هالة محمد سمير الحديدى | كلبة الهندسة | Electrical Engineering Department | PressureGuard: Real-Time Blood Pressure Prediction and Monitoring System | Pressure-Guard is an intelligent system designed for continuous blood pressure measurement using AI. By providing continuous monitoring, the proposed system can send immediate alerts if the pressure is out of the safe levels in order to prevent health setback
when patients are alone. The proposed project aims to estimate blood pressure (BP) using ECG and PPG signals. While both Machine Learning (ML) and Deep Learning (DL) could be used for this task, deep learning is the chosen due to the nature of the data and the
complexities involved. The dataset which is used in training AI model of this project is obtained from the Non-Invasive BP dataset, available at Kaggle. The continuous readings from two key biosensors used in BP estimation are used for monitoring BP. A mobile
application is developed to monitor blood pressure with key features such as: sending alerts if BP exceeds normal range, emergency notifications sent to companions, a locator for nearby doctors and hospitals, a video call feature between patients and companions,
medicine reminders for timely medication intake and correct dosage, a symptom checker to suggest potential conditions. |
هالة محمد سمير الحديدى | كلبة الهندسة | ُElectrical Engineering Department | SMART POWER MONITORING AND CONTROLLING SYSTEM FOR SMART HOMES | This project presents a cost-effective, open-source smart home automation system built around an Arduino micro controller and a web-based control interface. Designed for ease of use and accessibility, the system enables real-time monitoring of energy consumption and remote control of household appliances via a mobile application or web browser. It also features an automated shutdown mechanism for devices that exceed predefined energy usage limits. Key functionalities include live tracking of voltage, current, and power, remote switching capabilities, alert notifications for abnormal consumption, and tools to promote energy efficiency and reduce electricity costs. |
محمد حسام الجلاد | كلبة الهندسة | هندسة الاتصالات و الالكترونيات | IoT-Driven Smart Accident Recorder & Road Safety System | We are designing a system that can alert the car owner/contact authorities via text messages as
soon as an accident is detected. The system uses a temperature sensor to detect fire in the car, a collision
sensor to detect any impact force or strong vibrations, a gyroscope sensor to record data if the vehicle
swerved or rolled over during the accident, a GPS device and a GSM modem to send text messages with
GPS coordinates about the incident, and a camera that films the road and stores the videos. This entire
system is now powered by an ESP32 and a Raspberry Pi 4 to run the system. The controller similarly sends
an SMS notification with GPS coordinates on a map link for easy vehicle location tracking. In case any
sensor triggers abnormal activity, the Smart Accident Recorder starts storing all sensor data on a second-by-second basis in an SD card so that the investigation team can recover the data and study exactly what
happened during the accident. |
لمياء عادل محمود حلمى | كلية التجارة | Student | "The Moderating Role of Community Engagement in the Relationship Between School Principals’ Ethical Leadership and the Quality of School Life in Pre-University . | This study aims to examine the moderating role of community engagement in the relationship between school principals’ ethical leadership and the quality of school life in pre-university education. The research is based on the assumption that ethical leadership contributes to enhancing the quality of the school environment; however, the strength of this impact may vary depending on the level of community involvement in supporting the school. The study employed a descriptive-analytical method and included a sample of teachers and administrators from various schools. Data were collected using standardized questionnaires. The results revealed a statistically significant positive relationship between ethical leadership and school life quality. Moreover, community engagement was found to play a moderating role that strengthens this relationship, highlighting the importance of collaboration between school leadership and the local community in fostering a supportive educational environment. |
شادى على حبيشى | كلية التجارة | قسم اداره الاعمال DBA | | |
عالية السيد سالم | كلية التجارة | DBA | Users' Experience with Electronic Applications as a Mediating Variable in the Relationship Between the Use of Artificial Intelligence Technologies and the Quality of Therapeutic Services: A Field Study on the General Authority for Universal Health Insurance in Port Said Governorate | خبرة المتعاملين بالتطبيقات الإلكترونية كمتغير وسيط بين استخدام تقنيات الذكاء الاصطناعي
وجودة الخدمة العلاجية
دراسة ميدانية على الهيئة العامة للتأمين الصحي الشامل بمحافظة بورسعيد وترجع أهمية البحث إلى ما يلي:
أ- الأهمية العلمية: وتتمثل في الآتي:
1- تناول الدراسة لمتغيرات حديثة نسبيا في مجال إدارة الأعمال بصفة عامة، والموارد البشرية بشكل خاص وهم: استخدام الذكاء الاصطناعي، خبرة المتعاملين بالتطبيقات الإلكترونية، جودة الخدمة العلاجية.
2- محاولة استخلاص إطار مفاهيمي لمتغيرات الدراسة، بالإضافة إلى إلقاء الضوء على بعض المجالات البحثية المقترحة للاستفادة من هذه المتغيرات في المنظمات.
ب- الأهمية التطبيقية: وتتمثل في الآتي:
1- بلغ عدد المستفيدين من هيئة التأمين الصحي الشامل بجمهورية مصر العربية (6,1 مليون مواطن)، وفيما يتعلق بمجتمع الدراسة، فقد بلغ عدد المستفيدين من التأمين الصحي الشامل بمحافظة بورسعيد (920 ألف مستفيد)، وذلك بناء على أخر إحصائية لموقع وزارة الصحة المصرية.
2- اهتمام منظومة التأمين الصحي ببورسعيد بالخدمات العلاجية، ومن ثمَّ يعتبر استخدام التقنيات الرقمية من العوامل التي تعزز من جودة هذه الخدمات. وتتبلور مشكلة وتساؤلات البحث:
في ضوء نتائج الدراسة الاستطلاعية، يمكن صياغة مشكلة الدراسة في التساؤلات التالية:
1- هل يوجد تأثير مباشر لاستخدام الذكاء الاصطناعي على أبعاد جودة الخدمة العلاجية لدى الهيئة العامة للتأمين الصحي الشامل بمحافظة بورسعيد ؟
2- هل يوجد تأثير مباشر لاستخدام الذكاء الاصطناعي وأبعاد خبرة المتعاملين بالتطبيقات الإلكترونية لدى الهيئة العامة للتأمين الصحي الشامل بمحافظة بورسعيد ؟
3- إلى أي مدى تؤثر أبعاد خبرة المتعاملين بالتطبيقات الإلكترونية على أبعاد جودة الخدمات العلاجية بالهيئة العامة للتأمين الصحي الشامل بمحافظة بورسعيد ؟
4- هل يوجد تأثير غير مباشر لاستخدام الذكاء الاصطناعي على جودة الخدمة العلاجية بتوسيط خبرة المتعاملين بالتطبيقات الإلكترونية بالهيئة العامة للتأمين الصحي الشامل بمحافظة بورسعيد ؟ |
منة الله إبراهيم مصطفى | كلية الاداب | جغرافيا ونظم المعلومات الجغرافية | | |
نرمين محمد عبد الغني | كلية التجارة | الدراسات العليا - دكتوراة DBA | | |
كريم عبدالحليم حواس | كلية التجارة | إدارة الأعمال | The role of artificial intelligence tools in crisis management through centralized digital leadership | |
هبه فؤاد الخولي | كلية التمريض | Medical surgical nursing | | |
مروه محمود موسى | كلية التربية | قسم مناهج و طرق تدريس( لغة انجليزية ) | Effectiveness of Personalized Learning Model in Developing Critical English Reading Skills among Preparatory Stage Pupils | The present study aimed at investigating effectiveness of Personalized Learning Model in developing Critical Reading Skills .The quasi -experimental design was used .The participants were (57) second year preparatory stage pupils at Fatima Al-Zahraa Governmental Language School at Port Said. There were two intact groups: a control group and an experimental one.The instruments of the study included a reading skills questionnaire and a pre-post critical reading skills test .Both groups were pre-tested .Then , the experimental group pupils were taught using the model and it is applied during the first term of the academic year 2019/ 2020.Both groups were tested using SPSS program .Results revealed the effectiveness of the model in developing critical reading skills.
Keywords: Critical Reading Skills , Personalized Learning Model, Preparatory Stage Pupils |
نورهان مجدي عبد الفتاح | كلية التمريض | باطنه وجراحه | | |
اية ممدوح محمد | كلية التمريض | ماجستير في إدارة التمريض | Management support, strategic directions and required resources for digital transformation among nurses at Port Said health care sector | Digitization is the process of converting information into a digital format (Asif, 2021). The process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies is called digital transformation (Vial, 2019). Furthermore, Verhoef et al. (2021) claimed that digital transformation affects many aspects of organizations, such as the acquisition of digital resources, the design of digital growth strategies, the change of internal organizational structure, and goals. So that, digital transformation is considered one of the most effective responses to improve the quality of health services and reduce costs. (Saifudin, Aima, Sutawidjaya, & Sugiyono, 2021). Digital transformation requires good leadership, right policies, technology, infrastructure and financial resources (Desveaux, Soobiah, Bhatia& Shaw, 2019). While, poor leadership or lack of leadership, has been associated with failed implementation and poor uptake of digital health (Laukka, Huhtakangas, Heponiemi & Kanste, 2020). Furthermore, the support of the top management is needed because it can encourage and motivate employees to utilize the appropriate human resources technology in their practices. So, if top management lacks good support, it will affect the human resources digital transformation process (Banu, 2019). |
شيماء خالد حسن | كلية الاداب | الجغرافيا | Historical and urban development of port foad city | Historical and urban development of port foad city from1986 until 2023 |
آية الله هشام حسن | كلية التجارة | ادارة الاعمال | The Role of Intelligent Chatbots in Promoting Sustainable Marketing: A Field Study on Bank Customers | Abstract
This study aims to investigate the role of intelligent chatbots in promoting sustainable marketing within the banking sector by analyzing customer’s experiences with this emerging technology. Sustainable marketing represents a strategic approach that balances economic objectives with environmental and social
considerations. Modern banks seek to achieve this balance by integrating artificial intelligence technologies—particularly chatbots—into their service and marketing systems. A descriptive-analytical methodology is adopted, utilizing a structured questionnaire to collect data from a sample of bank customers who use chatbot services. Expected results will highlight the pivotal role of intelligent chatbots in supporting sustainable marketing strategies by improving customer experience,
reducing environmental costs, and fostering trust and loyalty. The study concludes with practical recommendations to optimize chatbot implementation in line with banks' sustainability and competitive goals. |
منة الله إبراهيم مصطفى | كلية الاداب | جغرافيا ونظم المعلومات الجغرافية | Geoarchaeology of Farma Area | |
احمد محمد عبد الملك احمد | كلية التمريض | التمريض الباطني الجراحي | | |
زهوة ناصر عبد المعطي محمد علي | كلية التمريض | قسم الامومه والنساء والتوليد | Knowledge, Attitude, and Practice Among Women Regarding Utilization of Family Planning Methods in North Sinai Governorate | Family planning is one of the most effective public health interventions to improve maternal and child health and reduce morbidity and mortality, particularly in low- and middle-income countries. The effectiveness of family planning programs largely depends on women's knowledge, attitudes, and practices regarding contraceptive methods. Despite the availability of family planning services, a significant gap often exists between knowledge and actual use, influenced by various social, cultural, and religious factors. Studies have shown that increased awareness and positive attitudes towards contraception are associated with higher rates of utilization, which contributes to preventing unintended pregnancies and promoting the overall well-being of families and communities. Global health organizations, including the World Health Organization (WHO), emphasize the importance of enhancing women's knowledge and fostering supportive attitudes as part of comprehensive reproductive health strategies. |
الاء وليد باسم | كلية العلوم | الرياضيات وعلوم الحاسب - برنامج الحوسبة والمعلوماتية الحيوية | From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers | Cancer is one of the leading causes of death globally, making early and accurate diagnosis crucial for effective treatment. While deep learning models have shown promising results in cancer detection, most studies have focused on binary classification, leaving a gap in multi-class classification. Additionally, the role of microRNA (miRNA) molecules in cancer development and their potential use as biomarkers requires further exploration. This study aimed to develop a hybrid deep learning model for accurate cancer classification (both binary and multi-class) using gene expression data, and to identify tumor-suppressive miRNA molecules across multiple cancer types. A hybrid model called Deep Long Short-Term Memory (D-LSTM) was developed and trained on gene expression profile data extracted from the GEO database, specifically from the dataset GSE203024. This dataset included 50,675 genes profiled across 2,845 peripheral blood samples, comprising 14 types of cancer, colon polyps, and normal samples. The age of the subjects ranged from 18 to 97 years, with a sex distribution of 1,547 males and 1,013 females. Data preprocessing involved the selection of significant genes using the ANOVA-F test and Correlation-based Feature Selection (CFS) technique. Class imbalance was addressed using the synthetic minority oversampling technique (SMOTE). The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score. Subsequently, key genes were analyzed to identify associated miRNA molecules using the miRNet database, and regulatory networks were visualized using Cytoscape. The model achieved 98.38% accuracy in binary classification (cancer vs. non-cancer) and 99.88–100% accuracy in multiclass classification. The analysis revealed that hsa-miR-133b functions as a tumor suppressor in 14 different cancer types and colon polyps, targeting genes, such as CTNND1, CCNB1, and SUZ12. Our model demonstrates high accuracy in cancer classification, positioning it as a promising noninvasive diagnostic tool. The identification of hsa-miR-133b as a conserved tumor suppressor highlights its significance in cancer research. Further biological validation is required to confirm the potential clinical applicability of this method.
Keywords: Cancer classification, deep learning, gene expression, miRNA, tumor suppressors |
جنه محمد مرعي | كلية العلوم | الرياضيات وعلوم الحاسب - برنامج الحوسبة والمعلوماتية الحيوية | From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers | Cancer is one of the leading causes of death globally, making early and accurate diagnosis crucial for effective treatment. While deep learning models have shown promising results in cancer detection, most studies have focused on binary classification, leaving a gap in multi-class classification. Additionally, the role of microRNA (miRNA) molecules in cancer development and their potential use as biomarkers requires further exploration. This study aimed to develop a hybrid deep learning model for accurate cancer classification (both binary and multi-class) using gene expression data, and to identify tumor-suppressive miRNA molecules across multiple cancer types. A hybrid model called Deep Long Short-Term Memory (D-LSTM) was developed and trained on gene expression profile data extracted from the GEO database, specifically from the dataset GSE203024. This dataset included 50,675 genes profiled across 2,845 peripheral blood samples, comprising 14 types of cancer, colon polyps, and normal samples. The age of the subjects ranged from 18 to 97 years, with a sex distribution of 1,547 males and 1,013 females. Data preprocessing involved the selection of significant genes using the ANOVA-F test and Correlation-based Feature Selection (CFS) technique. Class imbalance was addressed using the synthetic minority oversampling technique (SMOTE). The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score. Subsequently, key genes were analyzed to identify associated miRNA molecules using the miRNet database, and regulatory networks were visualized using Cytoscape. The model achieved 98.38% accuracy in binary classification (cancer vs. non-cancer) and 99.88–100% accuracy in multiclass classification. The analysis revealed that hsa-miR-133b functions as a tumor suppressor in 14 different cancer types and colon polyps, targeting genes, such as CTNND1, CCNB1, and SUZ12. Our model demonstrates high accuracy in cancer classification, positioning it as a promising noninvasive diagnostic tool. The identification of hsa-miR-133b as a conserved tumor suppressor highlights its significance in cancer research. Further biological validation is required to confirm the potential clinical applicability of this method.
Keywords: Cancer classification, deep learning, gene expression, miRNA, tumor suppressors |
عبدالرحمن مجدي الشاذلي | كلية العلوم | الرياضيات وعلوم الحاسب - برنامج الحوسبة والمعلوماتيه الحيوية | From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers | Cancer is one of the leading causes of death globally, making early and accurate diagnosis crucial for effective treatment. While deep learning models have shown promising results in cancer detection, most studies have focused on binary classification, leaving a gap in multi-class classification. Additionally, the role of microRNA (miRNA) molecules in cancer development and their potential use as biomarkers requires further exploration. This study aimed to develop a hybrid deep learning model for accurate cancer classification (both binary and multi-class) using gene expression data, and to identify tumor-suppressive miRNA molecules across multiple cancer types. A hybrid model called Deep Long Short-Term Memory (D-LSTM) was developed and trained on gene expression profile data extracted from the GEO database, specifically from the dataset GSE203024. This dataset included 50,675 genes profiled across 2,845 peripheral blood samples, comprising 14 types of cancer, colon polyps, and normal samples. The age of the subjects ranged from 18 to 97 years, with a sex distribution of 1,547 males and 1,013 females. Data preprocessing involved the selection of significant genes using the ANOVA-F test and Correlation-based Feature Selection (CFS) technique. Class imbalance was addressed using the synthetic minority oversampling technique (SMOTE). The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score. Subsequently, key genes were analyzed to identify associated miRNA molecules using the miRNet database, and regulatory networks were visualized using Cytoscape. The model achieved 98.38% accuracy in binary classification (cancer vs. non-cancer) and 99.88–100% accuracy in multiclass classification. The analysis revealed that hsa-miR-133b functions as a tumor suppressor in 14 different cancer types and colon polyps, targeting genes, such as CTNND1, CCNB1, and SUZ12. Our model demonstrates high accuracy in cancer classification, positioning it as a promising noninvasive diagnostic tool. The identification of hsa-miR-133b as a conserved tumor suppressor highlights its significance in cancer research. Further biological validation is required to confirm the potential clinical applicability of this method. Keywords: Cancer classification, deep learning, gene expression, miRNA, tumor suppressors |
ايمان عصام ابراهيم | كلية الاداب | جغرافيا ونظم المعلومات | | |
حكمت عبد الحسيب عباده | كلية العلوم | environmental science | Assessment of seasonal and spatial variations in Heavy metal Accumulation by aquatic macrophytes in polluted Drains -port said,Egypt | Polluted drainage systems represent a significant threat to aquatic ecosystems, particularly when their contaminated waters are used for irrigation, posing serious environmental and health hazards. This study aimed to assess heavy metal contamination in water and aquatic plants collected from three sites along the Bahr El-Baqar drain in Port Said, Egypt. Water samples, along with two macrophyte species—Pontederia crassipes (P. crassipes) and Pistia stratiotes (P. stratiotes)—were collected seasonally during the summer, autumn, winter, and spring of 2024.The study sites included: Site 1: A branch of the sub-drain Ezbet Abass, site 2: A branch of the main drain Elmahasna and site 3: The main course of Bahr El-Baqar at Zidan El Gamel. The concentrations of heavy metals (Fe, Mn, Zn, and Cu) in water and different plant parts (both aboveground and belowground parts) varied across sites and seasons. The highest concentration of iron (Fe) in water was recorded at Site 3 in summer (18.3 mg/L) below P. crassipes, while the lowest concentration (2.04 mg/L) was observed at Site 1 in autumn under P. stratiotes. Manganese (Mn) reached a peak concentration of 0.7 mg/L at Site 3 in summer under P. crassipes, with the lowest value (0.04 mg/L) detected at Site 1 in autumn in a mixed plant population. Copper (Cu) levels were highest (0.28 mg/L) at Site 2 during spring under a mixed population, and lowest (0.013 mg/L) at Site 1 in autumn under P. stratiotes. Zinc (Zn) showed maximum concentration (0.33 mg/L) at Site 2 in summer and a minimum (0.021 mg/L) at Site 1 in winter under P. stratiotes. Heavy metal accumulation in plant tissues (both aboveground and belowground parts) also varied significantly. The highest Fe concentration (45,372.3 mg/kg) was found in P. stratiotes roots within a in mixed community of two macrophytes at Site 2 in summer, whereas the lowest (89.8 mg/kg) was detected in P. crassipes shoots within a mixed community at Site 1 in autumn. Manganese reached its maximum concentration (6836 mg/kg) in P. stratiotes roots at Site 1 during summer, and its minimum (23.16 mg/kg) in P. crassipes shoots in a mixed community at the same site in autumn. For Cu, the highest level (26.3 mg/kg) was recorded in P. stratiotes roots within a mixed community at Site 2 in summer, while the lowest (2.11 mg/kg) occurred in P. crassipes shoots within a mixed community at Site 1 in autumn. Zinc accumulation peaked (37.23 mg/kg) in P. stratiotes roots within a mixed community at Site 2 in summer, and the lowest level (2.18 mg/kg) was found in P. crassipes shoots within a mixed community at Site 1 in autumn. These findings suggest that P. crassipes and P. stratiotes exhibit strong potential for use in the phytostabilization of heavy metal-contaminated environments, functioning effectively as metal excluders. |
لؤي فوزي العتر | كلية العلوم | الرياضيات وعلوم الحاسب - برنامج الحوسبة والمعلوماتية الحيوية | From Deep LSTM-Based Gene Expression Modeling to microRNA Disease Association; hsa-miR-133b identified as a Potentially Functionally Conserved Tumor Suppressor Across Cancers | Cancer is one of the leading causes of death globally, making early and accurate diagnosis crucial for effective treatment. While deep learning models have shown promising results in cancer detection, most studies have focused on binary classification, leaving a gap in multi-class classification. Additionally, the role of microRNA (miRNA) molecules in cancer development and their potential use as biomarkers requires further exploration. This study aimed to develop a hybrid deep learning model for accurate cancer classification (both binary and multi-class) using gene expression data, and to identify tumor-suppressive miRNA molecules across multiple cancer types. A hybrid model called Deep Long Short-Term Memory (D-LSTM) was developed and trained on gene expression profile data extracted from the GEO database, specifically from the dataset GSE203024. This dataset included 50,675 genes profiled across 2,845 peripheral blood samples, comprising 14 types of cancer, colon polyps, and normal samples. The age of the subjects ranged from 18 to 97 years, with a sex distribution of 1,547 males and 1,013 females. Data preprocessing involved the selection of significant genes using the ANOVA-F test and Correlation-based Feature Selection (CFS) technique. Class imbalance was addressed using the synthetic minority oversampling technique (SMOTE). The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score. Subsequently, key genes were analyzed to identify associated miRNA molecules using the miRNet database, and regulatory networks were visualized using Cytoscape. The model achieved 98.38% accuracy in binary classification (cancer vs. non-cancer) and 99.88–100% accuracy in multiclass classification. The analysis revealed that hsa-miR-133b functions as a tumor suppressor in 14 different cancer types and colon polyps, targeting genes, such as CTNND1, CCNB1, and SUZ12. Our model demonstrates high accuracy in cancer classification, positioning it as a promising noninvasive diagnostic tool. The identification of hsa-miR-133b as a conserved tumor suppressor highlights its significance in cancer research. Further biological validation is required to confirm the potential clinical applicability of this method. Keywords: Cancer classification, deep learning, gene expression, miRNA, tumor suppressors |
أحمد مجدي السيد | كلية العلوم | Bioinformatics | A Modular RDKit-Based Pipeline for Ligand–Protein Interaction Analysis and Pre-Docking Prioritization" | Abstract
Efficient early-stage screening of ligand–protein interactions is a cornerstone of modern drug discovery. This study introduces a modular and fully open-source computational pipeline that integrates cheminformatics and basic molecular docking techniques to evaluate ligand compatibility with protein targets. Utilizing RDKit as a core tool, the pipeline performs SMILES-to-3D conversion, conformational optimization using UFF, physicochemical property extraction, and shape/electrostatic-based docking estimations. Additionally, deep learning is employed through a pre-trained classification model to predict ligand activity using molecular descriptors and protein embeddings. The pipeline supports pocket extraction from PDB files, distance matrix generation for interaction mapping, and torsional angle analysis for conformational stability. By enabling batch processing of ligand libraries and automated docking scoring, the framework serves as a fast and lightweight filtering tool before proceeding to more rigorous simulations with software like AutoDock or GNINA. This approach is especially useful in academic settings or low-resource environments where rapid decision-making is required. |
مصطفى ابراهيم مصطفى | كلية العلوم | حوسبه | | |
مي محمود نعمت الله | كلية التربية | قسم المنلهج وطرق التدريس وتكنولوجيا التعليم | Using Games-Based Assessment to Develop English Oral Reading Prosody Skills of Primary Stage Pupils | Abstract
This study explored the impact of a games-based assessment (GBA) approach on developing English oral reading prosody among fifth-grade primary pupils. Using a quasi-experimental design, the research compared a GBA-treated experimental group with a traditionally taught control group. The GBA treatment, delivered over 12 sessions using games and the ELSA app, significantly improved prosodic elements—word stress, pausing, intonation, and tone. Results showed higher gains for the experimental group, supporting GBA as an effective method to enhance reading prosody and fluency in EFL primary learners. |
دينا أيمن عبد الرحمن | كلية التربية | مناهج وطرق تدريس اللغه الانجليزية | Using Instructional Scaffolding in a Hybrid Learning Environment to Develop EFL Listening Comprehension Skills among Primary Stage Pupils | The study investigated the use of instructional scaffolding in a hybrid learning environment to develop EFL listening comprehension skills of primary stage pupils. This study adopted the quasi-experimental design as two intact groups: experimental and control groups. The participants were (30) pupils each. The experimental participants were taught through face to face and online sessions. The control group received regular instruction. A listening comprehension questionnaire and pre-post listening comprehension test were designed and administered Results of the study proved the positive effect of utilizing instructional scaffolding in a hybrid environment in developing EFL listening comprehension skills among primary stage pupils. |
على محمد بدوي | كلية العلوم | Bioinformatics | | |
يوسف محمد الدالي | كلية العلوم | الحوسبة والمعلوماتية الحيوية | | |
روميساء فاروق السيد الاصبح | كلبة الهندسة | Civil engineering | Sustainable Development and Management Plan of Water Resources in El Moghra Area, Northwestern Desert, Egypt | Egypt has increasingly relied on groundwater reserves to supply water duties for ongoing reclamation activities in the Western Desert. Among these initiatives is the national project for reclaiming vast areas in El Moghra region, using the groundwater from El Moghra nonrenewable aquifer. This study assesses the long-term sustainability of this agricultural development under climate change scenarios and evaluates the impacts of extensive groundwater extraction on aquifer storage and salinity. Using meteorological data from CORDEX-Africa under two representative concentration pathways (RCP4.5 and RCP8.5), four climate models were downloaded to project changes in crop yield and water demand until 2100. According to Egyptian government policies, barley, sugar beet, quinoa, and canola were considered seasonal crops while date palms, olives, and jojoba were allowed as permanent crops for El Moghra. Results illustrated that crop yield would decline significantly under future climate conditions. The required crop area and irrigation demands were expected to increase over time, but both were still below the policies government. Therefore, the multi-objective genetic algorithm optimization was performed to maximize the crop yield and net revenue. The optimization analysis indicated the potential for expanding cultivated land, which would require additional irrigation requirements, and indicated the viability of the longterm development plan. Afterwards, a coupled groundwater flow and solute transport model was developed to assess the impact of intensive groundwater abstraction and climate change on the potential depletion and salinization of the aquifer. The developed model was successfully calibrated and validated against recently observed heads and salinities, achieving reasonable accuracy. The aquifer's behaviour was simulated under RCP4.5 and RCP8.5 scenarios till 2055. The results showed a significant reduction in groundwater levels and a notable inland migration of saline water under proposed pumping conditions. Under the worst-case climate change scenario, further declines in water levels and additional saltwater intrusion were projected, exacerbated by expected sea level rise. To sustain the agribusiness activities in the region, the cultivated area was reduced by half. The findings highlight that groundwater-dependent agribusiness in El Moghra region is at risk due to the potential long-term depletion and salinization. |
يوسف العربي فوزي | كلية العلوم | الحوسبة و المعلوماتية الحيوية | Multi-Modal prediction Machine Learning Framework for Brain Cancer Diagnosis using Omics Data and Medical Imaging | Multi-Modal prediction Machine Learning Framework for Brain Cancer Diagnosis using Omics Data and Medical Imaging
Background, Brain cancer poses a major health challenge, emphasizing the need for accurate and early diagnosis. Aim of the work, this project aims to present a comprehensive diagnostic framework by integrating multi-omics data (mRNA, protein, mutation) with image processing and analysis for MRI to enhance brain cancer classification. Materials and methods, the workflow included data exploration, preprocessing (normalization, encoding, augmentation), model building, evaluation, and deployment through a web application using Flask. Random forest models were developed for each omics dataset, The models were then combined into a flexible ensemble model capable of handling incomplete patient data. while a VGG16 convolutional neural network was used to classify MRI images. Results, the mRNA-based model achieved the highest individual accuracy (91.7%), followed by the protein (75%) and mutation (61.1%) models. The integrated ensemble model improved overall diagnostic performance, reaching 86.1% accuracy. Meanwhile, the VGG16 model for MRI data achieved accuracy up to 99%. Finally, a web application was developed to enable user-friendly access to the predictive tool. Conclusion, this study successfully established an integrated diagnostic approach for brain cancer, combining multi-omics and MRI data. The flexible ensemble model and the VGG16-based MRI analysis contribute to a more comprehensive and adaptable diagnostic pipeline, ultimately improving patient care. |
غادة سمير مصطفي المهدي | كلية التمريض | التمريض الباطني والجراحي | Relation between Self-Efficacy and Compliance to Treatment with Injectable Medication for Type 2 Diabetes | Background: Diabetes is a chronic disease that requires self-management, with patients responsible for at least 99% of their care. The long-term success of type 2 diabetes maintenance therapy relies heavily on patient compliance and self-efficacy, a crucial factor influencing self-care behavior in these patients. Aim: This study aimed to explore the relationship between self-efficacy and compliance with treatment with injectable medication for type 2 Diabetes. Subjects and Method: A descriptive correlational design was used in this study. A purposeful sample consisting of 165 individuals diagnosed with type 2 diabetes who are undergoing insulin therapy in outpatient facilities affiliated with Egypt health care authority hospitals. (El-Salam General Hospital and Al-Shifa Medical Complex Hospital in Port Said City). Tools: Patient assessment data consisted of two parts: First part: personal characteristics of the patient; second part: clinical characteristics of the patient. Compliance questionnaire to insulin therapy, Insulin therapy self-efficacy scale (ITSS). Results: According to this study, 73% of the individuals examined had positive compliance towards insulin therapy, and (72.1%) of the participants in the study had good self-efficacy toward insulin therapy, The study patients' positive compliance toward insulin therapy and their level of self-efficacy were positively correlated (p<0.05). Conclusion: Self-efficacy and treatment compliance with injectable medication were revealed to be significantly positively correlated by the study, indicating that higher self-efficacy levels lead to better compliance with treatment. Recommendation: Creating a guidebook entail providing information on administering various forms of insulin. Keywords: self-efficacy, compliance, insulin, type 2 diabetes |
نجلاء محمد بحيري | كلية التربية | مناهج وطرق تدريس اللغة الإنجليزية | A Program Based on Online Professional Learning Communities to Develop Authentic Assessment Skills of EFL Teachers | Abstract
This paper investigates the synergistic relationship between online professional learning communities (OPLCs) and authentic assessment within the context of situated learning theory. The paper aim is to evaluate the effectiveness of an OPLCs program in developing authentic assessment skills among English as a Foreign Language (EFL) teachers from Port Said Governorate in preparatory schools, enabling them to create, implement, and evaluate authentic assessments proficiently. A mixed-method approach with a concurrent triangulation design with one group of 30 EFL teachers in preparatory schools was employed OPLCs program during the first semester of the academic year 2023-2024. The study involved a ten-session OPLCs program. The researcher administered three instruments: pre-post authentic assessment skills test, pre-post authentic assessment skills rubric and OPLCs’ Satisfaction questionnaire. The results demonstrated statistically significant improvements in overall cognitive and performance authentic assessment skills, highlighting the efficacy of the program in enhancing EFL teachers' abilities to create, implement, and evaluate authentic assessments.
Keywords: Online professional learning communities (OPLCs), authentic assessment skills. |
امال مصطفى عامر | كلية التمريض | تمريض باطني وجراحي | | |
أحمد زكريا محمد الإمام الشريف | كلية العلوم | الرياشيات وعلوم الحاسب | "A Comparative Assessment of Machine Learning Models for Predicting Liver Diseases in Clinical Data" | Chronic diseases remain a leading cause of death and disability worldwide, placing immense pressure on healthcare systems and affecting quality of life for millions. Among these, liver diseases constitute a major public health burden due to their high morbidity and mortality rates. Early and accurate diagnosis of liver-related conditions is essential to prevent progression to critical stages such as cirrhosis or liver cancer. In light of this, various machine learning models have been developed to support early detection. This study presents a comparative evaluation of three machine learning algorithms K-Nearest Neighbors (KNN), Gaussian Naive Bayes (Gaussian NB), and Random Forest (RF) to assess their effectiveness in predicting liver disease. Utilizing a large dataset of 32,000 patient records, we first apply preprocessing techniques to handle missing or inconsistent data. Our findings indicate that the Random Forest algorithm significantly outperforms the others, achieving superior results across multiple evaluation metrics: 97.3% accuracy, 97% precision, 96% recall, and a 95% F1-score. These results highlight the potential of Random Forest as a reliable tool for enhancing clinical decision-making in the early detection and management of liver diseases. |
اسامة محمد الزيادي | كلبة الهندسة | قسم الهندسة المدنية | Estimation of reference evapotranspiration by using machine learning models | Water resources management has become one of the main global challenges worldwide. In these terms, determination of reference evapotranspiration (〖ET〗_o) is an important issue that can be used to improve water resources management by providing accurate and reliable estimates of the water requirements of a reference crop. Providing accurate 〖ET〗_o values is crucial for optimizing irrigation scheduling and reducing water consumption in agriculture without harming crops production.
The main objective of this work is to develop new regional meteorological-based machine learning models using daily meteorological-based features and the clustering methodology to obtain projected reference evapotranspiration values. |
ندى محمد محمود | كلية التربية | مناهج وطرق تدريس اللغة الإنجليزية | Using Technology and Scaffolding to Solve Reading and Writing Problems of Secondary Stage Students | Using Technology and Scaffolding to Solve Reading and Writing Problems of Secondary Stage Students ABSTRACT
The main purpose of the study was to investigate the effect of using technology and scaffolding on solving the reading and writing problems of secondary stage students. The study used the one group pre-posttest quasi experimental design. The participants were 16 students of third grade of the secondary stage in the study year (2021-2022). The study adopted the mixed methods approach that qualitative (pre-post interviews) and quantitative (pre-posttest) instruments were used for examining the effectiveness of the treatment in solving the students' reading and writing problems. The findings of the study showed statistically significant effectiveness of using technology and scaffolding to solve reading and writing problems of the secondary stage students. Keywords: Technology, Scaffolding, Reading problems, Writing problems, Secondary Stage Students. |