International Journal of Imminent Science & Technology.

A Cross-Platform Vehicle Tracking System for Pabna University of Science and Technology with Android and Web Interfaces

2024-07-10T06:03:44+00:00

In recent years, the development of cross-platform applications has gained increasing popularity due to their ability to enhance accessibility, usability, and functionality across multiple devices. This paper proposes a cross-platform vehicle tracking system for Pabna University of Science and Technology (PUST), addressing key challenges in vehicle management, transportation efficiency, and security. The system includes both android and web interfaces designed to provide real-time location monitoring of university vehicles. The primary problems identified are the inefficient tracking of university vehicles, lack of security in vehicle usage, and suboptimal route management. To address these issues, our system leverages modern technologies such as the Google Maps API, Node.js, and the Real-time Firebase Database. The Android app and web-based interfaces allow authorized users to track the real-time location of university vehicles, access detailed vehicle information, and review historical routes. Additionally, the system issues real-time alerts for unauthorized usage or irregular activities. The embedded system uses a Global Positioning System (GPS) and Global System for Mobile Communication (GSM) for tracking and positioning vehicles. The ESP8266 NodeMCU is interfaced serially with a GPS module Ublox NEO6M Receiver to continuously monitor and report vehicle status. The GPS module sends the vehicle’s position (latitude and longitude) to the real-time Firebase database, which then transmits this data to the mobile and web interfaces. The proposed system underwent comprehensive user acceptance tests, yielding satisfactory results that attest to its potential for enhancing vehicle management, transportation efficiency, and security at PUST. This paper details the design, development, and evaluation of the system, highlighting its benefits and outlining future research directions.

Optimized Convolutional Neural Network Architecture for High-Accuracy Classification of Rose Leaf Diseases

2024-10-06T11:46:48+00:00

Roses, a vital agricultural commodity in tropical and subtropical regions, face significant threats from various leaf diseases that reduce both yield and quality. This study focuses on improving rose leaf disease classification in Bangladesh by leveraging deep learning techniques. The proposed method utilizes a Convolutional Neural Network (CNN) to detect diseases such as rose rust and rose slug, which are common threats to rose cultivation. By training models on a dataset of 40,022 images containing healthy and diseased leaves, classifiers such as VGG-16, VGG-19, ResNet-50, and LCNN were employed to identify and classify diseases with an accuracy of 80%, 82%, 83%, and 82%, respectively. Preprocessing steps included data augmentation, cleaning, and normalization to ensure robustness in the model's performance. While image-based detection is highly effective, this research suggests that incorporating environmental and text-based data could further enhance disease detection accuracy and provide a more holistic approach to managing rose leaf diseases.

Efficiency Enhancement of Lead-Free CsSnGeI3-Based Perovskite Solar Cells Using ZnSe as an Electron Transport Material and Spiro-MeOTAD as the Hole Transport Material

2024-07-18T08:35:33+00:00

Perovskite solar cells are an emerging technology in the field of photovoltaic cells, they have higher efficiency, relatively low production cost, and are more versatile than traditional silicon solar cells. In this research, a cesium tin–germanium triiodide (CsSnGeI₃) perovskite solar cell has achieved high power conversion efficiency and extreme air stability. In this study, lead-free Cs-based perovskite solar cells have been quantitatively analyzed to explore the effect of absorber layer thickness, defect density of the absorber layer, working temperature, series and shunt resistance, acceptor doping concentration using a solar cell capacitance simulator software. For this perovskite solar cell structure, ZnSe is used as a buffer layer, and CsSnGeI3 is used as an absorber layer. ITO material is used as an electron transport layer and Spiro-MeOTAD Transport Layer. Gold is used to make the back contact of this proposed solar cell. In this simulation, the environment-friendly perovskite solar cell achieved an efficiency of 31.22% when the thickness of the buffer and absorber layers was 0.06µm and 1.5µm. The designed outputs will be efficient for the convenient fabrication of the perovskite solar cell.

Comparative Performance Analysis of Ensemble Models for Breast Cancer Classification

2024-11-12T04:58:51+00:00

Breast cancer remains a prevalent global health issue, accounting for approximately 2.3 million new cases and 670,000 deaths worldwide in 2022. Early detection and accurate diagnosis are crucial to improving patient outcomes, as delayed identification can lead to severe complications. Advances in machine learning (ML) have facilitated improvements in cancer diagnosis, with various algorithms enhancing predictive accuracy. This study proposes a novel ensemble model for breast cancer classification, utilizing 31 features from the University of Wisconsin Breast Cancer dataset. We applied six algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and XGBoost—and combined them with ensemble techniques, specifically Hard Voting, to develop a high-accuracy model. The model was evaluated on classification performance metrics, achieving improvements in accuracy, precision, recall, and F1 score. Results indicate that the proposed ensemble model outperforms individual classifiers and other ensembles, showing potential as a reliable tool for early breast cancer detection.

Harnessing Machine Learning for Operational Excellence: Transforming Business Efficiency Across Sectors

2024-10-06T09:56:51+00:00

Machine learning (ML) has become a vital tool for optimizing business operations, offering significant improvements in efficiency, cost reduction, and decision-making. This paper examines the application of ML in key operational areas such as predictive maintenance, inventory management, customer segmentation, and demand forecasting. By leveraging advanced algorithms, businesses can analyze large datasets to identify patterns, predict outcomes, and automate routine tasks. The study draws on real-world examples and case studies across various industries, illustrating the substantial benefits of ML integration. It also addresses challenges such as data quality, algorithm selection, and implementation barriers, providing insights into how businesses can overcome these obstacles. The research emphasizes the importance of aligning ML strategies with business objectives to fully realize its potential. The findings demonstrate that businesses adopting ML can achieve greater operational efficiency and maintain a competitive edge in today’s data-driven market. The paper provides practical recommendations for implementing ML technologies, highlighting the need for a systematic approach to ensure successful adoption and long-term impact.