Search Results (33)

Purpose: To evaluate the potential clinical role and reliability of Sonazoid-enhanced ultrasound (SEUS) as a diagnostic tool for liver metastatic lesions. Methods: An extensive literature search was conducted across five electronic databases, PubMed, Scopus, Embase, Cochrane Library, and Web of Science, from their inception up to January 2024 to identify all studies evaluating the use of Sonazoid-enhanced ultrasonography for detecting hepatic metastases. A meta-analysis was performed to assess diagnostic accuracy using the Meta-DiSc 2.0 software. Results: A total of 31 studies were included, 16 of which were eligible for meta-analysis and diagnostic test accuracy evaluation. A total of 13 studies in the meta-analysis evaluated the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) for 1347 metastatic and 1565 non-metastatic liver lesions. The pooled sensitivity and specificity for CEUS were 0.88 (95% CI: 0.82–0.92) and 0.92 (95% CI: 0.84–0.96), respectively. The combined positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 11.89 (95% CI: 5.42–26.09), 0.12 (95% CI:0.08–0.19), and 91.99 (95% CI: 32.15–263.17), respectively. Additionally, four studies of the meta-analysis assessed the diagnostic performance of contrast-enhanced intraoperative sonography (CE-IOUS) in detecting 664 metastatic and 246 non-metastatic liver lesions. The pooled sensitivity and specificity for CE-IOUS were 0.93 (95% CI: 0.82–0.97) and 0.84 (95% CI: 0.65–0.93), respectively. The aggregated positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated as 5.95 (95% CI: 2.32–15.25), 0.07 (95% CI: 0.02–0.24), and 77.68 (95% CI: 10.33–583.86), respectively. Conclusions: CE-IOUS and CEUS are reliable approaches for diagnosing liver metastatic lesions. CE-IOUS, in particular, exhibits higher accuracy in identifying liver metastatic lesions, indicating its potential effectiveness in clinical practice. Full article

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset. Full article

Due to the Internet’s explosive growth, network security is now a major concern; as a result, tracking network traffic is essential for a variety of uses, including improving system efficiency, fixing bugs in the network, and keeping sensitive data secure. Firewalls are a crucial component of enterprise-wide security architectures because they protect individual networks from intrusion. The efficiency of a firewall can be negatively impacted by issues with its design, configuration, monitoring, and administration. Recent firewall security methods do not have the rigor to manage the vagueness that comes with filtering packets from the exterior. Knowledge representation and reasoning are two areas where fuzzy Petri nets (FPNs) receive extensive usage as a modeling tool. Despite their widespread success, FPNs’ limitations in the security engineering field stem from the fact that it is difficult to represent different kinds of uncertainty. This article details the construction of a novel packet-filtering firewall model that addresses the limitations of current FPN-based filtering methods. The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area of firewall packet filtering that are characterized by imprecise knowledge. Because of SNPNs’ symbolic ability, the packet filtration model can be quickly and easily established, examined, enhanced, and maintained. Based on the idea that the ambiguity of a packet’s movement can be described by if–then fuzzy production rules realized by the truth-membership function, the indeterminacy-membership function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. In addition, we simulate the dynamic behavior of the tracking system in light of the ambiguity inherent in packet filtering by presenting a two-level filtering method to improve the ranking of the filtering rules list. Results from experiments on a local area network back up the efficacy of the proposed method and illustrate how it can increase the firewall’s susceptibility to threats posed by network traffic. Full article

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset. Full article

Cadmium is an environmental toxicant that instigates cognitive deficits with excessive glutamate excitatory neuroactivity in the brain. Topiramate, a glutamate receptor antagonist, has displayed favorable neuroprotection against epilepsy, cerebral ischemia, and Huntington’s disease; however, its effect on cadmium neurotoxicity remains to be investigated. In this study, topiramate was tested for its potential to combat the cognitive deficits induced by cadmium in rats with an emphasis on hippocampal oxidative insult, apoptosis, and autophagy. After topiramate intake (50 mg/kg/day; p.o.) for 8 weeks, behavioral disturbances and molecular changes in the hippocampal area were explored. Herein, Morris water maze, Y-maze, and novel object recognition test revealed that topiramate rescued cadmium-induced memory/learning deficits. Moreover, topiramate significantly lowered hippocampal histopathological damage scores. Mechanistically, topiramate significantly replenished hippocampal GLP-1 and dampened Aβ42 and p-tau neurotoxic cues. Notably, it significantly diminished hippocampal glutamate content and enhanced acetylcholine and GABA neurotransmitters. The behavioral recovery was prompted by hippocampal suppression of the pro-oxidant events with notable activation of SIRT1/Nrf2/HO-1 axis. Moreover, topiramate inactivated GSK-3β and dampened the hippocampal apoptotic changes. In tandem, stimulation of hippocampal pro-autophagy events, including Beclin 1 upregulation, was triggered by topiramate that also activated AMPK/mTOR pathway. Together, the pro-autophagic, antioxidant, and anti-apoptotic features of topiramate contributed to its neuroprotective properties in rats intoxicated with cadmium. Therefore, it may be useful to mitigate cadmium-induced cognitive deficits. Full article

Alzheimer’s disease (AD) is a devastating illness with limited therapeutic interventions. The aim of this study is to investigate the pathophysiological mechanisms underlying AD and explore the potential neuroprotective effects of cocoa, either alone or in combination with other nutraceuticals, in an animal model of aluminum-induced AD. Rats were divided into nine groups: control, aluminum chloride (AlCl₃) alone, AlCl₃ with cocoa alone, AlCl₃ with vinpocetine (VIN), AlCl₃ with epigallocatechin-3-gallate (EGCG), AlCl₃ with coenzyme Q10 (CoQ10), AlCl₃ with wheatgrass (WG), AlCl₃ with vitamin (Vit) B complex, and AlCl₃ with a combination of Vit C, Vit E, and selenium (Se). The animals were treated for five weeks, and we assessed behavioral, histopathological, and biochemical changes, focusing on oxidative stress, inflammation, Wnt/GSK-3β/β-catenin signaling, ER stress, autophagy, and apoptosis. AlCl₃ administration induced oxidative stress, as evidenced by elevated levels of malondialdehyde (MDA) and downregulation of cellular antioxidants (Nrf2, HO-1, SOD, and TAC). AlCl3 also upregulated inflammatory biomarkers (TNF-α and IL-1β) and GSK-3β, leading to increased tau phosphorylation, decreased brain-derived neurotrophic factor (BDNF) expression, and downregulation of the Wnt/β-catenin pathway. Furthermore, AlCl₃ intensified C/EBP, p-PERK, GRP-78, and CHOP, indicating sustained ER stress, and decreased Beclin-1 and anti-apoptotic B-cell lymphoma 2 (Bcl-2) expressions. These alterations contributed to the observed behavioral and histological changes in the AlCl₃-induced AD model. Administration of cocoa, either alone or in combination with other nutraceuticals, particularly VIN or EGCG, demonstrated remarkable amelioration of all assessed parameters. The combination of cocoa with nutraceuticals attenuated the AD-mediated deterioration by modulating interrelated pathophysiological pathways, including inflammation, antioxidant responses, GSK-3β-Wnt/β-catenin signaling, ER stress, and apoptosis. These findings provide insights into the intricate pathogenesis of AD and highlight the neuroprotective effects of nutraceuticals through multiple signaling pathways. Full article

Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated Essay Scoring (AES) provides a feasible approach to completing this process. Interest in this area of study has exploded in recent years owing to the difficulty of simultaneously improving the syntactic and semantic scores of an article. Ontology enables us to consider the semantic constraints of the actual world. However, there are several uncertainties and ambiguities that cannot be accounted for by standard ontologies. Numerous AES strategies based on fuzzy ontologies have been proposed in recent years to reduce the possibility of imprecise knowledge presentation. However, no known efforts have been made to utilize ontologies with a higher level of fuzzification in order to enhance the effectiveness of identifying semantic mistakes. This paper presents the first attempt to address this problem by developing a model for efficient grading of English essays using latent semantic analysis (LSA) and neutrosophic ontology. In this regard, the presented work integrates commonly used syntactic and semantic features to score the essay. The integration methodology is implemented through feature-level fusion. This integrated vector is used to check the coherence and cohesion of the essay. Furthermore, the role of neutrosophic ontology is investigated by adding neutrosophic membership functions to the crisp ontology to detect semantic errors and give feedback. Neutrosophic logic allows the explicit inclusion of degrees of truthfulness, falsity, and indeterminacy. According to the comparison with state-of-the-art AES methods, the results show that the proposed model significantly improves the accuracy of scoring the essay semantically and syntactically and is able to provide feedback. Full article

Cadmium (Cd) is a widespread environmental pollutant that triggers testicular dysfunction. Dapagliflozin is a selective sodium-glucose co-transporter-2 inhibitor with notable antioxidant and anti-apoptotic features. It has shown marked cardio-, reno-, hepato-, and neuroprotective effects. Yet, its effect on Cd-evoked testicular impairment has not been examined. Hence, the goal of the current study was to investigate the potential positive effect of dapagliflozin against Cd-induced testicular dysfunction in rats, with an emphasis on autophagy, apoptosis, and oxidative insult. Dapagliflozin (1 mg/kg/day) was given by oral gavage, and testicular dysfunction, impaired spermatogenesis, and biomolecular events were studied via immunohistochemistry, histopathology, and ELISA. The current findings demonstrated that dapagliflozin improved relative testicular weight, serum testosterone, and sperm count/motility and reduced sperm abnormalities, signifying mitigation of testicular impairment and spermatogenesis disruption. Moreover, dapagliflozin attenuated Cd-induced histological abnormalities and preserved testicular structure. The testicular function recovery was prompted by stimulating the cytoprotective SIRT1/Nrf2/HO-1 axis, lowering the testicular oxidative changes, and augmenting cellular antioxidants. As regards apoptosis, dapagliflozin counteracted the apoptotic machinery by downregulating the pro-apoptotic signals together with Bcl-2 upregulation. Meanwhile, dapagliflozin reactivated the impaired autophagy, as seen by a lowered accumulation of SQSTM-1/p62 and Beclin 1 upregulation. In the same context, the testicular AMPK/mTOR pathway was stimulated as evidenced by the increased p-AMPK (Ser487)/total AMPK ratio alongside the lowered p-mTOR (Ser2448)/total mTOR ratio. Together, the favorable mitigation of Cd-induced testicular impairment/disrupted spermatogenesis was driven by the antioxidant, anti-apoptotic, and pro-autophagic actions of dapagliflozin. Thus, it could serve as a tool for the management of Cd-evoked testicular dysfunction. Full article

Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm’s mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS’ 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results. Full article

To combat malicious domains, which serve as a key platform for a wide range of attacks, domain name service (DNS) data provide rich traces of Internet activities and are a powerful resource. This paper presents new research that proposes a model for finding malicious domains by passively analyzing DNS data. The proposed model builds a real-time, accurate, middleweight, and fast classifier by combining a genetic algorithm for selecting DNS data features with a two-step quantum ant colony optimization (QABC) algorithm for classification. The modified two-step QABC classifier uses K-means instead of random initialization to place food sources. In order to overcome ABCs poor exploitation abilities and its convergence speed, this paper utilizes the metaheuristic QABC algorithm for global optimization problems inspired by quantum physics concepts. The use of the Hadoop framework and a hybrid machine learning approach (K-mean and QABC) to deal with the large size of uniform resource locator (URL) data is one of the main contributions of this paper. The major point is that blacklists, heavyweight classifiers (those that use more features), and lightweight classifiers (those that use fewer features and consume the features from the browser) may all be improved with the use of the suggested machine learning method. The results showed that the suggested model could work with more than 96.6% accuracy for more than 10 million query–answer pairs. Full article

Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER). Full article

Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and complex. MANET is used to build a broad range of applications, both commercial and non-commercial, with the majority of these apps obtaining access to internet resources. Since the gateways (GWs) are the central nodes in a MANET’s ability to connect to the internet, it is common practice to deploy numerous GWs to increase the capabilities of a MANET. Current routing methods have been adapted and optimized for use with MANET through the use of both conventional routing techniques and tree-based network architectures. Exploring new or tacking-failure GWs also increases network overhead but is essential given that MANET is a dynamic and complicated network. To handle these issues, the work presented in this paper presents a modified gateway discovery approach inspired by the quantum swarm intelligence technique. The suggested approach follows the non-root tree-based GW discovery category to reduce broadcasting in the process of exploring GWs and uses quantum-inspired ant colony optimization (QACO) for constructing new paths. Due to the sequential method of execution of the algorithms, the complexity of ACO grows dramatically with the rise in the number of paths explored and the number of iterations required to obtain better performance. The exploration of a huge optimization problem’s solution space may be made much more efficient with the help of quantum parallelization and entanglement of quantum states. Compared to other broad evolutionary algorithms, QACO s have more promise for tackling large-scale issues due to their ability to prevent premature convergence with a simple implementation. The experimental results using benchmarked datasets reveal the feasibility of the suggested approach of improving the processes of exploring new GWs, testing and maintaining existing paths to GWs, exploring different paths to existing GWs, detecting any connection failure in any route, and attempting to fix that failure by discovering an alternative optimal path. Furthermore, the comparative study demonstrates that the utilized QACO is valid and outperforms the discrete binary ACO algorithm (AntHocNet Protocol) in terms of time to discover new GWs (27% improvement on average), time that the recently inserted node takes to discover all GWs (on average, 70% improvement), routing overhead (53% improvement on average), and gateway’s overhead (on average, 60% improvement). Full article

The extensive use of chemical fertilizers is responsible for numerous environmental problems including low food quality, soil degradation, and toxicity to beneficial living organisms in the soil. Nano-fertilizers (NFs) application may be a promising solution for combat these challenges. The current study focused on the efficiency of applying small amounts of NFs incorporated with conventional nitrogen, phosphorus, and potassium (NPK) fertilizers to reduce the quantities of conventional fertilizers (CFs) in lettuce cultivated in sandy soil. This study evaluated the effect of these incorporations on plant growth, yield, phytochemical accumulation, leaf nutrient, and leaf nitrate. A pot experiment was conducted during the winter seasons of 2020/2021 and 2021/2022 using the following treatments: CF100: 100% CFs, CF75NF25: 75% CFs + 25% NFs, CF50NF50: 50% CFs + 50% NFs, CF25NF75: 25% CFs + 75% NFs, and NF100: 100% NFs (=10% of CFs). Our findings displayed that the CF75NF25 and CF50NF50 treatments recorded the highest plant growth parameter values (plant length, root length, number of leaves, and fresh and dry biomass). The maximum of chlorophyll fluorescence measurements (photosystem II efficiency) were obtained in plants fertilized with CF75NF25, followed by CF50NF50 and CF100. The improvement ratios of photosynthetic pigments (Chlorophyll (Chl) a, b, and total) for CF75NF25 were 23.77, 50, and 23.72% in the first season and 10.10, 51.0, and 24.90% in the second season for Chl a, b, and total, respectively, as compared with the CF100 treatment. A similar tendency was observed for the CF50NF50 treatment. Generally, CF75NF25 significantly raised the content of total phenolic compounds (TPC), total flavonoid content (TFC), and antioxidant activity (AOA) in lettuce plants by 36.09, 47.82, and 40.16% in the first season and by 30.39, 37.53, and 32.43% in the second season, respectively, compared with CF100. In addition, the levels of leaf nutrient content and uptake of N, P, and K were significantly higher in plants fertilized with CF75NF25 compared to the other treatments, whereas CF25NF75 had the lowest values among the different treatments across both seasons for most of the tested traits. The nitrate content in lettuce leaves (NO₃⁻) for both seasons was lower than the acceptable level for human consumption. These results indicate that incorporating a low concentration of NFs into CFs could be a promising strategy to reduce the amount of CFs to 75% or 50% of lettuce NPK requirements without significant adverse effects on the growth and productivity of lettuce plants cultivated in sandy soil. Full article

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs. Full article

Indoor object detection is a fundamental activity for the development of applications of mobility-assistive technology for visually impaired people (VIP). The challenge of seeing interior objects in a real indoor environment is a challenging one since there are numerous complicated issues that need to be taken into consideration, such as the complexity of the background, occlusions, and viewpoint shifts. Electronic travel aids that are composed of the necessary sensors may assist VIPs with their navigation. The sensors have the ability to detect any obstacles, regardless of whether they are static or dynamic, and offer information on the context of an interior scene. The characteristics of an interior scene are not very clear and are subject to a great deal of variation. Recent years have seen the emergence of methods for dealing with issues of this kind, some of which include the use of neural networks, probabilistic methods, and fuzzy logic. This study describes a method for detecting indoor objects using a rotational ultrasonic array and neutrosophic logic. A neutrosophic set has been seen as the next evolution of the fuzzy set because of its indeterminate membership value, which is absent from conventional fuzzy sets. The suggested method is constructed to reflect the position of the walls (obstacle distance) and to direct the VIP to move freely (ahead, to the right, or to the left) depending on the degree of truthiness, the degree of indeterminacy, and the degree of falsity for the reflected distance. The results of the experiments show that the suggested indoor object detecting system has good performance, as its accuracy rate (a mean average precision) is 97.2 ± 1%. Full article