Journal of Telecommunication, Electronic and Computer Engineering (JTEC)

Evaluation Metrics for Air Quality Optimization Utilizing Machine Learning: PRISMA Review

2024-07-26T01:28:45+00:00

Air quality, both indoors and outdoors, is crucial for public health as it affects respiratory conditions and overall well-being. Machine learning (ML) techniques offer innovative solutions for monitoring and predicting air quality. However, choosing the right ML algorithms and evaluation metrics is essential for creating accurate air quality prediction models, as these choices directly impact the accuracy and reliability of the results. This study aims to explore and summarize the literature on the use of ML techniques in predicting and optimizing air quality, addressing the urgent issue of air pollution. The review analyzed papers from two electronic databases, namely Scopus and Science Direct. Information on ML techniques for predicting air quality and identifying the main sources of pollution was extracted from 26 studies. The study focuses on common ML techniques employed in air quality prediction, including classification, deep learning, regression, ensemble learning, and combinations of regression and deep learning. It also identifies the evaluation metrics used to assess the performance of these models, such as recall, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). By synthesizing existing knowledge, this study provides a comprehensive understanding of the metrics researchers use to measure the overall effectiveness of ML algorithms. It serves as a benchmark for future research and guides the selection of appropriate evaluation metrics in the field of air quality prediction.

IoT-Integrated Smart Gardening System for Real-Time Monitoring and User-Controlled with Smart Film

2024-12-18T08:13:43+00:00

Internet of things (IoT) refers to devices connected to the internet that enable human-to-human or human-to-computer communication. It facilitates the collection and exchange of data between these devices and users. This study presents an IoT-based Smart Garden integrated with smart film technology, designed to predict light intensity and monitor daily plant growth. Despite a strong interest in gardening, many individuals lack the time to properly care for their plants. Therefore, this study proposes a simple and efficient method for plant monitoring and control using a smartphone or other connected devices. The system incorporates three primary sensors: the DHT11 sensor (for temperature and humidity), a soil moisture sensor, and a light intensity module sensor. The water pump and smart film function as actuators, which can be controlled remotely or via a smartphone. This IoT-based Smart Garden with smart film collects real-time data and transmits it to users via a mobile application, ensuring convenient remote monitoring and control. This research demonstrates that smart gardening systems significantly reduce human intervention, making plant care more efficient. Moreover, the sensors continuously gather and update environmental data, allowing users to stay informed about plant conditions in real time without needing to be physical present.

Energy Optimized YOLO: Quantized Inference for Real-Time Edge AI Object Detection

2024-09-19T05:11:01+00:00

Efficient real-time object detection is a critical requirement in edge computing applications, such as smart surveillance, where resource constraints pose significant challenges. Existing deep learning methods often struggle to balance accuracy and efficiency, particularly when deployed on hardware with limited computational resources. This work focuses on developing a quantized object detection system utilizing advanced deep learning models to improve inference performance on edge devices, Zedboard and Jetson Nano. The Zedboard, an FPGA platform without GPU acceleration, executes a quantized YOLOv3-tiny model with ultra-low power consumption of 2.2W but requires over 3 seconds per inference, making it unsuitable for real-time applications. In contrast, the Jetson Nano, running an optimized YOLOv7-tiny model with FP16 quantization and GPU acceleration, achieves a processing speed of 38 FPS with mAP of 46.3%, while maintaining a low power consumption of 5.1W. Based on the results, this work presents a practical solution for real-time object detection in resource-constrained environments by demonstrating the benefits of combining quantized deep learning models with GPU acceleration. Future work could focus on fine-tuning models for specific applications, such as traffic monitoring, to improve the detection of vehicles, pedestrians, and traffic signs in dynamic environments.

Evaluation of Transformer-Based Models for Sentiment Analysis in Bahasa Malaysia

2024-12-18T03:25:38+00:00

This study investigates the application of advanced Transformer-based models, namely BERT, DistilBERT, BERT-multilingual, ALBERT, and BERT-CNN, for sentiment analysis in Bahasa Malaysia, addressing unique challenges such as mixed-language usage and abbreviated expressions in social media text. Using the Malaya dataset to ensure linguistic diversity and domain coverage, the research incorporates robust preprocessing techniques, including synonym mapping and sentiment-aware tokenization, to enhance feature extraction. Through rigorous evaluation, BERT-CNN exhibits the best accuracy (96.3%), followed by BERT-multilingual (89.84%) and BERT (89.5%). DistilBERT and ALBERT delivered competitive performance (88.96% and 88.76%, respectively) while offering reduced computational requirements, highlighting the trade-offs between performance and efficiency. The study emphasizes optimized strategies for handling challenges in positive sentiment classification and demonstrates the efficacy of transformer architectures in nuanced sentiment detection for low-resource languages. These findings contribute to advancing Natural Language Processing (NLP) for scalable sentiment analysis across domains.

Assessing the Impact of Artificial Intelligence and Machine Learning Tools on Software Development Efficiency in Agile Frameworks: A Structured Evaluation Using Machine Learning Models

2025-01-14T09:29:27+00:00

Adopting artificial intelligence (AI) and machine learning (ML) in software development processes presents an opportunity to systematically assess improvements in efficiency, accuracy, and project management. However, evaluating these technologies requires structured assessment models rather than generalized claims. This study utilizes a Kaggle dataset and applies linear regression, random forest classifiers, and K-means clustering to examine the impact of AI tools within Agile frameworks. The analysis reveals that AI tools enhance decision-making, productivity, and resource allocation in Agile environments. The linear regression model predicts willingness to adopt AI tools based on key variables, while the random forest classifier achieves high precision and recall in distinguishing AI tool users. Additionally, K-means clustering uncovers distinct adoption patterns among various roles, offering further insights into how AI adoption varies within Agile teams. Rather than assuming AI and ML’s impact, this study systematically evaluates their role in software development efficiency, providing a structured evaluation beneficial to both researchers and practitioners. While the findings highlight AI’s potential for optimizing Agile processes, they are constrained by the dataset’s scope. Future research should incorporate real-world industry validation and broader datasets to further substantiate AI’s effectiveness in Agile frameworks. This research contributes to the ongoing discourse on AI and ML adoption in software development, advocating for data-driven approaches in achieving scalable, efficient, and reliable software development processes.