LC International Journal of STEM (ISSN: 2708-7123)

Enhancing Vehicle Classification Accuracy: A Convolutional Neural Network (CNN) Based Model

2024-04-29T14:19:24+00:00

Using the convolutional neural network (CNN) fine-tuned method, this article introduces a vehicle categorization system. The system's goal is to properly categorize popular vehicle types in the domestic market, which will help with traffic control, monitoring, and traffic accident prevention. The efficacy of VGG-16 and Inception V3 architectures is demonstrated by their evaluation of a real-world dataset consisting of 2000 photos of vehicles. While VGG-16 attains an accuracy of 99.11%, Inception V3 reaches an accuracy of 96.43%. In terms of overall accuracy, VGG-16 outperforms Inception V3, highlighting the importance of architectural decisions in achieving accurate vehicle classification. The suggested technique significantly improves computer vision applications in the domain of vehicle classification, making valuable contributions to traffic management and accident prevention efforts.

Research on Intelligent Control of a 10-Channel Microwave Input Heating Microwave Oven

2024-02-22T06:48:20+00:00

The increasing demand for precise temperature control and specialized process control in industrial microwave ovens has led to the exploration of advanced control algorithms. To address these challenges, innovative neural network control algorithms have been introduced. This article delves into the heating mechanism of a 10-channel high-power industrial microwave oven and offers a mathematical explanation for the microwave heating process in the chamber. Through MATLAB simulations, the heating process and the RBF neural network adaptive control system were investigated, demonstrating promising performance. An intelligent control system was then designed, incorporating components such as a 10-channel magnetron, microwave cavity, temperature sensor, and STM-32 microcontroller. Utilizing an RBF neural network adaptive control algorithm, this system independently adjusts 10 microwave inputs to achieve heating and maintain the desired temperature. Subsequently, a 10kW 10-channel high-power industrial microwave oven RBF neural network adaptive control system was implemented and experimentally validated for its effectiveness. This innovative approach offers adaptive intelligent control, enhancing performance across diverse operating conditions.

What Factors are Challenging to Manage a Project in Industry 4.0?

2024-02-17T18:58:35+00:00

One of the main causes of the earlier industrial revolutions was the speed at which technology was developing. However, in terms of technological advancement and socioeconomic impact, it is anticipated that the fourth industrial revolution (Industry 4.0) and its integrated technology dissemination progress will expand dramatically. Industry 4.0 creates new organizational business models and human-centered manufacturing systems that have an effect on society, the environment, and the entire value chain. The Industry 4.0 is improving things so much that they are improving things even more. However, there are dangers and difficulties associated with developing a project in any Industry 4.0 area. Making a project will undoubtedly require some sort of difficulties. This essay examines the difficulties of putting Industry 4.0 into practice.

Enhancing Face Recognition through Dimensionality Reduction Techniques and Diverse Classifiers

2024-05-02T06:49:42+00:00

Face recognition is essential component of various applications including computer vision, security systems and biometrics. By examining the efficacy of several dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), and Non-Negative Matrix Factorization (NMF), this paper offers a novel approach to face recognition. These techniques are combined with diverse classifiers, including Support Vector Machines (SVM), Random Forest, LightGBM, and k-Nearest Neighbors (KNN), are employed to evaluate their impact on face recognition accuracy. Experiments were conducted on Olivetti faces data set. We have demonstrated the comparative analysis of different dimensionality reduction techniques classifiers in terms of accuracy, precision, recall, f1score. Results shows the potential of integrating PCA with diverse classification models to enhance face recognition accuracy and highlights its applicability in real-world scenarios.

Review Paper on IoT Based Smart Applications, Home Automation

2024-05-03T16:03:06+00:00

This paper discusses internet of things and their applications in various domains such as healthcare, manufacturing, retail, transportation, etc. It highlights the importance of IoT technology in enabling devices and sensors to communicate and exchange data, leading to more efficient and connected systems. The paper explores different applications of IoT, including smart agriculture, smart cities, smart energy, and smart traffic monitoring systems, smart environment, and smart home automation. It also addresses the challenges and problems associated with IoT, such as privacy and security issues, handling big data, connectivity, data transmission, and compatibility. The literature review section examines the development of IoT in smart homes, identifies challenges and hindrances to widespread adoption, and discusses intelligent home automation systems. The survey analysis focuses on the gaps in IoT implementation, including security, interoperability, scalability, data management, ethical concerns, edge computing, and legal/regulatory frameworks. Overall, the paper provides an overview of IoT-based smart applications, their benefits, challenges, and future prospects.

Plant Leaf Disease Detection Using Deep Learning

2024-05-07T15:12:25+00:00

Plant leaf diseases pose a danger to food security, and their rapid identification is made more difficult in many areas by a lack of infrastructure. This thesis is a concentrated attempt to address this important problem by utilizing state-of-the-art deep learning techniques, with a focus on the YOLOv5 model, to offer a dependable and effective solution for plant leaf disease detection in agriculture. The introduction emphasizes the serious effects that plant diseases have on a global and financial level, underscoring the critical necessity for early detection to lessen these effects. Driven by the promise of technology to revolutionize agriculture, this work carefully investigates the complex use of deep learning techniques. YOLOv5 is trained to demonstrate its ability to distinguish between healthy and diseased plant leaves using a carefully chosen tomato dataset. The dataset contains nine different types of illnesses. The model achieves an impressive 92.6 percent average precision, indicating a high degree of disease detection accuracy. Plant leaf disease detection in agriculture faces many complicated obstacles, and the successful deployment of the trained model through the Flask framework represents a significant leap in the practical application of deep learning to address these issues. Our multimodal approach places our research at the forefront of efforts to improve agricultural technology and guarantee global food security while also making a significant contribution to the scientific understanding of disease identification and laying the foundation for future advances.