In this paper, some series of new complexes of Mn(II), Co(II), Ni (II) Cu(II) and Hg(II) are prepared from the Schiff bases (L1,L2). (L1) derived from 4-aminoantipyrine and O-phenylene dia mine then (L2) derived from (L1) and 2-benzoyl benzoic acid. Structural features are obtained from their elemental microanalyses, molar conductance, IR, UV–Vis, 1H, 13CNMR spectra and magnetic susceptibility. The magnetic susceptibility and UV–Vis, IR spectral data of the ligand (L1) complexes get square–planar and tetrahedral geometries and the complexes oflig and (L2) get an octahedral geometry. Antimicrobial examinations show good results in the sharing complexes.

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection

Modern machine-learning applications require GPUs, and modern platforms can leverage numerous GPUs on one or more machines to increase performance. Contemporary deep-learning models are too huge for CPU or GPU training. Training these models with many GPUs without performance degradation is necessary to train them rapidly and maximize GPU consumption. Thus, training deep convolutional neural networks (DCNN) with multiple GPUs has become necessary for improving training. Therefore, we presented a parallel design and development of an efficient model for enhancing face mask CNN performance and improving resource efficiency. This DCNN model is a parallel training system over multiple GPUs, a multi-core CPU, and a multi-process GPU platform wit

            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the

This work presents a canned-food defect-detection method using the EfficientDet model with four backbones (MobileNet, EfficientNet, Swin-T, and ConvNeXt). The dataset included 8046 images with a resolution of 512 × 512 pixels. The performance criteria in this study accuracy “mAP”, computational cost “FLOPs”, and Frames Per Second “FPS”. The lightweight backbone with EfficientDet achieves a mean Average Precision (mAP) of 90% with MobileNet, while EfficientNet achieves mAP accuracy of 92% and 94%. The heavy backbone for EfficientDet (Swin-T, and ConvNeXt) achieves mAP accuracy of 96% and 98%. The main contribution of this study is to optimize the speed of conveyor belts in industrial production lines to considers the

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

The synthesized ligand (3-(2-amino-5-(3,4,5-tri-methoxybenzyl)pyrimidin-4-ylamino)-5,5-dimethylcyclohex-2-enone] [H1L1] was characterized via fourier transform infrared spectroscopy (FTIR), 1H, 13C – NMR, Mass spectra, (CHN analysis), UV-vis spectroscopic approaches. Analytical and spectroscopic techniques like chloride content, micro-analysis, magnetic susceptibility UV-visible, conductance, and FTIR spectra were used to identify mixed ligand complexes. Its (ML13ph) mixed ligand complexes [M= Co (II), Ni (II), Cu (II), Zn (II), and Cd (II); (H1L1) = β-enaminone ligand=L1 and (3ph) =3-aminophenol= L2]. The results demonstrate that the complexes are produced with a molar ratio of M: L1:L2 (1:1:1). To generate the appropriate compl

Background: Pleomorphic adenoma is the most common benign salivary gland tumor and shows a pronounced morphological complexity and diversity; for this The immunoprofiles and clinical course of PA differed according to cellular differentiation. Therefore, it is important to assess potential biomarkers in diagnostic and therapeutic trials. This study evaluates the immunohistochemical expression of D2-40, VEGF and PCNA as markers of lymphangiogenesis, angiogenesis and proliferation of PA and their correlation with clinicpathological parameters and with each other. Materials and Methods: Twenty five formalin – fixed, paraffin – embedded tissue blocks were included in this study. After histopathological reassessment of haematoxylin & eosin