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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 65 Documents
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Search results for , issue "Vol 34, No 3: June 2024" : 65 Documents clear
Revolutionization of augmented reality in tourism via deep learning Yasmin Chuupa Essa; Saumya Chaturvedi; Shiraz Khurana
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2055-2064

Abstract

Tourism has become an integral part of social and economic development across the globe. It does not only serve as a recreational activity but also as a source of revenue for the nation. The paper systematically explores the potential enhancements in the tourist experience through cutting-edge technology. Employing deep learning methods, the study specifically concentrates on refining augmented reality encounters for visitors. The proposed approach utilizes deep learning algorithms to optimize and tailor tourists’ augmented reality experiences, addressing current sectoral challenges like customization and engagement shortcomings. The methodology’s selection is predicated on it is capability to elevate user experience, accurately identify objects, offer visual guided tours, integrate historical context, and ultimately propel augmented reality adoption in tourism. Notably, the investigation culminates in a noteworthy average accuracy of 99% when incorporating deep learning to enhance augmented reality in tourism.
An efficient and low cost realization of LoRa based real-time forest protection system Gobinda Prasad Acharya; Lavanya Poluboyina; Jayaprakasan Veeragamoorthi; Chattopadhyay Joydeb
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1452-1462

Abstract

The forest is a natural habitat for a variety of fauna and flora, and helps to maintain the ecosystem equilibrium. However, wildfire incidents and deforestation lead to forest degradation. Moreover, most of the existing methods, to preserve the forest resources, are ineffective due to their large establishment cost, more power consumption, and poor coverage. This paper brings out a sustainable solution by developing a forest protection system (FPS) that uses internet of things (IoT) technology together with long range (LoRa) communication. The work focuses on the development of an IoT framework for the detection of any intrusion into the forest as well as the detection of fire incidents in the vicinity of the equipment. Powering the equipment through solar energy makes the system cost-effective. The system is examined in terms of acquisition of data from sensor nodes pertaining to forest protection, relaying the same to the cloud using LoRa wide area network (LoRaWAN) technology and analyzing using cloud based visualization tools. The developed system has been deployed at Eturnagaram Wildlife Sanctuary, Mulugu district, Telangana, India for validation in the forest environment. The obtained results have shown that the system has an accuracy of 97.14% for intrusion detection and 100% for fire detection.
A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis Thotakura Venkata Sai Krishna; Tummalapalli Siva Rama Krishna; Srinivas Kalime; Chinta Venkata Murali Krishna; Sadineni Neelima; Raja Rao PBV
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1904-1914

Abstract

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.
Factors driving business intelligence adoption: an extended technology-organization-environment framework Radhakrishnan Subramaniam; Prashobhan Palakeel; Manimuthu Arunmozhi; Manikandan Sridharan; Uthayakumar Marimuthu
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1893-1903

Abstract

Business intelligence (BI) is a vital component for businesses of all scales, offering actionable insights crucial for timely decision-making. This technology has become integral across diverse enterprises. Recognizing the factors influencing BI adoption is imperative, and this article employs the organization, complexity, knowledge, technology, user perception and experience, economic, environmental, and social (OCKTUEES) framework to identify key aspects. Building upon the TOE framework, it pinpoints significant variables, emphasizing the importance of factors like user perception and experience, technology, social, economical, and environmental. Employing structural equation modelling on primary data yields actionable insights to address BI adoption challenges. Analysis reveals the user perception and experience, technology, social, economic, and environmental as the top factors. However, the organization appears vulnerable, necessitating a mitigation strategy for successful BI adoption. The study predicts insignificant variables requiring mitigation, such as high costs, inadequate resources, organizational size, security and privacy concerns, risk of open-source adoption, and perception of analytics impacting jobs. This research aids those navigating the BI implementation journey.
Enhancing EEG-based brain-computer interface systems through efficient machine learning classification techniques Ferdi Ahmed Yassine; Ghazli Abdelkader
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2045-2054

Abstract

Advances in the fields of neuroscience and computer science have greatly enhanced the human brain’s ability to communicate and interact with the surrounding environment. In addition, recent steps in machine learning (ML) have increased the use of electroencephalography (EEG)-based BCIs for artificial intelligence (AI) applications. The prevailing challenge in recording EEG sensor data is that the captured signals are mixed with noise, which makes their effective use difficult. Therefore, strengthening the classification stage becomes extremely important and plays a major role in addressing this problem. In this study, we chose five most widely used classification models that obtained the best results in this field and tested them on two open-source databases. We also focused on improving the hyperparameters of each algorithm to obtain best results. Our results indicate excellent results on the first dataset and acceptable for most models on the second, while RF showed superior performance on both with an accuracy of 100% on the first dataset and 86.47% on the second. This was achieved with the lowest training costs, and better performance compared to previous works we evaluated that used the same databases. These results provide valuable insights and advance the development of brain-computer interface (BCI) technology and design.

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