Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

This research proposes the application of the dragonfly and fruit fly algorithms to enhance estimates generated by the Fama-MacBeth model and compares their performance in this context for the first time. To specifically improve the dragonfly algorithm's effectiveness, three parameter tuning approaches are investigated: manual parameter tuning (MPT), adaptive tuning by methodology (ATY), and a novel technique called adaptive tuning by performance (APT). Additionally, the study evaluates the estimation performance using kernel weighted regression (KWR) and explores how the dragonfly and fruit fly algorithms can be employed to enhance KWR. All methods are tested using data from the Iraq Stock Exchange, based on the Fama-French three-f

With the proliferation of both Internet access and data traffic, recent breaches have brought into sharp focus the need for Network Intrusion Detection Systems (NIDS) to protect networks from more complex cyberattacks. To differentiate between normal network processes and possible attacks, Intrusion Detection Systems (IDS) often employ pattern recognition and data mining techniques. Network and host system intrusions, assaults, and policy violations can be automatically detected and classified by an Intrusion Detection System (IDS). Using Python Scikit-Learn the results of this study show that Machine Learning (ML) techniques like Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) can enhance the effectiveness of an Intrusi

Malicious software (malware) performs a malicious function that compromising a computer system’s security. Many methods have been developed to improve the security of the computer system resources, among them the use of firewall, encryption, and Intrusion Detection System (IDS). IDS can detect newly unrecognized attack attempt and raising an early alarm to inform the system about this suspicious intrusion attempt. This paper proposed a hybrid IDS for detection intrusion, especially malware, with considering network packet and host features. The hybrid IDS designed using Data Mining (DM) classification methods that for its ability to detect new, previously unseen intrusions accurately and automatically. It uses both anomaly and misuse dete

Software Defined Network (SDN) is a new technology that separate the ‎control plane from the data plane. SDN provides a choice in automation and ‎programmability faster than traditional network. It supports the ‎Quality of Service (QoS) for video surveillance application. One of most ‎significant issues in video surveillance is how to find the best path for routing the packets ‎between the source (IP cameras) and destination (monitoring center). The ‎video surveillance system requires fast transmission and reliable delivery ‎and high QoS. To improve the QoS and to achieve the optimal path, the ‎SDN architecture is used in this paper. In addition, different routing algorithms are ‎used with different steps. First, we eva

The importance of our research is that it examines the causes and sources of the security challenges in the internal security environment of the GCC countries, and aims to address the most important issues that are of great interest, namely, the issue of inter-GCC differences and addressing the issues of regional security for the Gulf region, After it is one of the most dynamic and more polarized areas for the emergence of threats and challenges because of the multiplicity of sources of threat and their complexity due to the specificity of the strategic environment and the negative repercussions it can have on the Gulf region, especially the issue of regional security of the Gulf Cooperation Council Which has become a magnet for competing i

The irony pushes us to inquire about what is in the text of contradiction, irony, suspense, and other acts of paradox, as well as a departure from what is logical, or familiar, that attracts the attention of the addressee, and this is what drives us to introspect the text and interrogate it in order to get to know the intended product of the text or its real or metaphorical intent. On the other hand, the irony is more in the literary text than in the scientific texts. Therefore, critics add the word literature to it in their definition.

 As it is represented by the paradox, we will seek to study the paradox of the title and the problematic that it may pose as the beginning of the text, and i

The present paper applies Fanon Psychological reading of the problem of the Black introduced in his book Black Skin White Mask to Crooks, The black Character in John Steinbeck’s  Of Mice and Men. The analysis of this character infers three essential points regarding the artistic achievement of the author. First, he uses a fictional character that offers a psychological interpretation of the black problem of alienation and loneliness in a way that does not disgrace the black. Second, he applies Fanon’s way of showing the various attitudes that the black adopts in contact with the white society. Third, he affirms that the black inferiority complex comes as a result of double proc