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5th World Symposium on Communication Engineering, WSCE 2022

作者： Metallidou, Chrysi Psannis, Kostas E. Sarigiannidis, Panagiotis Michalas, Angelos Vergados, Dimitrios D. Goudos, Sotirios University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Dept of Economics/Computer Science University of Western Macedonia Kastoria Greece University of Western Dept. of Electrical & Computer Engineering Kozani Greece University of Piraeus Dept. of Informatics Piraeus Greece Aristotle University Dept. of Physics Thessaloniki Greece

ISBN: (数字)9781665450577

ISBN: (纸本)9781665450577

Nowadays., the advancement of artificial intelligence has led to the application of robotic systems in the industry, especially in the hotel industry, with a view to providing intelligent, automated and luxury services. At the same time, the knowledge gained from the Covid19 pandemic outbreak that convulses the world community, highlights the issue of sustainability in the hotel industry, solved by utilizing artificial intelligence achievements, remote robot systems, so as to guarantee reliability and security of a hotel's functionality, by providing not only convenient accommodation to guests, but safety, as well. In this paper, we focus on the development of a remote robot system, utilizing the Internet of Things, that has various kinds of sensors and cameras. Both the owners of the hotel and the guests can collaborate with the robot efficiently and accurately, since the robot is able to react with them. Specifically, the developed robot is able to serve clients, by providing them with hotel supplies and by giving them any requested information. In return, the guests are requested to give feedback to the robot upon check out. In addition, the robot browses throughout the hotel to inspect the normal and efficient functionality of all technical systems. The developed robot is also a Covid19 inspector, since it inspects whether one's pandemic measures are applied among guests in public areas and in case of a patient, the robot detects people connected to the patient, isolates the patient and gives medical care, food and supplies, so as to prevent humans from closely contacting the patient. © 2022 IEEE.

关键词： Sustainable development

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Big Data, Big Decisions: Choosing the Right Database

arXiv

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arXiv 2024年

作者： Hassan, Mohamed Dept. of computer science College of Engineering and Applied Sciences University of Colorado BoulderCO United States

In the burgeoning era of big data, selecting the optimal database solution has become a critical decision for organizations across every industry. Big data[1] demands a powerful database solution. Traditionally, SQL Database[2] Database ruled, offering a structured approach familiar to many organizations. However, big data's complexity and unstructured nature challenge SQL Database's limitations. Enter NoSQL Database: flexible and scalable, making them ideal for big data's ever-changing nature. We'll explore the key differences between SQL and NoSQL Database. Performance-wise, SQL Database shines for structured queries. Its standardized language (SQL)[3] ensures data consistency and complex analysis. But for big data's unstructured formats, this rigidity becomes a hurdle. NoSQL[4] offers a welcome contrast. Its flexible schema allows for diverse data formats and evolving structures, perfect for undefined or frequently changing data models. Additionally, NoSQL boasts superior horizontal scalability, distributing data across multiple servers for cost-effective growth. Understanding these key differentiators empowers organizations to choose the optimal database for their big data needs. © 2024, CC BY.

关键词： Big data

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YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesion Classification Using the HAM10000 Dataset

YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesio...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Utsha Saha Imtiaj Uddin Ahamed Md Ashique Imran Imam Uddin Ahamed Al-Amin Hossain Ucchash Das Gupta Dept. of Computer Science North Dakota State University Fargo ND USA Agricultural Data Analytics Unit North Dakota State University Fargo ND USA Dept. of Computer Science University of South Dakota Vermillion SD USA Dept. of Applied Computer Science The University of Winnipeg Winnipeg Canada Dept. of Computer Science Liverpool John Moores University Liverpool UK

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Skin cancer is a prevalent and potentially fatal disease that requires early detection for effective treatment. We trained and evaluated five YOLOv8 classification model variants (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls) on the HAM10000 dataset, which contains 10,015 dermatoscopic images of common pigmented skin lesions. The models were trained for 30 epochs using data augmentation techniques to enhance generalization. Performance was assessed using metrics including accuracy, precision, recall, F1-score, and inference time. The YOLOv8x-cls model achieved the highest accuracy of 86.2% and precision of 82.1%, while the YOLOv81-cIs model demonstrated the best balance with the highest F1-score of 77.0%. Compared to previous ensemble approaches, our single YOLOv8 models achieved superior performance with lower computational overhead. The YOLOv8n-cls variant showed the fastest inference time of 0.5 ms, making it suitable for real-time applications. Our results demonstrate the potential of YOLOv8-based models for accurate and efficient skin lesion classification, which could aid in early skin cancer detection and improve patient outcomes.

关键词： Measurement Deep learning Accuracy Computational modeling Skin Real-time systems Lesions Ensemble learning Skin cancer Diseases

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Generative Adversarial Networks as Weight Generators in Ensemble Classification

Generative Adversarial Networks as Weight Generators in Ense...

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Artificial Intelligence, Robotics and Control (AIRC), International Conference on

作者： Asmaa Yehia Sherif Fahmy Ahmed Fahmy Basic & Applied Science Dept. AASTMT Giza Egypt Computer Engineering Dept. AASTMT Cairo Egypt

Ensemble classifiers are constructed from various component classifiers by aggregating the results of the individual classifiers in some manner. While there are many ways to do this, one of the least computationally intensive is to use a voting ensemble, where the vote of each classifier is weighted by a number representing its performance. Choosing the appropriate weights is an involved process, that ranges from using trial and error to using sophisticated machine learning algorithms. In this paper, we proposed a novel technique that uses generative adversarial networks to customize the weights used for each individual input to the ensemble. Specifically, we propose to use conditional generative adversarial networks to generate the weights, with the input image acting as the label to the network and the appropriate weight being the output. We test the proposed system on an image classification task and show that the proposed method produces better results than using fixed weights by 2.43 percent.

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Plant Disease Recognition Using Different CNN Models 14

Plant Disease Recognition Using Different CNN Models

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14th International Conference on Cloud Computing, Data science and Engineering, Confluence 2024

作者： Gupta, Shivam Gilotra, Shrey Rathi, Shivam Choudhury, Tanupriya Kotecha, Ketan School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Graphic Era Deemed to Be University Cse Dept. Uttarakhand Dehradun248002 India Symbiosis Institute of Technology Symbiosis International University Symbiosis Centre for Applied Artificial Intelligence Pune411045 India Cse Dept. Maharashtra Pune412115 India

ISBN: (纸本)9798350344837

The research paper highlights the remarkable threats that plant diseases pose to global agricultural productivity and food security. The early detection and accurate recognition of these diseases are crucial for the effective management of plants. Deep learning models, such as CNN, Resnet34, Resnet50, and VGG16, have shown propitious results in computer vision tasks, including plant disease recognition. The study explores the effectiveness of these models using a comprehensive dataset consisting of images of healthy and diseased plants. The pretrained versions of the models are fine-tuned using transfer learning techniques with performance evaluation based on metrics like accuracy, precision, recall and f1 score. Comparative analysis also identifies each model's strengths and weaknesses in classifying plant diseases, considering factors like model architecture dept. and complexity. The overall findings of this research contribute towards the development of automated plant disease recognition systems integrated into real-time monitoring systems. © 2024 IEEE.

关键词： Deep learning

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Automatic transformation of UML-RT models into nuXmv model checker

Automatic transformation of UML-RT models into nuXmv model c...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Ruth Schorr Dept. of Computer Science and Engineering Frankfurt Unviersity of Applied Sciences Frankfurt Germany

The use of diagram-based development approach especially Model Driven Development (MDD), where the executable code can be generated directly from the diagrammatic system model, has gained much popularity in the recent years. The use of automated code generation allows to focus primarily on the core design aspects of system design without having to deal with the developmental challenges. Moreover, the approach also avoids any gap between the system design and the developed system, which can be very common when code development is manual. However, at present, these generated programs have to be manually checked for semantics correctness and hence, the possibility of an automated model level verification of such MDD artifacts is the next big step in this domain. There are multiple tools today that implement MDD and are being used by the research world for exploring the possible verification alternatives. In this paper we look closely into the verification of one such standardized profile, UML for Real-Time (UML-RT), developed particularly for MDD of Real-Time systems. Eclipse Papyrus-RT is an open-source modeling tool that supports UML-RT for specifying software systems. nuXmv is a model checker, that can verify system models against a set of properties defined using temporal logics. This paper presents a system that automatically translates Papyrus-RT models into a behaviorally equivalent nuXmv model.

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SIMHEURISTIC AND LEARNHEURISTIC FOR SOLVING STOCHASTIC AND/OR DYNAMIC PORTFOLIO OPTIMIZATION PROBLEMS 11

SIMHEURISTIC AND LEARNHEURISTIC FOR SOLVING STOCHASTIC AND/O...

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11th Operational Research Society Simulation Workshop, SW 2023

作者： Li, Yuda Polat, Onur Juan, Angel A. Calvet, Laura Kizys, Renatas Dept. of Computer Science Universitat Oberta de Catalunya Barcelona Spain Dept. of Applied Statistics and Operations Research Universitat Politcnica de Valncia Alcoy Spain Dept. of Public Finance Bilecik Şeyh Edebali University Bilecik Turkey Dept. of Telecomm. and Systems Engineering Autonomous University of Barcelona Sabadell Spain Dept. of Banking and Finance Southampton Business School University of Southampton Southampton United Kingdom

ISBN: (纸本)9781713870951

Constructing portfolio by proper asset selection to maximize return and minimize risk has been considered an essential task for investment activities. Rich portfolio optimizations with realistic constraints are NP-hard problems and are commonly solved using metaheuristics. However, financial markets are characterized by their high volatility and uncertainty, and metaheuristics do not fully account for these random and/or dynamic components, which renders them unrealistic in the presence of heightened uncertainty and dynamism in financial markets. Therefore, this paper proposes a simulationoptimization approach specifically, a simheuristic algorithm to deal with the stochastic version of the problem and a learnheuristic algorithm for solving the dynamic version of the problem. Computational experiments are performed on a benchmark instance to illustrate the advantages of the proposed methodologies and analyze how the solutions change in response to a different degree of stochasticity, dynamism, and minimum required return. © SW *** rights reserved

关键词： Financial markets

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Mathematical Model of Asset and Liability Management in the Presence of Normative and Internal Constraints 5

Mathematical Model of Asset and Liability Management in the ...

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5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhukova, Aleksandra Flerova, Anna Chernov, Aleksey Pilnik, Nikolay FRC CSC of RAS Dept. of Mathematical Modelling of Economic Systems MIPT Moscow Russia FRC CSC RAS Dept. of Mathematical Modelling of Economic Systems Moscow Russia Moscow Institute of Physics and Technology MIPT-School of Applied Mathematics and Computer Science Moscow Russia

ISBN: (纸本)9798350309126

In this paper we present the mathematical formulation of the problem of planning the Asset Liability Management if the Presence of Normative and Internal Constraints. As a nonlinear optimal control problem, it has specifics due to the state constraints. The maximum principle involves verification of regularity property in order to apply computational methods. We reformulate the problem as the problem with convex objective function and linear, but mixed system of constraints. This enables application of optimization methods to compute the optimal controls, and study the dependence of controls on the parameters that describe the external environments and the management objective. © 2023 IEEE.

关键词： Optimal control systems

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An app-based Spaced Repetition Learning Environment with User Generated Video Content

An app-based Spaced Repetition Learning Environment with Use...

引用

IEEE International Symposium on Multimedia

作者： Florian Schimanke Robert Mertens Lars Kölker Dept. of Computer Science and Engineering HSW University of Applied Sciences Hameln Germany

Spaced repetition is a learning task that helps users repeat learning items at optimally computed intervals. It is best suited for small learning units such as learning cards. In a typical learning scenario these learning cards do not stand by themselves, rather they are the last step in the students’ work process, summarizing the students’ interaction with the learning material. In this paper a framework is developed that allows learning material curators and students to create spaced repetition learning units with back-references to multimedia learning content, i.e., learning videos. With the advent of video learning nuggets, a wealth of content was created that can now be used in spaced repetition learning. The approach also allows for linking to specific parts of those learning videos to enable students to quickly review learning content specific to the learning unit currently worked on.

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Federated Learning for Lung Cancer Detection: Comparative Analysis and Visual Interpretability

Research Square

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Research Square 2025年

作者： Osei, Isaac Aabaah, Iven Benjamin, Appiah Dept. Of Computer Science Ho Technical University Ho Ghana Dept. Of Information System and Technology C. K. Tedam University of Technology and Applied Science Kumasi Ghana

Artificial Intelligence (AI) has become a crucial tool in the detection of lung can-cer through medical image segmentation. However, traditional AI approaches, which require centralizing sensitive patient data for model training, raise sig-nificant privacy concerns. This project investigates the efficiency of Federated Learning (FL) frameworks in comparison to a conventional centralized AI model. We evaluated seven different state-of-the art Federated Learning frameworks to assess their performance in maintaining model accuracy and scalability. Among these, Per-FedAvg and FedOpt demonstrated efficiency compared to the cen-tralized framework. To further understand the performance of these models, we utilized Gradient-weighted Class Activation Mapping (Grad-CAM) to visually interpret their predictions, ensuring that they focus on medically relevant fea-tures. This project highlights that Federated Learning frameworks, specifically Per-FedAvg and FedOpt, offer promising alternatives to traditional AI methods by providing enhanced performance in lung cancer detection. © 2025, CC BY.

关键词： Federated learning

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