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High-Dimensional Data Integration Model to Improve the Plants Quality

High-Dimensional Data Integration Model to Improve the Plant...

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

作者： Hasibuan, Nelly Astuti Sihombing, Poltak Nababan, Erna Budhiarti Amalia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331511166

High Dimensional Data is data that has many dimensions or features with the aim of providing more complete and detailed information about an object or phenomenon. Plant images are objects that have data with many dimensions or features. Plant images can be analyzed to determine plant quality. For example, coffee plant images can be analyzed to determine the quality of coffee plants. Determination of coffee plant quality in previous studies only used one object such as leaves or stems and or fruit. In this study, the quality of coffee plants is measured using leaves, stems, fruit, and hypotheses from coffee plant experts. Analysis of coffee plant images can produce information in the form of size, color and health of plants. This information can be found by extracting image features. However, the large number of features is an obstacle in grouping. Clustering techniques are used to group the large amount of data in the dataset. Expert hypotheses will be used to validate the dataset, determine centroid values and determine weight values to determine plant quality. The obstacle in analyzing multidimensional data is the difficulty of visualizing the number of variables because the available methods generally only process low-dimensional data. The purpose of this study is to produce an integration model for analyzing plant quality datasets by optimizing machine learning to group high-dimensional data. © 2024 IEEE.

关键词： Data quality

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Optimation Cavacity Vehicle Routing Problem with K-Nearest Neighbor in Classification of Goods Ditribution Route 7

Optimation Cavacity Vehicle Routing Problem with K-Nearest N...

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7th IEEE International Conference of computer science and information technology, ICOSNIKOM 2023

作者： Novelan, Muhammad Syahputra Efendi, Syahril Sihombing, Poltak Mawengkang, Herman Universitas Sumatera Utara Doctoral Program of Computer Science Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Department of Mathematics Medan Indonesia

ISBN: (纸本)9798350360752

Industrial and distribution service problems that belong to combinatorial optimization include vehicle routing with Vehicle Routing Problem. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification in the influence of the number of 'nodes' on vehicle routes with machine learning. The problem of imbalance in vehicle route classification has been a challenge in the classification process and attracted the attention of a number of researchers. The purpose of the model in general is to gain an understanding of the mechanism in the problem so as to classify the imbalanced vehicle route data based on JNE delivery routes. The solution method that will be used by applying k-nearest neighbor to determine the amount of carrying capacity of the goods will then determine the delivery location point with the vehicle routing problem. So that this model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. © 2023 IEEE.

关键词： Machine learning

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Visual programming Concept for Infineon XMC Series Microcontrollers 47

Visual Programming Concept for Infineon XMC Series Microcont...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Vulić, Lukrecia Kaučić, Luka Aleksi, Ivan Matić, Tomislav Computer Science And Information Technology Osijek Faculty Of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350382495

In today's modern world, efficient programming is a necessity. To speed up code generation nowadays, programming code is generated using different graphical tools. Although efficient, this technology is scarcely used in embedded system development. In this paper, we present a procedure for block-based visual programming with applications in embedded systems development. We present several examples of blocks using the Blockly open-source visual programming tool and test the implementation on selected Infineon XMC-based boards. The presented programming solution shows successful implementation of block-based visual programming in embedded systems development. © 2024 IEEE.

关键词： Embedded systems

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Channel Estimation Model and Their Role in Optimizing Customer Lifetime Value with Robust Optimization 24

Channel Estimation Model and Their Role in Optimizing Custom...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Elveny, Marischa Nasution, Mahyuddin K.M. Purnamasari, Fanindia Wook, Tengku Siti Meriam Tengku Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (纸本)9798400717833

In a dynamic and competitive business world, optimizing customer lifetime value (CLV) is an important goal for companies to maximize the effectiveness of their marketing strategies. Utilizing effective channel estimation techniques and adapting robust statistical methodologies is critical to accurately understanding and improving CLV. The study aims to investigate the part that channel estimation plays in CLV optimization by using the robust M-estimation and iteratively reweighted least squares (IRLS) approaches. This study specifically integrates RFM (recency, frequency, and monetary) analysis to measure and improve customer value predictions. We integrate the RFM variable as the main predictor in the regression model to estimate CLV based on different channel interactions. The research results show an R-square value of 0.94, which means 94% of data variability shows very good agreement between models. An RMSE value of 0.20 means that the average model prediction deviates from the actual value by around 0.20 units. A low RMSE indicates high prediction accuracy. Channel estimation techniques, combined with RFM and robust methods, provide significant insight into the factors influencing CLV. Increased email interactions and social media engagement are associated with higher CLV values, indicating that businesses should invest more in digital marketing strategies. We have proven that the frequency of current customer interactions and engagement through optimal channels has the most significant positive impact on increasing CLV. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Measuring Student Satisfaction Based on Analysis of Physical Parameters in Smart Classroom 12

Measuring Student Satisfaction Based on Analysis of Physical...

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12th International Conference on information and Education technology, ICIET 2024

作者： Shiri, Farhad Mortezapour Perumal, Thinagaran Mustapha, Norwati Mohamed, Raihani Ahmadon, Mohd Anuaruddin Bin Yamaguchi, Shingo University Putra Malaysia Faculty Of Computer Science And Information Technology Malaysia Graduate School Of Sciences And Technology For Innovation Yamaguchi University Japan

ISBN: (纸本)9798350371772

Several factors related to the physical environment, including temperature, environmental noise, CO2 levels, air pressure, humidity, and the lecturer's vocal delivery, significantly impact student satisfaction. Evaluating, quantifying, and enhancing these factors can significantly contribute to enhancing students' satisfaction with lecture quality. The objective of this study is to assess students' satisfaction regarding the quality of lectures through an analysis of data obtained from a variety of smart devices that record physical environmental parameters. In this study, the machine learning techniques employed encompass Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), K-nearest neighbors (KNN), and Stochastic Gradient Descent (SGD). Additionally, the deep learning models utilized comprise Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM). The findings of this study suggest that, on the whole, deep learning models demonstrate superior performance when it comes to classifying data about this particular problem, as compared to traditional machine learning models. Notably, the CNN model emerges as the top performer among all the models assessed, achieving an impressive accuracy rate of 74.62%. it is worth mentioning that the decision tree exhibits the highest accuracy among classical machine learning models. © 2024 IEEE.

关键词： Long short-term memory

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Intercommunication Method between Local Edge Computing Devices Using QUIC-Based CYPHONIC

Intercommunication Method between Local Edge Computing Devic...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Horisaki, Shota Matama, Kazushige Goto, Ren Naito, Katsuhiro Suzuki, Hidekazu Meijo University Graduate School of Science and Technology Nagoya Japan Aichi Institute of Technology Graduate School of Business Administration and Computer Science Toyota Japan Aichi Institute of Technology Faculty of Information Science Toyota Japan Meijo University Faculty of Information Engineering Nagoya Japan

ISBN: (纸本)9798350324136

Internet of Things (IoT) devices are installed in various locations to realize smart cities, smart factories, smart homes, etc., and are connected to the Internet for data communication. Most current IoT systems are built on a client-server model using servers built on the cloud. On the other hand, edge computing services are increasing and there are still demands to manage sensitive data in a local environment, and a local edge computing is emerging as a new concept to address these demands. Next-generation IoT services are expected to increase the importance of interoperability between different IoT systems, and connectivity to the local edge computing environment will be required. The authors have proposed QUIC-based CYPHONIC (CYber PHysical Overlay Network over Internet Communication) as a communication architecture that simultaneously achieves communication connectivity and mobility transparency in a mixed IPv4/IPv6 environment. This paper shows that QUIC-based CYPHONIC can be used to realize end-To-end encrypted communication between local edge devices located in different local networks over the actual Internet environment with practically acceptable performance. © 2024 IEEE.

关键词： End-To-End Connectivity Local Edge Computing Overlay Network QUIC

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Correcting Language Model Bias for Text Classification in True Zero-Shot Learning 30

Correcting Language Model Bias for Text Classification in Tr...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhao, Feng Wan, Xianlin Yan, Cheng Loo, Chu Kiong Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology China Faculty of Computer Science & Information Technology University of Malaya Malaysia

ISBN: (纸本)9782493814104

Combining pre-trained language models (PLMs) and manual templates is a common practice for text classification in zero-shot scenarios. However, the effect of this approach is highly volatile, ranging from random guesses to near state-of-the-art results, depending on the quality of the manual templates. In this paper, we show that this instability stems from the fact that language models tend toward predicting certain label words of text classification, and manual templates can influence this tendency. To address this, we develop a novel pipeline for annotating and filtering a few examples from unlabeled examples. Moreover, we propose a new method to measure model bias on label words that utilizes unlabeled examples as a validation set when tuning language models. Our approach does not require any pre-labeled examples. Experimental results on six text classification tasks demonstrate that the proposed approach significantly outperforms standard prompt learning in zero-shot settings, achieving up to 19.7% absolute improvement and 13.8% average improvement. More surprisingly, on IMDB and SST-2, our approach even exceeds all few-shot baselines. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Zero-shot learning

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Recognition of Student Engagement and Affective States Using ConvNeXtlarge and Ensemble GRU in E-Learning 12

Recognition of Student Engagement and Affective States Using...

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12th International Conference on information and Education technology, ICIET 2024

ISBN: (纸本)9798350371772

The advent of online learning has revolutionized the educational landscape, significantly enhancing global access to and affordability of education. However, a critical challenge in this domain is the efficacy of online classes, particularly in the context of the feedback loop between instructors and students. A pivotal aspect of this challenge is the assessment of student engagement and other affective states in online learning environments, a task that is inherently complex due to its nuanced nature. In this paper, we introduce a novel spatio-temporal hybrid deep learning model that combines the strengths of ConvNeXtLarge with an ensemble of gated recurrent unit (GRU and Bi-GRU) models to detect and classify affective states of students from video data. This model adeptly handles the multi-label, multi-class nature of the problem, employing four parallel fully connected (Dense) layers for effective classification. Our model's performance is evaluated using the DAiSEE dataset, a publicly available resource that captures a range of student affective states in an online learning environment. The proposed model demonstrates promising results in identifying four key affective states of boredom, engagement, confusion, and frustration, with accuracy rates of 54.89%, 56.46%, 70.18%, and 79.32% respectively. These findings underscore the efficacy of our model in accurately gauging student affective states, thereby offering significant insights into the potential of spatio-temporal hybrid networks in this realm. The study demonstrates the feasibility of employing such models to enhance the quality of online education. © 2024 IEEE.

关键词： Students

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Evaluation of REAL-ESRGAN Using Different Types of Image Degradation 11

Evaluation of REAL-ESRGAN Using Different Types of Image Deg...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Andriadi, Kus Zarlis, Muhammad Heryadi, Yaya Chowanda, Andry Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta11480 Indonesia School of Computer Science Binus University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389654

This study evaluates the performance of the REAL-ESRGAN [1] model on images with varying levels of degradation using the DIV2K dataset [2], such as the Wild, the Mild, the Difficult, and the x8 subsets. REAL-ESRGAN was created to solve super-resolution problems and aims to produce high-resolution images from low-resolution images. Experiments were conducted at scales of x2 and x4, and performance was measured using Full-Reference metrics (LPIPS, PSNR, SSIM) and No-Reference metrics (NIQE, MANIQA, CLIPIQA, and PI). The Results were good, especially with the x2 scale;it has higher PSNR and SSIM scores, lower LPIPS and NIQE values, and enhanced visual and perceptual quality. The model faced more significant challenges with the wild and the difficult datasets because they have more complex degradations and compression artifacts;it can be seen with unstable results of Full-Reference and No-Reference metrics. On the contrary, the Mild and x8 datasets yielded better results in both metrics;not only that, even the computational cost for Mild and x8 outperforms the rest of the dataset. This study shows the strengths and limitations of REAL-ESRGAN in handling different levels of image degradation. For future research, the model needs enhancement to tackle the degradation format of the wild and the difficult dataset. It would be good if the REAL-ESRGAN improvement could also maintain the computational cost. © 2024 IEEE.

关键词： Gallium nitride

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An effective medical image fusion method utilizing moth-flame optimization and coupled neural P systems

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Neural Computing and Applications 2025年 1-41页

作者： Dinh, Phu-Hung Le, Thi-Hong-Ha Giang, Nguyen Long Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Hong Duc University 565 Quang Trung Dong Ve Thanh Hoa Viet Nam Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi Viet Nam

Medical image fusion is crucial for various applications, yet existing fusion models often encounter issues such as low quality, information loss, and insufficient contrast. This paper presents a novel approach to medical image fusion that directly addresses these challenges by enhancing brightness and contrast, improving overall image quality, and preserving essential structural features. Our method begins by decomposing input images into a base component and two detail components using a three-component decomposition (TCD) method with a truncated Huber filter (THF). The detail components are subsequently fused using an integrated approach combining the structure tensor saliency detection operator (STSDO), distance-weighted regional energy (DWRE), fast guided filter (FGF), and coupled neural P system (CNPS). The base components are then fused through an adaptive method guided by the moth-flame optimization (MFO) algorithm. To evaluate our approach, we used a dataset of 120 pairs of medical images and assessed performance with metrics such as QMLI, QCI, QAG, QAB/F, QP, QW, QE, and QCB. Experimental results demonstrate that the proposed model significantly enhances brightness and contrast and more effectively preserves information compared to recent methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image fusion

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