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2nd IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values. © 2023 IEEE.

关键词： Image fusion

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Big Data Analytics for Business Sustainability: Review Study 4

Big Data Analytics for Business Sustainability: Review Study

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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Akour, Iman Elhoseny, Mohamed Al-Amawi, Amaal Akila, Ayman Department of Information Systems College of Computing & Informatics University of Sharjah United Arab Emirates Department of Management Information Systems Isra University Jordan Department of Electrical Engineering University of Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

Recently, many businesses and industries involved sustainability in their strategies. The demand for sustainable development in business has drawn the attention of the government and academia and emerged as a broadly well-known trend. The aim of this project is to develop an integrated approach of the sustainable business from a theoretical perspective integrated with the current wave of big data analytics. To achieve this, aim an advance methodology is employed for the assessment of business sustainability based on the proposed big data analytics model from consumer perception dimension. In addition, this research will quantify the effect of big data analytics on business sustainability. It also proposes a scale for measuring and monitoring sustainability in the industry benchmarking the experiences of big data analytics usage globally and locally. The method used to achieve the mentioned aim is a qualitative approach. Articles published from 2015 to 2022 will be reviewed to get the findings and results of this review. This study contributes to the advancement of knowledge and literature by offering a comprehensive scale based on big data analytics to measure sustainability in the industries. Moreover, this research project is expected to produce research findings that will enhance our understanding of the importance of data analytics in business sustainability. Many literatures showed the impact of data analytics on business performances, but there are less articles that demonstrated the impact of big data analytics on business sustainability which aimed to be explored by this research project. © The Institution of engineering & Technology 2023.

关键词： Sustainable development goals

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Profane Words on Twitter: An analysis of the use of swear words in different countries 2022

Profane Words on Twitter: An analysis of the use of swear wo...

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6th International Conference on Compute and Data Analysis, ICCDA 2022

作者： Phoey Lee, Teh Cheng, Chi-Bin Department of Computing and Information Systems School of Engineering and Computing Sunway University Malaysia Department of Information Management Tamkang University Tamsui Taiwan

ISBN: (纸本)9781450395472

Decreasing the usage of profanity terms can reduce a toxic environment, create a clean language culture towards the z-generation who is now exploring the online world. To understand how intensive is profanity usage in social media platforms, we collected a sample of tweets for the year 2020, from Jan 2020 to Dec 2020. A total of 4,175,516 tweets that contained the reference list of various profanity words is collected, with its geographical country compiled. Each of the tweets included at least a profanity term from the categories set of profanity. This study intends to visualise the profanity classes in text communication among users from different countries. This idea is to create awareness of language usage and suggest a possible mechanism to assist or investigate a potential crime, such as racism or cyberbully detection. © 2022 ACM.

关键词： Social networking (online)

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Sepsis Risk Assessment with XGBoost: A Data-Intensive Approach

Sepsis Risk Assessment with XGBoost: A Data-Intensive Approa...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Gayathri, M. Kavitha, V. School of Computing Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Kattankulathur India School of Computing Department of DataScience and Business Systems SRM Institute of Science and Technology Tamilnadu Kattankulathur India

ISBN: (纸本)9798350319200

In sepsis, immunological response to infection fails, causing systemic inflammation and organ failure. Sepsis progresses quickly and is commonly misdiagnosed, making early discovery and treatment crucial for patient outcomes. Augmented patient care and earlier intervention are possible using machine learning sepsis prediction systems. This research investigates if the well-known gradient boosting technique XGBoost can predict sepsis. Our goal is to construct a viable predictive model that can identify sepsis patients at risk of deterioration utilizing a huge dataset of EHRs from various healthcare facilities. The information includes vitals, test results, and patient details. Methodology includes "data preprocessing,""feature engineering,"and model development". Train the XGBoost model to predict sepsis onset and then build a threshold-based system to alert patients when their risk surpasses a certain level. Some indicators of model efficacy include sensitivity, specificity, and AUC-ROC. Sepsis prediction appears promising using the XGBoost model. If doctors can detect at-risk patients early and administer early treatments, fatality rates and patient outcomes may decrease. The study emphasizes the need of machine learning in healthcare to improve clinical decision support systems and assist critical care physicians. © 2023 IEEE.

关键词： Disease prediction Healthcare Machine learning Sepsis XGBoost

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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Computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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ROS Integration and Evaluation for Robot Navigation in a Narrow-Path Indoor Environment 14

ROS Integration and Evaluation for Robot Navigation in a Nar...

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14th IEEE International Conference on Control System, computing and engineering, ICCSCE 2024

作者： Ung, Lilian Wei Fang Wong, Richard T.K. Eu, Kok Seng Jasser, Muhammed Basheer Issa, Bayan Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia Taylor's University Selangor Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350364507

This project explores the implementation of ROS for robot navigation in an indoor environment with narrow pathways typically found in a grocery shop or library. The study discusses the design of the integration of different components including hardware such as sensors and actuators, and algorithms in ROS. The system uses LiDAR to first map the real-world environment to determine the static components of the environment. The purpose of the project is to determine the optimal system integration to achieve obstacle detection and avoidance specifically on dynamic obstacles in environments with narrow pathways. The results show 100% collision-free navigation either through avoiding obstacles or re-routing to an obstacle-free course. © 2024 IEEE.

关键词： Integration

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Reinforcement Learning-based Optimization of EBike Charging infrastructure 11

Reinforcement Learning-based Optimization of EBike Charging ...

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11th IEEE/ACM International Conference on Big Data computing, Applications and Technologies, BDCAT 2024

作者： Sharif, Muddsair Seker, Huseyin Birmingham City University Faculty of Computing Engineering and the Built Environment BirminghamB5 5JU United Kingdom The University of Sharjah College of Computing and Informatics Department of Information Systems Sharjah United Arab Emirates

ISBN: (纸本)9798350367300

This paper presents a novel technique for enhancing the allocation of resources in the charging infrastructure for e-bikes by employing deep reinforcement learning (DRL) in a context-specific manner. With the evolving transportation land-scape and the increasing popularity of electric micro-mobility solutions, efficient resource management plays a vital role. The methodology involves utilizing deep Q networks (DQN) in virtual environments to dynamically allocate charging resources based on contextual parameters. Through empirical analysis, we demonstrate the efficacy of our approach in achieving optimal resource allocation while considering the needs of diverse stakeholders. Findings suggest that the DQN model, when integrated with context-awareness and stakeholder objectives, enables efficient resource allocation, maximize fleet utilization, ensures charging station availability, enhances user satisfaction, and promotes environmental sustainability. The findings of this study offer valuable insights for policymakers, urban planners, and industry stakeholders seeking to enhance the sustainability and effectiveness of micro-mobility services in urban settings. Overall, this study contributes to the advancement of sustainable urban mobility solutions and underscores the potential of DRL in addressing complex urban transportation challenges. © 2024 IEEE.

关键词： Federated learning

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DESIGNING AN INTEGRATED MULTI-ECHELON, MULTI-PERIOD HEALTHCARE SUPPLY CHAIN NETWORK 51

DESIGNING AN INTEGRATED MULTI-ECHELON, MULTI-PERIOD HEALTHCA...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Rahman, Rafat Sultana, Farzana Farhan, Mahmudur Rahman Ahmed, Sayem Chakrabortty, Ripon K. Department of Mechanical & Production Engineering Ahsanullah University of Science & Technology Dhaka Bangladesh School of Systems & Computing University of New South Wales CanberraACT2610 Australia

The healthcare sector faces unique supply chain management challenges because of the wide range of product life cycles, growing levels of outsourcing, and globalization. Selecting cooperators and forming alliances across different supply chain echelons is particularly crucial when designing a multi-echelon network. This study presents a mixed-integer linear programming (MILP) model to design a multi-echelon, multi-period healthcare supply chain network (HSCN) in the context of a developing country. The model aims to improve operational efficiency and cost-effectiveness by selecting optimal partners, production planning, and inventory control at each echelon. The results demonstrate that the proposed model achieves a 100% customer service level (CSL), ensuring that all demands are met without shortages. Additionally, the model minimizes the total supply chain costs, which are determined to be $183,329, while maintaining a low average inventory level of 9 units across all nodes. These findings underscore the effectiveness of the model in optimizing strategic, tactical, and operational planning for a highly efficient and competent healthcare supply chain. Finally, a 23 factorial experimental design is carried out, considering demand variability, capacity variability for selected partners, and transportation capacity variability as the main factors. Among these factors, demand variability has the largest positive effect on total cost, while both partner capacity variability and transportation capacity variability have negative effects on total cost. This framework offers valuable insights for decision-makers and provides the foundation for future research to explore more complex scenarios. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Decision making

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From Deep Learning to Interpretable and Explainable Deep Learning in Medical Image computing: Balancing Innovation with Ethics and Responsibilities 28th

From Deep Learning to Interpretable and Explainable Deep Lea...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Chehri, Abdellah Ahmed, Imran Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

The utilization of artificial intelligence (AI) and other cutting-edge techniques in the field of medical image analysis has exhibited significant potential. Nevertheless, a significant obstacle that impedes the extensive implementation of these models in the healthcare sector is their restricted interpretability. The concept of explainability is a subject of extensive discussion and debate within the context of utilizing artificial intelligence in the healthcare domain. Notwithstanding the empirical evidence demonstrating the superior performance of AI-driven systems compared to humans in certain analytical tasks, particularly in the field of medical image computing, these systems still encounter challenges due to their limited explainability. The present study provides a comprehensive assessment of the significance of explainability in the field of medical artificial intelligence and performs an ethical analysis of the influence of explainability on the incorporation of AI-driven tools in data engineering in medicine and health care. The paper examines various subjects including data security, confidentiality, privacy, fairness, and discrimination, among others. © 2024 The Authors.

关键词： Data Analytics Deep Learning Ethics Explainable AI Medical Imaging Responsibilities

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