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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 24 Documents
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Search results for , issue "Vol 11, No 1: March 2023" : 24 Documents clear
Automatic Caption Generation for Aerial Images: A Survey Parag Jayant Mondhe; Manisha P. Satone; Gajanan K. Kharate
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4342

Abstract

Aerial images have attracted attention from researcher community since long time. Generating a caption for an aerial image describing its content in comprehensive way is less studied but important task as it has applications in agriculture, defence, disaster management and many more areas. Though different approaches were followed for natural image caption generation, generating a caption for aerial image remains a challenging task due to its special nature. Use of emerging techniques from Artificial Intelligence (AI) and Natural Language Processing (NLP) domains have resulted in generation of accepted quality captions for aerial images. However lot needs to be done to fully utilize potential of aerial image caption generation task. This paper presents detail survey of the various approaches followed by researchers for aerial image caption generation task. The datasets available for experimentation, criteria used for performance evaluation and future directions are also discussed.
Enhanced Emotion Recognition in Videos: A Convolutional Neural Network Strategy for Human Facial Expression Detection and Classification Arselan Ashraf; Teddy Surya Gunawan; Fatchul Arifin; Mira Kartiwi; Ali Sophian; Mohamed Hadi Habaebi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4449

Abstract

The human face is essential in conveying emotions, as facial expressions serve as effective, natural, and universal indicators of emotional states. Automated emotion recognition has garnered increasing interest due to its potential applications in various fields, such as human-computer interaction, machine learning, robotic control, and driver emotional state monitoring. With artificial intelligence and computational power advancements, visual emotion recognition has become a prominent research area. Despite extensive research employing machine learning algorithms like convolutional neural networks (CNN), challenges remain concerning input data processing, emotion classification scope, data size, optimal CNN configurations, and performance evaluation. To address these issues, we propose a comprehensive CNN-based model for real-time detection and classification of five primary emotions: anger, happiness, neutrality, sadness, and surprise. We employ the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV) video dataset, extracting image frames from the video samples. Image processing techniques such as histogram equalization, color conversion, cropping, and resizing are applied to the frames before labeling. The Viola-Jones algorithm is then used for face detection on the processed grayscale images. We develop and train a CNN on the processed image data, implementing dropout, batch normalization, and L2 regularization to reduce overfitting. The ideal hyperparameters are determined through trial and error, and the model's performance is evaluated. The proposed model achieves a recognition accuracy of 99.38%, with the confusion matrix, recall, precision, F1 score, and processing time further quantifying its performance characteristics. The model's generalization performance is assessed using images from the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Extended Cohn-Kanade Database (CK+) datasets. The results demonstrate the efficiency and usability of our proposed approach, contributing valuable insights into real-time visual emotion recognition.
Days of autonomy for optimal Battery Sizing in Stand-alone Photovoltaic Systems Meriem Andam; Jamila El Alami; Younes Louartassi; Rabie Zine
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4417

Abstract

The main purpose of our article is to optimize the battery sizing by identifying the most appropriate number of autonomy days. A case study has been established and simulated to define the optimal number. In the others current researches, only a small importance has been attributed to the battery autonomy. The objective is generally to ensure a continuous presence of energy especially for isolated systems while this is not always optimal nor economical and does not necessarily guarantee a safe supply. Nevertheless, an over dimensioning of the battery will lead to a consequent cost and a loss of energy. The results show that the number of days of autonomy must correspond to the minimum ratio linking the lack of energy to the surplus during a specific period.
SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis Anuradha Vishwajit Yenkikar; C. Narendra Babu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4381

Abstract

Sentiment Analysis has been a topic of interest for researchers due to its increasing usage by Industry. To measure end-user sentiment., there is no clear verdict on which algorithms are better in real-time scenarios. A rigorous benchmark evaluation of various algorithms running across multiple datasets and different hardware architectures is required that can guide future researchers on potential advantages and limitations. In this paper, proposed SentiMLBench is a critical evaluation of key ML algorithms as standalone classifiers, a novel cascade feature selection (CFS) based ensemble technique in multiple benchmark environments each using a different twitter dataset and processing hardware. The best trained ensemble model with CFS enhancement surpasses current state-of-the-art models, according to experimental results. In a study, though ensemble model provides good accuracy, it falls short of neural networks accuracy by 2%. ML algorithms accuracy is poor as standalone classifiers across all three studies. The supremacy of neural networks is further stamped in study three where it outperforms other algorithms in accuracy by over 10%. Graphical processing unit provide speed and higher computational power at a fraction of a cost compared to a normal processor thereby providing critical architectural insights into developing a robust expert system for sentiment analysis.

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