International Journal of Innovations in Science & Technology
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Architectural Framework for Scalable On-Demand Service Aid Platform
Full text linkThis innovative web-based platform designed to connect skilled workers with service seekers, addressing challenges such as accessibility, affordability, and trustworthiness in the skilled labor market. The platform provides a seamless experience for users, enabling them to post job requirements, browse available professionals, and hire based on specific needs, all within an intuitive interface. Built with modern technologies, the platform\u27s front end is developed using HTML, CSS, JavaScript, and Bootstrap, ensuring responsiveness and ease of use. The back end, powered by Java with Spring Boot and integrated with a robust MySQL database, ensures efficient data management and secure user interactions. The platform’s architecture facilitates smooth navigation and quick access to essential features. Service seekers can easily sign up and explore a pool of skilled workers categorized by their expertise, location, and ratings. The system includes a user feedback mechanism that enhances trust by allowing users to rate and review workers based on their experiences. Workers, in turn, benefit from consistent job opportunities and the ability to showcase their skills to a wider audience. This project also addresses the unique challenges of underserved regions, such as Hyderabad, where digital platforms for hiring local services are scarce. By providing a comprehensive solution that caters to diverse household needs, from plumbing to carpentry, the platform bridges the gap between demand and supply in the skilled labor market. The research stands out as a transformative tool, creating economic opportunities for skilled workers while offering service seekers a reliable and efficient solution for their everyday needs. It fosters a sense of empowerment and convenience, making it a vital contribution to the digital transformation of the Labor market
Digital Retinal Fundus Imaging: An AI-Assisted Effective Machine Learning Model for Detecting Ocular Pathology
Full text linkOcular pathology is the study of employing digital fundus imaging to diagnose various eye-related diseases. Macular degeneration, cataracts, glaucoma, and diabetic retinopathy are among these eye diseases. To distinguish between these illnesses, a manual examination of the human eye is performed. Since the work is arduous, we have used many complex machine learning techniques in this paper to automatically identify eye disorders using digital retinal fundus imaging. In our initial stage, the dataset is de-noised to avoid misclassification. Additionally, we use Contrasted Limited Adaptive Histogram Equalization (CLAHE) to enhance the images. By adjusting the histograms\u27 adaptive equalization parameters, it is possible to improve the fundus image on each of the RGB channels separately. With the help of three distinct deep CNN models; AlexNet, GoogLeNet, and ResNet50, high-quality features were extracted in the second phase. After merging the features, a composite feature vector was created. This is done to choose characteristics of superior quality. The Bag of Deep Features (BoDF) was used to choose features of the highest caliber. BoDF will assist in lowering the size of the feature so that it can be recognized quickly. Using Mutual Information (MI), comparable features were also eliminated. Support Vector Machine (SVM) and Decision Tree (DT) were then used to classify the model\u27s output to identify ocular diseases. The STARE dataset is used in this research. When compared to current state-of-the-art models, the proposed model is more appropriate and provides an overall classification performance of 94.8% in almost 3 seconds
Bioremediation of Textile Disperse Dyes using White-Rot Fungi Trametes versicolor
Full text linkDisperse dyes, frequently used in textile dyeing processes, present a particular challenge because of their recalcitrant nature. With an emphasis on wastewater effluent treatment, white-rot fungi Trametes versicolor were used. The fungus was cultured on different media and optimized various biochemical parameters (temperature, pH, inoculum size, dye concentration, and culturing time). After their biomass, disperse Red-I (DR1) and disperse Blue-I (DB1), and textile wastewater were biodegraded with the fungi T. versicolor. The growth of T. versicolor is time taking but maximum degradation by T. versicolor (0.02 to -0.11 during 3 days) is observed. In DB1 solutions and wastewater, absorbance values started at different points. However, the efficiency of fungi was found to be more than 80%. The potential of degradation of fungi in wastewater treatment can be further maximized to reduce environmental impact
Enhancing Open-Source Projects: The Synergy Between Code Readability Metrics and User Experience
Full text linkIntroduction /Importance of Study: The open-source project is a key driver of innovation in the so-called open ecosystem. However, the readability of code is still a major obstacle in having users successfully engaged and contributing.
Objective: This study explores how Code Readability Metrics Impact User Experience (UX) in OSS projects.
Novelty Statement: We examine code comments, structure of the code, and version control to discover their impact on user understanding and satisfaction.
Material and Method: For this, a survey has been conducted. In this survey, handed out to upper division (computing major) or first-year computer science students at university/graduates and post-grads in similar positions), we gathered feedback on projects written in Kotlin, Python, Swift, JavaScript, and Flutter.
Results and Discussion: Results show that readability correlates positively with a user\u27s perceived experience. The clarity in your structure, commenting on all parts of the code, and great version control lead to better user reception. The study’s findings show that when code is well-organized and understandable, users tend to have more positive experiences and like to use the software.
Concluding Remarks: Our study has demonstrated that better code readability translates into enhanced user experiences, which can inform developers and project managers on how best they can improve their practices
Comparative Evaluation of Machine Learning and Deep Learning Models for Real Estate Price Prediction
Full text linkAccurate real estate price prediction plays a vital role in informed decision-making for investors, policymakers, and stakeholders. This study evaluates various machine learning and deep learning models for predicting real estate prices using the House Prices 2023 dataset which contains 168,000 entries of Pakistani property data. In our proposed methodology we performed data preprocessing and features engineering to standardize the data. We performed extensive experiments by using machine learning (ML) and deep learning (DL) models on our preprocessed data. The model’s performance was evaluated based on the R-squared (R²) score and Mean Squared Error (MSE) metrics. Based on the provided metrics, the Decision Tree achieved the highest performance with an R² of 0.9968 and an MSE of 0.0021, followed by Random Forest with an R² of 0.990 and MSE of 0.0007. Similarly, other ML models like Gradient Boosting and XG Boost also outperformed by achieving (R² 0.9959, MSE 0.0028 R² 0.9747, and MSE 0.0170) respectively. In contrast, models like AdaBoost, Neural Network, and Convolutional Neural Network (CNN) showed comparatively lower performance due to the nature of the data. The study emphasizes that ensemble-based models like Decision Trees and Random Forests are highly effective at identifying patterns in real estate prices. Additionally, applying optimization techniques improves the models ability to generalize and perform well on unseen data
Challenges and Practices Identification via Systematic Literature Review in the Design of Green/Energy-Efficient Embedded Real-Time Systems
Full text linkAs most embedded devices are portable, that is they are operated by batteries, early battery exhaustion is likely to cause the failure of the embedded real-time systems (ERTS). Therefore, developers and users enjoy the services of the ERTS but face green and energy consumption challenges. Studies show that attempting to design green ERTS may lead to some serious issues or deteriorate some of the quality characteristics of the embedded systems. Energy conservation in ERTS has continued to be an area of interest in the past years. Energy efficiency or certain quality features are considered while designing ERTS, but these two factors are not often considered together because they have direct impact on each other in ERTS. The purpose of this research is to identify the challenges in the design of green ERTS and the solutions that can be employed to address those challenges. A review of the relevant literature was conducted to define the problems and practices under consideration. Based on a comprehensive Systematic Literature Review (SLR), we have found 8 challenges and 34 practices from 65 papers in the green ERTS context. The results of our SLR will help us develop a framework for creating green ERTS in the future
An Enhanced Novel IoT-Based Car Accident Detection and Alert System
Full text linkThe excessive use of vehicles for our day-to-day tasks in this revolutionized era has become a necessity, making our lives convenient and technology-dependent. This rise in the use of vehicles has led to a greater number of road accidents that have affected the lives of humans dramatically resulting in an increased fatality rate. According to the World Health Organization, about 50 million people are injured due to road accidents every year. This is mainly due to the unavailability of timely emergency health services. Objective: This study is presented to address this critical issue by leveraging the unmatched capabilities of the Internet of Things (IoT). Novelty statement: A novel IoT-based car accident detection and alerting system considering various car parameters simultaneously for more precise results is proposed which is designed in two stages. First, the accident that has occurred is detected via sensors considering the key vehicle parameters like speed, pressure, acceleration, and gravitation force. Second, upon detecting an accident an emergency alert containing all relevant information regarding the driver, vehicle as well as the exact location of the accident calculated through a GPS module along with its severity is sent to the nearby hospital, police, and driver’s emergency contacts using the GSM module. The proposed approach is employed on a toy car to show its significance and outperforms the existing systems in terms of accuracy 98% and responsiveness
Design of a Modified Wilkinson Power Divider for Ultra-Wideband Antipodal Vivaldi Antenna Arrays
Full text linkThis paper presents the design of a two-way Modified Wilkinson power divider (MWPD) feeding network for a two-element Antipodal Vivaldi Antenna (AVA) array, operating in the 3–10 GHz ultra-wideband (UWB) frequency range. The proposed feeding network is optimized by incorporating bent corners, which help reduce unintended radiation and improve signal distribution. Additionally, the antenna design is enhanced using a compact structure to improve radiation performance and impedance matching. The design, simulation, and optimization of both the feeding network and antenna array are conducted using CST Microwave Studio. Experimental validation confirms that the proposed array meets UWB specifications, making it a suitable candidate for wideband communication and imaging applications
Improving Cardiovascular Disease Prediction Accuracy with Three-Way Decisions
Full text linkCardiovascular Disease (CVD) is a leading cause of death worldwide, making accurate and early risk prediction crucial for better patient outcomes. Traditional CVD prediction models often rely on binary decision-making, which struggles with uncertain or borderline cases, leading to misclassification and ineffective treatment strategies. This research proposes an advanced predictive model that combines machine learning algorithms with a three-way decision approach to improve the accuracy and reliability of CVD risk assessment. The three-way decision model, based on rough set theory, divides decisions into three categories acceptance, rejection, and deferment—allowing for more detailed and informed predictions. Using the Cleveland Heart Disease dataset, this study applies machine learning techniques such as Random Forest (97.14% accuracy), Logistic Regression (91.30% accuracy), Naïve Bayes (88.24% accuracy), and Support Vector Machine (89.74% accuracy) to evaluate the model’s effectiveness. The results show that integrating three-way decisions with machine learning improves predictive performance, especially for unclear cases, enhancing clinical decision-making. However, the model’s reliance on dataset quality and threshold selection poses some limitations that need further investigation. This research introduces an intelligent and flexible approach to CVD prediction, which could reduce diagnostic errors and support early interventions for high-risk patients
The Design and Development of 1-DOF Assisted Physiotherapeutic Device for Rehabilitation of Frozen Shoulder
No full textThe growing prevalence of limb-related issues has increased the demand for rehabilitation services. A key focus has been on restoring upper limb functionality and controllability, which are essential for patients to reintegrate into society and improve their quality of life. In this project, a prototype for an assisted physiotherapy device was designed to rehabilitate frozen shoulders. The device supports both external and internal rotation of the shoulder, allowing movement from 0 to 90 degrees. Its adjustable speed control lets patients move their shoulders based on their personal needs and abilities. Additionally, the device provides real-time feedback on the angle, speed, and force applied during rehabilitation. Experimental results show that this prototype is both practical and effective in rehabilitating shoulder rotation