The increasing complexity of assaults necessitates the use of innovative intrusion detection systems (IDS) to safeguard critical assets and data. There is a higher risk of cyberattacks like data breaches and unauthorised access since cloud services have been used more frequently. The project's goal is to find out how Artificial Intelligence (AI) could enhance the IDS's ability to identify and classify network traffic and identify anomalous activities. Online dangers could be identified with IDS. An intrusion detection system, or IDS, is required to keep networks secure. We must create efficient IDS for the cloud platform as well, since it is constantly growing and permeating more aspects of our daily life. However, using standard intrusion detection systems in the cloud may provide challenges. The pre-established IDS design may overburden a cloud segment due to the additional detection overhead. Within the framework of an adaptively designed networked system. We demonstrate how to fully use available resources without placing undue load on any one cloud server using an intrusion detection system (IDS) based on neural networks. To even more successfully detect new threats, the suggested IDS make use of neural network machine learning (ML).
Explainable Artificial Intelligence (XAI) techniques enable transparency and trust in automated visual inspection systems by making black-box machine learning models understandable. While XAI has been widely applied, prior reviews have not addressed the specific demands of industrial and medical inspection tasks. This paper reviews studies applying XAI techniques to visual inspection across industrial and medical domains. A systematic search was conducted in IEEE Xplore, Scopus, PubMed, arXiv, and Web of Science for studies published between 2014 and 2025, with inclusion criteria requiring the application of XAI in inspection tasks using public or domain-specific datasets. From an initial pool of studies, 75 were included and categorized in
... Show MoreRoads irrespective of the type have specific standard horizontal distance measured at 90 degrees from a lot boundary to a development known as a setback. Non-observance of the recommended setbacks accommodated in any urban center’s master plan creates noise hazard to the public health and safety as the movement of vehicular traffic is not without the attendant noise. This study assessed noise intrusion level in shops along a section of Ibadan-Abeokuta road with due consideration to compliance with the recommended building structure setback. Analysis of noise descriptors evaluated in this study gave A-weighted equivalent sound pressure level average of 91.3 dBA, the daytime average sound level (LD) 92.27 dBA,
... Show MoreThe demand for single photon sources in quantum key distribution (QKD) systems has necessitated the use of weak coherent pulses (WCPs) characterized by a Poissonian distribution. Ensuring security against eavesdropping attacks requires keeping the mean photon number (µ) small and known to legitimate partners. However, accurately determining µ poses challenges due to discrepancies between theoretical calculations and practical implementation. This paper introduces two experiments. The first experiment involves theoretical calculations of µ using several filters to generate the WCPs. The second experiment utilizes a variable attenuator to generate the WCPs, and the value of µ was estimated from the photons detected by the BB
... Show MoreThe problem of informal settlements in Iraqi is one of the most serious problems due
to its economic, social and security impacts which jeopardize the society safety and stability.
This study investigates the present situation of informal settlements in the Baghdad
governorates as well as approaches followed in making decisions to either upgrade or remove
those areas. Requirements of each decision are also discussed.
Study methods relied on both office and field studies to collect data and information
needed. The study revealed important findings that would help take appropriate measures that
deal with the informal settlements in Iraqi. The study also proposes effective mechanisms to
preclude both an increase in c
Since the widespread use of the concept of human security in 1994, and as stated in the report of human development (UNDP) issued by the United Nations Development Programmer,And framed it with multiple dimensions of political, economical, social and cultural. This concept has became beyond the state and its security ,and covered all what, can threaten human life and humanitarian groups, according to the humanitarian needs in the following aspects: The economic, the food,health,environmental , individual, community and the political aspect,which totally means ,the disability of the security traditional perspective to deal with these issues.The achievement of human security,that handles the maintenance of human dignity for meeting their b
... Show MoreIn current article an easy and selective method is proposed for spectrophotometric estimation of metoclopramide (MCP) in pharmaceutical preparations using cloud point extraction (CPE) procedure. The method involved reaction between MCP with 1-Naphthol in alkali conditions using Triton X-114 to form a stable dark purple dye. The Beer’s law limit in the range 0.34-9 μg mL-1 of MCP with r =0.9959 (n=3) after optimization. The relative standard deviation (RSD) and percentage recoveries were 0.89 %, and (96.99–104.11%) respectively. As well, using surfactant cloud point extraction as a method to extract MCP was reinforced the extinction coefficient(ε) to 1.7333×105L/mol.cm in surfactant-rich phase. The small volume of organi
... Show MoreThis study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K
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