ABSTRACT: This paper focuses on using natural language processing (NLP) in chatbots to manage stress in war-affected countries. A Java-based chatbot was designed to alleviate stress using two algorithms: TextRank and Stanford_CoreNLP. The problem was solved by integrating different languages using a plugin. The chatbot was tested with fifteen people and received positive feedback. Modifications were made based on user feedback, with journaling being a winner. However, the chatbot faced limitations like a lack of Arabic language support and voice chat features.

This study provides a comprehensive evaluation of the compliance of key Libyan government websites with the Web Content Accessibility Guidelines (WCAG) 2.2, the latest international standard for digital accessibility published in October 2023. The assessment focuses on the nine new success criteria introduced in WCAG 2.2, which aim to improve accessibility for users with low vision, cognitive, and motor disabilities. By conducting thorough automated and manual testing, this research identifies the specific strengths and weaknesses of the evaluated websites in meeting WCAG 2.2 requirements at the A, AA, and AAA levels. The findings reveal significant areas for improvement across the government's online presence and provide actionable recommendations for Libyan institutions to enhance their digital accessibility efforts and create a more inclusive online environment for all citizens. KEYWORDS: digital accessibility, web accessibility, compliance, Libyan government websites

Daily incidents significantly impact the workflow of ambulance and healthcare personnel, whose critical role involves providing immediate medical treatment and facilitating transportation to hospitals. This study presents the design of a medical expert system aimed at enhancing first-aid response in ambulances and educating users on fundamental first-aid principles. The proposed system integrates a comprehensive knowledge base that catalogs disease symptoms and corresponding treatments, functioning similarly to a medical professional's guidance. While the system relies on pre-programmed symptoms, it allows for the continuous addition of new symptoms and diseases, ensuring adaptability in emergencies. This expert system is particularly beneficial for novice healthcare providers, equipping them with reliable diagnostic support and improving patient outcomes during medical emergencies.

The Libyan healthcare system faces significant challenges in medication management, with high rates of medication errors posing serious risks to patient safety. Digital transformation, particularly through the adoption of electronic medical prescriptions, offers a promising solution to enhance prescription accuracy, improve patient outcomes, and streamline healthcare processes. This technical paper examines the implementation of digital medical prescriptions in Libya, focusing on the role of health informatics, the validation of prescriptions, and the potential barriers to success. The paper also highlights efforts in Arab and African countries similar to Libya, showcasing best practices and lessons learned.

Keywords: Digital prescriptions, Libya healthcare system, Medication errors, Patient safety, Digital transformation, Prescription practices, Prescription accuracy, Handwritten prescriptions, Prescription software, electronic health record systems (EHRS).

The article discusses exploring the Photovoltaic (PV) emulator for simulating photovoltaic systems for Renewable Energy Analysis (REA) focuses on creating and applying a PV emulator to simulate photovoltaic systems and concludes by emphasizing the value of precise simulation tools in comprehending and enhancing photovoltaic system performance. The first portion of the article emphasizes how renewable energy sources especially solar energy are becoming more and more important in tackling the world's energy problems. As a result, the pv output along with the voltage and current have been presented

This paper employs Monte Carlo Simulation (MCS) to assess how randomly power flow is distributed in Vehicle-to-Grid (V2G) operations. V2G technology facilitates a bidirectional flow of electricity by allowing Electric Vehicles (EVs) to feed excess power back into the grid in addition to drawing power from it. The researchers used Monte Carlo simulation as a computational technique that involves generating random samples to analyze the behaviour of a system, to assess the impact of random variations in power flow on V2G operations. They considered various factors such as EV charging and discharging patterns, grid conditions, and user behaviour to create a realistic simulation environment. The main objective function in this study wants to achieve is to reduce the dependency on the grid and develop a stochastic framework for the efficient management of microgrids with the presence of EVs. reduce cost. As a result, the analysis of the MCS shows the performance of the bidirectional operation, and the output power of the integrated sources is obtained.

Alzheimer's disease (AD) is a complex neurological illness that has several deep reasons. According to recent research, the use of machine learning techniques (ML) on MRI images can assist in identify the brain regions and the connections between them that are implicated in dementia. The study aims to review literature from 2017 to 2023 on the use of machine learning algorithms to identifying and categorize AD. The precision of each machine learning model is assessed, and mind map models are employed to illustrate the study and compare the outcomes.

Abstract. One innovative initiative that shows how technology and creativity can save lives in dire circumstances is the creation of a smart autonomous drone system for Search and Rescue Operations in Libya. The Search and Rescue Drone is a ray of hope that is intended to transform rescue operations by offering a quick and effective way to find persons who are in trouble or who may have been lost in Libya's desert or Mediterranean Sea. A Raspberry Pi, a Pixhawk flying controller, the Internet of Things, and a specially created mobile application are the main parts of the study. With the help of the YOLOv4-tiny module and object detection algorithms, the system enables users to operate the drone and quickly and accurately identify those who go missing in challenging environments. By fusing technological innovation with a humanitarian goal, this paper paves the way for future search and rescue operations in Libya and other nations to be safer and more responsive. The work shows how technology can save lives in dire circumstances and serves as an example of how it can benefit humanity at its most vulnerable times.

ABSTRACT: As more due to the obvious quickening pace of the digital transformation revolution, information and cyber security are

becoming major challenges in industries like banking, telecommunications, and energy. As a branch of cyber security,

network security is concerned with organizing and putting into practice safeguards against software and network hacking and illegal access. To guarantee security, the CIA security tried confidentiality, integrity, and availability must exist. Malicious actors frequently exploit distributed denial of service (DDoS) attacks to compromise availability. When it comes to DDoS detection and prevention, open-source software is thought to be the most economical option. The purpose of this study is to investigate the efficacy and potential of open-source DDoS detection and prevention techniques. To ascertain the best open-source solution, how to launch attacks, and how to measure system performance metrics like CPU, RAM usage, packet loss, and delay, an experimental testbed was set up and assessed. Because PfSense firewall includes widely used intrusion detection/prevention systems (IDS, IPS) packages (such as Snort and Suricata), they are deployed to verify the system performance.

This paper presents the development of an advanced system designed to efficiently process Arabic queries in today’s data-driven landscape. By using the T5 sequence-to-sequence model developed by Google and fine-tuning it specifically for transforming textual and verbal input into SQL queries, the system becomes adept at accurately comprehending and interpreting Arabic queries.

The resulting system serves as a user-friendly tool that automates the generation of SQL queries. Users can input their queries in written or recorded Arabic utterances, eliminating the need for manual translation and query construction.

This involved fine-tuning T5 models on a SQL dataset, splitting the dataset, tokenizing it, and setting training parameters. The implementation phase included loading the fine-tuned model, which incorporated PICARD to generate valid queries effectively. This paper also explores the impact of Arabic translation on the performance and accuracy of the model.

The optimal testing on test set accuracy achieved was 63.11%, the real-world testing accuracy: T5-base without PICARD scored 56.96% and 51.36% on English and Arabic questions, respectively, while T5-large with PIARCD scored 77.74% and 70.34% on English and Arabic questions.