Cybersecurity involves protecting computer networks, systems, and data from unauthorized access and disruptions using advanced technologies. The purpose of this research is to establish a novel cyber security framework for strengthening cloud data protection. In this paper, we propose a novel Dung Beetle optimization-redefined Intelligent Random Forest (DB-IRF) for accurate detection of intrusions in a cloud environment. We obtained a dataset that includes cloud system logs and network traffic data, including normal and malicious activities, to train our proposed model. We utilized z-score normalization to pre-process the gathered raw data. Our suggested model enhances classification accuracy by integrating DB optimization with the

Objectives: This study aimed to identify and analyse ATP7B variants in Iraqi adults with Wilson disease (WD) by long-read next-generation sequencing. Methods: This cross-sectional study was conducted at the Poisoning Consultation Center at Ghazy Al-Hariri Hospital for Surgical Specialties and the Gastroenterology Consultation Clinic at Baghdad Teaching Hospital, Medical City in Baghdad, Iraq. Unrelated patients with clinical and biochemical features suggestive of WD were recruited between October 2022 and October 2023. DNA was extracted from peripheral blood samples. Variants in the ATP7B gene were identified using long-read next-generation sequencing and then analysed by in-silico tools. Results: A total of 45 patients were recruited in

ABSTRACT:

              Interest rates are one of the important aspects that affect the banking business directly, which is characterized by unstable dynamic dynamics, which must be viewed on a daily and continuous basis through the macroeconomic view, which directly affects the bank’s income realized from loans as interest received or interest paid on its deposits as an expense. Hence the earnings per share. The relationship between interest rates and between net income and earnings per share was measured and a correlation was found between them, and then the effect between them was measured using regression equations and they were applied and th

Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classifica

Precise and interpretable classification of autism-related behaviors is important for initial diagnosis, personalized intervention, and support arrangements. This study proposes an interpretable machine learning (ML) model using Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to classify behavioral patterns into four categories (normal, mild, moderate, and severe) associated with Autism Spectrum Disorder (ASD) based on a custom 377-instance survey dataset from Iraqi parents and teachers of children aged 6-12. The model observes 16 key features across communication and social interaction, repetitive behaviors, language, and adaptive skills, preprocessed via interquartile range (IQR) outlier removal, me

Physicians are likely to expend significant labor and time while manually calculating blood smears. Automatic computer-based methods for classifying acute lymphoblastic leukemia have trouble correctly lighting stained white blood cell microscopy images and accurately separating cells that touch or overlap. Additionally, incorporating machine learning techniques into medical services is very hard because doctors can deal with rough guesses as long as the results aren't too bad, but they can't use these calculations for actual medical care. Enabling a A deep network having knowledge of the accuracy of its own predictions is a fascinating and crucial issue. Most instances segmentation frameworks weigh the mask quality during the instance

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

A novel demountable shear connector for precast steel‐concrete composite bridges is presented. The connector uses high‐strength steel bolts, which are fastened to the top flange of the steel beam with the aid of a special locking nut configuration that prevents slip of bolts within their holes. Moreover, the connector promotes accelerated construction and overcomes typical construction tolerances issues of precast structures. Most importantly, the connector allows bridge disassembly, and therefore, can address different bridge deterioration scenarios with minimum disturbance to traffic flow, i.e. (i) precast deck panels can be rapidly uplifted and replaced; (ii) connectors can be rapidly removed and replaced; and (iii) steel beams can b