International Journal of Innovations in Science & Technology
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Machine Learning-Based Asthma Diagnosis Prediction Using Lung Function and Demographic Features
Get PDFAsthma is a prevalent chronic respiratory disease, which poses significant diagnostic challenges because of its multifactorial nature. This study aims to develop a machine-learning approach for predicting asthma diagnosis using key features such as body mass index (BMI), age, lung function parameters (FEV1 and FVC), and demographic information. A dataset containing clinical and demographic records was utilized to train and evaluate models, including Random Forest, Neural Networks, and XGBoost classifiers. The performance of the following models was assessed using metrics such as precision, recall, accuracy, and F1-score, with Random Forest showing/exhibiting the highest predictive performance. In addition to traditional performance metrics, advanced visualization techniques like SHAP (Shapley Additive ex Planation’s) values were employed to interpret model predictions and assess feature importance. Results demonstrate that age, BMI, and lung function are key predictors of asthma diagnosis, with lung function parameters showing/exhibiting the strongest correlation with diagnosis outcomes. The study also explores various 3D and interactive visualizations to enhance the interpretability of the models. The proposed approach demonstrates that machine learning models when combined with clinical data, can accurately predict asthma diagnosis and potentially aid healthcare professionals in early detection and personalized treatment plans. This research highlights the potential of data-driven models in improving asthma diagnosis and contributing to better clinical decision-making
Vortex Powerplant Implementation in A Coastal Community
Get PDFA gravitational water vortex power plant is an eco-friendly device that generates electricity from renewable energy sources. In this system, a turbine extracts energy from the vortex created by tangentially channeling water into a circular basin. This article aims to explore the feasibility of implementing vortex power plant technology in coastal communities using an experimental model. The study investigates the potential of wastewater as a renewable energy resource by analyzing the relationship between flow rate, torque, and efficiency under different material and pipe configurations, particularly in urban areas. For experimental purposes, Gujrat city was selected. The wastewater outlet points near Bolley Bridge discharge approximately 74,714,000 liters per day. Based on our survey, the average household water usage in Gujrat city is 500 liters per day. An experimental model was designed to estimate potential energy generation. The model\u27s design focused on optimizing the basin shape, inlets, outlets, and turbine configuration.
Using different pipes (cast iron and steel), the average water velocity and discharge rates were evaluated. The steel pipe produced higher velocity. Efficiency and production were further analyzed using LED lights, revealing that at 60 RPM, the system achieved significant efficiency and output voltage
Performance Analysis of Motorbike Engine Using Bioethanol Gasoline Blends
Get PDFThe increasing demand for sustainable energy and reduced reliance on fossil fuels has driven the exploration of alternative fuel options. This study aims to evaluate the performance of a motorcycle engine using bioethanol-gasoline blends. Simulations were conducted using AVL Boost software. By applying AVL Boost in innovative ways, the research provides new insights into improving the performance of motorcycle engines powered by bioethanol-gasoline blends, contributing to more eco-friendly transportation. A numerical model of a single-cylinder engine was developed, and various fuel blends were tested to assess performance characteristics at engine speeds ranging from 1000 to 4000 RPM. Single-cylinder spark ignition engines are commonly used in many types of motorcycles. The results showed that the E20 blend achieved a 4% increase in power and improved performance characteristics during tests on engines running on lower ethanol blends
Mobile Legends Win Rate Prediction and Team Recommendation Using Switched Hero Roles
Get PDFMobile Legends Bang Bang (MLBB) falls under the category of a Multi Online Battle Arena game. Games like MLBB require players to have strong skills and strategic gameplay; team composition is an important factor influencing the chances of winning the game. Although there is data currently available for MLBB, two aspects of this game that remain unexplored include: i) win rate prediction using nontraditional roles in heroes, and ii) team composition with switched hero roles. While picking heroes for each team, a team chooses heroes that they know perform well using a traditional role. However, nothing has been mentioned as to what happens when heroes are selected using a nontraditional role. This research aims to address this question by predicting the win rate of heroes with switched roles. This unpredictability will lead to the formation of a team that can have a significant advantage over the enemy team thus leading to victory. The dataset for this study was formulated by focusing on 67 heroes in the game. The win rates were generated with real-time simulations where the ally team members remained unchanged to avoid biased results. Using two model-building approaches, win rate predictions were made using 12 regression algorithms under 5 feature selection settings. The research has shown that LightGBM with AdaBoost as the base estimator provides better results and was used to formulate 5 teams. A recommendation system was designed to optimize team composition from the win rate prediction analysis. To validate the results, we simulated 50 matches with each team, with ally team players remaining the same to avoid biased results. This resulted in a 94% win rate with 47 wins and 3 losses out of a total of 50 matches
An Efficient Read and Mark Mechanism for Multiple-choice Questions Using Optical Character Recognition
Get PDFThis research paper focuses on modifying the grading of multiple-choice questions (MCQs) to better the efficiency and incorrectness of educational tests. Conventional grading systems, such as optical mark recognition (OMR), have fundamental drawbacks, excluding the necessity for precise shading, time-wasting, and the use of special OMR sheets and OMR scanners. This conceptualization can be expensive and error-prone, especially if the MCQs papers are folded or unmarked. In comparison, the suggested OCR-based approach gives fundamental benefits in all necessary areas. It is less costly to use a simple scanner and software alternatively to costly OMR equipment. The method is motivated to be simple to set up and use. It importantly reduces error rates and marking time by employing precise OCR algorithms and processing greater amounts of answer sheets quickly. Moreover, the system is extremely accurate and scalable, allowing it to handle a rising amount of paper efficiently. It also has limited trust in external tools and is highly flexible and adaptable to different MCQ formats and grading settings. In General, the OCR-based approach outperforms existing methods by eliminating their shortcomings and delivering a trustworthy, time-saving alternative for automated MCQ grading
Impact of Rhizospheric and Phyllospheric Mycobiota on Plant Health of Tomato
Get PDFTomato (Solanum lycopersicum) is a significant crop produced globally but suffers from numerous biotic and abiotic stresses when cultivated in fields. Among all the biological stresses, fungal diseases cause a sharp decline in yield and quality but may remain non-pathogenic and symptomless under certain fungal species throughout the plant\u27s entire life cycle. This work aimed to isolate and purify the mycobiota from various parts of the tomato plant—stem, root, fruit, leaf, and rhizospheric soil—to determine the fungal communities present. Morphological and molecular identification established the presence of various fungal species, including Aspergillus fumigatus, Acremonium spp., Pythium spp., Geotrichum candidum, Aspergillus parasiticus, Aspergillus carbonarius, Aspergillus terricola, Aspergillus flavus, Aspergillus oryzae, and Alternaria alternata. The density and distribution of these fungi varied among different plant parts and soil, with A. fumigatus showing the highest frequency (80%) among all isolates. Fungal diversity analysis revealed notable differences in species richness and evenness across plant parts. The rhizospheric soil showed the highest fungal diversity (Shannon index = 2.31), followed by roots (2.05), while the leaf and fruit tissues exhibited lower diversity indices. The Simpson\u27s index values also confirmed greater dominance and lower evenness in aboveground plant parts, indicating a more selective fungal colonization. A heat map was constructed to visually compare diversity metrics across plant parts. Moreover, the effect of microbiomes on tomato plant health, especially on chlorophyll content in the field, was also examined. The results indicate that tomato plant mycobiota play a positive role in plant health based on their interaction. Further studies need to be conducted to investigate the specific possible positive impact of individual fungal species and their interactive effect on plant health of tomato crops
Improved Improved Millimeter Wave Patch Antenna for Next-Generation and Beyond Networks
No full textThis paper presents an optimization of a compact, ultra-wideband (UWB) rectangular microstrip patch antenna (MSPA), tailored for next-generation mm-wave wireless applications. The proposed UWB antenna offers significant enhancements in gain and bandwidth. It achieves an impressive bandwidth of 36 GHz, covering the V-band (40–75 GHz), essential for high-capacity satellite communication, as well as the 61.25GHz ISM band and most of the 60GHz WiGig band. Simulations performed using CST MW Studio 2021 demonstrate that the antenna achieves a maximum efficiency of 93.3% at 44.2 GHz and a minimum efficiency of 63.1% at 66.2 GHz. A maximum realized gain is 10.2 dB at 55.8 GHz, with the lowest realized gain being 4 dB at 65 GHz. These results underscore the antenna\u27s suitability for future 5G handheld devices and other high-frequency applications. Comparative analysis with existing designs is provided, highlighting the proposed antenna’s superior performance metrics
A Compact Slotted Micro-Strip Patch Antenna Operating at 28 GHz for 5 G-IoT Applications
Get PDFThis paper aims to present a compact slotted microstrip patch antenna for 5 G-IoT applications operating at a 28 GHz frequency. The antenna structure is modeled on an FR4 substrate with a compact size of 12 mm × 13 mm (substrate height = 1.6 mm, Epsilon = 4.3, and loss tangent = 0.02). The antenna comprises a patch on top of a dielectric substrate and a defected ground plane (DGS) on the bottom side. Slots and curves are incorporated in the patch radiator to achieve the desired resonating frequency of 28 GHz. Simulation results demonstrate a return loss of –22 dB, a bandwidth of 4.64 GHz, a VSWR of 1.16, a gain of 3.2 dBi, and an efficiency of 60%. These attributes make the antenna appropriate for a range of 5 G-IoT applications, including smart cities, industrial IoT, and autonomous systems where high data throughput and reliable connectivity are essential. The overall results depict that the proposed design is a good candidate for deployment in 5 G-enabled IoT ecosystems
Enhancing Predictive Business Process Monitoring in Call Centers through Multimodal Data Fusion and Heterogeneous Time-Aware LSTM-Based Multi-Task Learning
Get PDFThe optimization of call center operations and the enhancement of customer service are greatly supported by predictive business process monitoring. Traditional methods often overlook valuable multimodal data, such as conversations occurring in contact centers, because they typically rely on sequence data from business IT systems. This limitation hinders a complete understanding of business processes. In this study, we introduce a unique time-aware LSTM-based framework for predictive business process monitoring, which leverages both IT system data and dialogue data from contact centers. Our approach combines multiple data sources to improve the accuracy of forecasting ongoing business activities. To address challenges related to multi-task learning and to better utilize the rich information embedded in various data types, we propose a heterogeneous multi-task learning architecture called Heterogeneous Multi-gate Mixture-of-Experts (H-MMoE). Experimental results show that our method outperforms established baseline models such as Transformer, CNN, and standard LSTM. These findings demonstrate the potential of time-aware LSTM models to improve process monitoring, optimize workflows, and drive operational success in call center environments
University Auto-Gate Management through AI-Driven License Plate Recognition
Get PDFThe rapid growth in the number of vehicles and transportation systems has made Automatic Number Plate Recognition (ANPR) an essential tool for modern traffic management and security. With the rising vehicle count, manual monitoring and control of traffic have become increasingly difficult. ANPR, a complex field within computer vision, faces challenges due to variations in license plate styles, sizes, orientations, and lighting conditions. License plate recognition, leveraging advanced image processing techniques, represents a promising research domain, especially in the context of IoT and smart city development. With the exponential rise in the number of vehicles, automated systems are essential for retaining vehicle information for various purposes. Researchers are increasingly focused on developing reliable ANPR systems, spurred by advancements in portable electronics and machine learning techniques. Although numerous ANPR approaches have been documented for surveillance systems and intelligent transportation applications, creating a robust system remains a challenging research problem. This research aims to investigate the utilization of ANPR for managing vehicle access at the entrance gates or parking areas of private or government universities and colleges. The system aims to maintain a record of vehicles entering and exiting the premises, as the performance of existing techniques depends on various factors and local conditions. The study introduces an AI-powered ANPR system that restricts access to authorized vehicles by capturing and identifying license plates. This technology can be used to track vehicle entry and exit at university campus gates, improving traffic regulation and security during peak hours