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All Journal IJIE IJOT IJEFSD IJHCS
Shynu T
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Neuroscience

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Journal : IJOT

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A Visual Approach for Detecting Tyre Flaws That Makes Use of The Curvelet Characteristic S. Suman Rajest; Shynu T; R. Regin*; Steffi. R
International Journal on Orange Technologies Vol. 5 No. 4 (2023): International Journal on Orange Technologies
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijot.v5i4.4264

Abstract

Automatic flaw identification is a crucial and difficult subject in the realm of industrial quality inspection for many different types of businesses. After the tyres have been manufactured, we use the curvelet transform to do an analysis on each tyre in order to locate imperfections on the tire's outer surface. In this paradigm, deep image features can be learned, and then later used for detection, classification, and retrieval tasks using bigger coefficients in the sub-highest frequency band represented by the curvelet feature. Curvelets are a type of wavelet transform that are used to represent curvelets. We investigate image categorization challenges using deep learning with the goal of applying our findings to practical, real-world applications. The findings of the experiments demonstrate that the method that was developed is capable of accurately locating and segmenting flaws in tyre images.
Application of Machine Learning to the Process of Crop Selection Based on Land Dataset S. Suman Rajest; S. Silvia Priscila; R. Regin; Shynu T; Steffi. R
International Journal on Orange Technologies Vol. 5 No. 6 (2023): International Journal on Orange Technologies
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijot.v5i6.4546

Abstract

We are well recognised that the vast majority of Indians work in agriculture. Most farmers always grow the same thing, always use the same amount of fertilizer, and always plant what the people want. Recently, there have been many breakthroughs in the use of machine learning in many fields of study and business. Thus, we intend to establish a framework for the application of machine learning in agriculture for the benefit of farmers. India's economy relies heavily on the nation's agricultural output. Agriculture, then, has the potential to serve as the backbone of the Philippine economy. Choosing the right crop every time is crucial when making agricultural plans. Researchers have utilised machine learning to explore agricultural issues such as crop yield, weather prediction, soil categorization, and crop labelling. Our Indian economy really needs the agricultural sector to undergo significant reforms. Simple applications of machine learning systems in agriculture have the potential to significantly enhance this industry. In addition to the considerable role played by improvements in farming machinery and technology, functional information about many subjects also plays an important role. The main idea of this study is to use the crop selection approach in order to address numerous issues in farming. This increases the wealth of India by increasing crop yields to their maximum potential. In our study, we use the method Random Forest (RF) to estimate a crop and then evaluate its performance relative to that of competing techniques
Android Application for Remote Control of Personal Computers Shynu T; S. Suman Rajest; R. Regin; Steffi. R
International Journal on Orange Technologies Vol. 5 No. 12 (2023): International Journal on Orange Technologies (IJOT)
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijot.v5i12.5126

Abstract

The development of mobile devices, particularly in these modern times, has brought about a significant transformation in the way business is conducted. The capability of a mobile phone device is frequently anticipated to be comparable to that of a computer. In this day and age, the majority of people who use mobile phones feel that performing some things on their computers is inconvenient. The majority of people find that switching postures when sitting or stretching not only makes them feel more comfortable, but also makes them feel more at ease when they are browsing through their laptops. Standing five or ten feet away from the computer while being restricted to using only the keyboard and mouse can be an impractical situation. Additionally, the system provides the features to access the files that are available on the computer; if the file appears to be a media file, this Android app can play, pause, stop, mute, and turn on/off full-screen mode of the respective media. As a result, the application that is being proposed is intended to transform the handphone into a wireless keyboard or mouse that also includes a touch pad. The wireless network is the medium through which the link is established (WiFi). It has been demonstrated that this prototype is capable of performing the majority of the functions that are often associated with a computer.
Using a Deep Convolutional Neural Network to Identify Vehicle Driver Activity Shynu T; S. Suman Rajest; R. Regin; Steffi. R
International Journal on Orange Technologies Vol. 6 No. 1 (2024): International Journal on Orange Technologies (IJOT)
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijot.v6i1.5185

Abstract

Driver actions and judgments are the most critical aspects influencing passenger safety in a vehicle. Common things that drivers do include: driving safely, talking on the phone, texting, eating, reaching behind, altering hairstyle or makeup, operating the radio, and operating mobile phones. The first four are considered typical driving duties, whereas the latter seven are considered distractions. The strategy is to take in the live feed from the dashboard camera, process the frames with pre-trained convolutional neural network (CNN) models, and then refine it with the transfer learning method to identify the driver's activities. An activity recognition system for drivers is developed utilising deep Convolutional Neural Networks in order to monitor their actions (CNN). A warning is sent to the driver based on the identified driver's actions. In addition to activating the warning lights, the system gently slows down the vehicle or forces the driver to shift to the side lane and come to a complete stop in order to protect everyone in the vehicle and the surrounding area.
Co-Authors R. Regin R. Regin* S. Silvia Priscila S. Suman Rajest Steffi. R