International Journal of Innovations in Science & Technology
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Design of a Miniaturized Flexible Patch Antenna with Shorting-Pin Integration for Enhanced Gain in RFID, ISM, and Wearable Biomedical Applications
Full text linkAntennas are integral parts of wireless communication because they can ensure that signals are transmitted and received effectively, encompassing a variety of frequencies, such as those used in IoT and overall RF systems. This paper introduces a miniaturized single-band antenna of 9 × 31.6 × 0.254 mm3 with a specific design for wearable applications, which was made on flexible Rogers RT5880 substrate. Using the CST Microwave Studio 2024 microchip as the design and analysis tool, the proposed antenna consists of a rectangular slot-based radiating structure with a shorting pin and probe feed, which can achieve stable design performances both in the free-space and on-body situation services. Compliant with the wearable safety requirements and operating at a low level specific absorption rate (SAR) less than 1.6 W/kg at the resonant frequency. The antenna has good radiation performance, with a maximum efficiency of 82% and a maximum gain of 4.1 dBi, having a bidirectional radiation pattern in the elevation plane and an omnidirectional radiation pattern in the azimuth plane. The introduction of shorting-pin as a strategic method of reducing the length of resonance allows the approach of substantial reduction in the resonant length without compromising radiation characteristics. Simulation results also provide further confidence scales of stabilizing impedance performance and omnidirectional radiation pattern properties in the target 5.725-5.875 GHz ISM band, which demonstrates the expectation of shorting-pin strategies for the development of small-size, high biocompatibility, and flexible antennas for next-generation wearable, body-area network, and radio frequency identification (RFID) communication systems
An Efficient and Robust Deep Learning Approach for Vehicle Recognition using Light-weight Deep Network
Full text linkIn the realm of intelligent transportation systems, automatic number plate detection has emerged as a crucial research topic due to its wide range of applications, including traffic violation monitoring, support for autonomous vehicles, vehicle speed tracking, automated toll collection, stolen vehicle identification, and overall traffic management. The goal of automatic number plate detection is to accurately identify vehicles based on their number plates. This study proposes a hierarchical approach for detecting number plates. In the initial phase, a lightweight deep learning model, Mobile Net-SSD, is employed to detect number plates. Subsequently, the alphanumeric characters from the detected number plates are extracted using an Optical Character Recognition (OCR) technique. The model is, built on a convolutional neural network, and efficiently uses depth wise and pointwise convolution layers, making it suitable for mobile and embedded systems. Additionally, we introduce a dataset of 30,613 vehicle number plate images to foster further research. Experimental evaluations show that the proposed method achieves 95% accuracy on this dataset, significantly enhancing real-time number plate detection and making it suitable for large-scale implementations in smart cities and intelligent transportation networks
Developing a Quranic QA System: Bridging Linguistic Gaps in Urdu Translation Using NLP and Transformer Model
Full text linkThe limited access to Quranic knowledge for Urdu speakers is due to inadequate Natural Language Processing (NLP) tools, which hinder precise Quranic understanding and retrieval. This research introduces a Transformer-based Urdu Quranic Question-Answering (QA) system, a novel approach that enhances semantic accuracy and retrieval precision, unlike existing Arabic- and English-based models. This study primarily leverages Transformer-based technology to develop a context-aware Urdu Quranic chatbot, unlike conventional systems, which primarily support Arabic and English Quranic texts. The system addresses the missing linguistic gaps in Quranic QA by enhancing both precision and semantic interpretation for Urdu users. The system was trained using Fateh Muhammad Jalandhari’s Urdu Quranic translation and fine-tuned with Roberta for enhanced semantic text analysis. It integrates TF-IDF with SBERT for improved question-answering performance. The NLP system went through multiple evaluation metrics were used to assess its precision and overall capability. The chatbot achieved high retrieval accuracy with a Mean Average Precision of 0.85, an Exact Match of 0.82, and an F1 Score of 0.88. User satisfaction reached 92%, indicating its effectiveness in providing precise Quranic answers. Future updates will introduce that include voice detection features, expanded language support, and integration with Tafsir and Hadith databases for improved contextual understanding. This study enhances Urdu Quranic information retrieval by providing an improved NLP-based solution for automated Islamic knowledge dissemination
Reducing the Environmental Impact of Leather Production and Assessing the Potential of Cactus-Based Vegan Leather
Full text linkGlobal warming and the environmental and health risks linked to animal-based leather products have increased the demand for sustainable alternatives. Vegan leather has gained attention as a promising solution to these issues, encouraging eco-friendly fashion. To reduce its environmental impact, the leather industry is shifting from animal-derived to plant-based materials. Traditional leather production involves slaughtering over a billion cattle each year, releasing harmful substances like chromium and lead that pollute water sources and threaten public health. This study explores the potential of cactus-based vegan leather as an eco-friendly substitute for conventional leather. The process involved harvesting mature cactus pads, drying them in the sun, and transforming them into a sturdy material that mimics the properties of real leather. Mechanical tests showed that cactus leather offers similar durability, flexibility, and aesthetic appeal to traditional leather. The results emphasize the environmental, economic, and functional advantages of cactus leather, positioning it as a scalable alternative to reduce the negative ecological effects of animal-based leather production
Network Traffic Classification in SDN Networks Using PCA Integrated Boosting Algorithms
Full text linkIn recent years, internet traffic has increased as a result of the introduction of new services and apps. As a result, managing network traffic has grown more challenging. To accomplish this, several classification techniques for network traffic were proposed. Several researchers have used the most advanced deep learning and machine learning models for the suggested challenge. The suggested work can also make use of boosting methods. Boosting algorithms take advantage of the decision tree idea. They take little training time, and model training does not require a powerful system. Thus, boosting algorithms like Extreme Gradient Boosting Model (XGBM), Light Gradient Boosting Model (LGBM), Cat Boost, and Ada Boost with the integration of Principle component analysis (PCA) are used in the proposed study to classify network traffic. The results of these models are compared in terms of confusion matrix, accuracy, precision, recall, and F-Measure. The Network traffic android malware dataset, which was utilized in the proposed study, is publicly accessible online on Kaggle.com. For simulation, Python and its libraries such as sci-kit-learn, tensor flow, keras, and matplotlib are utilized. Following the simulation, the results showed that the XGBM had 90.41% accuracy, 96.39% precision, 89.72% recall, and 92.91% f-measures, while the LGBM had 89.02% accuracy, 90.04% precision, 89.8% recall, and 89.83% f-measures. 86.87% accuracy, 83.97% recall, 89.43% precision, and 86.61% f-measure were attained with Cat Boost. Following that, ada boost obtained 83.07% accuracy, 80% recall rate, 85.25 precision, and 82.58% f-measures. After the integration of the proposed boosting algorithms with PCA, we achieved a very significant enhancement in results. After the integration, it has been achieved that the accuracy rate of XGBoost has improved to 95.56%, while the recall rate is 94.39%, precision is 96.72% and the F-Measure rate has improved to 93.91%. Similarly, the performance of the light Gbm model is also improved with the integration of PCA. It achieved an accuracy rate of 93.41%, precision of 93.72%, recall of 92.39%, and f-measures of 92.91%. Following this, the performance of PCA integrated cat boost could also be seen as improved, as it achieved an accuracy rate of 94.41%, precision rate of 93.72%, recall of 92.39%, and F-measures of 93.91%. Similarly, the performance of a boost has also gained improvement by achieving an accuracy rate of 94.56%, precision rate of 94.72%, recall of 93.39%, and F-measure score of 93.91%. After all the simulations and performance evaluations, it has been achieved that the integration of PCA with the boosting algorithm is a simple trick to improve the performance of boosting algorithms. As here the performance of each model is improved to approximately 10%
An AI-Powered Browser Extension Using Roberta and XAI for Phishing Email Detection and Security Awareness
Full text linkPhishing attacks are a common and serious cybersecurity threat today. They exploit human weaknesses by stealing sensitive information by sending fake emails and harmful links. Traditional email filtering systems like rule-based methods and black-box models, struggle to detect phishing. Rule-based filters fail when attackers use new tricks, and black-box models lack transparency, which limits user awareness.
This work introduces a smart browser extension that uses deep learning and Explainable AI (XAI) for phishing detection. We use a transformer-based model, Roberta, trained on a large email dataset, achieving 98.12% accuracy in classifying email content. For checking URLs, we use VirusTotal, which gathers threat intelligence from multiple sources. We also apply XAI tools to highlight key parts of the text that contributed to the classification of the email content, and a large language model (LLM) to provide simple explanations about phishing.
Our hybrid approach combines explainable deep learning with multi-source URL verification. This helps users understand phishing threats better and improves their ability to spot attacks on their own
Optimization of MPPT in PV Systems Using Machine Learning Under Partial Shading Conditions
Full text linkPhotovoltaic (PV) systems are an important solution to the increasing global demand for electricity and the declining availability of fossil fuels. However, under Partial Shading Conditions (PSC), the Power-Voltage (P-V) curve can have multiple local peaks, which leads to significant power losses and makes it harder to find the true Maximum PowerPoint (MPP). Traditional algorithms like Perturb and Observe (P&O) and Incremental Conductance (INC) often mistake these local peaks for the global ones, making it difficult to accurately track the Global Maximum PowerPoint (GMPP) during shading. To overcome this issue, Machine Learning (ML)-based Maximum Power Point Tracking (MPPT) methods are explored as a data-driven alternative. These aim to improve accuracy and reduce energy loss in PV systems affected by shading. The study evaluates several ML techniques—Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), and Weighted K-Nearest Neighbours (WK-NN) using both synthetic and real-world weather data from Johannesburg, South Africa. To test their effectiveness, the models are simulated and implemented on a hardware-based PV system. Results show that ML-based MPPT methods significantly enhance tracking performance and reliability. For example, SVM achieves an efficiency of 96.76% under normal conditions and 83.66% during heavy shading, while ANN reaches 99.58% efficiency in stable sunlight. RF and WK-NN also maintain over 95% efficiency in changing conditions due to their adaptability. Despite the promising results, some challenges remain. These include computational complexity, real-time deployment limitations, and the ability of models to generalize under varying sunlight levels. Still, this study demonstrates that AI-powered MPPT systems can greatly improve energy management and grid stability in next-generation solar technologies. Future research should focus on deep learning-based MPPT, hardware-efficient AI models, and real-time optimization to reduce processing demands and improve scalability in embedded MPPT controllers
Machine Learning-Based Fish Species Recommendation Using Water Quality Parameters
Full text linkThe integration of machine learning (ML) in aquaculture enables data-driven fish species recommendations based on water quality parameters. Traditional fish farming faces challenges like manual monitoring, inefficient species selection, and unpredictable water conditions, leading to economic losses. This paper presents a software-based fish recommendation system using ML models to analyze seven key water parameters—pH, Temperature, Turbidity, TDS, Dissolved Oxygen, Nitrate, and Ammonia. Various ML algorithms, including Random Forest, XGBoost, and SVM, were evaluated, with the optimized model achieving over 90% accuracy. A graphical user interface (GUI) allows users to input parameters and receive real-time recommendations, enhancing efficiency and sustainability in aquaculture
Investigation of Improvement in Current Carrying Capacity of Various Power Cables Using a Novel Arrangement
Full text linkPower cables are essential components of electrical systems, and their ability to carry current depends directly on the size of the conductor. With the rising cost of copper, efficiently utilizing a conductor\u27s current-carrying capacity has become increasingly important. To maximize this capacity, it is crucial to limit the temperature rise, either through effective heat dissipation or by optimizing the cable orientation in a trench. This study introduces new cable arrangements designed to lower the operating temperature of power cables, which in turn increases their current-carrying capacity. Different cable orientations for laying three-phase power cables in both single and double circuit configurations were examined. A high-resolution thermal imager was used to accurately measure the temperature. Two of the proposed orientations led to a significant reduction in operating temperature compared to the cable arrangements specified in BS 7671. These novel orientations can increase the current-carrying capacity by approximately 6% without the need to increase the cable size
Optimized Mode Selection in D2D Enabled B5G Networks: A Game Theory Approach
Full text linkBeyond Fifth Generation (B5G) networks aim to revolutionize wireless communication with unprecedented data rates, extremely low latency and high throughput. This research is motivated to optimize Device-to-Device (D2D) communication within cellular networks, where the goal is to improve wireless communication gains and performance. This research identifies the key components necessary for ensuring proper Quality of Services (QoS) provisioning for D2D and cellular users, given the challenges to ensuring appropriate QoS for both D2D and cellular users done by a holistic method to ensure the user experience is smooth while the network capability is fully utilized, which aims to improve the efficiency and performance in wireless communication. Leveraging the power of game theory, this study develops practical solutions for the identified challenges. Furthermore, this work presents a proposed solution, goals, architecture and methodology of the proposed game-theoretic model, paving the way for enhanced D2D communication in future B5G networks