35 research outputs found
Optimizing cybersecurity incident response decisions using deep reinforcement learning
The main purpose of this paper is to explore and investigate the role of deep reinforcement learning (DRL) in optimizing the post-alert incident response process in security incident and event management (SIEM) systems. Although machine learning is used at multiple levels of SIEM systems, the last mile decision process is often ignored. Few papers reported efforts regarding the use of DRL to improve the post-alert decision and incident response processes. All the reported efforts applied only shallow (traditional) machine learning approaches to solve the problem. This paper explores the possibility of solving the problem using DRL approaches. The main attraction of DRL models is their ability to make accurate decisions based on live streams of data without the need for prior training, and they proved to be very successful in other fields of applications. Using standard datasets, a number of experiments have been conducted using different DRL configurations The results showed that DRL models can provide highly accurate decisions without the need for prior training
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General queueing network models for computer system performance analysis. A maximum entropy method of analysis and aggregation of general queueing network models with application to computer systems.
In this study the maximum entropy formalism [JAYN 57] is suggested
as an alternative theory for general queueing systems of computer
performance analysis. The motivation is to overcome some of the
problems arising in this field and to extend the scope of the results
derived in the context of Markovian queueing theory.
For the M/G/l model a unique maximum entropy solution., satisfying
locALl balance is derived independent of any assumptions about the service
time distribution. However, it is shown that this solution is identical
to the steady state solution of the underlying Marko-v process when the
service time distribution is of the generalised exponential (CE) type.
(The GE-type distribution is a mixture of an exponential term and a unit
impulse function at the origin). For the G/M/1 the maximum entropy
solution is identical in form to that of the underlying Markov process,
but a GE-type distribution still produces the maximum overall similar
distributions.
For the GIG11 model there are three main achievements:
first, the spectral methods are extended to give exaft formulae for
the average number of customers in the system for any G/G/l with rational
Laplace transform. Previously, these results are obtainable only through
simulation and approximation methods.
(ii) secondly, a maximum entropy model is developed and used to obtain
unique solutions for some types of the G/G/l. It is also discussed how
these solutions can be related to the corresponding stochastic processes.
(iii) the importance of the G/GE/l and the GE/GE/l for the analysis of
general networks is discussed and some flow processes for these systems
are characterised.
For general queueing networks it is shown that the maximum entropy
solution is a product of the maximum entropy solutions of the individual
nodes. Accordingly, existing computational algorithms are extended to
cover general networks with FCFS disciplines. Some implementations are
suggested and a flow algorithm is derived. Finally, these results are
iised to improve existing aggregation methods.
In addition, the study includes a number of examples, comparisons,
surveys, useful comments and conclusions
A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis
Over the past few years, much work has been done to develop machine learning models that perform Arabic sentiment analysis (ASA) tasks at various levels and in different domains. However, most of this work has been based on shallow machine learning, with little attention given to deep learning approaches. Furthermore, the deep learning models used for ASA have been based on noncontextualized embedding schemes that negatively impact model performances. This article proposes a novel deep learning-based multilevel parallel attention neural (MPAN) model that uses a simple positioning binary embedding scheme (PBES) to simultaneously compute contextualized embeddings at the character, word, and sentence levels. The MPAN model then computes multilevel attention vectors and concatenates them at the output level to produce competitive accuracies. Specifically, the MPAN model produces state-of-the-art results that outperform all established ASA baselines using 34 publicly available ASA datasets. The proposed model is further shown to produce new state-of-the-art accuracies for two multidomain collections: 95.61% for a binary classification collection and 94.25% for a tertiary classification collection. Finally, the performance of the MPAN model is further validated using the public IMDB movie review dataset, on which it produces an accuracy of 96.13%, placing it in second position on the global IMDB leaderboard
Topical co-delivery of indomethacin and nigella sativa L. essential oil in poly-cappa-caprolactone nanoparticles: in vitro study of anti-inflammatory activity
Indomethacin is a potent, nonselective Non-steroidal Antiinflammatory
Drug (NSAID) but its low water-solubility precludes its
use as topical dosage form. As with other NSAIDs, the systemic
delivery is associated with high risk of serious gastrointestinal adverse
events including bleeding, ulceration and perforation of stomach and
intestines. Here we demonstrate a safer way of administration i.e via
topical demonstrating synergistic effects when co-delivered with
Nigella sativa L. seeds essential oil (NSSEO) in the form of coencapsulated
particles (~200 nm) of poly--caprolactone. The particles
showed penetrability across stratum corneum to dermis layer in ex-vivo
human skin. Further study in the xyline-induced ear edema in mice was
performed, and co-encapsulated particles demonstrated highest antiinflammatory
effect compared to indomethacin particles and
indomethacin gels. Despite slower onset compared to indomethacin
gels, the inflamed ear continued to show reduction in thickness over 8
hours of observation demonstrating synergistic and pro-longed effect
contributed by NSSEO. In immunohistochemistry study of CD45+, the
mice ears treated with co-encapsulated particles showed considerable
reduction in lesions, epidermal-dermal separation and inflammatory
cells (lymphocytes and neutrophils) infiltration as compared to other
formulation. Based on microscopic evaluation, the anti-inflammatory
inhibition effect of co-encapsulated particles is the highest (90%)
followed by indomethacin particles (79%) and indomethacin gel (49%).
The findings suggest not only skin permeability of indomethacin
significantly improved but also the therapeutic effects, all provided by
the presence of NSSEO in the particles. This study paves the way to more co-encapsulation of any other contemporary medicines in
combination with this wholesome natural oil, NSSEO
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model
Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning
Social media networks have grown exponentially over the last two decades, providing the opportunity for users of the internet to communicate and exchange ideas on a variety of topics. The outcome is that opinion mining plays a crucial role in analyzing user opinions and applying these to guide choices, making it one of the most popular areas of research in the field of natural language processing. Despite the fact that several languages, including English, have been the subjects of several studies, not much has been conducted in the area of the Arabic language. The morphological complexities and various dialects of the language make semantic analysis particularly challenging. Moreover, the lack of accurate pre-processing tools and limited resources are constraining factors. This novel study was motivated by the accomplishments of deep learning algorithms and word embeddings in the field of English sentiment analysis. Extensive experiments were conducted based on supervised machine learning in which word embeddings were exploited to determine the sentiment of Arabic reviews. Three deep learning algorithms, convolutional neural networks (CNNs), long short-term memory (LSTM), and a hybrid CNN-LSTM, were introduced. The models used features learned by word embeddings such as Word2Vec and fastText rather than hand-crafted features. The models were tested using two benchmark Arabic datasets: Hotel Arabic Reviews Dataset (HARD) for hotel reviews and Large-Scale Arabic Book Reviews (LARB) for book reviews, with different setups. Comparative experiments utilized the three models with two-word embeddings and different setups of the datasets. The main novelty of this study is to explore the effectiveness of using various word embeddings and different setups of benchmark datasets relating to balance, imbalance, and binary and multi-classification aspects. Findings showed that the best results were obtained in most cases when applying the fastText word embedding using the HARD 2-imbalance dataset for all three proposed models: CNN, LSTM, and CNN-LSTM. Further, the proposed CNN model outperformed the LSTM and CNN-LSTM models for the benchmark HARD dataset by achieving 94.69%, 94.63%, and 94.54% accuracy with fastText, respectively. Although the worst results were obtained for the LABR 3-imbalance dataset using both Word2Vec and FastText, they still outperformed other researchers’ state-of-the-art outcomes applying the same dataset
Optimization of Multidimensional Energy Security: An Index Based Assessment
This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization. Based on criteria, expert participation, and reliability testing, 27 indicators were identified and weighted based on dimension reduction utilizing the Varimax Rotation technique. As a result of robust evaluation framework, there has been a considerable change in Pakistan’s energy security when compared to other studies such as the energy security indicator of Pakistan (ESIP) and the energy security index of Pakistan (ESIOP). According to the findings, energy security decreased by 25% between 1991 and 2012, followed by a modest increase through 2020. During the study period, the “Affordability” dimension improved; however, the other four dimensions, namely “Availability,” “Technology,” “Governance,” and “Environment,” regressed. Few goals under the petroleum policy (1991), petroleum policy (2012), and power policy (2013) were partially met, while conservation programs, such as the renewable policy (2006) and national climate change policy (2012), fell short. Indicators such as price, reserves, governance, corruption, and consumption contributed to PMESI across five dimensions. Thus, PMESI and indices guiding policymakers to focus on improving governance and exploiting local energy resources in order to provide affordable and sufficient energy in the long run
Optimization of Multidimensional Energy Security: An Index Based Assessment
This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization. Based on criteria, expert participation, and reliability testing, 27 indicators were identified and weighted based on dimension reduction utilizing the Varimax Rotation technique. As a result of robust evaluation framework, there has been a considerable change in Pakistan’s energy security when compared to other studies such as the energy security indicator of Pakistan (ESIP) and the energy security index of Pakistan (ESIOP). According to the findings, energy security decreased by 25% between 1991 and 2012, followed by a modest increase through 2020. During the study period, the “Affordability” dimension improved; however, the other four dimensions, namely “Availability,” “Technology,” “Governance,” and “Environment,” regressed. Few goals under the petroleum policy (1991), petroleum policy (2012), and power policy (2013) were partially met, while conservation programs, such as the renewable policy (2006) and national climate change policy (2012), fell short. Indicators such as price, reserves, governance, corruption, and consumption contributed to PMESI across five dimensions. Thus, PMESI and indices guiding policymakers to focus on improving governance and exploiting local energy resources in order to provide affordable and sufficient energy in the long run
The prevalence of prostate cancer in Pakistan: A systematic review and meta-analysis
Background: Prostate cancer is a significant public health issue, ranking as the second most common cancer and the fifth leading cause of cancer-related deaths in men. In Pakistan, the prevalence of prostate cancer varies significantly across published articles. This study aimed to determine the pooled prevalence of prostate cancer and its associated risk factors in Pakistan. Methods: MEDLINE (via PubMed), Web of Science, Google Scholar, and local databases were searched from inception until March 2023, using key search terms related to the prevalence of prostate cancer. We considered a random-effects meta-analysis to derive the pooled prevalence and relative risks with 95% CIs. Two investigators independently screened articles and performed data extraction and risk of bias analysis. We also conducted meta-regression analysis and stratification to investigate heterogeneity. This study protocol was registered at PROSPERO, number CRD42022376061. Results: Our meta-analysis incorporated 11 articles with a total sample size of 184,384. The overall pooled prevalence of prostate cancer was 5.20% (95% CI: 3.72–6.90%), with substantial heterogeneity among estimates (I2 = 98.5%). The 95% prediction interval of prostate cancer was ranged from 1.74%–10.35%. Subgroup meta-analysis revealed that the highest pooled prevalence of prostate cancer was in Khyber Pakhtunkhwa (8.29%; 95% CI: 6.13–10.74%, n = 1), followed by Punjab (8.09%; 95% CI:7.36–8.86%, n = 3), while the lowest was found in Sindh (3.30%; 95% CI: 2.37–4.38%, n = 5). From 2000 to 2010 to 2011–2023, the prevalence of prostate cancer increased significantly from 3.88% (95% CI: 2.72–5.23%) to 5.80% (95% CI: 3.76–8.24%). Conclusions: Our meta-analysis provides essential insights into the prevalence of prostate cancer in Pakistan, highlighting the need for continued research and interventions to address this pressing health issue