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12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021

作者： Leonardou, Angeliki Rigou, Maria Garofalakis, John University of Patras Dept. of Computer Engineering and Informatics Patra Greece University of Patras Dept. of Management Science and Technology Patra Greece

ISBN: (纸本)9781665400329

The COVID-19 pandemic among other radical changes it imposed onto our typical way of living, working, and interacting, established online education in both synchronous and asynchronous forms. This in many cases has given alternative forms of education like digital games, more practical potential. Teachers during this period have proposed digital games as a part of the teaching and learning procedure more often. The digital games that managed to prove their value in more effective learning will probably gain a place in the classroom and teachers' role is significant towards this change. This study presents the results of an online survey with the participation of 184 primary school teachers in Greece. The purpose of the survey is to investigate teachers' attitudes towards digital games in general and the Multiplication Game (MG) in particular and the findings are positive as teachers' opinion for both MG and digital games is high. © 2021 IEEE.

关键词： COVID-19

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An Intelligent Ground Station Selection Algorithm in Satellite Optical Communications via Deep Learning

An Intelligent Ground Station Selection Algorithm in Satelli...

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Mediterranean Conference on control and Automation (MED)

作者： Andrea Wrona Emanuele De Santis Francesco Delli Priscoli Francesco Giacinto Lavacca Dept. of Computer Control and Management Engineering (DIAG) University of Rome “La Sapienza” Rome Italy Consortium for the Research in Automation and Telecommunication (CRAT) Rome Italy Dept. of Information Engineering Electronics and Telecommunications (DIET) University of Rome “La Sapienza” Rome Italy

Among the most common issues to be faced in optical satellite communications, weather conditions play a fundamental role for a correct transmission of the information. In the case of heavy rain, hailstorm snow, or even dense clouds, a communication channel between the satellite and a optical ground station (OGS) may suffer significant interference, causing errors in delivering information. Since satellite transmissions cover in general very widespread areas, it usually happens that different zones are characterized by different weather conditions. This property is exploited by the site diversity technique, that tries to limit bad weather effects on the overall availability of the communication channel. When implementing such a site diversity technique, the satellite should be able to switch between the OGSs, by evaluating the rain events probability either through direct measurement campaigns or exploiting statistical models. The setup studied in this work involves a geostationary satellite equipped with two laser communication terminals (LCTs). In order to dynamically decide the OGSs to be pointed by those LCTs, a Deep-Learning based proactive control algorithm for site diversity, performing weather forecasting and consequent preventive LCT switching on the basis of current and past weather conditions has been developed. Simulative results show the ability of our proposed algorithm in achieving the maximum possible link availability, which is bounded by the weather conditions of all the OGSs.

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Design and Implementation of a Mobile Application for Intelligent Attendance Tracking

Design and Implementation of a Mobile Application for Intell...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik Nadhiya S Deepak Jain Anupama Verma Saurabh Kumar S. Bhavadharani Dept. of Computer Science Engineering Chandigarh University Chandigarh India Dept. of AI & Data Science Dhanalakshmi Srinivasan College Coimbatore India Dept. of Management IIM Indore Indore India Dept. of Business Management & Comm Usha Martin University Jharkhand India Dept. of Business Management & Commerce Mangalayatan University Aligarh India Dept. of Commerce Meenakshi A.H. Edu. & Research Chennai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Taking attendance is an important task in schools, universities, and other institutions. However, traditional attendance taking methods such as paper-based attendance sheets or manual entry into an electronic system are time-consuming and prone to errors. In recent years, the emergence of facial recognition technology has opened up new possibilities for attendance management systems. This research paper discusses the development and implementation of a Smart Attendance System using Facial Recognition technology using OpenCV in educational institutions. The study explores the benefits, limitations, and ethical considerations associated with this technology and its implications for the future of attendance management systems. This model takes use of LBPH and gja, exfilaface recognition to increase accuracy over conventional models. After this, the connection of recognized faces is compared with the kdatabase containing student’s faces. This model will be an effective method for controlling student attendance.

关键词： Privacy Accuracy Face recognition Scalability Refining Organizations Real-time systems Mobile applications Security Faces

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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads

Dynamic Optimization of Provider-Based Scheduling for HPC Wo...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Jacopo Marino Fulvio Risso Mauro Bighi Dept. of Control and Computer Engineering Politecnico di Torino Torino Italy Dept. of Information Technology PUNCH Torino S.p.A Torino Italy

The vast array of cloud providers present in today’s market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module’s primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous Machine Learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase.

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Designing of Creator’s Information Hub using Machine Learning

Designing of Creator’s Information Hub using Machine Learni...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Diksha Gabhane Ujwalla Gawande Sanjana Gaikwad Swati Gajarlewar Rita Bhawalkar Dept. of Computer Science & Design Yeshwantrao Chavan College of Engineering Nagpur India Dept. of Information Technology Yeshwantrao Chavan College of Engineering Nagpur India NCPharmacy Nagpur India Dept. of Cyber Security and Data Science G H Raisoni College of Engineering and Management Pune India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide.

关键词： Industries Technological innovation Video on demand Machine learning algorithms Education Ecosystems Collaboration Medical services Machine learning Web sites

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Pose-free visual servoing from 3D measurements

Pose-free visual servoing from 3D measurements

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European control Conference (ECC)

作者： Adrian Burlacu Radu L. Rosca Andrei I. Iancu Enric Cervera Dept. of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Romania Dept. of Computer Science and Engineering Jaume I University Spain

In this paper, a novel approach to visual servo control robotic systems is proposed. It is focused on developing a solution using 3D point features without recovering the rigid object’s pose. Pose-free motion is achieved using motion parameterization techniques based on dual numbers and dual vectors. Considering an imposed velocity field over the motion of the 3D point features ensemble, this work proposes a close-form solution to a visual servoing problem. The solution provides stable motion control while preserving the image features in the field of view. However, when some point features leave the field of view, their contribution to the control law is dropped without losing stability. The proposed solution is easy to tune and implement. Various scenarios are used in simulations and real experiments to show how the proposed solution overcomes classic servoing problems.

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GANs for Synthetic Environmental Data Generation (Air Quality) for Low-Resource Regions

GANs for Synthetic Environmental Data Generation (Air Qualit...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Seema Verma Neha Jain Madhumita Mahapatra Sonia Setia Vrinda Sachdeva Manoj Kumar Dept. of Computer Science & Engineering Delhi Technical campus Greater Noida Uttar Pradesh India Dept. of Computer Science & Engineering Sharda University Greater Noida Uttar Pradesh India Dept. of Computer Science & Engineering G.L Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Bharat Electronics Limited (BEL) Ghaziabad Uttar Pradesh India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Due to the significant expenses and logistical difficulties involved in deploying monitoring equipment, many areas, especially those in developing nations, lack adequate environmental data. This work presents a novel approach that uses Generative Adversarial Networks (GANs) for creating artificial environmental data. The model of GAN is trained for generating synthetic data for under-monitored or unmonitored areas by utilizing high-resolution environmental data from well-monitored regions. The paper discusses the generation of synthetic data and its comparison with real data based on statistical properties. The analysis reveals that the characteristics of the created data closely align with the actual data. With the help of this synthetic data, researchers and policymakers will be able to make more informed decisions by having precise estimations of important environmental indicators like air quality.

关键词： Training Predictive models Generative adversarial networks Air quality Data models Real-time systems Predictive analytics Monitoring Synthetic data Resilience

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High Efficiency and High Gain Hybrid DC-DC Converter with Switched Capacitor

High Efficiency and High Gain Hybrid DC-DC Converter with Sw...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Brenna Theodora Machado Matos Rodrigues Mauricio Albuquerque Moura Edilson Mineiro Sá Junior Kleber Cesar Alves de Sousa Dept. Electrical and Computer Engineering Federal University of Ceara(UFC) Sobral Brazil Dept. Control and Industrial Processes Federal Institute of Caera Sobral Brazil

This work presents the study and development of a high-gain hybrid DC-DC converter with switched capacitor for photovoltaic energy applications. Qualitative analyzes and quantitative values of the converter are proposed. The proposed converter is based on the converter boost integrated into switched capacitive cells with the addition of a small inductor resonant. A mathematical modeling of the converter was developed to determine the value of the resonant inductance, where the converter was analyzed for inductance values of 1uH at 2uH. This proposal presented some advantages: such as extended static gain, reduction of voltage stress in semiconductors and reduction of current peaks in switches. A 200 W prototype was developed and its respective results are presented. Maximum efficiency obtained was 97.1% for a voltage gain of 10.

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A Novel Hybrid Deep Learning Model for Detecting Breast Cancer

TechRxiv

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TechRxiv 2023年

作者： Banerjee, Avishek Chakraborty, Debasis Bhattacharyya, Basanti Nath, Ira Dey, Debashis Garg, Deepak Dept. of Computer Science and Business Systems Asansol Engineering College West Bengal 713305 India Dept. of Computer Science and Engineering Asansol Engineering College Asansol713305 India Dept. of Computer Science and Engineering Pailan College of Management and Technology West Bengal 700139 India Dept. of Computer Science and Engineering JIS College of Engineering West Bengal 741235 India Dept. of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal 700064 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

Cancer is a deformity of the body cells that grow out of control and spread to other parts of body. According to the American Cancer Society, early identification of cancer resulted in a 99% chance of survival in the localized stage. In this article, a benchmark cancer dataset is considered for the purpose of investigation. After data preprocessing, we attempt to predict cancer type using our proposed deep learning model named as CNN-Soft-T that combines the feature selection (FS) and classification. FS is introduced using histogram technique for anomaly detection and dimension reduction. We have compared the performance of this model to other cutting-edge machine learning approaches including Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) and Decision Tree (DT) in order to demonstrate its utility. We have experimentally exhibited the exceptional empirical effectiveness and biological applicability of our suggested hybrid model for cancer diagnostics and medication development. © 2023, CC BY.

关键词： Decision trees

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Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision

Class-incremental Continual Learning for Instance Segmentati...

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International Conference on computer Vision (ICCV)

作者： Yu-Hsing Hsieh Guan-Sheng Chen Shun-Xian Cai Ting-Yun Wei Huei-Fang Yang Chu-Song Chen Dept. Computer Science and Information Engineering National Taiwan University Taiwan Dept. Information Management National Sun Yat-sen University Taiwan

Instance segmentation requires labor-intensive manual labeling of the contours of complex objects in images for training. The labels can also be provided incrementally in practice to balance the human labor in different time steps. However, research on incremental learning for instance segmentation with only weak labels is still lacking. In this paper, we propose a continual-learning method to segment object instances from image-level labels. Unlike most weakly-supervised instance segmentation (WSIS) which relies on traditional object proposals, we transfer the semantic knowledge from weakly-supervised semantic segmentation (WSSS) to WSIS to generate instance cues. To address the background shift problem in continual learning, we employ the old class segmentation results generated by the previous model to provide more reliable semantic and peak hypotheses. To our knowledge, this is the first work on weakly-supervised continual learning for instance segmentation of images. Experimental results show that our method can achieve better performance on Pascal VOC and COCO datasets under various incremental settings 1 .

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