Drug consultation is an important part of pharmaceutical care. mobile phone call or text message can serve as an easy, effective, and implementable alternative to improving medication adherence and clinical outcomes by providing the information needed significantly for people with chronic illnesses like diabetes and hypertension particularly during pandemics like COVID-19 pandemic.

The problem of divorce from the phenomena that characterized the nature of privacy,
although their impact beyond the individual to include the community as a whole, the parties
to the relationship affected by divorce caused them harm moral and material for a long time,
resulting imbalance in the personal relationship and family and social relations because of the
high divorce rates, particularly in Iraq high rates of 28690 thousand cases in 2004 to 59 515
thousand cases in 2011 and an increase of more than (100%) during the period above, and this
rise caused by aggravation of many of the problems led the reasons for social, economic and
incompatibility spouses, health and lack of reproduction, not spending The wife a

Background Epilepsy is one of the most widespread neurological illnesses in the world with a prevalence rate of 2.8–19.5 per 1000 of the general population, and it is more prevalent in childhood. The understanding of the teachers about epilepsy has a significant impact on the academic performance and social skills of schoolchildren with epilepsy. Aim of Study This study aimed to assess teachers’ knowledge, attitude, and practice regarding epilepsy in schoolchildren and the association of certain studied variables with the level of schoolteachers’ knowledge, attitude, and practice. Subjects and Methods This was a cross-sectional study that included 402 participants and was conducted in Al-Rusafa/Baghdad during the period from November

Objective (s): to assess nurses' practices regarding discharge planning post-cardiac surgery and to find out the relationship between nurse practices and demographic characteristic (age, gander, level of education, years of experiences, and years of experiences in surgical ward).
Methodology: A descriptive study was carried out at cardiac centers and hospitals in Baghdad (Ibn Al- Bitar specialized center for cardiac surgery; Iraqi center for heart disease and Ibn al Nafees hospital). For the period of 6th February 2017 up to 1st of June 2018. A non-probability (purposive) sample of (58) nurses (male & female) who were working in surgical wards. The data was collected, by using of a questionnaire, which consists of two parts, part

Abstract
Objectives: The study is carried out to assess functional performance for heart's valve replacement patients and find out relationship with sociodemographic data and clinical data
Methodology: Descriptive design is carried out at cardiac surgery centers in Baghdad ; Ibn -Al Betar Specialized for cardiac surgery center and Al-Iraqi center for cardiac disease. its initiation from December28the 2013 to September 1st 2014,A non- probability (purposive) sample of 50 adults patients are attended cardiac surgery centers at Baghdad city and who have heart valves replacement. The data collection through development of questionnaire which is composed from three parts(socio demographic data, clinical information, functional performa

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat