ANNU Digital Library

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Communities in DSpace

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Recent Submissions

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Automated Sample Preparation System
(2023) Jenan Abualrub; Yaqout Salameh
Analytical chemistry occupies a crucial role in the medical field for being the deterministic criteria in diagnosis. It primarily relies on performing chemical laboratory tests on patients’ samples , which are widely done manually and vulnerable to errors . In light of these challenges , the development of an automated sample preparation system (ASPS) provides a viable alternative to overcome the flaws of the manual process. The ASPS eliminates the need for manual intervention from the la boratory technician, as a needle would move automatically towards the samples and the reagents based on the selected test. The needle would then sense the substance and precisely dispense the required amount for the test in the reaction cup . Simultaneously , a syringe would help draw ing in and expelling the substances. Throughout the test, the needle is cleaned to prevent any contamination. Additionally , the laboratory technician has the ability to schedule up to 4 tests, which the system would perform seque ntially . The ASPS was developed using Arduino mega, motors, and a sensing circuit built upon capacitance manipulation.
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Customizable Service Provider Platform
(2024) Shahd Lubbadeh; Yaqout Salameh
The Customizable Service Provider Platform is a versatile solution comprising a mo- bile application and a website tailored to empower a wide range of service providers, whether they are established companies or individual freelancers. This project aims to streamline the service request process for clients and enhance e ciency for ser- vice providers, addressing complexities across various domains such as homecare, gardening, electrical services, maintenance, and more. The platform integrates fea- tures from existing platforms, o ering a uni ed and highly customizable space for service providers to personalize their o erings. Noteworthy functionalities include service categorization, pricing, and request management for providers, along with a straightforward booking process for customers. The application facilitates commu- nication through a noti cation system and chat functionality. Di erent user types enjoy varying privileges, with administrators having full control over customization. The platform's signi cance lies in its ability to cater to the diverse needs of service providers and clients in a uni ed manner, acknowledging the varied structures of ser- vice providers, be they companies or freelancers. With a focus on customization and e ciency, the project responds to market demands for a comprehensive, adaptable service provider solution that accommodates the nuances of di erent organizational types. The platform is developed using modern tools and technologies. React was used for web front-end development, React Native for mobile app development, and Spring Boot for the robust back-end infrastructure.
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Predicting Type 2 Diabetes Risk and Identifying its Risk Factors through a Machine Learning Model
(2024-02-07) Ghaith Maqboul
Abstract As one of the most prevalent chronic conditions worldwide, diabetes, particularly type 2 diabetes, poses a significant health challenge affecting millions of individuals and placing a considerable global economic burden. Our goal was to develop predictive models aimed at identifying universal risk factors for type 2 diabetes. The intention is to advance early diagnosis and intervention strategies and also reduce medical costs. The dataset started with 441,456 participants and 330 features. After preprocessing and cleaning, it was narrowed down to 42,340 participants and 18 features, incorporating 10,348 type 2 diabetes cases from the 2015 Behavioral Risk Factor Surveillance System (BRFSS), a survey conducted by the U.S. Centers for Disease Control and Prevention (CDC). This binary classification project strategically selected features to inform a comprehensive analysis for public health strategies. Employing multiple machine learning models, such as AdaBoost, Neural Network, Logistic Regression, Decision Tree, K Nearest Neighbors, Naive Bayes, and Random Forest, we delved into feature importance, with the Random Forest classifier scrutinizing risk factors associated with type 2 diabetes. Our study evaluates various predictive models for type 2 diabetes, all demonstrating notable performance with an AUC range of (74.7%-79.2%). AdaBoost excels with the highest test accuracy (78.2%), with sensitivity (33.5%), and specificity (92.7%). Neural Network and Logistic Regression also perform well. K Nearest Neighbors prioritizes specificity (92.8%), while Naive Bayes showcases notable sensitivity (57.8%), Random Forest had the highest sensitivity (72.9%), this classifier has been used to evaluate the importance of features associated with type 2 diabetes, identifying the top five significant contributors: Age (14.4%), Income (12.1%), MentalHealth (8.3%), Education (8.2%), and PhysicalHealth (7.7%). Among 7 models, including Neural Network, AdaBoost, and Logistic Regression, a convergence is seen with (77.4%-78%) accuracy, sensitivity (32%-34.6%), and (91.2%-92.7) specificity, yielding a closely aligned AUC of (78.7%-79.2%). Notably, Random Forest excels in sensitivity at 72.9%, despite a 71.7% accuracy, it is crucial for feature importance, and it is preferred for type 2 diabetes initial screening due to its balanced overall results.
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Tourpulse Explore, Plan, and Administer Tours Website
(2024-02-05) Ghazal Masri; Sameh Essa; Motasem Ayyash
Abstract "Tourpulse: Explore, Plan, and Administer Tours" is a Website that acts as an intermediary between travel agencies and travelers, it is designed to give the Agency the ability to offer their Tour packages and to provide the user with a Gallery of all Tours that have been offered, and it allow the user to review his experience which delivers feedback that helps the agency to improve it services, and give the other user a good ideas to know what to choose. Tourpulse is an intelligent website, we added a recommendation system that provides the user with the most suitable tour picks for him according to his interaction with the website.
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Energy generation using palm waste for Heating applications, case study for Palestine.
(2024-03-12) Ahmad Abuarra; Dirar Hara; Firas Mnasour
Abstract Date palm trees, especially Alhayani, Barhi, and Majhool, have a rich history in Palestine. However, the waste produced by these trees, such as unripe dates, date pits, and palm fronds, is usually burned on farms, leading to environmental concerns, or collected them inside the farms for long periods, which leads to the emergence of the red date palm weevil, which works to damage palm trees.These wastes are a significant source of excellent biomass that can be used in many applications such as energy production, livestock feed, fertilizer for soil, and wooden boards, and can even be used as insulating materials. This project focuses on utilizing agricultural waste from date palm trees in Palestine by grinding and transforming it into wooden blocks for use in heating, bakery ovens, household wood stoves, and other applications. The effectiveness of these waste materials for use as excellent heat value resources has been demonstrated, especially when compared to other tree waste. The heating values for date kernel (17.1267 MJ/Kg), palm leaf (16.8873 MJ/Kg), and palm frond petiole (15.9897 MJ/Kg) indicate their promising potential for use in heating applications. A feasibility study was conducted for a production line that converts these waste materials into wooden blocks for use in heating applications. The annual profits were estimated at approximately 150,240 NIS, with a payback period of around 2.4 years and a return-on-investment rate of 36.3%