Tor (The Onion Routing) network was designed to enable users to browse the Internet anonymously. It is known for its anonymity and privacy security feature against many agents who desire to observe the area of users or chase users’ browsing conventions. This anonymity stems from the encryption and decryption of Tor traffic. That is, the client’s traffic should be subject to encryption and decryption before the sending and receiving process, which leads to delay and even interruption in data flow. The exchange of cryptographic keys between network devices plays a pivotal and critical role in facilitating secure communication and ensuring the integrity of cryptographic procedures. This essential process is time-consuming, which causes delay and discontinuity of data flow. To overcome delay or interruption problems, we utilized the Software-Defined Network (SDN), Machine Learning (ML), and Blockchain (BC) techniques, which support the Tor network to intelligently speed up exchanging the public key via the proactive processing of the Tor network security management information. Consequently, the combination network (ITor-SDN) keeps data flow continuity to a Tor client. We simulated and emulated the proposed network by using Mininet and Shadow simulations. The findings of the performed analysis illustrate that the proposed network architecture enhances the overall performance metrics, showcasing a remarkable advancement of around 55%. This substantial enhancement is achieved through the seamless execution of the innovative ITor-SDN network combination approach.
The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita
... Show MoreA new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification
... Show MoreIn this paper, we propose a new and efficient ferroelectric nanostructure metal oxide lithium niobate [(Li1.075Nb0.625Ti0.45O3), (LNTO)] solid film as a saturable absorber (SA) for modulating passive Q-switched erbium-doped fiber laser (EDFL). The SA is fabricated as a nanocomposite solid film by the drop-casting process in which the LNTO is planted within polyvinylidene fluoride-trifluoroethylene [P(VDF-TrFE)] as host copolymer. The optical and physical characteristics of the solid film are experimentally established. The SA is incorporated within the cavity of EDFL to examine its capability for producing multi-wavelength laser. The experimental results proved that a multi-wavelength laser is produced, where stable four lines with central
... Show MoreThe COVID-19 pandemic has had a huge influence on human lives all around the world. The virus spread quickly and impacted millions of individuals, resulting in a large number of hospitalizations and fatalities. The pandemic has also impacted economics, education, and social connections, among other aspects of life. Coronavirus-generated Computed Tomography (CT) scans have Regions of Interest (ROIs). The use of a modified U-Net model structure to categorize the region of interest at the pixel level is a promising strategy that may increase the accuracy of detecting COVID-19-associated anomalies in CT images. The suggested method seeks to detect and isolate ROIs in CT scans that show the existence of ground-glass opacity, which is fre
... Show MoreThis work involved the successful synthesis of three new Schiff base complexes, including Ni(II), Mn(II), and Cu(II) complexes. The Schiff base ligand was created by reacting the malonyldihydrazide molecule with naphthaldehyde, and the final step involved reacting the ligand with the corresponding metallic chloride yielding pure target complexes. FTIR, 1 H NMR, 13 C NMR, mass, and UV/Vis spectroscopies were used to comprehensively characterize the produced complexes. These substances have been employed in this study to photo-stabilize polystyrene (PS) and lessen the photo-degradation of its polymeric chains. Several methods, including FTIR, weight loss, viscosity average molecular weight, light and atomic force microscopy, and energy disper
... Show MoreThis paper presents L1-adaptive controller for controlling uncertain parameters and time-varying unknown parameters to control the position of a DC servomotor. For the purpose of comparison, the effectiveness of L1-adaptive controller for position control of studied servomotor has been examined and compared with another adaptive controller; Model Reference Adaptive Controller (MRAC). Robustness of both L1-adaptive controller and model reference adaptive controller to different input reference signals and different structures of uncertainty were studied. Three different types of input signals are taken into account; ramp, step and sinusoidal. The L1-adaptive controller ensured uniformly bounded
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