Advanced Detection Approaches Research Articles

Implementation and validation of MICaFVi: A highly efficient nanotechnology-based method for coronaviruses detection

Coronaviruses have emerged as a significant public health concern due to the global impact of Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which cause COVID-19. The development of sensitive and accurate detection methods is critical for early diagnosis, disease management, and outbreak control. In a previous study, we developed and optimized a nanobased detection methodology called Magnetic Immuno-Capture Followed by Flow Virometry (MICaFVi) using virus-mimicking silica nanoparticles and MERS-CoV/SARS-CoV-2 pseudoviral particles. In the present study, we have extended this methodology to evaluate its specificity and sensitivity for detecting wild-type MERS-CoV and SARS-CoV-2 in human and camel samples. Our results demonstrated that MICaFVi successfully detected MERS-CoV and SARS-CoV-2 viruses with high sensitivity and specificity, although it showed reduced performance for samples with Ct values of 30 or lower compared to qPCR. Despite some limitations in detection speed and sensitivity, MICaFVi represents a significant advancement in diagnostic methodologies by combining nanotechnology with flow cytometry. Additionally, we adapted the MICaFVi methodology to simultaneously detect MERS-CoV and SARS-CoV-2 in a single multiplex assay. The successful implementation of this advanced detection approach has important implications for improving early detection, surveillance, and control of both current and future viral or bacterial pandemics. Our results underscore the potential of MICaFVi as a valuable tool for monitoring the spread of these viruses and highlight its role in advancing diagnostic technologies. This extension of our earlier work offers new insights into the application of nanotechnology and flow cytometry for viral detection.

Hydrogen Sulfide Signaling in Plants.

Significance: Hydrogen sulfide (H2S) is a multitasking potent regulator that facilitates plant growth, development, and responses to environmental stimuli. Recent Advances: The important beneficial effects of H2S in various aspects of plant physiology aroused the interest of this chemical for agriculture. Protein cysteine persulfidation has been recognized as the main reduction-oxidation (redox) regulatory mechanism of H2S signaling. An increasing number of studies, including large-scale proteomic analyses and functional characterizations, have revealed that H2S-mediated persulfidations directly regulate protein functions, altering downstream signaling in plants. To date, the importance of H2S-mediated persulfidation in several abscisic acid signaling-controlling key proteins has been assessed as well as their role in stomatal movements, largely contributing to the understanding of the plant H2S-regulatory mechanism. Critical Issues: The molecular mechanisms of the H2S sensing and transduction in plants remain elusive. The correlations of H2S-mediated persulfidation with other oxidative post-translational modifications of cysteines are still to be explored. Future Directions: Implementation of advanced detection approaches for the spatiotemporal monitoring of H2S levels in cells and the current proteomic profiling strategies for the identification and quantification of the cysteine site-specific persulfidation will provide insight into the H2S signaling in plants. Antioxid. Redox Signal. 39, 40-58.

Conventional and advanced detection approaches of fluoride in water: a review.

Fluorine is a naturally occurring element found in soil, water, food materials, and natural minerals such as fluorapatite, sellaite, and cryolite and exists as fluoride compounds with other elements because of high reactivity. The exposure of fluoride to the environment and human beings are industrial factors, food, water, and geogenic factors that impact the health of millions of human beings worldwide. Overexposure to fluoride exceeding the permissible limit (1.5mg/l as per WHO) causes several diseases in human beings, such as teeth mottling, thyroid inflammation, dental fluorosis, skeletal fluorosis, lesions in the kidney, and other organs. To overcome the deleterious impact of fluoride, its detection at an early stage is very much required. Therefore, feeling the importance of the same, immense efforts have been made to the selective and sensitive determination of fluoride in water by numerous researchers. This review paper summarizes the various conventional methods such as spectroscopic, ion chromatography, ICP-OES, and gas chromatography-mass spectrometry, their advantages, and drawbacks leading to the development of advanced ready-to-use detection strategies such as stamartphones for on-the-spot fluoride detection. This review paper also discusses future directions, which will assist scientists in achieving a new benchmark in developing a reliable, cost-effective, and user-friendly fluoride detector.

An efficient quantum multiverse optimization algorithm for solving optimization problems

<p>Due to the recent trend of technologies to use the network-based systems, detecting them from threats become a crucial issue. Detecting unknown or modified attacks is one of the recent challenges in the field of intrusion detection system (IDS). In this research, a new algorithm called quantum multiverse optimization (QMVO) is investigated and combined with an artificial neural network (ANN) to develop advanced detection approaches for an IDS. QMVO algorithm depends on adopting a quantum representation of the quantum interference and operators in the multiverse optimization to obtain the optimal solution. The QMVO algorithm determining the neural network weights based on the kernel function, which can improve the accuracy and then optimize the training part of the artificial neural network. It is demonstrated 99.98% accuracy with experimental results that the proposed QMVO is significantly improved optimization compared with multiverse optimizer (MVO) algorithms.</p>

A Hybrid Model is Proposed Based in The Combination of Genetic and MAFS in Cloud Environment

Cloud computing is being heralded as an important trend in information technology throughout the world. Data security has a major issue in cloud computing environment; An intrusion detection system (IDS) is a component that helps to detect various types of malicious network traffic which cannot be detected by a conventional firewall. Many IDS have been developed based on machine learning techniques. In recent growth, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning technique. The feature representation method is an important pattern classifier that facilitates correct classifications, however, there have been very few related studies focusing how to extractor representative features for normal connections and effective detection of attacks. The objective of this paper is to suggest new security mechanisms using various trust approaches in broker based federated cloud architecture, ranking the providers with the help of regression tree approach using Service Measurement Index security attributes and new hybrid computation intelligence built on the combination of genetic with Artificial Fish Swarm in Intrusion Detection system.

A new evolutionary neural networks based on intrusion detection systems using multiverse optimization

Building an intrusion detection system (IDS) has become an increasingly urgent issue for detecting network security breaches in computer and network systems. However, an effective and flexible IDS is imperative. In this paper, a new natural evolutionary algorithm (EA) called multiverse optimizer (MVO) is investigated and combined with an artificial neural network (ANN) to develop advanced detection approaches for an IDS. Under this context, the combination of ANN and EA produce evolutionary neural network (ENN). ENN makes this combination a new improved system for solving problems encountered by ANNs. The main idea of this work is to use an MVO to train a feed forward multilayer artificial neural network (MVO-ANN) to identify new attacks. This approach is applied to NSL-KDD and the new benchmark dataset called UNSW-NB15. In this manner, the effectiveness of our approach on detecting various forms of attack is demonstrated. Our results using UNSW-NB15 is better than those that were obtained using NSL-KDD. Furthermore, the efficacy of our proposed method is confirmed by performing better when compared to other well-known heuristic-based approaches such as practical swarm optimizer and artificial neural network (PSO-ANN).

A study on intrusion detection using neural networks trained with evolutionary algorithms

Intrusion detection has been playing a crucial role for making a computer network secure for any transaction. An intrusion detection system (IDS) detects various types of malicious network traffic and computer usage, which sometimes may not be detected by a conventional firewall. Recently, many IDS have been developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating evolutionary algorithms and neural networks have shown better detection performance than general machine learning approaches. The present study reports two new hybrid intrusion detection methods; one is based on gravitational search (GS), and other one is a combination of GS and particle swarm optimization (GSPSO). These two techniques have been successfully implemented to train artificial neural network (ANN) and the resulting models: GS-ANN and GSPSO-ANN are successfully applied for intrusion detection process. The applicability of these proposed approaches is also compared with other conventional methods such as decision tree, ANN based on gradient descent (GD-ANN), ANN based on genetic algorithm (GA-ANN) and ANN based on PSO (PSO-ANN) by testing with NSL-KDD dataset. Moreover, the results obtained by GS-ANN and GSPSO-ANN are found to be statistically significant based on the popular Wilcoxon's rank sum test as compared to other conventional techniques. The obtained test results reported that the proposed GS-ANN and GSPSO-ANN could achieve a maximum detection accuracy of 94.9 and 98.13 % respectively. The proposed models (GS-ANN and GSPSO-ANN) could also achieve good performance when tested with highly imbalanced datasets.

CANN: An intrusion detection system based on combining cluster centers and nearest neighbors

The aim of an intrusion detection systems (IDS) is to detect various types of malicious network traffic and computer usage, which cannot be detected by a conventional firewall. Many IDS have been developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning techniques. The feature representation method is an important pattern classifier that facilitates correct classifications, however, there have been very few related studies focusing how to extract more representative features for normal connections and effective detection of attacks. This paper proposes a novel feature representation approach, namely the cluster center and nearest neighbor (CANN) approach. In this approach, two distances are measured and summed, the first one based on the distance between each data sample and its cluster center, and the second distance is between the data and its nearest neighbor in the same cluster. Then, this new and one-dimensional distance based feature is used to represent each data sample for intrusion detection by a k-Nearest Neighbor (k-NN) classifier. The experimental results based on the KDD-Cup 99 dataset show that the CANN classifier not only performs better than or similar to k-NN and support vector machines trained and tested by the original feature representation in terms of classification accuracy, detection rates, and false alarms. I also provides high computational efficiency for the time of classifier training and testing (i.e., detection).

Constrained Parity-Check Code and Post-Processor for Advanced Blue Laser Disc Systems

We propose an advanced detection approach based on capacity-approaching constrained parity-check codes and multiple-error-event correction post-processing for high-density blue laser disc systems. Simulation results for an idealized blue laser disc system show that at high recording density, a performance improvement of 1.7 dB is achieved over the standard 17PP code without parity.

Parity-check codes and post-processing for d=1 optical recording channels

We have developed an advanced detection approach with parity-check (PC) codes and post-processing for optical recording channels. In seeking the advanced detection approach, we investigated different types of PC codes, existing as well as new. We developed a novel remedy scheme to minimize the miscorrection of error events that are split across codeword boundaries. Simulation shows that the performance of the developed PC codes approaches the corresponding bit-error-rate bounds, at both nominal density and high density.