Abstract: In light of the aspiration to achieve sustainability in the field of education, especially after the development of artificial intelligence technologies in general and artificial neural network technologies in particular, the process of improving e-learning has become one of the most important strategies for achieving sustainability. This study aims to evaluate the impact of using artificial intelligence techniques and artificial neural networks on improving cloud e-learning by extracting and mining data from the electronic cloud and analyzing it to provide a more efficient and effective educational experience. Through several methodologies, including descriptive methodology to describe the factors influencing the improvement of machine learning through the use of artificial intelligence techniques and the mechanism for using these techniques, quantitative methodology was used in collecting data and analytical scientific methodology to analyze the results of the proposed model, which is a hybrid of convolutional artificial neural networks (CNN) and recursive neural networks (RNN). Through the model, it is possible to extract and analyze user data such as interaction with content and duration of study to extract patterns and behaviors that support improving the learning experience, and personalizing individual learning paths. Predicting the academic performance of students using machine learning techniques. The results showed an improvement in the learning experience of 86% and an improvement in platform performance of 85%. Machine learning indicators, such as accuracy (94%), recall (93%) and f1 score (91%), also indicated the success of the model used in

الملخص

يواجه استقرار الجهد تحديات كبيرة بسبب الطلب المتزايد على الكهرباء والاعتماد على خطوط النقل الطويلة، خاصة في شبكات الجهد المتوسط بجنوب ليبيا. تركز هذه الدراسة على شبكة سبها بجهد 66 كيلو فولت، التي تعاني من عدم استقرار الجهد نتيجة الاعتماد على مصادر توليد الطاقة الشمالية وخسائر الطاقة التفاعلية العالية. باستخدام برنامج، NEPLAN تم تقييم فعالية جهازين من تقنيات، FACTS وهما المعوض المتزامن الثابت (SVC) والمعوض المتزامن الديناميكي، (STATCOM) في تحسين استقرار الجهد.

تناولت الدراسة ثلاثة سيناريوهات: بدون تعويض، باستخدام، SVC وباستخدام. STATCOM أظهرت النتائج أن كلا الجهازين يحسنان استقرار الجهد بشكل ملحوظ ويقللان من خسائر الطاقة، مع تفوق STATCOM في الاستجابة الديناميكية والكفاءة الإجمالية. تؤكد هذه النتائج أهمية تقنيات التعويض المتقدمة في ضمان استقرار وموثوقية توصيل الطاقة في الظروف التشغيلية الصعبة.

This research deals with estimating the likely switch of car ownership drivers and private transport vehicles modes

users to public transportation system (PT) modes in the main cities as Tripoli Capital city of Libya due to reduced

shopping travel time of all trips influenced by age, household size. Car ownership and shopping distance through

the introducing the public transport modes such as public buses. The data on other factors (variables) that could

potentially reason a model switch of from car ownership drivers and private transport vehicles modes users to

public transport were collected through a survey (questionnaire) with a specified predilection method. Modeswitch models to describe the switch of conduct of own car operators to PT system are developed. A binary

logistic regression model was used to assessment the transport displacement model for shopping trips. Probability

curves have been developed to change the position so that it is a user-friendly tool for analysing the potential

transformation Model of a variety of variables. One way to achieve this objective is by establishing reserved

public transportation lanes on main urban Tripoli city roads. The providing of private road lanes will increase of

service road level and reduce the level of road traffic congestion and this may effect in a switch of all or some

private transport mode users to public transport modes.

Key words: Own car, Public transport modes, logit model, shopping Travel time, shopping distance, age, car

ownership and switch probabilities, likelihood..