RLIS: resource limited improved security beyond fifth generation networks using deep learning algorithms.

This study explores the feasibility of allocating finite resources beyond fifth generation networks for extended reality applications through the implementation of enhanced security measures via offloading analysis (RLIS). The quantification of resources is facilitated through the utilization of parameters, namely energy, capacity, and power, which are equipped with proximity constraints. These constraints are then integrated with activation functions in both multilayer perceptron and long short term memory models. Furthermore, the system model has been developed using vision-based computing, which involves managing data queues in terms of waiting periods to minimize congestion for data transmission with limited resources. The major significance of the proposed method is to utilize allocated spectrums for future generation networks by allocating necessary resources and therefore high usage of resources by all users can be avoided. In addition the advantage of the proposed method is secure the networks that operate beyond 5G where more number of users will try to share the allocated resources that needs to be provided with high security conditions

A multimodel-based screening framework for C-19 using deep learning-inspired data fusion.

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19. However, these models require inadequate computing resources and are not suitable for lightweight environments. We propose a multimodal screening framework that leverages deep learning-inspired data fusion models to enhance screening results. A Variation Encoder (VEN) design proposes to measure skin temperature using Regions of Interest (RoI) identified by YoLo. Subsequently, the multi-data fusion model integrates electronic records features with data from wearable human sensors. To optimize computational efficiency, a data reduction mechanism is added to eliminate unnecessary features. Furthermore, we employ a contingent probability method to estimate distinct feature weights for each cluster, deepening our understanding of variations in thermal and sensory data to assess the prediction of abnormal COVID-19 instances. Simulation results using our lab dataset demonstrate a precision of 95.2%, surpassing state-of-the-art models due to the thoughtful design of the multimodal data-based feature fusion model, weight prediction factor, and feature selection model

Intravenous Formulation of HET0016 Decreased Human Glioblastoma Growth and Iimplicated Survival Benefit in Rat Xenograft Models

Glioblastoma (GBM) is a hypervascular primary brain tumor with poor prognosis. HET0016 is a selective CYP450 inhibitor, which has been shown to inhibit angiogenesis and tumor growth. Therefore, to explore novel treatments, we have generated an improved intravenous (IV) formulation of HET0016 with HPssCD and tested in animal models of human and syngeneic GBM. Administration of a single IV dose resulted in 7-fold higher levels of HET0016 in plasma and 3.6-fold higher levels in tumor at 60 min than that in IP route. IV treatment with HPssCD-HET0016 decreased tumor growth, and altered vascular kinetics in early and late treatment groups (p \u3c 0.05). Similar growth inhibition was observed in syngeneic GL261 GBM (p \u3c 0.05). Survival studies using patient derived xenografts of GBM811, showed prolonged survival to 26 weeks in animals treated with focal radiation, in combination with HET0016 and TMZ (p \u3c 0.05). We observed reduced expression of markers of cell proliferation (Ki-67), decreased neovascularization (laminin and alphaSMA), in addition to inflammation and angiogenesis markers in the treatment group (p \u3c 0.05). Our results indicate that HPssCD-HET0016 is effective in inhibiting tumor growth through decreasing proliferation, and neovascularization. Furthermore, HPssCD-HET0016 significantly prolonged survival in PDX GBM811 model

Machine learning methods for service placement : a systematic review

With the growth of real-time and latency-sensitive applications in the Internet of Everything (IoE), service placement cannot rely on cloud computing alone. In response to this need, several computing paradigms, such as Mobile Edge Computing (MEC), Ultra-dense Edge Computing (UDEC), and Fog Computing (FC), have emerged. These paradigms aim to bring computing resources closer to the end user, reducing delay and wasted backhaul bandwidth. One of the major challenges of these new paradigms is the limitation of edge resources and the dependencies between different service parts. Some solutions, such as microservice architecture, allow different parts of an application to be processed simultaneously. However, due to the ever-increasing number of devices and incoming tasks, the problem of service placement cannot be solved today by relying on rule-based deterministic solutions. In such a dynamic and complex environment, many factors can influence the solution. Optimization and Machine Learning (ML) are two well-known tools that have been used most for service placement. Both methods typically use a cost function. Optimization is usually a way to define the difference between the predicted and actual value, while ML aims to minimize the cost function. In simpler terms, ML aims to minimize the gap between prediction and reality based on historical data. Instead of relying on explicit rules, ML uses prediction based on historical data. Due to the NP-hard nature of the service placement problem, classical optimization methods are not sufficient. Instead, metaheuristic and heuristic methods are widely used. In addition, the ever-changing big data in IoE environments requires the use of specific ML methods. In this systematic review, we present a taxonomy of ML methods for the service placement problem. Our findings show that 96% of applications use a distributed microservice architecture. Also, 51% of the studies are based on on-demand resource estimation methods and 81% are multi-objective. This article also outlines open questions and future research trends. Our literature review shows that one of the most important trends in ML is reinforcement learning, with a 56% share of research

Targeting bone marrow to potentiate the anti-tumor effect of tyrosine kinase inhibitor in preclinical rat model of human glioblastoma

Antiangiogenic agents caused paradoxical increase in pro-growth and pro-angiogenic factors and caused tumor growth in glioblastoma (GBM). It is hypothesized that paradoxical increase in pro-angiogenic factors would mobilize Bone Marrow Derived Cells (BMDCs) to the treated tumor and cause refractory tumor growth. The purposes of the studies were to determine whether whole body irradiation (WBIR) or a CXCR4 antagonist (AMD3100) will potentiate the effect of vatalanib (a VEGFR2 tyrosine kinase inhibitor) and prevent the refractory growth of GBM. Human GBM were grown orthotopically in three groups of rats (control, pretreated with WBIR and AMD3100) and randomly selected for vehicle or vatalanib treatments for 2 weeks. Then all animals underwent Magnetic Resonance Imaging (MRI) followed by euthanasia and histochemical analysis. Tumor volume and different vascular parameters (plasma volume (vp), forward transfer constant (Ktrans), back flow constant (kep), extravascular extracellular space volume (ve) were determined from MRI. In control group, vatalanib treatment increased the tumor growth significantly compared to that of vehicle treatment but by preventing the mobilization of BMDCs and interaction of CXCR4-SDF-1 using WBIR and ADM3100, respectively, paradoxical growth of tumor was controlled. Pretreatment with WBIR or AMD3100 also decreased tumor cell migration, despite the fact that ADM3100 increased the accumulation of M1 and M2 macrophages in the tumors. Vatalanib also increased Ktrans and ve in control animals but both of the vascular parameters were decreased when the animals were pretreated with WBIR and AMD3100. In conclusion, depleting bone marrow cells or CXCR4 interaction can potentiate the effect of vatalanib

Topic Modeling based text classification regarding Islamophobia using Word Embedding and Transformers Techniques

Islamophobia is a rising area of concern in the current era where Muslims face discrimination and receive negative perspectives towards their religion, Islam. Islamophobia is a type of racism that is being practiced by individuals, groups, and organizations worldwide. Moreover, the ease of access to social media platforms and their augmented usage has also contributed to spreading hate speech, false information, and negative opinions about Islam. In this research study, we focused to detect Islamophobic textual content shared on various social media platforms. We explored the state-of-the-art techniques being followed in text data mining and Natural Language Processing (NLP). Topic modelling algorithm Latent Dirichlet Allocation is used to find top topics. Then, word embedding approaches such as Word2Vec and Global Vectors for word representation (GloVe) are used as feature extraction techniques. For text classification, we utilized modern text analysis techniques of transformers-based Deep Learning algorithms named Bidirectional Encoders Representation from Transformers (BERT) and Generative Pre-Trained Transformer (GPT). For results comparison, we conducted an extensive empirical analysis of Machine Learning algorithms and Deep Learning using conventional textual features such as the Term Frequency-Inverse Document Frequency, N-gram, and Bag of words (BoW). The empirical based results evaluated using standard performance evaluation measures show that the proposed approach effectively detects the textual content related to Islamophobia. In the corpus of the study under Machine Learning models Support Vector Machine (SVM) performed best with an F1 score of 91%. The Transformer based core NLP models and the Deep Learning model Convolutional Neural Network (CNN) when combined with GloVe performed best among all the techniques except SVM with BoW. GPT, SVM when combined with BoW and BERT yielded the best F1 score of 92%, 92% and 91.9% respectively, while CNN performed slightly poor with an F1 score of 91%

Combination of vatalanib and a 20-HETE synthesis inhibitor results in decreased tumor growth in an animal model of human glioma

BACKGROUND: Due to the hypervascular nature of glioblastoma (GBM), antiangiogenic treatments, such as vatalanib, have been added as an adjuvant to control angiogenesis and tumor growth. However, evidence of progressive tumor growth and resistance to antiangiogenic treatment has been observed. To counter the unwanted effect of vatalanib on GBM growth, we have added a new agent known as N-hydroxy-N\u27-(4-butyl-2 methylphenyl)formamidine (HET0016), which is a selective inhibitor of 20-hydroxyeicosatetraenoic acid (20-HETE) synthesis. The aims of the studies were to determine 1) whether the addition of HET0016 can attenuate the unwanted effect of vatalanib on tumor growth and 2) whether the treatment schedule would have a crucial impact on controlling GBM. METHODS: U251 human glioma cells (4×10(5)) were implanted orthotopically. Two different treatment schedules were investigated. Treatment starting on day 8 (8-21 days treatment) of the tumor implantation was to mimic treatment following detection of tumor, where tumor would have hypoxic microenvironment and well-developed neovascularization. Drug treatment starting on the same day of tumor implantation (0-21 days treatment) was to mimic cases following radiation therapy or surgery. There were four different treatment groups: vehicle, vatalanib (oral treatment 50 mg/kg/d), HET0016 (intraperitoneal treatment 10 mg/kg/d), and combined (vatalanib and HET0016). Following scheduled treatments, all animals underwent magnetic resonance imaging on day 22, followed by euthanasia. Brain specimens were equally divided for immunohistochemistry and protein array analysis. RESULTS: Our results demonstrated a trend that HET0016, alone or in combination with vatalanib, is capable of controlling the tumor growth compared with that of vatalanib alone, indicating attenuation of the unwanted effect of vatalanib. When both vatalanib and HET0016 were administered together on the day of the tumor implantation (0-21 days treatment), tumor volume, tumor blood volume, permeability, extravascular and extracellular space volume, tumor cell proliferation, and cell migration were decreased compared with that of the vehicle-treated group. CONCLUSION: HET0016 is capable of controlling tumor growth and migration, but these effects are dependent on the timing of drug administration. The addition of HET0016 to vatalanib may attenuate the unwanted effect of vatalanib

Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system

Intravenous Formulation of HET0016 Decreased Human Glioblastoma Growth and Implicated Survival Benefit in Rat Xenograft Models

Glioblastoma (GBM) is a hypervascular primary brain tumor with poor prognosis. HET0016 is a selective CYP450 inhibitor, which has been shown to inhibit angiogenesis and tumor growth. Therefore, to explore novel treatments, we have generated an improved intravenous (IV) formulation of HET0016 with HPßCD and tested in animal models of human and syngeneic GBM. Administration of a single IV dose resulted in 7-fold higher levels of HET0016 in plasma and 3.6-fold higher levels in tumor at 60 min than that in IP route. IV treatment with HPßCD-HET0016 decreased tumor growth, and altered vascular kinetics in early and late treatment groups (p \u3c 0.05). Similar growth inhibition was observed in syngeneic GL261 GBM (p \u3c 0.05). Survival studies using patient derived xenografts of GBM811, showed prolonged survival to 26 weeks in animals treated with focal radiation, in combination with HET0016 and TMZ (p \u3c 0.05). We observed reduced expression of markers of cell proliferation (Ki-67), decreased neovascularization (laminin and αSMA), in addition to inflammation and angiogenesis markers in the treatment group (p \u3c 0.05). Our results indicate that HPßCD-HET0016 is effective in inhibiting tumor growth through decreasing proliferation, and neovascularization. Furthermore, HPßCD-HET0016 significantly prolonged survival in PDX GBM811 model

Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system

Underwater wireless communications (UWC), based on acoustic waves, radio frequency waves, and optical waves, are currently deployed using underwater communications networks. Wireless sensor communications are among the most common UWC technologies because they offer connectivity over long distances. However, the UWC complex problems include restricted bandwidth, multitrack loss, limited battery power, and latency in propagation. Hence in this paper, Artificial Intelligence based Effective Data Interpretation Approach (AI-EDIA) has been proposed to improve the underwater wireless sensor network communication and less computational Time in IoT platform. The proposed AI-EIDA utilizes the discrete cosine transform (DCT) with frequency modulation multiplexing (FMM) for underwater acoustic communication. Underwater acoustic channels are categorized as double Time and frequency distribution channels. Therefore, the reverse DCT structure provides the orthogonal characteristic of the traditional FMM with the additional advantages of reduced execution and improved speed when the actual calculations are needed. Thus the experimental results show that AI-EDIA decreases energy usage and less delay rate to 0.45 s