The Arabic Language is the native tongue of more than 400 million people around the world,  it is also a language that carries an important religious and international weight.  The Arabic language has taken its share of the huge technological explosion that has swept the world, and therefore it needs to be addressed with natural language processing applications and tasks.

This paper aims to survey and gather the most recent research related to Arabic Part of Speech (APoS), pointing to tagger methods used for the Arabic language, which ought to aim to constructing corpus for Arabic tongue. Many AI investigators and researchers have worked and performed POS utilizing various machine-learning methods, such as Hidden-Mark

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

Suicidal ideation is one of the most severe mental health issues faced by people all over the world. There are various risk factors involved that can lead to suicide. The most common & critical risk factors among them are depression, anxiety, social isolation and hopelessness. Early detection of these risk factors can help in preventing or reducing the number of suicides. Online social networking platforms like Twitter, Redditt and Facebook are becoming a new way for the people to express themselves freely without worrying about social stigma. This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim o

The foreguts of a total of 515 fish of Chondrostoma regium (Heckel, 1843) (locally: Bala’aot Malloky) were studied. These fish were collected from Tigris River at Salah Al-Deen Province (between Al-Hagag & Yathrib) for 20 months between March and October of the next year. Detritus, plant in origin materials (19.6%, 23.0% & 24.9%); green and blue green algae, mostly Cladophora, Cosmarium and Merismpedia sp. (17.1%, 12.9% & 12.2%) and diatoms, mostly Diatoma, Chanathes, Amphora and Cyulbella sp. (16.9%, 8.8% & 8.2%) were the main food categories taken by these fishes according to occurrence (O%), volumetric methods (V%) and ranking index (R%). Debris (not part of the diet) took 45.3% of the studied fish foreguts by volume. Detritus was also

This study was conducted in 2018, at Technical College of Applied Sciences, Sulaimani Polytechnic University, and Kurdistan Region-Iraq. The aim of the study was to determinate nutritional compositions and some elemental contents in marketable white button mushroom (Agaricus bisporus) that is collected in local markets of Kurdistan Region-Iraq. Five different samples (A: Penjwen product fresh, B: Sulaimani product fresh, C: American caned, D: Valencia Netherlandcaned and E: Erbil product fresh) were collected to be observed. The elements were analyzed by Atomic Absorption Spectrometry methods, and their chemical compassions were determined, too. The collected data were analyzed by One Way ANOVA. The highest fat, protein, fiber and dry matte

This study was conducted in 2018, at Technical College of Applied Sciences, Sulaimani Polytechnic University, Kurdistan Region-Iraq. The aim of the study was to determinate nutritional compositions and some elemental contents in marketable white button mushroom (Agaricus bisporus) that is collected in local markets of Kurdistan Region-Iraq. Five different samples (A: Penjwen product fresh, B: Sulaimani product fresh, C: American caned, D: Valencia Netherlandcaned and E: Erbil product fresh) were collected to be observed. The elements were analyzed by Atomic Absorption Spectrometry methods, and their chemical compassions were determined, too. The collected data were analyzed by One Way ANOVA. The highest fat, protein, fiber and d

Background: (ABO) Blood type have an effect on general health including oral health as salivary physicochemical characteristics differ among different type of blood and as consequence these affect the severity of dental caries. The aim of the present study is an assessment of the prevalence of caries experience among different blood type in relation to salivary physicochemical characteristic. Materials and Methods: Two hundred and fifty females' college students in Al-Qadisyia University aged 18 years old were selected on random basis; they were divided to four groups according to their blood type, Dental experience was diagnosed and recorded according to DMFs (Mülemman, 1976) Index, this allows recording decayed lesion by severity. A su

Reconstruction project management in the cities of Mosul, Anbar, and Tikrit, in Iraq still faces major obstacles that impede the comprehensive performance of these projects. It is thus necessary to improve the arising challenge estimation in the implementation of reconstruction projects and evaluate their components: time, cost, quality, and scope. This study used the Analytical Hierarchy Process (AHP) to prioritize major and minor criteria in the influential causes of challenges and formulate a mathematical model to help decision-makers estimate them. Using the Super Decisions software, the final results indicated that changes in scope reached 40.8%, which is the greatest difficulty, followed by changes in cost at 27.6%, changes in

The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is