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检索条件"机构=Dept. of Informatics and Computer Technology"

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A Procedure for Optimization of Placement Products in Large Industrial Areas of a Greek Soft Drinks Company

A Procedure for Optimization of Placement Products in Large ...

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2024 International Conference on Applied Mathematics and computer Science, ICAMCS 2024

作者： Tsakiridis, Sotirios Tsimpiris, Alkiviadis Angeioplastis, Athanasios Papaioannou, Nikolaos Mastorocostas, Paris Papakonstantinou, Apostolos Kamilali, Angeliki Varsamis, Dimitrios International Hellenic University Dept. Computer Informatics and Telecommunications Engineering Serres Greece University of West Attica Dept. Informatics and Computer Engineering Egaleo Greece Cyprus University of Technology Dept Civil Engineering and Geomatics Lemesos Cyprus

ISBN: (纸本)9798350363807

In this paper we present the process of optimal placement of industrial products (pallets) in mapped ware-houses and specifically in a soft drink company in Greece. The optimization criterion is to minimize the cost of moving the lifting machine that places the products in specific positions. The computational method of optimal placement of industrial products receives data from the automatic product count of the mapped warehouse and from the sales predictive model. Therefore, the computational method, having knowledge of the location and number of products in the warehouse and the prediction of sales for each product, places the products produced in an optimal way. Automatic product counting is carried out using an unmanned aerial vehicle (UAV) which developed and applied in soft drink company in Greece. The sales prediction is carried out through a prediction model which developed in a soft drink company in Greece. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Sentiment Analysis Twitter (X) Users Towards the Boycott of Pro-Israel Brand Using Support Vector Machine Method with SMOTE Approach

Sentiment Analysis Twitter (X) Users Towards the Boycott of ...

引用

2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Mawarnie, Hana Patricia Widodo Nugraheni, Murien Universitas Negeri Jakarta Dept. of Informatics and Computer Jakarta Indonesia Universitas Negeri Jakarta Dept. of Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798331511166

The Israel-Palestine conflict that occurred on October 7, 2023, resulted in a boycott of brands known to be pro-Israel. Twitter or X, as one of the largest social media platforms in the world, became a platform for people to voice their opinions regarding the boycott. This study aims to analyze the sentiment of Twitter or X users towards the boycott of pro-Israel brands using the Support Vector Machine (SVM) method with the SMOTE (Synthetic Minority Oversampling Technique) approach. The Support Vector Machine (SVM) method is a machine learning method used for classification and regression. SMOTE is a technique used to overcome data imbalance. The data used in this study is Indonesian language tweet data from October 7, 2023, to April 7, 2024. The data was collected using data crawling techniques. The results of the study from the 80:20 data split, show that the Support Vector Machine (SVM) polynomial kernel model with the SMOTE technique has better performance in analyzing the sentiment of Twitter or X users towards the boycott of pro-Israel brands, with an accuracy value of 98,47%. © 2024 IEEE.

关键词： Tweets

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Handling of Computationally Demanding Digital Controllers in Power System Dynamic Simulations 3

Handling of Computationally Demanding Digital Controllers in...

引用

3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Jafari, Mehran Bureau, Gautier Chiaramello, Marco Guironnet, Adrien Panciatici, Patrick Aristidou, Petros Cyprus University of Technology Computer Eng. & Informatics Dept. of Electrical Eng. Limassol Cyprus France

ISBN: (纸本)9798350375923

Nowadays Smart grids utilize digital controllers to guarantee the reliable and efficient operation of their components. However, the numerical simulation of these types of controllers is challenging due to the numerous discrete time events introduced. The interpolation-based method (IBM) can effectively simulate smart digital controllers alongside the differential-algebraic equations of the system under control. Compared to the traditional step-reduction method (SRM) that reduces the time step for each time event during the simulation, IBM allows the use of variable time steps and solves the system while ensuring simulation accuracy. However, in the case of computationally demanding controllers, the computational performance of IBM suffers significantly. In this paper, a modified version of IBM is introduced, which can handle computationally demanding controllers in dynamic simulation of power systems. The performance of the proposed method is assessed and compared to SRM and IBM by simulating a test system with different controllers. © 2024 IEEE.

关键词： Smart power grids

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A Deep Ensemble Learning Approach for Venomous & Non-Venomous Serpents and Insect Species Identification 3

A Deep Ensemble Learning Approach for Venomous & Non-Venomou...

引用

3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Samarasinghe, Buddhin Saroj Lakmali, K.B.N. Informatics Institute of Technology Dept. of Computer Science Colombo 06 Sri Lanka Informatics Institute of Technology Department of Computer Science Colombo 06 Sri Lanka

ISBN: (纸本)9798350302288

In recent years snake and insect attacks have become a huge problem worldwide. Most species have similar colors and shapes, which makes it hard to tell them apart using typical techniques. Similarly, identifying different species of bees and wasps can be challenging, especially for non-experts in the field. Therefore, developing a reliable and effective method for recognizing these animals is essential to reducing the issues caused by snake and insect attacks and supporting wildlife conservation efforts. The proposed method in this research utilizes deep ensemble learning and transfer learning techniques to identify snakes, wasps, and bees accurately. Deep ensemble learning involves combining several machine learning models to make better predictions, and transfer learning is a technique that allows pre-trained models to be re-used for new tasks, saving time and computational resources. The proposed method utilizes deep ensemble learning and uses three base transfer learning models, DenseNet201, MobileN etV2, and InceptionResN etV2, for classification. The study obtained a training accuracy of 93%. The research classified 11 types of snakes, four types of bees, and four types of wasps, including all 19 classes. Moreover, the author developed a mobile application for user interaction and utilized Flask API for the logic tier. The mobile application enables users to take pictures or upload images of a snake, wasp, or bee and identify the species accurately. This feature makes the proposed method more accessible to people who encounter these animals daily. Overall, this research represents a promising step towards developing an accurate and effective system for identifying snakes, wasps, and bees, with practical implications for human safety and biodiversity conservation. © 2023 IEEE.

关键词： Learning systems

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir University of Oslo Dept. of Informatics Oslo Norway University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom Illinois Institute of Technology Dept. of Electrical and Computer Engineering Chicago United States Northwest Univesity School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350351255

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024 IEEE.

关键词： Quality of service

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

引用

24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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Asynchronous Complete Visibility Algorithm for Luminous Robots on Grid 11

Asynchronous Complete Visibility Algorithm for Luminous Robo...

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Kim, Yonghwan Katayama, Yoshiaki Wada, Koichi Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan Hosei University Dept. of Applied Informatics Tokyo Japan

ISBN: (纸本)9798350306941

An autonomous mobile robot system is a distributed system consisting of mobile computational entities (called robots) that autonomously and repeatedly perform three operations: Look, Compute, and Move. Various problems related to autonomous mobile robots, such as gathering, pattern formation, or flocking, have been extensively studied to understand the relation between each robot's capabilities and the solvability of these problems. In this study, we focus on the complete visibility problem, which involves relocating all the robots on an infinite grid plane so that each robot is visible to every other robot. We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (not transparent). In this paper, we propose an algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) when a set of robots is given. The algorithm ensures that complete visibility is achieved even when robots operate asynchronously and have no knowledge of the total number of robots on the grid plane using only two colors. © 2023 IEEE.

关键词： Visibility

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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Augmented Reality-Based Navigation: Bridging Indoor and Outdoor Environments through Immersal Integration 1

Augmented Reality-Based Navigation: Bridging Indoor and Outd...

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1st IEEE International Symposium on Consumer technology, ISCT 2024

作者： Maulana Achmad, Zacky Sukaridhoto, Sritrusta Agus Zainuddin, Muhammad Savila, Aurent Firmansyah, Ardian Mega Saputra, Septa Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Multimedia Creative Technology Surabaya Indonesia

ISBN: (纸本)9798350365191

A navigation system is an essential tool designed to assist users in determining and following a route from one location to another. Navigation systems are typically categorized into two types: outdoor navigation systems designed for open areas and indoor navigation systems used within enclosed spaces. However, transitioning between indoor and outdoor environments has limitations, often requiring users to switch applications, such as using GPS for outdoor navigation and a different application for indoor navigation. Therefore, we propose the integration of indoor and outdoor navigation based on Augmented Reality. The development of AR navigation system begins with the creation of 3D assets of the Politeknik Elektronika Negeri Surabaya (PENS) campus, which is the site of our research including three buildings for indoor navigation and the connecting roads between the buildings for outdoor navigation. The development of this navigation system uses the Immersal SDK as a library for Spatial Mapping, Localization and System Integration. Several features are included, such as indoor-outdoor navigation, multilevel floor navigation, and zero additional devices. The system testing results are based on user testing, integration testing, and multilevel floor testing. From User Testing with PIECES Framework, 32 respondents expressed satisfaction with the proposed system. Integration Testing showed that the system could navigate between indoor and outdoor environments. And Multilevel Floor Testing demonstrated that the system could navigate within buildings with multiple floors. © 2024 IEEE.

关键词： Augmented reality

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Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network 24

Emotion Recognition from Video Frame Sequence using Face Mes...

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24th International Seminar on Intelligent technology and Its Applications, ISITIA 2023

作者： Adi, Derry Pramono Yuniarno, Eko Mulyanto Wulandari, Diah Puspito Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350313956

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe's face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of 'Anger,' 'Fearful,' 'Happy,' and 'Sad.' For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of ∼2.3 seconds (achieved using the SqueezeNet model). © 2023 IEEE.

关键词： Emotion Recognition

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