighting for them to engage in their activities. It is seen that many open spaces waste energy as the lights have been on throughout the nights irrespective of usage of the space. In this paper, the authors propose a security aware lighting control system that helps minimize the energy waste without compromising on security. A prototype implementation was tested and the results were positive promising real field implementations. © 2023 IEEE.

关键词： Control systems

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Detection of Cataracts from Retinal Fundus Image Using Ensemble Machine Learning Approach

Detection of Cataracts from Retinal Fundus Image Using Ensem...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Rajshahi University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350316001

Cataract is a common eye condition that causes clouding of the eye's natural lens, resulting in blurry vision and it is very common in older ages people. However, it can also occur in younger aged people due to various genetical effects, diabetes, smoking, and prolonged exposure to sunlight. Cataracts can be treated through surgery to remove the clouded lens and replaced by an artificial one, which can significantly improve the vision. This problem can be reduced through early detection, regular eye examinations etc. In this research, a Contrast Limited Histogram Equalization (CLAHE) of retinal fundus images is applied for a better presentation of the above cataract effects. These improved images are fed to a machine learning-based model for effective detection. Finally, the ensembling of the proposed machine learning-based classifiers is performed for the accurate detection of cataracts. The proposed model was tested experimentally on a real dataset and achieves excellent performance with accuracy, precision, and recall scores of 99.67%, 100%, and 99.25%, respectively which outperform the baseline methods. The robustness of the proposed ensemble model is evaluated through 5-fold cross-validation and achieved an average accuracy of 99.6%. The implementation code can be accessed from the GitHub repository: https://***/nahid-tech/cataract-detection. © 2023 IEEE.

关键词： Image enhancement

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Empathy-Aware Behavior Trees for Social Care Decision Systems

Empathy-Aware Behavior Trees for Social Care Decision System...

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2023 International Conference on ENTERprise information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care information Systems and Technologies, HCist 2023

作者： Dell'Acqua, Pierangelo Costantini, Stefania Dyoub, Abeer Gasperis, Giovanni De Monaldini, Andrea Rafanelli, Andrea Dept. of Science and Technology Linkoping University Norrkoping60351 Sweden Dept. of Information Science and Engineering and Mathematics University of Aquila L'Aquila67100 Italy Dept. of Computer Science University of Pisa Pisa56100 Italy

There is growing attention on the importance of building intelligent systems where humans and Artificial Intelligence-based systems (AIs) form teams exploiting the potentially synergistic relationships between humans and automation. In the last decade, the computational modeling of empathy has gained increasing attention. Empowering interactive agents with empathic capabilities leads, on the human's side, to more trust, increases engagement, and thus interaction length, helps cope with stress. These findings suggest that agents endowed with empathy may enhance social interaction in educational applications, artificial companions, medical assistants, and gaming applications. This article focuses on modeling the empathic behavior of virtual agents interacting with humans. We propose a formal model that enables virtual agents to exhibit empathic, emotional behavior. Specifically, we extend the modeling of empathy via behavior trees with a new type of node allowing the specification of various kinds of empathy. Using the proposed extension, we show how different agents' reactive behavior can be modeled. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Intelligent systems

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Customer Segmentation Using Hierarchical Clustering 9

Customer Segmentation Using Hierarchical Clustering

引用

9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Afzal, Areeba Khan, Laiba Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan Information Technology University Lahore Department of Computer Engineering Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (纸本)9798350394474

In the dynamic landscape of retail, understanding customer behavior is paramount for businesses seeking to optimize marketing strategies and enhance the shopping experience. This study explores the utilization of hierarchical clustering techniques for mall customer segmentation, with a focus on the paper titled 'MALL CUSTOMER SEGMENTATION USING MACHINE LEARNING' as the benchmark. Our dataset encompasses a diverse range of mall customers, spanning demographics and behavioral attributes. Hierarchical clustering systematically groups customers into clusters, revealing distinct segments within the mall's customer base. A comprehensive analysis of these clusters unveils profound insights into customer tendencies, preferences, and purchasing habits. These insights form a solid foundation for tailored marketing campaigns, personalized recommendations, and resource allocation within the mall. The study contributes significantly to customer analytics, providing retailers with a powerful tool to gain a competitive edge in the retail sector. By leveraging hierarchical clustering for mall customer segmentation, businesses can enhance customer satisfaction, drive sales, and foster lasting customer relationships. This paper underscores the importance of data-driven methodologies in understanding customer behavior and offers a practical framework for businesses to harness the potential of hierarchical clustering for strategic decision-making in the retail industry. © 2024 IEEE.

关键词： Consumer behavior

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Data driven approach for high-power laser ablation phenomena 10

Data driven approach for high-power laser ablation phenomena

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10th Euro-Asian Pulsed Power Conference, 25th International Conference on High-Power Particle Beams and 20th International Symposium on Electromagnetic Launch technology, EAPPC-BEAMS-EML 2024

作者： Sasaki, Toru Iwasa, Momoka Wang, Innu Harada, Takatoshi Suzuki, Tsuneo Takahashi, Kazumasa Kikuchi, Takashi Nagaoka University of Technology Dept. Science of Technology Innovation Nagaoka Japan University of Technology Dept. Electrical Electronics Information Engineering Nagaoka Nagaoka Japan Nagaoka University of Technology Dept. Nuclear Technology Nagaoka Japan

ISBN: (纸本)9789038661353

This study introduces a comprehensive data-driven approach to decipher the intricate dynamics of high-power laser ablation phenomena and a vast dataset of experimental observations. Laser ablation experiments were performed using a high repetition rate laser (>1 kHz) or a low repetition rate laser (10 Hz) at a wavelength of 1064 nm. The target sample was an aluminum plate placed on a vacuum chamber. The laser fluence was set to the same value above the ablation threshold. Plasma parameters and laser power were observed by photodiode signals from the orifice probe and laser exit. The drift velocity and ion rectification current were considered a function of laser fluence to confirm the relationship between the plasma parameters. © 2024 Eindhoven University of technology.

关键词： Laser ablation

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Analyzing the Performance of Naive Bayes, Logistic Regression, SVM and Random Forest for Identifying Hate speech from Twitter Social Media 7

Analyzing the Performance of Naive Bayes, Logistic Regressio...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Wankhede, Disha S. Gaikwad Vidya, S. Manikjade, Akshay Meher, Nikita Atkale, Tejas Ghule, Aishwarya Gujar, Deepika Vishwakarma Institute of Information Technology Dept. of Computer Engg Pune India Vishwakarma Institute of Information Technology Dept. of Mechanical Engg Pune India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304268

The spread of hate on social media and other platforms is of great concern because it has the implicit to be a serious detriment to society and the country and is disastrous. The development of mass media has led to lower exposure of hate speech and discrimination. detest speech generally refers to a person or group of people predicated on race, color, race, gender, race, religion, etc It's defined as humiliating communication grounded on certain characteristics. The donation of Structure through named images, type of caption and words used in the textbook of ) can explain the causes of virality and what is associated with it. Fake news and hate speech are not the result of the internet age. Fake news and hate have been around since the morning of mortal history, that is, for times- people have fabricated and fabricated all the time. still, the phenomenon of social media has changed how, where and what it's associated with fake news and hate speech. Where lies and culmination formerly appeared on the internet, now fake news and hate speech are taking their place on social media. The thing of the design is to propose results that stoners can use to identify and filter to count hate speech on Twitter. Using colorful bracket styles, we can determine whether tweets are hate speech. We use traditional shadowing algorithms Analogous as Naive Bayes, Logistic Regression, SVM and Random Forest. From data collection, preprocessing, point engineering and type to, we will estimate and give the delicacy and quality of all classifiers and their results in discrimination quests on Twitter.. © 2023 IEEE.

关键词： classification Fake news hate speech Naive Bayes Random Forest social media SVM

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Investigating the Efficacy of Diverse Machine Learning Classifiers for Parkinson's Disease Detection 15

Investigating the Efficacy of Diverse Machine Learning Class...

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

作者： Roy, Sourabarna Debbarma, Tina Pal, Tannistha Duraisamy, Prakash National Institute of Technology Agartala Dept. of Computer Science and Engineering Agartala India University of Wisconsin- Green Bay Dept. of Computer Science and Engineering WI54311 United States

ISBN: (纸本)9798350370249

To detect Parkinson's disease, we compare the effectiveness of K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM), and Random Forest (RF) algorithms. Utilizing a dataset with clinical and biomedical features, we preprocess the data to handle missing values and standardize the features. Subsequently, we train each algorithm with the preprocessed data and evaluate their performance using metrics like accuracy, precision, recall, and F1-score. Our results indicate that all four algorithms achieve excellent accuracy in diagnosing Parkinson's disease, with KNN slightly outperforming the others. However, the selection of the algorithm may depend on specific needs such as interpretability and computational efficiency. Additionally, we conduct a feature importance analysis to identify the most relevant features for Parkinson's disease identification, offering insights that can aid in early diagnosis and disease management. © 2024 IEEE.

关键词： Logistic regression

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Analysis of Arabs Publications In the Field of computer Networks 14

Analysis of Arabs Publications In the Field of Computer Netw...

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14th International Conference on information and Communication Systems, ICICS 2023

作者： El-Bashabsheh, Emran Younes, Mutaz Shatnawi, Mohammed Jordan University of Science and Technology Dept. of Computer Information Systems Irbid Jordan Jordan University of Science and Technology Dept. of Computer Science Irbid Jordan

ISBN: (纸本)9798350307870

Increasing technology led to increasing collaboration between the countries in general, especially the Arabic world. In recent years, many impacts affect the growth of research work in different Arabic countries. In this research, we show the growth of research work and its quality between Arabs and the rest of the world. We collected over 2500 publications and used it to create a new table that contains the intersection between each Arab country and each co-author's continent to show the relationship between each Arabic country with the rest of the world. We divided the data into two periods, before and after 2015, then we used Hue to apply our analysis on the data. We focused on the number of publications and the number of citation for each Arabic country. We noticed the rapid growth in the last period in some Arabic countries such as Jordan, Qatar, United Arab Emirates, and Saudi Arabia. In contrast, the rest of the countries had slow or decreasing growth. © 2023 IEEE.

关键词： Big data

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A Proposed Model for Machine Learning-Based Early Chronic Kidney Disease Detection 3

A Proposed Model for Machine Learning-Based Early Chronic Ki...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Bhandurge, Sonam Sambrekar, Kuldeep Allagi, Shridhar Rajpurohit, Vijay S. Sannakki, Sanjeev Angadi Institute of Technology and Management Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Gogte Institute of Technology Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Kle Institute of Technology Hubli Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering India

ISBN: (纸本)9798350354171

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient's death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector Machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of 100% by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness. © 2024 IEEE.

关键词： Support vector regression

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Classifying wireless IOT ICU traffic with machine learning models 19

Classifying wireless IOT ICU traffic with machine learning m...

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19th International Conference on Future Networks and Communications, FNC 2024 / 21st International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2024 / 14th International Conference on Sustainable Energy information technology, SEIT 2024

作者： Sibai, Fadi N. Sibai, Ahmad Electrical & Computer Engineering Dept. Gulf University for Science and Technology Mishref Kuwait Computer Science & Engineering Dept. University of South Florida TampaFL33620 United States

In this work, we classified the wireless internet of things (IoT) traffic of the IoT Health intensive care unit (IHI) dataset which belongs to three general classes: patient monitoring, environment monitoring, and network attack. We trained and tested 7 machine learning (ML) models with Orange 3 including kNN, Decision Tree (tree), SVM, Random Forest (RF), Neural Network (NN), Gradient Boosting (GB), and AdaBoost (AB). With the original dataset, 5 ML models performed perfect classification. After pruning the dataset columns by keeping the features with the highest correlations with the label in the dataset, good classifications were obtained with only 4 TCP/IP features by the Gradient Boosting, kNN, and RF models with MSEs in the range 0.008-0.011, and R2s in the range 0.978-0.984. With only 6 MQTT features, Gradient Boosting, RF, Tree, and NN were the top classifiers with MSEs in the range 0.073-0.074, and R2s in the range 0.856-0.859. This work demonstrates the effectiveness of guiding the feature pruning process by the values of the correlation coefficients in order to minimize the long training times of ML models while achieving good accuracies. © 2024 The Authors. Published by Elsevier B.V.

关键词： correlation coefficient feature pruning Internet of things IOT health intensive care unit dataset machine learning network security

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