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22nd IEEE International Symposium on Intelligent Systems and informatics, SISY 2024

作者： Zigulic, Nikola Glucina, Matko Frank, Domagoj Lorencin, Ivan Sverko, Zoran Matika, Dario University of Rijeka Faculty of Engineering Rijeka Croatia Istrian University of Applied Sciences Department of Engineering Pula Croatia University North Department of Computer Science and Informatics Koprivnica Croatia Juraj Dobrila University of Pula Faculty of Informatics Pula Croatia University North Department of Electrical engineering Varaždin Croatia

ISBN: (纸本)9798350385601

The development of artificial intelligence is one of the most significant technological innovations that contributes to humanity with its characteristics and facilitates, secures, and improves everyday life. However the challenge arises when the programmer-engineer learns the algorithm of artificial intelligence to perform the proposed task, that is, the challenge lies in the issue of available hardware resources. Artificial intelligence algorithms perform many human-impossible tasks, such as detection and counting, i.e. calculating the interrelationships of individual objects, segmentation of tumors and other malignant diseases, i.e. tissues, classification of specific states of classes of a scene, and many other similar technologies. This research paper examines the possibility of implementing the You Only Look Once algorithm of the tenth generation on certain devices such as an affordable Raspberry Pi, and will discuss the advantages and disadvantages of changes in detection parameters, i.e. inferences to the applied model. In addition, a mock-up of the device will be shown, which will serve to provide timely information about criminals and suspicious persons who possess firearms in different situations, such as normal weather conditions in a populated place or in shops where petty robberies are frequent. The testing will be done using recorded videos in real-time scenarios. Finally, real-time inference or detection in real-time will be tested and the actions that Raspberry will perform will be simulated. The results indicate that the optimal model achieved a precision of 0.938, recall of 9.863, mAP50 of 0.91, and mAP50-95 of 0.739. This was achieved using an image size of 640, IOU threshold of 0.7, confidence threshold of 0.6, and training for 600 iterations with the Stochastic Gradient Descent optimizer, without augmentations, and employing the ONNX inference format. © 2024 IEEE.

关键词： Stochastic systems

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Assessing Predictor Influence in LSTM Models for Enhanced Solar Energy Forecasting 26

Assessing Predictor Influence in LSTM Models for Enhanced So...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Ciobanu, Alexandru-Adrian Bacanin, Nebojsa Sanders, Tilman Stoean, Catalin University of Craiova Department of Computer Science Craiova Romania Singidunum University Faculty of Informatics and Computing Belgrade Serbia FH Muenster Department of Electrical Engineering and Computer Science Steinfurt Germany

ISBN: (纸本)9798331532833

Accurate forecasting of solar energy production is highly important for an adequate integration of renewable energy into the power grid. This study explores the importance of various predictors for enhancing the accuracy of solar energy forecasting using two distinct Long Short-Term Memory models: one that relies solely on historical power production data and past exogenous weather data, and another that additionally incorporates future exogenous weather data. Various settings are tried to improve the results and relatively similar parameters are used by both, yielding more accurate results for the model that also uses information from the future. The results also demonstrate that predictor importance varies significantly between the two models. These findings suggest that different models benefit from distinct predictor combinations and incorporating exogenous data can significantly enhance forecasting performance. © 2024 IEEE.

关键词： Prediction models

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Learner Assessment System in e-Learning with OBE Approach: Activity Performance, Ability Level and Recommendation

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 572-602页

作者： Yuniarti, Wenty D. Hartati, Sri Priyanta, Sigit Surjono, Herman D. Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Information Technology Universitas Islam Negeri Walisongo Semarang50185 Indonesia Department of Informatics Engineering Education Faculty of Engineering Universitas Islam Negeri Walisongo Yogyakarta55281 Indonesia

E-learning can lead learners to achieve learning outcomes if it is designed based on several principles. One is applying assessments that motivate and inform ability levels. In Outcome-based Education (OBE), assessment is integral to the system. However, e-learning has limitations in providing assessment instruments according to needs, such as assessing complex and detailed aspects and accommodating a variety of numerical and linguistic assessment data. Moreover, the presence and involvement of learners affect their performance and learning outcomes. This study proposes a learner assessment system in e-learning with the OBE approach, including learning design, activity performance analysis, ability level determination, and recommendations. This system adds the e-rubric to e-learning to overcome instrument limitations and accommodate comprehensive assessments. Various numerical and linguistic assessment data are unified using 2-tuple fuzzy linguistics, producing ability levels as two tuples. Performance analysis was based on event log data using descriptive statistical technique and alignment-based conformance checking, from frequency, time, and sequence of activity objects, resulting in five activity performance variables. The performance value of each variable is converted into High, Medium, or Low levels. The ability and performance levels are processed using rule-based methods to produce recommendations for learning stages and activity performance directions. The results of this research can be used as input for academic stakeholders and online learning providers and potentially be applied to the advancement of e-learning in higher education. © Authors retain all copyrights.

关键词： Contrastive Learning

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Indonesian Stock Prices Prediction using Bidirectional Long Short-Term Memory 7

Indonesian Stock Prices Prediction using Bidirectional Long ...

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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Dinata, I Gede Angga Yudistira, Novanto Muflikhah, Lailil Department of Informatics Engineering Faculty of Computer Science Brawijaya University Indonesia

ISBN: (纸本)9781450397117

This paper aims to know how well Bidirectional Long Short-Term Memory (BiLSTM) is in predicting Indonesian stock prices. First, the best hyperparameter of BiLSTM is searched through hyperparameter tuning. After finding the best hyperparameter, we train the data train containing close prices from Indonesian stock. After that, we try to find optimal days to predict by measuring the error rate using Mean Absolute Error (MAE). We also compared BiLSTM with Recurrent Neural Network (RNN) and LSTM by comparing the MAE from each method. Finally, we also tried using multivariate BiLSTM using Indonesian stock. The evaluations yield the best hyperparameters setting and how many days suitable for predicting BiLSTM performance. BiLSTM performed better than RNN and LSTM. Moreover, univariate BiLSTM performs better than multivariate BiLSTM in predicting Indonesian stock prices. © 2022 ACM.

关键词： Long short-term memory

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Mobile Robot Tracker for the Presence of Gas in a House Prototype Using Left Hand Rule Based on PID

Mobile Robot Tracker for the Presence of Gas in a House Prot...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jayadi, Akhmad Adhinata, Faisal Dharma Putri, Novia Utami Setiawan, Ridho Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung Indonesia Institut Teknologi Telkom Purwokerto Faculty of Informatics Jawa Tengah Indonesia

ISBN: (纸本)9798350369359

The designed mobile robot can follow the arena automatically and can detect gas automatically without human assistance. This mobile robot can run from a predetermined starting point, and will detect gas at a predetermined point. This mobile robot is built with an Arduino Uno microcontroller which uses the C programming language and is equipped with 2 ultrasonic sensors and an MQ-6 sensor. The results obtained after testing in this study are the establishment of an intelligent mobile robot system that is able to navigate walls with the help of ultrasonic sensors and is able to detect gas wells. In this study using the PID value, namely kp=40ki= 0.0001kd=2 and for gas readings the MQ-6 sensor detects the presence of gas of 800 PPM. Thus, this mobile robot is expected to be implemented so that it can be used to facilitate the role of humans in detecting gas leaks based on intelligent mobile robots. © 2023 IEEE.

关键词： Gases

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Arabic Named Entity Recognition Using Clustered Word Embedding 19th

Arabic Named Entity Recognition Using Clustered Word Embeddi...

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Sabty, Caroline Elmahdy, Mohamed Abdennadher, Slim Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Faculty of Informatics and Computer Science German International University Cairo Egypt

ISBN: (纸本)9783031237928

Named Entity Recognition in Arabic is a challenging topic because of morphological and lexical richness of Arabic. In this paper, we propose an Arabic NER system that is based on word embedding. Word embedding hold semantic information about the context of the words. We hypothesized that the integration of word embedding features to the conventional lexical and contextual features could improve Arabic NER performance. The Conditional Random Field (CRF) sequence classifier was used. Since most CRF implementations only support categorical features, continuous word embedding vectors are clustered. In this paper, we are mainly investigating the effect of the number of clusters on NER performance. Moreover, the combination of fine and coarse grained clusters has resulted in further recognition improvement. © 2023, Springer Nature Switzerland AG.

关键词： Embeddings

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One-Class Classification of 3D Point Clouds Using Dynamic Graph CNN 7

One-Class Classification of 3D Point Clouds Using Dynamic Gr...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Gencer, Zeki Sahin, Yusuf H. Unal, Gozde Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Anomaly detection is defined as a binary classification of normal and abnormal samples in a given data. In this task, anomalous samples are considered very rare and thus they are not used in training the deep learning model. Anomaly detection is thoroughly studied in 2D images, but 3D applications are not common yet. In this work, we combine studies in the field of 3D point cloud and anomaly detection and present a one-class classification method for 3D data. This method uses a pretrained Dynamic Graph Convolutional Neural Network (DG-CNN) to extract features from 3D point cloud data, and then fits a separate multivariate Gaussian distribution for each class. In the test phase, features are extracted from the test samples, and the distances to the distribution obtained in training indicate how anomalous the sample is for that class. Our method achieves 94.2% AUROC accuracy for the given setup. This is the first attempt to handle 3D point cloud classification as an anomaly detection case, and thus our findings and methods serve as a baseline for further studies. © 2022 IEEE.

关键词： Anomaly detection

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Implementation of PID Control as a One Axis Balancing Control System on the Bicopter Vehicle 2

Implementation of PID Control as a One Axis Balancing Contro...

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2nd International Conference on Converging Technology in Electrical and Information engineering, ICCTEIE 2023

作者： Jayadi, Akhmad Adhinata, Faisal Dharma Sembiring, Jaka Persada Putri, Novia Utami Pranita, Elka Setiawan, Arif Universitas Teknokrat Indonesian Faculty of Engineering and Computer Science Lampung Indonesia Institute Technology Telkom Purwokerto Faculty of Informatics Central Java Indonesia

ISBN: (纸本)9798350370645

This Bicopter is an unmanned aircraft with two brushless motor drive systems connected to propellers in a horizontal parallel position. The problem that often occurs in bicopters is stability or flight balance. When the vehicle cannot control or maintain its balance angle, it is most likely that the bicopter vehicle will fall while flying. Thus, a research design is carried out that can develop and overcome problems in Bicopter stability through PID Control Implementation as a One Axis Balancing Control System on the Bicopter Vehicle. This study aims to find out how to apply the PID control system as a balance controller on the bicopter and to see the results of the dynamic response generated by the PID control system using testing methods using different load weights and load positions. The research results show that the prototype of the bicopter vehicle with PID controller has been successfully made and can work well in carrying out balance control. By providing a value of Kp = 2.4329, Ki = 0.000821, Kd = 603.98901, and a Throttle of 1600 rpm, it produces a dynamic response in the form of the smallest Steady State Error value of 0.16 and the largest value of 9.29, the smallest Rise Time is 0.22s and the largest is 1.92s, The biggest overshoot is 6.68 and the smallest is -0.17. The biggest settling time is 3.36s, and the smallest is 0.92s © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Leveraging Transformer-Based Topic Modeling using BERTopic for Publication Venue Recommendation 11

Leveraging Transformer-Based Topic Modeling using BERTopic f...

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11th International Conference on Electrical engineering, computer Science and informatics, EECSI 2024

作者： Badieah Chaerul Haviana, Sam Farisa Mulyono, Sri Salim, Channa Abdullah Adli, Muhammad Fatih Febriansah, Moh Taufik Wiktasari Sulaiman, Noor Suhana Yaqob, Azliza Universitas Islam Sultan Agung Informatics Engineering Department Semarang Indonesia Politeknik Negeri Semarang Computer Engineering Technology Semarang Indonesia University College of TATI Faculty of Computer Media and Technology Management Terengganu Malaysia

ISBN: (纸本)9798350355314

Topic modeling has become essential in a variety of text mining applications, such as document clustering and recommendation systems. This study investigates the potential of BERTopic, a transformer-based method that leverages BERT embeddings to recommend publication venues. Traditional topic modeling methods are made better by BERTopic, which uses the deep contextual embeddings that BERT (Bidirectional Encoder Representations from Transformers) offers. This investigation assesses the efficacy of BERTopic in clustering academic papers according to their content in order to suggest appropriate publication venues. We also compare the performance between different kinds of pre-trained sentence embedding models and input types (title, abstract, and title+abstract). The experimental results indicate that BERTopic performs well with the GARUDA dataset, which comprises 5272 records from three different research subjects. The results emphasize the transformative influence of the transformer-based model. The best performance of all cases was 99.17% mean average precision in the Top 3 position, using Title+Abstract as input data and all-mpnet-base-v2 as the sentence embedding model. © 2024 IEEE.

关键词： Recommender systems

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Measuring Bug Reporter's Reputation and Its Effect on Bug Resolution Time Prediction 7

Measuring Bug Reporter's Reputation and Its Effect on Bug Re...

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7th International Conference on computer Science and engineering, UBMK 2022

作者： Yucel, Muhammed Kadir Tosun, Ayse Istanbul Technical University Faculty of Computer and Informatics Engineering Istanbul Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

In software projects, bug reports remain open and get updates from team members during the related bug's lifetime. It is an important task to predict when a bug would be resolved so that managers plan timeline and allocate team resources accordingly. Prior works show that reporter information is the most effective feature for predicting resolution time. Previous work only considers bug reporting activity to define reporter reputation and misses other activities. In this paper, we propose a new reputation calculation method that considers all activities of a reporter within a bug tracking system. We collected bug reports of Chromium and WebRTC projects and calculated reputations of each bug reporter within the dataset using our weighted, activity based calculation method as well as other methods from previous work in order to compare performance. We trained a Doc2Vec model to utilize textual information in bug reports to build a base model for comparing different reputation methods. Bug reports are classified into two categories as FAST and SLOW according to their resolution time. Stochastic Gradient Descent (SGD) and Extreme Gradient Boost (XGB) classifier algorithms are employed. XGB classifier resulted with 55 % - 72 % F -Scores for FAST and SLOW respectively. The results also show that data characteristics of project affects the effectiveness of the reputation calculation method on bug resolution time prediction. © 2022 IEEE.

关键词： Forecasting

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