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

KE Sharquie, HM Al-Hamamy, AA Noaimi, IA Al-Shawi, Journal of the Saudi Society of Dermatology & Dermatologic Surgery, 2011 - Cited by 9

Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and p

Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data t

Based on the results of standard penetration tests (SPTs) conducted in Al-Basrah governorate, this research aims to present thematic maps and equations for estimating the bearing capacity of driven piles having several lengths. The work includes drilling 135 boreholes to a depth of 10 m below the existing ground level and three standard penetration tests (SPT) at depths of 1.5, 6, and 9.5 m were conducted in each borehole. MATLAB software and corrected SPT values were used to determine the bearing capacity of driven piles in Al-Basrah. Several-order interpolation polynomials are suggested to estimate the bearing capacity of driven piles, but the first-order polynomial is considered the most straightforward. Furthermore, the root means squar

This paper proposes a new encryption method. It combines two cipher algorithms, i.e., DES and AES, to generate hybrid keys. This combination strengthens the proposed W-method by generating high randomized keys. Two points can represent the reliability of any encryption technique. Firstly, is the key generation; therefore, our approach merges 64 bits of DES with 64 bits of AES to produce 128 bits as a root key for all remaining keys that are 15. This complexity increases the level of the ciphering process. Moreover, it shifts the operation one bit only to the right. Secondly is the nature of the encryption process. It includes two keys and mixes one round of DES with one round of AES to reduce the performance time. The W-method deals with

Manganese sulfate and Punica granatum plant extract were used to create MnO2 nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO2 NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencies were 32.79%, 75

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi