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Vertex
Published by Institute of Computer Science
ISSN : 2089385X     EISSN : 28296761     DOI : https://doi.org/10.35335/Vertex
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Articles published in Vertex include original scientific research results (top priority), new scientific review articles (non-priority), or comments or criticisms on scientific papers published by Vertex. The journal accepts manuscripts or articles in the field of engineering from various academics and researchers both nationally and internationally. The journal is published every June and December (2 times a year). Articles published in Vertex are those that have been reviewed by Peer-Reviewers. The decision to accept a scientific article in this journal is the right of the Board of Editors based on recommendations from the Peer-Reviewers. Since 2011, Vertex only accepts articles derived from original research (top priority), and new scientific review articles (non-priority).
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 11 No. 2 (2022): June: Engineering" : 5 Documents clear
Adaptive traffic control at complex intersections using fuzzy logic multi-agent approach Zainal Yang Xu; Rivera Smith Ager; Niu Wylie Sjödin; Mubashar Wylie Pintrich
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/0meps156

Abstract

In an increasingly dense urban environment, efficient and adaptive traffic management is essential to maintain smooth mobility and reduce congestion. In this effort, the Multi-Agent Fuzzy Logic method emerges as a promising approach to overcome the complexity and fluctuation of traffic conditions. This study aims to investigate the potential application of the Multi-Agent Fuzzy Logic method in controlling traffic lights at complex crossroads. Within this conceptual framework, a mathematical formulation model and a programming algorithm are developed that enable the simulation of traffic light settings with the Multi-Agent Fuzzy Logic approach. By fuzzifying input variables to convert numeric data into linguistic variables. Furthermore, applying fuzzy rules to make adaptive decisions based on traffic conditions and coordination between agents in a multi-agent system. The results of this system are then validated through simulations, with evaluation metrics such as average waiting time, energy efficiency, and traffic smoothness. The results of this study indicate that the Multi-Agent Fuzzy Logic method is capable of producing traffic light settings that are responsive to changes in traffic conditions on each lane. By coordinating between agents, the average waiting time can be reduced, energy efficiency can be increased, and traffic flow can be improved.
Optimizing face sensor-based attendance system using wavelet method for enhanced security and efficiency Priya Shimizu; Hagihara Kobatake; Subasi Tziritas Ezema; Maiti Eneh Bhanot; Das Junior
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/629k6b98

Abstract

In today’s digital era, efficient and accurate attendance management is essential in various sector. This research presents an innovative approach in the development of facial sensor-based attendance systems using the wavelet method. This approach aims to address the challenges of recognizing faces under varying lighting conditions, rotations, and facial details. Image pre-processing is used to improve the quality of the face image, followed by a discrete wavelet transform to decompose the image into wavelet coefficients at various scales. This model is strengthened by strong security techniques, considering the importance of individual data privacy. This research offers a modern alternative in attendance management by utilizing facial recognition technology and the wavelet method. Although the mathematically formulated model provides a clear framework, practical implementation requires advanced techniques in image processing and artificial intelligence.
Design of an expert system for diagnosing eye disease using the certainty factor method Birch Shah; Zeeshan; Asghar Sadiq; Übeyli Ung; Shanbhag Gilmore
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/vw11e535

Abstract

Accurate eye disease diagnosis is essential for therapy and vision preservation. Complexity and diversity of symptoms often confound established diagnostic methods. We suggest designing an expert system for eye illness diagnosis utilizing the certainty factor method, which handles ambiguous and imprecise medical diagnoses. A systematic technique to improve diagnostic accuracy is proposed in this research to bridge medical experience and computational reasoning. The research begins with a mathematical framework for symptoms, test data, and diagnostic conclusions. The system uses medical-inspired rule-based inference to aid evidence-based reasoning. The certainty factor technique quantifies diagnosis confidence, ensuring transparency and justifiability. A numerical example shows how to apply the strategy. The simple example shows how the expert system can analyze various criteria and make well-supported diagnoses. It emphasizes symptom analysis, test results, and the certainty factor method's capacity to handle uncertainty. This research emphasizes the interaction between artificial intelligence and medical competence and is conceptual. Real-world application involves medical practitioner participation, intensive testing, and patient data validation. This research advances medical diagnostic tools by combining computational and clinical knowledge. It symbolizes a shift toward efficient, accurate, and transparent diagnostic methods to improve patient care and healthcare.
Fake news detection using naive bayes classifier and forward selection in the digital era Traore Lei Ogilvie; Monti Sharma; Zhou Xei Huu
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/rxg67q35

Abstract

This study investigates the application of the Naive Bayes Classifier Method with the Forward Selection Technique in an effort to detect hoax news. Through a hypothetical numerical example, this study illustrates the basic steps involved in this approach. The Naive Bayes method is used to estimate class probabilities based on the selected text features, while the Forward Selection technique is used to select the most informative features. The results and implications of this approach are discussed in the context of potential real-world applications. This research provides an initial understanding of the use of these techniques in dealing with the challenge of detecting fake news in the digital age. While this research does not describe the more complex aspects of real-world fake news detection, it highlights a foundation that can be developed for further efforts to improve information integrity in an increasingly complex digital environment.
Men's facial foam selection decision support system based on skin type Jonhariono Sihotang; Roma Sinta Simbolon; Amran Manalu
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/vxydbw52

Abstract

This research presents the development of a Decision Support System (DSS) aimed at assisting men in selecting facial foam products based on their skin types. The growing interest in skincare and grooming among men has led to an abundance of facial care products in the market, making it challenging for consumers to choose the most suitable option for their individual needs. The WDSS addresses this predicament by intelligently analyzing user input, classifying skin types, and generating personalized product recommendations. The conceptual framework of the WDSS combines content-based filtering and collaborative filtering techniques to ensure accuracy and relevance in recommending facial foam products. The Decision Support System offers a valuable tool for men seeking the most suitable facial foam products based on their individual skin types. The system's ability to provide personalized recommendations contributes to improved self-confidence and promotes proactive self-care practices among users. Continuous efforts in refining algorithms and updating the product database are essential to ensure the DSS's accuracy and relevancy as the skincare industry continues to evolve. The research seeks to empower men in their skincare journey, fostering a positive impact on their overall well-being and self-image.

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