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14th International Conference on Advanced computer Information technologies, ACIT 2024

作者： Shevchuk, Ruslan Karpinski, Mikolaj Yurchyshyn, Tetiana Lishchynskyy, Ihor Lyzun, Maria Melnyk, Andriy West Ukrainian National University Department of Computer Science Ternopil Ukraine University of Bielsko-Biala Department of Computer Science and Automatics Bielsko-Biala Poland Institute of Security and Computer Science University of the National Education Commission Krakow Poland Ivan Puluj National Technical University Department of Cyber Security Ternopil Ukraine Information and Communication Technologies West Ukrainian National University Department of Foreign Languages Ternopil Ukraine West Ukrainian National University Department of International Economics Ternopil Ukraine

ISBN: (纸本)9798350350036

Nowadays, ensuring human safety in yarious environments is of paramount importance. This paper introduces a context-aware approach and software solution aimed at enhancing alert notifications about personal safety. By leveraging contextual information such as location, environmental conditions, and user preferences, the proposed system tailors safety alerts to individual needs and situations. Integrating modern technologies such as the *** Core Web API framework, Google Maps API, OpenWeatherMap API, and the C # language, we designed software to enhance human safety. Through the integration of cutting-edge technologies, including context awareness and dynamic threshold adjustment, our software provides personalized and timely notifications regarding potential dangers. This paper outlines the development process, key features, and potential of the context-aware software, highlighting its significance in improving safety measures and mitigating risks in diverse settings. © 2024 IEEE.

关键词： Mobile applications

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EmoRoom: Unveiling Academic Emotions Through Interactive Visual Analytics in Classroom Videos 9th

EmoRoom: Unveiling Academic Emotions Through Interactive Vis...

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9th Future technologies Conference, FTC 2024

作者： Yuvaraj, Rajamanickam Faizal, Sahil Ratnavel, Rajalakshmi Yang, Wang Science of Learning in Education Centre Office of Education Research National Institute of Education Nanyang Technological University Singapore637616 Singapore Department of Computer Science and Engineering Tandon School of Engineering New York University New YorkNY11201 United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computing and Information Systems Singapore Management University Singapore188065 Singapore

ISBN: (纸本)9783031731211

Measuring emotions in educational settings can provide important information in predicting and explaining student learning outcomes. Knowledge of student’s classroom emotions can also help teachers understand their students’ learning behaviors, improve their teaching methods, and optimize students’ learning and development. However, it can be highly challenging for teachers to monitor and accurately understand student emotions within classroom or group contexts, especially while they are actively teaching and attending to many students simultaneously. Video recording classroom activity can address that issue as high-definition cameras can be used to continuously record groups of students, enabling online or offline analyses of student emotions and supplementing teacher monitoring, retrieval, and interpretation of those processes. Teachers find it difficult to use existing emotion recognition methods to analyze student behaviors in videos due to a lack of user-friendly interfaces that facilitate automatic analysis. To address this challenge, we developed EmoRoom, an open-source tool designed to simplify the annotation and analysis of videos from an emotional perspective. EmoRoom tool offers a practical solution for quantifying and visualizing the frequency of emotions for individuals of interest. Furthermore, it can assist teachers in refining their teaching methods by offering a comprehensive overview of students’ emotional experiences during learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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UAV Databased Temperature Patterns Analysis with Carbon Emission Detection Using Deep Neural Network

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 512-523页

作者： Mohanty, Sachi Nandan Dash, Bibhuti Bhusan Shanmugasundar, G. MGM, Johar Aswani, Inakollu Sundaram, Ajith Varghese, Issac K. School of Computer Science & amp Engineering (SCOPE) VIT -AP University Andhra Pradesh Amravati India School of Computer Applications KIIT Deemed to be University Bhubaneswar India Department of Mechanical Engineering Sri Sairam Institute of Technology Chennai 600044 India Management and Science University Shah Alam Selangor Malaysia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist.Andhra Pradesh Vaddeswaram 522302 India Amrita School of Business Kochi Amrita Vishwa Vidyapeetham Amrita Nagar Edappally Kerala Ernakulam 682041 India T A Pai Management Institute Manipal Academy of Higher Education Manipal India

Unmanned aerial vehicle (UAV) imaging methods have drawn a lot of interest lately from academics and industry professionals as an affordable option for agro-environmental uses. To improve the UAV capabilities of diverse applications, machine learning (ML) methods are specifically applied to UAV-based remote sensing data. Spatiotemporal properties were analysed and the city-level carbon emissions statistics were estimated. This research proposes novel technique in UAV-based climate temperature pattern analysis and carbon emission detection utilizing the deep learning (DL) model. Here the input is collected as UAV-based weather data which is processed for noise removal and smoothening. Processed data is extracted and classified utilizing Gaussian belief deep neural network and spatial convolutional Q-swarm colony metaheuristic optimization. A metaheuristic optimisation algorithm uses gradient information-free iterative evaluation of the objective function in order to identify a global optimum. Experimental analysis has been carried out in terms of detection accuracy, average precision, F-1 score, recall and AUC for various UAV-based weather dataset. The proposed technique attained mean average precision was 92%, recall was 94%, AUC was 87%, detection accuracy was 96% and F1-score was 93%. This work shows that remotely sensed data can be used to support more advanced evaluations that are more successful, particularly in areas with extensive selective logging and diverse forest conditions, and to help quantify carbon emissions from selective logging using conventional methodologies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Carbon emission detection Climate temperature Deep learning model Pattern analysis UAV

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Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting 17

Crowdsourcing on Sensitive Data with Privacy-Preserving Text...

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17th Linguistic Annotation Workshop, LAW 2023, LAW 2023, co-located with ACL 2023

作者： Mouhammad, Nina Daxenberger, Johannes Schiller, Benjamin Habernal, Ivan DIPF Leibniz Institute for Research and Information in Education Germany Summetix GmbH Germany Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany

ISBN: (纸本)9781959429838

Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included. Textual data often contains sensitive information like person names or locations. In this work, we investigate how removing personally identifiable information (PII) as well as applying differential privacy (DP) rewriting can enable text with privacy-relevant information to be used for crowdsourcing. We find that DP-rewriting before crowdsourcing can preserve privacy while still leading to good label quality for certain tasks and data. PII-removal led to good label quality in all examined tasks, however, there are no privacy guarantees given. © 2023 Association for Computational Linguistics.

关键词： Crowdsourcing

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Predicting House Price Model : A Comprehensive Analysis with Random Forest and Decision Tree Method 3

Predicting House Price Model : A Comprehensive Analysis with...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Sharma, Mansi Sharma, Deepak Burle, Rushikesh Patil, Pawan Joge, Isha Puri, Chetan Datta Meghe Institute of Higher Education and Research Department of Computer Science and Design Wardha India Department of Commerce & Management Sciences Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Machine Learning Wardha India Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798350381931

This paper explores the field of predicting property prices through a thorough comparison of several regression techniques. Linear regression, decision tree regression, random forest regression, K-neighbors regression, gradient boosting regression, and XGBoost regression are the regression models that are being examined. Finding the best model to reliably estimate house prices based on a set of features is the *** dataset used in this study includes location, bedroom count, square footage, and other relevant parameters that are known to affect property values. Each regression model is trained and tested on this dataset using a methodical evaluation procedure, and its performance is evaluated using important metrics including R-squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and overall correctness. predicting house prices mainly use harlfoxem (Owner) repository of housing dataset will contain the homes sold between May 2014 and May 2015 with 21613 entries and 21 attribute were used to evaluate the performance of machine learning prediction model. For practitioners and scholars working in real estate, housing market analysis, and related sectors, this study offers insightful information. It emphasizes how crucial it is to properly choose regression techniques depending on the particulars of the prediction task and the distinct qualities of the dataset. © 2024 IEEE.

关键词： Decision trees

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Transforming Agriculture with AI: Automated Crop Insect Identification Using InceptionV3 Transfer Learning 1

Transforming Agriculture with AI: Automated Crop Insect Iden...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Thyagharajan, K.K. Kalaiarasi, G. Nirmala, G. Saravanan, P. Vignesh, T. Makashkumar, V. R.M.D Engineering College Department of Electronics and Communication Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India R.M.D Engineering College Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350342796

Identifying crop insects and classifying them is a challenging task for agriculturalists. A transfer learning method is suggested in this paper to address this issue. The proposed approach uses InceptionV3 as a base deep learning model and it is given additional custom layers. The model is fine-tuned using a custom dataset made up of five classes with 1000 insects each. The insect and pest images available from the National Bureau of Agricultural Insect Resources datasets and the Xie1 dataset are used for testing the model. Preprocessing techniques are applied to resize the images, and data augmentation is employed to prevent overfitting. The model was analyzed on the validation dataset with Adam, Adagrad, and Nadam optimizers, and obtained recognition accuracies of 86.6%, 77.8%, and 88.56% respectively. The outcomes confirms that the model performs well with Nadam optimizer for insect identification. © 2023 IEEE.

关键词： Deep Learning InceptionV3 Insect Detection Smart Agriculture Transfer Learning

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Optimized Image Compression Using Multiple Compressed Sensing Techniques

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SN computer science 2025年第4期6卷 1-18页

作者： Puttegowda, Kiran Mohan, B.A. Veeraprathap, V. Vijay, C.P. Sudheesh, K.V. Kumar, D. S. Sunil Department of ECE Vidyavardhaka College of Engineering Karnataka Mysuru India Department of ISE B.M.S. Institute of Technology and Management Yelahanka Karnataka Bengaluru India Department of ECE ATME college of Engineering Karnataka Mysuru India Department of Computer Science Engineering (AI & ML) Vidyavardhaka College of Engineering Mysuru Karnataka India Department of Computer Applications East Point College of Higher Education Banglore India

In the digital era, the demand for efficient image compression techniques is ever-increasing due to the exponential growth of visual data. Compressed sensing (CS), a groundbreaking theory in signal processing, offers a promising solution by exploiting the inherent sparsity of images. The proposed work introduces a novel approach to image compression, leveraging compressed sensing techniques to achieve optimized results. Our method works by expressing image data in a higher-order non-raw and non-dense format hence dramatically decreasing the size of the entire data. This significantly reduces storage requirements while preserving image quality. By combining advanced signal processing with innovative image analysis techniques, our approach maintains low computational complexity, enabling real-time applications without sacrificing the fidelity of reconstructed images, even after compression. This makes our approach especially valuable for applications where storage space is limited but image integrity remains crucial. We present experimental results demonstrating the effectiveness of our technique across various image types and compression ratios. Our findings underscore the potential of compressed sensing as a transformative paradigm in the realm of image compression, paving the way for more efficient storage and transmission of visual content in diverse applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Compressed Sensing Compression Compression ratio Images Reconstruction

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of computer Applications and management 2025.

关键词： control strategies liquid transportation modeling of system

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Software for Implementing the Directed Cone Optimization Method 14

Software for Implementing the Directed Cone Optimization Met...

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14th International Conference on Advanced computer Information technologies, ACIT 2024

作者： Melnyk, Bohdan Stakhiv, Petro Melnyk, Nataliya Franko, Yuriy Seniv, Bohdan Martsenyuk, Yevgeniya Ivan Franko National University of Lviv Department of Information Systems of Management Lviv Ukraine Lviv Polytechnic National University Department of Theoretical and General Electrical Engineering Lviv Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Technologies Ternopil Ukraine Banking Business Department of Financial Technologies Ternopil Ukraine Ukrainian National University Department of Computer Science West Ternopil Ukraine

ISBN: (纸本)9798350350036

The article is devoted to the development of software for the implementation of the stochastic optimization method. The classical method of the directed cone and its modification are considered. Its shortcomings in the process of searching for the global minimum are pointed out. To improve the search, it is proposed to use algorithms with adaptation of optimization parameters. The architecture of software implementation of optimization algorithms of the directed cone method is proposed. Appropriate software was developed. It was tested on the example of optimization of the structured cable system of the telecommunications network. For this, a corresponding optimization problem is formulated. The results of its solution and application of optimization algorithms are analyzed. © 2024 IEEE.

关键词： Optimization algorithms

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A Study about the Impact of Rewards on Student's Engagement with the Flipped Classroom Methodology 14

A Study about the Impact of Rewards on Student's Engagement ...

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14th IEEE Global Engineering education Conference, EDUCON 2023

作者： Urquiza-Fuentes, Jaime Rey Juan Carlos University Móstoles ACTIVATE Teaching Innovation Group Laboratory of Information Technologies in Education - LITE Department of Computer Science & Statistics Madrid Spain

ISBN: (纸本)9798350399431

This work describes an incremental design of a course that implements the flipped classroom methodology. The last phase of this implementation was to include rewards based on self-evaluation tests that would impact the students' grades. This should foster students' engagement with the learning methodology. In this work, a deep analysis of the impact of the reward is performed. The analysis of the use of the out-class materials shows that the students' pace does not correspond to the flipped classroom methodology. The students' behaviour is more focused on achieving rewards rather than preparing in-class activities with out-class materials. It is hypothesized that, the main reasons for these behaviours could be that, on the one hand, since the reward is considered by students as another item of the course grade, they work to get the reward. On the other hand, students do not want to change their study methodology because getting involved in the flipped methodology requires from them a significant effort. © 2023 IEEE.

关键词： Students

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