An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

<p>Mobility management protocols are very essential in the new research area of Internet of Things (IoT) as the static attributes of nodes are no longer dominant in the current environment. Proxy MIPv6 (PMIPv6) protocol is a network-based mobility management protocol, where the mobility process is relied on the network entities, named, Mobile Access Gateways (MAGs) and Local Mobility Anchor (LMA). PMIPv6 is considered as the most suitable mobility protocol for WSN as it relieves the sensor nodes from participating in the mobility signaling. However, in PMIPv6, a separate signaling is required for each mobile node (MN) registration, which may increase the network signaling overhead and lead to increase the total handoff latency

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

This paper focuses on the concept of cognition and its clarification in the light of Islamic epistemology. Knowledge passes through two essential parts: conception and assent. Conception explains simple knowledge, while assent explains knowledge involving a judgment. The paper proceeded with the identification of the problem of relationship blurring between cognition and knowledge. The external and inner senses have explained the relationship between the stages of knowledge and cognition. The external senses receive stimuli and form primary conceptions. These conceptions transfer to the first part of the inner senses, which is common sense; it collects the sensations and transmits them to pictorial power. Secondary conceptions are f

The quote of a Canadian communication scientist (Marshall McLuhan) (“The world has become an electronic village”) has become an archaic information compared to the great and rapid development of communication in the last two decades of the 20th century and what will happen later in the 21st century, to the extent that the world is called, thanks to the internet, a “Small screen” and this fact is a sign of the great progress that has been made in this field. As for the other statement of the Canadian communication scientist mentioned before “the medium itself, is the message”, it has been renewed and developed in its meaning and it’s purpose. Each new technical development in the means of communication necessarily means a me

 Finding a path solution in a dynamic environment represents a challenge for the robotics researchers, furthermore, it is the main issue for autonomous robots and manipulators since nowadays the world is looking forward to this challenge. The collision free path for robot in an environment with moving obstacles such as different objects, humans, animals or other robots is considered as an actual problem that needs to be solved.  In addition, the local minima and sharp edges are the most common problems in all path planning algorithms. The main objective of this work is to overcome these problems by demonstrating the robot path planning and obstacle avoidance using D star (D*) algorithm based on Particle Swarm Optimization (PSO)

One study whose importance has significantly grown in recent years is lip-reading, particularly with the widespread of using deep learning techniques. Lip reading is essential for speech recognition in noisy environments or for those with hearing impairments. It refers to recognizing spoken sentences using visual information acquired from lip movements. Also, the lip area, especially for males, suffers from several problems, such as the mouth area containing the mustache and beard, which may cover the lip area. This paper proposes an automatic lip-reading system to recognize and classify short English sentences spoken by speakers using deep learning networks. The input video extracts frames and each frame is passed to the Viola-Jone

Pathology reports are necessary for specialists to make an appropriate diagnosis of diseases in general and blood diseases in particular. Therefore, specialists check blood cells and other blood details. Thus, to diagnose a disease, specialists must analyze the factors of the patient’s blood and medical history. Generally, doctors have tended to use intelligent agents to help them with CBC analysis. However, these agents need analytical tools to extract the parameters (CBC parameters) employed in the prediction of the development of life-threatening bacteremia and offer prognostic data. Therefore, this paper proposes an enhancement to the Rabin–Karp algorithm and then mixes it with the fuzzy ratio to make this algorithm suitable