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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Garg, Kamal Deep Gupta, Manik Sharma, Bhisham Dhaou, Imed Ben Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The expansion of the internet of things (IoT) and Big Data are two factors that have contributed to the rise in popularity of smart cities. The ability to anticipate the quality of the air in an area with precision and efficiency is one of the fundamental building blocks of a smart city. The amount of polluted air found in smart cities throughout the world has been gradually growing. Because of this, there has been a rise in the concentration of several air pollutants in the environment, including particulate matter (PM 10), sulphur dioxide (SO2), and PM 2.5, amongst others. Because of the possibility of uncontrollable repercussions, such as an increase in the severity of asthma and cardiovascular disease, this situation poses a risk to the country and to the people who live in it. Heavy industry and vehicle exhaust have been major contributors to the growth of air pollution in smart cities such as New Delhi, Bombay, Chandigarh, and Bengaluru in India. The purpose of this investigation is to compare and contrast the efficiency of a variety of machine learning methods in order to assess the precision of the air quality index (AQI) projection of PM 2.5 in Chandigarh, India. Models for predicting AQI are trained and tested using a variety of statistical techniques like Linear regression, Lasso regression, KNN regression, and Random Forest regression This Root Mean Square Error (RMSE) found for Linear regression, Lasso regression, KNN regression, and Random Forest regression are 31.01, 29,45, 37.09 and 28.3. From all four models, random forest regression was more accurate than the other three regression models in estimating PM 2.5 levels in India's smart city. © 2023 IEEE.

关键词： Diseases

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Zirkus Empathico 2.0: a multiplayer serious mobile game for children with autism spectrum disorder (ASD), with a focus on enhancing social and emotional development

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Multimedia Tools and Applications 2025年 1-24页

作者： Hassan, Ahmed Pinkwart, Niels Shafi, Muhammad Department of Computer Science and Informatics Berlin School of Business and Innovation Karl Marx Strasse 97 - 99 Berlin12043 Germany Institute for Informatic Humboldt University Berlin Rudower Chaussee 25 Berlin Berlin12489 Germany School of Computing Ulster University Belfast Campus United Kingdom Faculty of Computing & Information Technology Sohar University Sohar Oman

This study explores the effectiveness of the multiplayer serious mobile game "Zirkus Empathico (ZE) 2.0" in enhancing social-emotional behaviors in children diagnosed with autism spectrum disorder (ASD). It aims to assess how such games can foster emotional recognition, empathy, and prosocial behavior, thereby supporting the holistic development of children with ASD. An eight-week randomized controlled trial (RCT) was conducted between January and April 2021 at autism centers in Islamabad, Pakistan, and Berlin, Germany. The study involved 107 children aged 5–11 years, with 60 diagnosed with ASD and 47 typically developing children serving as an Active Control Group (ACG). Participants were engaged in sessions using the bilingual"ZE 2.0"game, incorporating multiplayer and emotional learning modules. The intervention required 120 min of weekly training supplemented by daily sessions. Post-intervention assessments demonstrated significant improvements in the ASD group compared to the control group in areas of empathy and emotional awareness, as evidenced by higher scores on the Griffith Empathy Measure (GEM) and multiplayer module evaluations. Notable was the ability of participants to apply learned social skills in dynamic, real-world contexts, which suggests the game’s effectiveness transcends the controlled settings typically associated with ASD interventions. ZE 2.0 adds to the social and teamwork competencies of children with ASD. Besides encouraging peers to be proactive in communications and cooperation, the multiplayer mode provides an immersion learning atmosphere for facilitating social and emotional development. Such an outcome underlines the perspectives of serious games for use as a framework for the treatment and education of children who have ASD. In this manner, serious games can be utilized as a supplementary or alternative means of treating ASD. Future research may focus on distinguishing the effects of intervention from those of other therapy modalit

关键词： Serious games

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Foundation Models in Healthcare: Opportunities, Risks & Strategies Forward

Foundation Models in Healthcare: Opportunities, Risks & Stra...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Thieme, Anja Nori, Aditya Ghassemi, Marzyeh Bommasani, Rishi Andersen, Tariq Osman Luger, Ewa Microsoft Health Futures United Kingdom Massachusetts Institute of Technology United States Stanford University United States Department of Computer Science University of Copenhagen Denmark Institute of Design Informatics The University of Edinburgh United Kingdom

ISBN: (纸本)9781450394222

Foundation models (FMs) are a new paradigm in AI. First pretrained on broad data at immense scale and subsequently adapted to more specific tasks, they achieve high performances and unlock powerful new capabilities to be leveraged in many domains, including healthcare. This SIG will bring together researchers and practitioners within the CHI community interested in such emerging technology and healthcare. Drawing attention to the rapid evolution of these models and proposals for their wide-spread adoption, we aim to demonstrate their strengths whilst simultaneously highlighting deficiencies and limitations that give raise to ethical and societal concerns. In particular, we will invite the community to actively debate how the field of HCI - with its research frameworks and methods - can help address some of these existing challenges and mitigate risks to ensure the safe and ethical use of the end-product;a requirement to realize many of the ambitious visions for how these models can positively transform healthcare delivery. This conversation will benefit from a diversity of voices, critical perspectives, and open debate, which are necessary to bring about the right norms and best practices, and to identify a path forward in devising responsible approaches to future FM design and use in healthcare. © 2023 Owner/Author.

关键词： Ethical technology

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HealthAir: A Crowdsourcing mHealth Platform for Air Pollution Effects on Citizens Health Status

HealthAir: A Crowdsourcing mHealth Platform for Air Pollutio...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Ioannis Katsidimas Sotiris Nikoletseas Stefanos H. Panagiotou Christina Spyropoulou Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press “Diophantus” (CTI) Greece

Crowdsourcing systems offer numerous advantages for acquiring data, which can contribute to the development of modern concepts such as Smart Cities. Air quality in urban areas has been identified as a crucial theme and requirement, towards ideal city of the future that can be adapted upon its citizens' needs and comfort. Thus, besides the sensing infrastructure, a complementary system must be implemented in order to enable the public authority and municipality awareness over citizens' personalised and subjective input, in a cost efficient way that can be easily upscaled. In this work, we present HealthAir, an mHealth application alongside its subsystems (namely a web application and a smartwatch version), that aims to increase and complement the sensing capacity of a Smart City, with respect to the air quality-wise health of the citizens. This is achieved based on questionnaires and surveys approved by the health community, and services and features that implement a bidirectional interaction between the system and the users. Finally, users' data and air quality measurements from a third party platform are combined and presented for the case study realization and demonstration.

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Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

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CervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification 4th

CervixNet: A Reward-Based Weighted Ensemble Framework for Ce...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Roy, Kaushiki Bhattacharjee, Deboparna Banik, Debapriya Krejcar, Ondrej Sarkar, Ram Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata700015 India Department of Computer Science and Engineering ICFAI University Tripura Agartala799210 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9789819726103

Cervical cancer is one of the most common causes of death among women worldwide. However, this fatal disease can be treated, and the mortality rate can be decreased if detected at an early stage. The Papanicolaou test is the gold standard for screening cervical cancer patients. However, the process of manual inspection is tedious and subject to manual errors. Thus, computer-based automatic screening is considered a viable alternative. To this end, in the present work, we have developed a novel system for classifying cervical cancer images as either normal or malignant. We have used the Herlev dataset in this study, which is the standard Pap smear image benchmark dataset. The proposed framework uses multiple base deep learning frameworks, namely Vision Transformer, Xception, VGG-19, VGG-16, and ResNet-101, for classification. Further, we have introduced a novel reward-based weighing technique to decide the weights of individual classifiers, which are, in turn, used to make decisions about the final class label of an input image using the weighted average technique. The proposed framework achieves an overall accuracy, precision, recall, F1-score, and AUC of 96%, 94%, 92%, 93%, and 94%, respectively, for the binary classification task that is normal vs. malignant. An elaborate study of the performance achieved by our proposed framework on the Herlev dataset shows it to be both robust and effective for the Pap smear image classification task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 75页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Novel SSD-Based Models for Detection of Multidimensional Pulmonary Nodules in CT Images 7

Novel SSD-Based Models for Detection of Multidimensional Pul...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Ji, Zhanlin Hao, Shengnan Pang, Jianhua Ganchev, Ivan Zhejiang A&F University College of Mathematics and Computer Science Hangzhou China North China University of Science and Technology Department of Artificial Intelligence Tangshan China University of Plovdiv 'Paisii Hilendarski' Department of Computer Systems Plovdiv Bulgaria Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria University of Limerick Telecommunications Research Centre Limerick Ireland

ISBN: (纸本)9798350368673

Lung cancer, as one of the diseases with the highest incidence and mortality rates in the world, has always been a big challenge that humans are constantly striving to address. Pulmonary nodules, as one of the early manifestations of lung cancer, constitute a breakthrough in early detection of this disease and its prompt treatment. Currently, significant progress has been made in object detection models based on deep learning in the context of increasingly scarce medical resources. To make pulmonary nodule detection more efficient and accurate, this paper proposes two novel models, based on the Single Shot MultiBox Detector (SSD) model. The proposed models adapt to the detection of small pulmonary nodules by changing the size of the feature map of the input prediction module. In addition, the prediction frame is modified to make greater use of the shallow feature layer. A group of up-sampling modules is added by increasing asymmetric convolution and a feature pyramid network (FPN) mechanism is used for improving the detection performance. The experimental results, obtained on the LUNA16 dataset, demonstrate that the proposed models achieve great performance improvement compared to the baseline (SSD). © 2024 IEEE.

关键词： Lung cancer

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Data Science System of Predicting and Detecting the Chikungunya Virus using Ensemble Techniques 4

Data Science System of Predicting and Detecting the Chikungu...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Reddy, Bommireddy Vijay Kumar Patel, Shail Singh, Anayna Singh, Nishant Ranjit, Sasanapuri Dantkale, Shreyas National Institute of Technology Srinagar Department Computer Science and Engineering Jammu and Kashmir India Vellore Institute of Technology Department Computer Science and Engineering Vellore India Niit University Department Computer Science and Engineering Neemrana India University of Delhi Department of Institute of Informatics and Communication Delhi India Pes University Department of Electronics and Communication Engineering Bangalore India Vishwakarma Institute of Technology Department of Electronics and Communication Engineering Pune India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

The ailment known as chikungunya fever is brought on by a virus that mosquitoes disseminate. Due to the infection's excruciating joint and muscular discomfort, the term translates to 'bends you up' or 'stooped walk.' The abrupt onset of a high temperature, headache, exhaustion, rash, nausea, and red eyes are other symptoms. Chikungunya symptoms frequently show up 2 to 7 days after being bitten by an infected mosquito. There isn't a vaccination yet. Additionally, there is no cure for the illness. However, fatalities or major complications with chikungunya are uncommon. Many people recover in a matter of weeks. However, some people experience joint and muscular discomfort that can linger for months or even years after they have been exposed to the virus. After one to two weeks, a lot of people are free of further problems. Others, though, can experience joint and muscular discomfort for years. This kind of arthritis is known as chronic chikungunya. At least 40persons who contract the virus are affected. Chikungunya is seldom fatal. But for certain people, the infection can lead to serious issues. Older persons, those with chronic illnesses like diabetes or high blood pressure, small children, and pregnant women who could pass the virus to their unborn children are at increased risk. Serious issues with the eyes, heart, and nerves can be complications. Once-infected individuals are likely to be resistant to subsequent infections. The main aim of the authors is to use the ensemble technqiue to find a novel approach and to compare the ensemble techniques. © 2022 IEEE.

关键词： Viruses

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Innovative Approaches to Arabic Author Identification: A Comprehensive Evaluation of Classical and Deep Learning Approaches 2

Innovative Approaches to Arabic Author Identification: A Com...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Almanaseer, Waref Alsekait, Deema Mohammed Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Abdelaziz, Amr Elsabbagh, Belal Essam, George Nader, Nour Abdelminaam, Diaa Salama Applied Science Private University Faculty of Information Technology Amman11931 Jordan Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information Technology Riyadh11671 Saudi Arabia Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Benha University Faculty of Computers and Artificial Intelligence Benha Egypt MEU Research Unit Middle East University Amman11831 Jordan

ISBN: (纸本)9798350362633

The amount of written text on the internet has grown exponentially. When pairing that with the difficulties in sourcing the author of a text, a need emerges to be able to verify claimed author of text and attribute anonymous text to its author. These tasks fall under the umbrella of author identification, which is the process of identifying who, among a set of authors, wrote a given text. For many reasons, it is a challenging task in the field of Natural Language Processing (NLP). This paper compares the performance of different machine learning and deep learning models on the task of Arabic author identification between. The results surprisingly show that most models failed except for the feedforward neural network and Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Arabic Deep Learning Machine Learning Natural Language Processing

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