Volume-13, Issue-3, September 2024 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org Exploring Innovation | ISSN:2277-3878(Online) | A Periodical Journal | Reg. No.: C/819981 | Published By BEIESP Tue, 24 Jun 2025 06:23:24 +0000 en-US hourly 1 https://www.ijrte.org/wp-content/uploads/2021/02/cropped-IJRTE-Logo-WS-32x32.png Volume-13, Issue-3, September 2024 – International Journal of Recent Technology and Engineering (IJRTE) https://www.ijrte.org 32 32 C814213030924 https://www.ijrte.org/portfolio-item/c814213030924/ Sat, 28 Sep 2024 12:41:52 +0000 https://www.ijrte.org/?post_type=portfolio&p=49480 The post C814213030924 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Security Considerations for Large Language Model Use: Implementation Research in Securing LLM-Integrated Applications
Nikhil Pesati

Nikhil Pesati, High School Senior, The Harker School, San Jose, California, USA.

Manuscript received on 21 July 2024 | Revised Manuscript received on 30 July 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024 | PP: 19-27 | Volume-13 Issue-3, September 2024 | Retrieval Number: 100.1/ijrte.C814213030924 | DOI: 10.35940/ijrte.C8142.13030924

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Large Language Models (LLMs) are rapidly being adopted in various applications due to their natural language capabilities that enable user interaction using human language. As system designers, developers, and users adopt generative artificial intelligence and large language models in various applications, they must understand the significant security risks associated with them. The paper describes a typical LLM-integrated application architecture and identifies multiple security risks to address while building these applications. In addition, the paper provides guidance on potential mitigations to consider in this rapidly evolving space, helping to protect systems and users from potential attack vectors. This paper presents the common real-world application patterns of LLMs trending today. It also provides an overview of generative artificial intelligence and related fields.

Keywords: Large Language Models, Security, Copilot, OWASP.
Scope of the Article: Security Technology

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The Impact of GHG Emissions on Human Health and its Environment using XAI
Stanley Ziweritin1, David Waheed Idowu2

1S. Ziiweritin, Department of Estate Management and Valuation, Akanu Ibiam Federal Polytechnic, Unwana-Afikpo, Nigeria.

2I. D. Waheed, Department of Computer Science, University of Portharcourt, Nigeria. 

Manuscript received on 20 July 2024 | Revised Manuscript received on 26 July 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published 30 September 2024 | PP: 7-14 | Volume-13 Issue-3, September 2024 | Retrieval Number: 100.1/ijrte.C814013030924 | DOI: 10.35940/ijrte.C8140.13030924

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Explainable AI(XAI) is a revolutionary concept in artificial intelligence that supports professionals in creating trust between people in the decisions of learning models. Greenhouse gases produced in the atmosphere are driving our weather to become more irregular and intense. This poses a threat to human health and impacts crops and plants. XAI techniques remain popular, but they cannot disclose system behaviour in a manner that facilitates analysis. Predicting greenhouse gas (GHG) emissions and their impact on human health is an essential aspect of monitoring emission rates by industries and other sectors. However, a handful of investigations have been used to examine the collective effect of sectors such as construction, transportation, and CO2, among others, on emission patterns. This research addresses a knowledge gap by presenting an explainable machine learning model. This framework employed a random forest classifier combined with two different explainable AI methodologies to give insights into the viability of the proposed learning model. The goal is to use XAI in determining the impact of GHG emissions on humans and the environment. A quantitative survey was conducted to investigate the possibilities of deciding GHG emission rates more accurately. We created a random forest model, trained on GHG emission data using SHAP and LIME techniques. This helped provide local and global explanations on model sample order by similarity, output value, and original sample ranking. The model yielded high accuracy and enhanced interpretability with XAI, enabling decision-makers to comprehend what the AI system truly indicates. LIME exceeded SHAP in terms of comprehension and satisfaction. In terms of trustworthiness, SHAP surpassed LIME.

Keywords: LIME, SHAP, Random Forest, Explainable AI, interpretability
Scope of the Article: Computer Science and Applications

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]]> C813913030924 https://www.ijrte.org/portfolio-item/c813913030924/ Sat, 28 Sep 2024 12:27:59 +0000 https://www.ijrte.org/?post_type=portfolio&p=49474 The post C813913030924 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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A Study on Effective Teaching of Fuzzy Controller Design Using ChatGPT
Dong Hwa Kim

Dong Hwa Kim, DSTSC (Daeduck S&T Social Cooperation, S. Korea. 

Manuscript received on 19 July 2024 | Revised Manuscript received on 31 July 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024 | PP: 28-42 | Volume-13 Issue-3, September 2024 | Retrieval Number: 100.1/ijrte.C813913030924 | DOI: 10.35940/ijrte.C8139.13030924

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper deals with an effective teaching method of fuzzy controller design using ChatGPT and Python at the University. Currently, ChatGPT and related technologies are penetrating all areas, including text, image, video, as well as AI and industry. ChatGPT technologies are strongly connected to the education of universities and K-12 schools due to their ease of use and accurate responses. There are two categories of AI-related education. The first is AI-supported education; the other is education (teaching and learning) that teaches AI. In any case, AI and the ChatGPT method should be taught in conjunction with theory and a process. It should be performed with S/W for the student’s understanding. This paper provides a method for utilising ChatGPT effectively in fuzzy controller design for education. To illustrate ChatGPT-based fuzzy controller design, this paper uses a ChatGPT prompt and suggests a suitable prompt skill.

Keywords: ChatGPT, Prompt, Fuzzy logic, Pendulum, AI, Education
Scope of the Article: Fuzzy Logics

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]]> C812713030924 https://www.ijrte.org/portfolio-item/c812713030924/ Sat, 28 Sep 2024 12:22:53 +0000 https://www.ijrte.org/?post_type=portfolio&p=49472 The post C812713030924 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Advanced Home Automation Using Light Fidelity
Varsha Shrivastava1, Kevin Fernandes2, Garric Mathias3, Shannon Pereira4

1Varsha Shrivastava, Department of Computer Engineering St. Francis Institute and Technology, Mumbai (M.H), India.

2Kevin Fernandes, Department of Computer Engineering St. Francis Institute and Technology, Mumbai (M.H), India.

3Garric Mathias, Department of Computer Engineering St. Francis Institute and Technology, Mumbai (M.H), India.

4Shannon Pereira, Department of Computer Engineering St. Francis Institute and Technology, Mumbai (M.H), India.  

Manuscript received on 18 May 2024 | Revised Manuscript received on 28 May 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024 | PP: 15-18 | Volume-13 Issue-3, September 2024 | Retrieval Number: 100.1/ijrte.C812713030924 | DOI: 10.35940/ijrte.C8127.13030924

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Light Fidelity or Li-Fi is an emerging technology that uses the visible light spectrum for communication. Home automation is a handy way to control various electronic devices used in a home for our day-to-day activities. This paper proposes the use of Li-Fi technology, which comprises light-emitting diode (LED) bulbs, as a means of connectivity. Data is sent through light spectrum on an optical wireless medium as signal propagation. This idea can be implemented as an android application connected to the cloud. All devices in a home can be monitored and controlled using this app, which is installed on a mobile phone. A user can change the state of any device (ON/OFF) with this app. Changes are made in the cloud, and a message is sent as a binary sequence to the hardware connected to the light source. After translation, the identity of the device that needs to be controlled is obtained along with the message to toggle it between the ON and OFF states. This message is transmitted securely using visible light communication, as the data remains within the confines of the four walls. Hardware on the receiving end senses the blinking light source and translates the blinks into a binary sequence, thus obtaining the original message. Then the device performs the necessary action and updates the cloud after completion. This is further reflected in the Android application on the user’s phone. Attackers cannot easily break into this system, as the device identity and the light source are required to manipulate it.

Keywords: Li-Fi; VLC; Light Spectrum; ESP 8266; Home Automation; Internet of Things (IoT); Wireless Data Communication; Cloud Database; Android.
Scope of the Article: Computer Science and Applications

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]]> C812413030924 https://www.ijrte.org/portfolio-item/c812413030924/ Sat, 28 Sep 2024 12:16:25 +0000 https://www.ijrte.org/?post_type=portfolio&p=49469 The post C812413030924 appeared first on International Journal of Recent Technology and Engineering (IJRTE).

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Optimization of Machining Parameters for Nimonic PE16 Using Machine Learning Models
Matthew Jansen1, Ibrahim Deiab2

1Matthew Jansen, College of Engineering and Physical Sciences, University of Guelph, Guelph, Canada.

2Ibrahim Deiab, College of Engineering and Physical Sciences, University of Guelph, Guelph, Canada. 

Manuscript received on 04 July 2024 | Revised Manuscript received on 11 July 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published 30 September 2024 | PP: 1-6 | Volume-13 Issue-2, September 2024 | Retrieval Number: 100.1/ijrte.C812413030924 | DOI: 10.35940/ijrte.C8124.13030924

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Machining high-temperature alloys such as Nimonic PE16 demands precise control of machining parameters to achieve desired outcomes while minimizing tool wear and optimizing surface finish. In this study, we propose using machine learning regression models combined with synthetic data and response surface methodology strategies to optimize machining parameters for PE16. We aim to develop a predictive model that accurately estimates optimal cutting speeds and feed rates based on key output parameters, including cutting forces and surface roughness. Our methodology involves collecting experimental data from controlled machining tests conducted on PE16 samples under varying conditions. We used the datasets to train and validate regression models, establishing correlations between input parameters and machining outcomes. The performance of each model is evaluated based on metrics such as mean absolute error and coefficient of determination. These metrics show relationships within the data and can determine a model’s success. The proposed machine learning framework provides a data-driven approach to optimise machining processes for PE16, thereby facilitating enhanced efficiency, productivity, and quality in nuclear and other high-performance applications. Our findings contribute to understanding machining dynamics in challenging materials and provide valuable insights for intelligent machining systems.

Keywords: Nimonic PE16, Machine Learning, Machinability, Regression, Response Surface Methodology, Synthetic Data.
Scope of the Article: Computer Science and Applications

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