Distributed denial of service (DDoS) attacks are a significant danger to network security, with SYN flood assaults being particularly known for exploiting the transmission control protocol (TCP) handshake to deplete server resources. This review paper analyzes the current research on classifying DDoS attacks using machine learning (ML) approaches, with a focus on SYN f lood scenarios. Traditional algorithms such as XGBoost, Random Forest, and k-Nearest Neighbors are examined alongside modern deep learning methods such as convolutional neural networks and long short-term memory networks. Deep learning, noted for its capacity to automatically learn complex properties from data, is particularly effective in dynamic contexts like the internet of things. The review analyzes the usefulness of various strategies, obstacles in feature engineering and model training, and their implications for real-time detection. This study presents a comprehensive overview of the accomplishments in employing ML and deep learning for TCP SYN flood attack classification and exposes gaps in the field that indicate options for further research.

Modifying the asphalt binder is one technique for improving asphalt pavement performance. When nanoparticles are utilized to modify asphalt binders, they exhibit unique and significant properties. Without a doubt, silica is the most commonly added element as nanoparticles to asphalts. The main objective of this study was to investigate the effect of mixing speed used to produce nanosilica (NS)-modified-asphalt cement on conventional physical properties of asphalt cement and prepare a model to predict the physical properties of NS-modified asphalt cement. For this purpose, four different nanosilica contents (1.5%, 3%, 4.5%, and 6% of the asphalt cement weight) were utilized. Each content was mixed at a temperature of 160°C using a high shear mixer set to four different speeds (1,000 rpm, 2,000 rpm, 3,000 rpm, and 4,000 rpm) for 30 min. Conventional asphalt cement tests, including penetration, softening point, rotational viscosity, and ductility tests, are conducted to evaluate the nanosilica-modified asphalt cement properties. The results showed that higher NS contents improve the physical properties of modified asphalt cement, but the improvement is more noticeable when mixing at a faster speed, as this lowers penetration and rotational viscosity and raises the softening point. By utilizing the Minitab statistical program, the physical properties prediction models of the NSmodified asphalt showed a direct correlation between input data (NS content, NS average size, mixing temperature, mixing rotational speed, mixing time, and neat asphalt properties) and predictive models, demonstrated by a high R2 value and acceptable standard deviation values.

Using seismic data is the most relevant and sound method to indicate the folding type in anticlines. However, when seismic data are not available or not allowed for use, then structural data are recommended to indicate the folding type of any anticline. Such usage is known and considered worldwide with excellent results. We have adopted a sound and published opinion of using structural data to indicate the folding type, and applied the method on three anticlines, namely, Qara Boutaq, Korek, and Mateen. We have selected the three anticlines from three different tectonic zones in the Iraqi Kurdistan Region, which is located in the northern and northeastern parts of Iraq, and forms the northeastern part of the Arabian Plate. The plate has been colliding with the Eurasian Plate since the Late Cretaceous and is still in collision; accordingly, all the existing anticlines are developed. We used satellite images to measure the required structural parameters, including aspect ratio, fold symmetry index, and Mountain Front Sinuosity Index in these anticlines. We also measured the dip amounts along the three anticlines from satellite images, where there are no dip measurements in the geological maps, to indicate the geometrical type of the three anticlines. Part of the interpreted data was checked and confirmed in the field, wherever it was accessible. Accordingly, we indicated the folding type to be Detachment Fold for the Qara Boutaq and Korek anticlines and Fault-bent Fold for the Mateen anticline.

This study evaluates radon concentrations and associated health risks in 40 commercially available cigarette brands from the Kurdistan Region. Two measurement techniques are employed: RAD-7 (active method) and CR-39 (passive method). Using the CR-39 detector, radon concentrations ranged from 101.99 ± 10.1 Bq·m⁻3 in the S6 (Cigaronne) sample to 339.98 ± 18.44 Bq·m⁻3 in the S32 (Gauloises Gold) sample, with an average concentration of 190.84 ± 13.67 Bq·m⁻3. The RAD-7 measurements show values between 96.01 ± 9.8 Bq·m⁻3 and 282.72 ± 16.81 Bq·m⁻3 in the same samples, averaging 180.30 ± 13.00 Bq·m⁻3. A strong correlation (R² = 0.9271) is observed between the two methods, confirming the reliability of the results. The effective radium content in all cigarette samples remains below internationally recommended safety limits, with Gauloises Gold showing the highest levels. Estimated annual effective doses ranged from 2.57 ± 1.61 mSv·y−1 to 8.58 mSv·y−1, remaining within acceptable limits established by the International Commission on Radiological Protection. Lung cancer risk due to radon exposure varies among brands, with an average of 208 ± 14.27 cases per million individuals. A significant correlation is found between ²²²Rn concentration and estimated annual lung cancer incidence. These findings highlight radon exposure from cigarette smoke as a contributing risk factor for lung cancer, underscoring the need for public health awareness regarding the radiological hazards of smoking.

Text classification is one of the severe challenges for categorizing large and high-dimensional text data accurately and efficiently. Many features confuse the classification process, and feature selection (FS) strategies should be used to deal with the problem of high dimensionality. This paper proposes a novel FS technique based on enhanced category-feature association measure (ECFAM). ECFAM utilizes the existence and elimination of terms and the complicated relationships among the terms across different sections. This one-of-a-kind approach emphasizes the key role of ancillary terms in classifying and differentiating categories. The comparison is done on two important datasets, Reuters-21578 and 20-Newsgroups, through two widely employed supervised machine learning classifiers and one deep learning algorithm. Throughout our experiments, we investigate the feature sizes in nine different feature sets, ranging from 50 to 4000. Experimental data show that ECFAM always performs better than other methods concerning accuracy and computational cost.

Computational ghost imaging encryption (CGIE) has gained increasing attention from researchers in the field of optical cryptography due to its unique phenomenon. However, traditional CGIE suffers from long imaging time, inherent system linearity, and an enormous number of random phase masks that must be transmitted as secret keys, which limits its application in practical communication. In this paper, a hybrid optical image encryption approach is proposed using CGIE based on integer wavelet transform and chaotic maps. In addition, Hadamard basis patterns are employed to reduce sampling times and improve reconstructed image quality. Simulation results demonstrate that the proposed system is robust against different types of attacks with high key sensitivity and low execution times of 0.03 s for encryption and 0.14 s for decryption. This approach will ensure broader adoption of this technology by facilitating its integration into cryptosystems.

The widespread commercial importance of selenium makes it an interesting element. It serves as an effective host matrix for chalcogenide alloys. However, pure selenium has a short lifetime and poor sensitivity. Therefore, specific chemical elements, such as tritium, have been used to overcome this problem. Se-Te alloys are preferred over selenium for their numerous advantages, such as increased electrical sensitivity, thermal stability, and applications in xerography. In this manuscript, the effects of partially substituting tellurium for selenium are studied for amorphous Se100-x Tex chalcogenide alloys prepared by melt quenching and spraying procedures to produce bulk and thin films, respectively, with varying tellurium concentrations (x = 10, 20, 30, and 40). X-ray diffraction of samples with different concentrations revealed that all samples had an amorphous (glassy) structure. Continuous electrical conductivity is also studied to determine the conduction mechanisms, effective energies, and densities of localized and extended states. The results of electrical conductivity measurements confirm the existence of two conduction modes (extended-state conduction at high temperatures and localized state conduction at intermediate and low temperatures in the tails of the conduction and valence bands). It is also found that the density of states, local and extended state coefficients, and activation energies are significantly affected by the partial substitution of selenium with tellurium. The optical properties of the Se₁₀₀₋ₓTeₓ films are studied using ultraviolet-visible spectroscopy, and it is found that the overall absorption increases while the energy gap decreases with increasing tellurium concentration.

Medical diagnostics require brain tumor classification to operate in real-time so the task demands accurate results with efficientprocessing abilities. A new hybrid deep learning solution merges convolutional neural networks (CNNs) with support vector machines(SVMs) to improve classification results as this paper describes. A total of four tumor categories including glioma, meningioma, andpituitary tumors together with no tumor appearance contribute to the magnetic resonance imaging (MRI) dataset are used for analysis. We applied and organized three pre-trained deep learning models: Alex-Net, DarkNet-19, and ResNet-50 for comparison. Anewly engineered compact CNN model linked with an SVM classifier brought decreased model dimensions while keeping excellent accuracy rates. Aproposed compact CNN model delivers 97.50% accuracy through its smaller 2.38 MB size and an additional SVM integration results in 97.45% accuracy using 1.43 MB. A Graphical User Interface (GUI) system comprising automated tumor classification capabilities is created to improve real-time systems that visualize MRI scans and illustrate predicted labels in addition to displaying confidence scores. A GUI enables smooth access to the trained model while being suitable for medical practice mobile healthcare environments and edge computing needs. The proposed system shows that lightweight architectures work excellently in real-time system applications especially when used for edge computing and mobile healthcare frameworks. The proposed solution demonstrates superiority over established models through its ability to scale efficiently.

A complex genetic diversity among the causative agent, Echinococcus granulosus, is documented. Antigen B (AgB) is a major antigenic fraction of hydatid fluid and hydatid cyst tissues. This study aims to investigate the role of antigen B subunit 2 (AgB2) gene polymorphism in the pathogenesis of cystic echinococcosis (CE) in murine model. Ovine liver hydatid cysts are obtained from Erbil Slaughterhouse. Protoscoleces from each isolate are separated into two batches. First preserved at −20°C for molecular analysis whereas the second is used for experimental infection in mice. Parasite DNA was extracted, and AgB2 genome was amplified and sequenced. The sequencing profile of six of the isolates (1, 2, 3, 5, 8, and 11) revealed a 100% analogy with AgB2 gene of E. granulosus genotype G2. Minor sequence polymorphisms, 1.67%, are observed in one of the isolates, whereas remarkable DNA sequence polymorphisms are noticed in three of the isolates. The polymerase chain reaction (PCR) products sequencing profiles revealed 100% polymorphisms in four of the isolates in comparison with the source gene (AY569356.1), instead, those isolates reveal various degrees of analogy, 80.33%, 80.87–89.05%, and 89.36% to G1, G3, and G6, respectively. Polymorphic sequencing profile of the PCR-amplified product (250 bp) of E. granulosus clone EgB2G2v13 AgB2 gene (Accession no.: AY569356.1) has no significant impact on the pathogenicity of the CE in murine model. To upgrade the diagnostic sensitivity rates of theimmunological techniques, a mixture of native hydatid antigens containing AgB is recommended to be used in the ser-diagnosis of this infection.

This research demonstrates that deep eutectic solvents (DESs) can eliminate sulfur compounds, which are corrosive and carcinogenic species, from model petroleum products through liquid-liquid extraction. Several monoprotic acids, including formic acid and acetic acid, are used to make DESs, along with 1-butyl-3-methylimidazolium chloride (BmimCl) as a hydrogen bond acceptor. These DESs are used for the first time to remove sulfur compounds (thiophene and dibenzothiophene) from an alkane as a model hydrocarbon (n-octane), which is used instead of crude oil as the latter contains a variety of species, including nitrogen compounds, hydrocarbons, and oxygen. The optimal parameters for the removal of sulfur are discussed, including the extraction temperature, reaction time, and mass ratio of DES to the model hydrocarbon, whilst the regeneration of DESs is also considered. H2O2 and iron oxide (Fe3O4) are also used as nanoparticle (NP) catalysts to enhance the sulfur removal process. Several characterization methods, including scanning electron microscopy, Fourier transform infrared, energy dispersive X-ray, and transmission electron microscopy, are used to determine the structural characteristics of the Fe3O4 NPs. The results show that acetic acid, as a monoprotic acid-based DES, is able to remove more than 86% of the sulfur molecules from model petroleum products when the mass ratio of DES to model petroleum products is 2:1, at 30°C and within 60 min. This research provides an important opportunity to advance our understanding of the role of DESs in removing carcinogenic and corrosive particles in industrial processes.