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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Indonesian Journal of Electrical Engineering and Computer Science
Veemaraj, Ebenezer
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Computer Science & IT

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Efficient traffic signal detection with tiny YOLOv4: enhancing road safety through computer vision Santhiya, Santhiya; Johnraja Jebadurai, Immanuel; Leelipushpam Paulraj, Getzi Jeba; Veemaraj, Ebenezer; Sharance, Randlin Paul; Keren, Rubee; Karan, Kiruba
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp285-296

Abstract

As decades go by, technology advances and everything around us becomes smarter, such as televisions, mobile phones, robots, and so on. Artificial intelligence (AI) is applied in these technologies where AI assists the computer in making judgments like humans, and this intelligence is artificially fed to the model. The self-driving technique is a developing technology. Autonomous driving has been a broad and fast-expanding technology over the last decade. This model is carried out using the tiny you only look once (YOLO) algorithm. YOLO is mainly used for object detection classification. Tiny YOLO model is explored for the traffic signal detection. ROBI FLOW dataset is used for object detection which contains 2000+ image data to train the tiny YOLO model for traffic signal detection in real time. This model gives an improved accuracy and lightweight implementation compared to other models. Tiny YOLO is fast and accurate model for real-time traffic signal detection.
Explainable artificial intelligence for traffic signal detection using LIME algorithm Santhiya, P.; Jebadurai, Immanuel Johnraja; Leelipushpam Paulraj, Getzi Jeba; Kirubakaran S, Stewart; Keren L., Rubee; Veemaraj, Ebenezer; Sharance J. S., Randlin Paul
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp527-536

Abstract

As technology progresses, so does everything around us, such as televisions, mobile phones, and robots, which grow wiser. Of these technologies, artificial intelligence (AI) is used to aid the computer in making decisions comparable to humans, and this intelligence is supplied to the machine as a model. As AI deals with the concept of Black-Box, the model’s decisions were poorly comprehended by the end users. Explainable AI (XAI) is where humans can understand the judgments and decisions made by the AI. Earlier, the predictions made by the AI were not as easy as we know the data now, and there was some confusion regarding the predictions made by the AI. The intention for the use of XAI is to improve the user interface of products and services by helping them trust the decisions made by AI. The machine learning (ML) model White-box shows us the result that can be understood by the people in that domain, wherein the end users cannot understand the decisions. To further enhance traffic signal detection using XAI, the concept called local interpretable model- agnostic explanation (LIME) algorithm has been taken into consideration and the performance is improved in this paper.
Revolutionizing agricultural efficiency with advanced coconut harvesting automation Davincy R., Yona; Veemaraj, Ebenezer; Edwin, E. Bijolin; Kirubakaran S., Stewart; Thanka, M. Roshni; Neola J., Dafny
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp537-546

Abstract

The precision coconut harvesting system aims to develop an efficient system for accurately detecting coconuts in agricultural landscapes using advanced image processing techniques. Coconut cultivation is vital to many tropical economies and precise monitoring is essential for optimizing yield and resource utilization. Traditional methods of coconut detection are labor-intensive and time-consuming. The proposed computer vision-based approach automates and enhances coconut detection by analyzing high-resolution images of coconut plantations. Pre-processing techniques improve image quality and object detection algorithms such as convolutional neural networks (CNNs) identify coconut clusters. Challenges like lighting variations and background clutter are addressed using feature extraction and pattern recognition. A user-friendly interface visualizes detection results, aiding farmers in timely decision-making. Extensive testing on diverse datasets evaluates system effectiveness. This model aims to advance precision agriculture, enhancing productivity and informing coconut farmers' decision-making processes. Using a CNN model, the accuracy of coconut detection based on its ripeness was 98.8%.
Smart agriculture monitoring system for outdoor and hydroponic environments Edwin, Bijolin; Veemaraj, Ebenezer; Parthiban, Pradeepa; Devarajan, Joseph Pushparaj; Mariadhas, Vargheese; Nainar, Ahila Arumuga; Reddy, Maheshwar
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1679-1687

Abstract

Agriculture plays an important role in economic aspects in most countries like India. Numerous problems associated with farming are continuously affecting the actions that are happening in the country. A potential resolution for such issues to be eradicated, one should combine the technological advancements with current ongoing agricultural practices. Good agricultural practice will increase crop productivity and reduce unwanted water usage. Many authors have done research on temperature, nutrition, and pH-controlled systems. But no one concentrated on alert messages sent to the mobile phone. The main objective of the proposed system measures various natural aspects that use a global system for mobile communication (GSM) module that is connected to an Arduino to transfer the data that is obtained by the sensors to an internet of things (IoT) application programming interface (API) which is a kind of cloud computing of obtained data, this data can be analyzed if needed, and an alert short message service (SMS) is sent to the cell phone/mobile phone. The alert message can be done through conversational artificial intelligence (CAI). It is the collection of technologies behind triggering the message that will be sent automatically to the mobile as an SMS if any of the sensor values that are generating are not under already specified threshold values.
Co-Authors Davincy R., Yona Devarajan, Joseph Pushparaj Edwin, Bijolin Edwin, E. Bijolin Jebadurai, Immanuel Johnraja Johnraja Jebadurai, Immanuel Karan, Kiruba Keren L., Rubee Keren, Rubee Kirubakaran S, Stewart Kirubakaran S., Stewart Leelipushpam Paulraj, Getzi Jeba Mariadhas, Vargheese Nainar, Ahila Arumuga Neola J., Dafny Parthiban, Pradeepa Reddy, Maheshwar Santhiya, P. Santhiya, Santhiya Sharance J. S., Randlin Paul Sharance, Randlin Paul Thanka, M. Roshni