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Science in Information Technology Letters
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27224139     DOI : https://doi.org/10.31763/SiTech
Core Subject : Science,
Computer Science & IT
Science in Information Technology Letters (SITech) aims to keep abreast of the current development and innovation in the area of Science in Information Technology as well as providing an engaging platform for scientists and engineers throughout the world to share research results in related disciplines. SITech is a peer reviewed open-access journal which covers four (4) majors areas of research that includes 1) Artificial Intelligence, 2) Communication and Information System, 3) Software Engineering, and 4) Business intelligence Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers. Finally, accepted and published papers will be freely accessed in this website.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 4 Documents
 1 
Search results for , issue "Vol 5, No 1 (2024): May 2024" : 4 Documents clear
Betta fish classification using transfer learning and fine-tuning of CNN models Munif, Rihwan; Prahara, Adhi
Science in Information Technology Letters Vol 5, No 1 (2024): May 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i1.1378

Abstract

Betta fish, known as freshwater fighters, are in demand because of their beauty and characteristics. These betta fish such as Crowntail, Halfmoon, Doubletail, Spadetail, Plakat, Veiltail, Paradise, and Rosetail are hard to recognize without knowledge about them. Therefore, transfer learning of Convolutional Neural Network models was proposed to classify the betta fish from the image. The transfer learning process used a pre-trained model from ImageNet of VGG16, MobileNet, and InceptionV3 and fine-tuned the models on the betta fish dataset. The models were trained on 461 images, validated with 154 images, and tested on 156 images. The result shows that the InceptionV3 model excels with 0.94 accuracies compared to VGG16 and MobileNet which acquire 0.93 and 0.92 accuracy respectively. With good accuracy, the trained model can be used in betta fish recognition applications to help people easily identify betta fish from the image.
Classification Of Plants By Their Fruits And Leaves Using Convolutional Neural Networks Irhebhude, Martins E.; Kolawole, Adeola O.; Chinyio, Chat
Science in Information Technology Letters Vol 5, No 1 (2024): May 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i1.1364

Abstract

The population growth of the world is exponential, this makes it imperative that we have an increase in food production. In this light, farmers, industries and researchers are struggling with identifying and classifying food plants. Over the years, there have been challenges that come with identifying fruits manually. It is time-consuming, labour intensive and requires experts to identify fruits because of the similarity in fruit’s leaves (citrus family), shapes, sizes and colour. A computerized detection technique is needed for the classification of fruits. Existing solutions to fruits classifications are majorly based on fruit or leave used as input. A new model using Convolutional Neural Network (CNN) is proposed for fruits classification. A dataset of 5 classes of fruits and fresh dry leaves plants (Mango, African almond, Guava, Avocado and Cashew) comprising of 1000 images each. The proposed model hyperparameters were: Conv2D layer, activation layer, dense layer, a learning and dropout rates of 0.001 and 0.5 respectively were used for the experiment. Various performances for accuracies of 91%, 97%, 78% and 97% were obtained for proposed model on local dataset, proposed model on benchmark dataset, benchmark model on local dataset and benchmark model on benchmark dataset. The proposed model is robust on both local and benchmark datasets and can be used for effective classification of plants
Determination of living quarters clutter for caregiver support Karungaru, Stephen
Science in Information Technology Letters Vol 5, No 1 (2024): May 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i1.1459

Abstract

Providing enough health caregivers due to an aging population has recently been challenging. To alleviate this problem, there's a growing demand for certain household monitoring tasks to be automated especially for elderly persons living independently to reduce the number of scheduled visits by caregivers. Moreover, gathering crucial data using AI technology about functional, cognitive, and social health status, is essential for monitoring daily physical activities at home. This paper proposes a system that determines a room's cleanliness (degree of clutter) to decide whether a caregiver visit is required. A Yolov5-based method is applied to recognize objects in the room including clothes, utensils, clothes, etc. However, due to background noise interference in the rooms and the insufficient feature extraction in YOLOv5, an improvement regime is proposed to improve the detection accuracy. The ECA (Efficient Channel Attention) is added to the network's backbone to focus on feature information, reducing the missed detection rate. The initial anchor box clustering algorithm is improved by replacing K-means with the K-means++ algorithm, enabling more effective adaptation to changing room views. The regression loss function EIoU (Enhanced Intersection over Union) is introduced to optimize the convergence speed and improve the accuracy. The room clutter is determined using set rules by comparing the detection results and prior information from the clean room using IOU. In 31 rooms, 9 subjects' evaluation was used to prove the effectiveness of the proposed system. Compared to the original Yolov5 algorithm, the method proposed in this paper achieved better performance
Analyzing event relationships in Andersen's Fairy Tales with BERT and Graph Convolutional Network (GCN) Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Ghosh, Anusua; Hernandez, Leonel
Science in Information Technology Letters Vol 5, No 1 (2024): May 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i1.1810

Abstract

This study explores the narrative structures of Hans Christian Andersen's fairy tales by analyzing event relationships using a combination of BERT (Bidirectional Encoder Representations from Transformers) and Graph Convolutional Networks (GCN). The research begins with the extraction of key events from the tales using BERT, leveraging its advanced contextual understanding to accurately identify and classify events. These events are then modeled as nodes in a graph, with their relationships represented as edges, using GCNs to capture complex interactions and dependencies. The resulting event relationship graph provides a comprehensive visualization of the narrative structure, revealing causal chains, thematic connections, and non-linear relationships. Quantitative metrics, including event extraction accuracy (92.5%), relationship precision (89.3%), and F1 score (90.8%), demonstrate the effectiveness of the proposed methodology. The analysis uncovers recurring patterns in Andersen's storytelling, such as linear event progressions, thematic contrasts, and intricate character interactions. These findings not only enhance our understanding of Andersen's narrative techniques but also showcase the potential of combining BERT and GCN for literary analysis. This research bridges the gap between computational linguistics and literary studies, offering a data-driven approach to narrative analysis. The methodology developed here can be extended to other genres and domains, paving the way for further interdisciplinary research. By integrating state-of-the-art NLP models with graph-based machine learning techniques, this study advances our ability to analyze and interpret complex textual data, providing new insights into the art of storytelling

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