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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： Samih, Islam Abdelsalam, Mohammed Moawad, Ibrahim Fathy Business Information Systems Department Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Abbassia Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura City Egypt

ISBN: (纸本)9789819947638

Increasing satisfaction of customers is the most important goal for any institution. Therefore, customer relationship management (CRM) is founded for this purpose to keep track of the interaction between the organization and their customers to satisfy them and to guarantee their loyalty. In this paper, we introduce a layered architecture model for customer relationship management in Education domain. This model integrates educational domain ontology and service-oriented architecture to improve the business processes and hence to increase the customer satisfaction in the educational institutions. The model design is presented in detail for a huge Egyptian educational institution called the Education City. The paper presents a workflow design for customer relationship management describing the interaction among stakeholders to achieve the different business processes in the Education City. As well as, a detailed ontology design for the Education City is presented to describe and to visualize the different scenarios for the services provided by the Education City. To achieve flexibility and reusability, the model was built based on service-oriented architecture for the possibility of reusing the model in more than one field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Reusability

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A Self-Supervised Learning-Based computer Recognition Platform for Orchidaceae Classification 11

A Self-Supervised Learning-Based Computer Recognition Platfo...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hu, Wu-Chih Huang, Guan-Zhi Huang, Xiang-Rui Wang, Wei-Chien Chen, Liang-Bi National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan The University of Sydney Faculty of Engineering and Information Technologies Department of Computer Science Sydney Australia

ISBN: (纸本)9781665492324

This paper proposes an orchid classification platform based on a self-supervised learning model. Users can view orchid information through the computer application platform and upload unknown orchid images to the platform. The orchid information is displayed on the visualization window. In terms of the recognition of orchids, fine-grained classification is used to classify orchids. The fine-grained classification model is usually applied to plants. This model is more suitable for the classification of hundreds of orchid species. The fine-grained classification of orchids is suitable for this task, and the classification system is built on the server side. The user only needs to install the client-side application and operate it directly. © 2022 IEEE.

关键词： Visualization

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Analysis of Lung Cancer for Developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Mahaveerakannan, R. Murali Dhar, M.S. Goswami, Sohan Krishna, P. Gopala Jayanthi, K. Kumar, M. Sathish The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Technique Polytechnic Institute Department of Computer Science and Technology West Bengal India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Pollachi Institute of Engineering and Technology Department of Computer Science and Engineering Pollachi Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350300857

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method. © 2023 IEEE.

关键词： Internet of things

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Semisupervised Breast MRI Density Segmentation Integrating Fine and Rough Annotations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 690-699页

作者： Xie, Tianyu Sun, Yue Yang, Hongxu Li, Shuo Song, Jinhong Yang, Qimin Chen, Hao Wu, Mingxiang Tan, Tao Macao Polytechnic University Faculty of Applied Sciences 999078 China Eindhoven University of Technology EindhovenAZ 5612 Netherlands Case Western Reserve University Department of Computer and Data Science Department of Biomedical Engineering ClevelandOH44106 United States Jiangsu JITRI Sioux Technologies Company Ltd. Suzhou215000 China University of Twente Department of Biomechanical Engineering EnschedeNB 7522 Netherlands Shenzhen People's Hospital Department of Radiology Shenzhen518020 China

This article introduces an enhanced teacher-student model featuring a novel Vnet architecture that integrates high-pass and low-pass filters to improve the segmentation of breast magnetic resonance imaging (MRI) images. The model effectively utilizes finely annotated, roughly annotated, and unannotated data to achieve precise breast tissue density segmentation. The teacher-student framework incorporates three specialized Vnet networks, each tailored to different types of annotations. By integrating cosine contrast loss functions between finely and roughly annotated models, and innovatively applying high-pass and low-pass filters within the Vnet architecture, the segmentation performance is significantly enhanced. This hybrid filtering approach enables the model to capture both fine-grained and coarse structural details, leading to more accurate segmentation across various MRI image datasets. Experimental results demonstrate the superiority of the proposed method, achieving Dice values of 0.833 on the finely annotated Shenzhen dataset and 0.780 on the Duke dataset, using 15 finely annotated, 15 roughly annotated, and 58 unlabeled samples provided by Shenzhen People's Hospital. These findings underscore its potential clinical application in breast density assessment. © 2024 IEEE.

关键词： Students

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Towards an Automatic Extracting UML Class Diagram from System's Textual Specification 24

Towards an Automatic Extracting UML Class Diagram from Syste...

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7th International Conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Babaalla, Zakaria Jakimi, Abdeslam Oualla, Mohamed Saadane, Rachid Chehri, Abdellah GL-ISI Team Department of Informatics Faculty of Sciences and Techniques Errachidia Morocco Electrical Engineering Department Hassania School of Public Works Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada

ISBN: (纸本)9798400709296

Developing a software system from natural language requirements is a complex and delicate task that requires a high level of design and programming expertise. Increasing the level of abstraction used to describe these requirements is the most natural solution. Model-Driven engineering (MDE) also takes this route, using abstract models as primary entities to generate source code automatically or semi-automatically. Among these models, the UML class diagram occupies a privileged place in object-oriented systems because it not only serves as a basis for communication between developers but also provides a closely aligned static representation of the system implementation. However, creating a UML class diagram from a textual system specification poses a significant challenge due to the inherent imprecision and ambiguity commonly found in natural language expressions. In this paper, we propose a model-centric approach based on deep learning for the automatic extraction of UML class diagrams from textual requirements. © 2024 ACM.

关键词： Object oriented programming

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An AI-based Approach for Mystery Shopping Audit in Customer Service

An AI-based Approach for Mystery Shopping Audit in Customer ...

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2024 International Conference on Information Technology, Data science, and Optimization, I-DO 2024

作者： Chuang, Christopher Zhang, Qiaoyun Lin, Yi-Ti Ho, Chia-Ling Chang, Chih-Yung Tamkang University Department of Computer Science and Information Engineering Taiwan Chuzhou University School of Computer and Information Engineering China Tamkang University Department of English Taiwan National Taipei University of Nursing and Health Sciences General Education Center Taiwan

ISBN: (纸本)9798400709180

In the era of intense business competition, the emergence of mystery shoppers provides companies with objective insights, enabling them to innovate and enhance their offerings to meet evolving customer needs and maintain a competitive edge. This paper introduces AMSA (AI-based approach for Mystery Shopping Audit), a service behavior identification mechanism for analyzing mystery shopping audit videos. AMSA identifies behaviors like five-finger guidance, hand offering, and maintaining good posture through two phases: coarse-grain and fine-grain identification. In the coarse-grain phase, an automated filtering and classification algorithm is proposed, utilizing YOLO for target detection. Subsequently, fine-grain identification employs 3DCNN for action classification, trained on enhanced videos of target actions. The Simulation results show that the proposed AMSA significantly improves accuracy of identification. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Competition

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Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： Thavavel Vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data Science Noroff University CollegeKristiansand4608Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly *** addition,SqueezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

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A Self-Driving Vehicle Navigation System 3

A Self-Driving Vehicle Navigation System

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Prakash, Somya Mali, Sabita Ali, Farida A. Mohapatra, Srikanta Kumar Mohanty, Jayashree University Faculty of Engineering & Technology Odisha India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India IIIT Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350303360

Population is growing in rapid manner and as compared to the growth of population the number of vehicles is increasing in significant numbers. Traffic accidents are becoming more prevalent in today's world as technology develops. The accidents are growing day by day in exponential manner due to the extended use of mobile phones, music systems in the car, infrastructure of most of the road and more traffic leads to the error of driver. There is a likelihood that the use of technology may reduce human error, making driving safer. So, the major challenge is to overcome the road safety approach. That is automated vehicles will run but main focus will be road safety. Every car participant is doing all they can to make it a reality. Audi, BMW, Ford, Google, General Motors, Tesla, Volkswagen, and Volvo are just some of the automakers working on or testing autonomous cars. Google's 'Waymo' is one of the most active projects in this area. In this effort, an Arduino Uno prototype of a driverless car with rudimentary functionality was created. To acquire the route, Gmaps was used to create a map with the origin and destination supplied. The route is then downloaded in Keyhole Markup Language (KML) format, and latitude and longitude are extracted using the *** service. The latitude and longitude were then supplied in array format in the Arduino UNO code at the proper spots. An application is developed to extract the vehicle's current movement or the real-time tracking of the vehicle movement as per the calculation of longitude and latitudes of the positional vehicle movement to the thingspeak cloud, all while displaying the coordinates on a real- time map. The mobile Assisted Global Positioning System (AGPS) sensor is built into most modern smartphones. The gmap route's latitudes and longitudes are saved in an array, and once the Arduino UNO gets the most up-to-date information from the Thingspeak cloud through the esp8266 Wi-Fi module, it begins calculating the nece

关键词： Python

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value 41

Minimally Modifying a Markov Game to Achieve Any Nash Equili...

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41st International Conference on Machine Learning, ICML 2024

作者： Wu, Young McMahan, Jeremy Chen, Yiding Chen, Yudong Zhu, Xiaojin Xie, Qiaomin Department of Computer Sciences University of Wisconsin-Madison MadisonWI United States Department of Computer Science Cornell University IthacaNY United States Department of Industrial and Systems Engineering University of Wisconsin-Madison MadisonWI United States

We study the game modification problem, where a benevolent game designer or a malevolent adversary modifies the reward function of a zero-sum Markov game so that a target deterministic or stochastic policy profile becomes the unique Markov perfect Nash equilibrium and has a value within a target range, in a way that minimizes the modification cost. We characterize the set of policy profiles that can be installed as the unique equilibrium of a game and establish sufficient and necessary conditions for successful installation. We propose an efficient algorithm that solves a convex optimization problem with linear constraints and then performs random perturbation to obtain a modification plan with a near-optimal cost. Copyright 2024 by the author(s)

关键词： Nash equilibrium

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