با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
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Edge Concierge: Democratizing Cost-Effective and Flexible Network Operations using Network Layer AI at Private Network Edges
Edge Concierge: دموکراتیک کردن عملیات شبکه با هزینه و مقرون به صرفه و انعطاف پذیر با استفاده ازهوش مصنوعی لایه لایه در لبه های شبکه خصوصی-2020 We observe two major revolutionary trends in network
operations: democratization of cost-effective and flexible
communication means for vertical players, such as public safety,
by private mobile networking combined with edge computing,
and automatic and autonomic network operations empowered
by Artificial Intelligence (AI). Further innovations are required
for making private networking readily available for vertical
players that are reluctant to acquire expertise in complex network
operations. We propose Edge Concierge, of which concept is to
democratize cost-effective and flexible network operations using
network layer AI at private network edges. Edge Concierge assists
smart network operations for private mobile network operators
and energy saving by changing working state of AI-empowered
anomaly detection applications by network layer AI. We also
employ unsupervised machine learning using Hidden Markov
Model (HMM) for estimating contexts by solely observing network
traffic at mobile edge computing (MEC) middle boxes. In
detail, we design a system of real-time and self-learning context
estimation by a multi-level probabilistic state transition model
trained by unsupervised learning, which is implemented in a
commodity PC. In order to evaluate our proposed system, we
take public safety context of smart cities as an example use case
and show the benefits. |
مقاله انگلیسی |
2 |
Application of deep reinforcement learning to intrusion detection for supervised problems
کاربرد یادگیری تقویتی عمیق برای تشخیص نفوذ برای مسائل تحت نظارت-2020 The application of new techniques to increase the performance of intrusion detection systems is crucial in modern data networks with a growing threat of cyber-attacks. These attacks impose a greater risk on network services that are increasingly important from a social end economical point of view. In this work we present a novel application of several deep reinforcement learning (DRL) algorithms to intru- sion detection using a labeled dataset. We present how to perform supervised learning based on a DRL framework. The implementation of a reward function aligned with the detection of intrusions is extremely diffi- cult for Intrusion Detection Systems (IDS) since there is no automatic way to identify intrusions. Usually the identification is performed manually and stored in datasets of network features associated with in- trusion events. These datasets are used to train supervised machine learning algorithms for classifying intrusion events. In this paper we apply DRL using two of these datasets: NSL-KDD and AWID datasets. As a novel approach, we have made a conceptual modification of the classic DRL paradigm (based on interaction with a live environment), replacing the environment with a sampling function of recorded training intrusions. This new pseudo-environment, in addition to sampling the training dataset, generates rewards based on detection errors found during training. We present the results of applying our technique to four of the most relevant DRL models: Deep Q- Network (DQN), Double Deep Q-Network (DDQN), Policy Gradient (PG) and Actor-Critic (AC). The best results are obtained for the DDQN algorithm. We show that DRL, with our model and some parameter adjustments, can improve the results of intrusion detection in comparison with current machine learning techniques. Besides, the classifier ob- tained with DRL is faster than alternative models. A comprehensive comparison of the results obtained with other machine learning models is provided for the AWID and NSL-KDD datasets, together with the lessons learned from the application of several design alternatives to the four DRL models. Keywords: Intrusion detection | Data networks | Deep reinforcement learning |
مقاله انگلیسی |
3 |
Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example
کاوش مکانیسم طب سنتی چینی با رویکرد هوش مصنوعی با استفاده از یادگیری ماشین بدون نظارت برای شباهت عملکردی سلولی ترکیبات در شبکه های ناهمگن ، گرانول های XiaoErFuPi به عنوان مثال-2020 ‘Polypharmacology’ is usually used to describe the network-wide effect of a single compound, but traditional
Chinese medicine (TCM) has a polypharmacological effect naturally based on the ‘multi-components, multitargets
and multi-pathways’ principle. It is a challenge to investigate the polypharmacology mechanism of TCM
with multiple components. In this study, we used XiaoErFuPi (XEFP) granules as an example to describe an
unsupervised learning strategy for polypharmacology research of TCM and to explore the mechanism of XEFP
polypharmacology against multifactorial disease function dyspepsia (FD). Unsupervised clustering of compounds
based on similarity evaluation of cellular function fingerprints showed that compounds of TCM without similar
targets and chemical structure could also exert similar therapeutic effects on the same disease, as different
targets participate in the same pathway closely associated with the pathological process. In this study, we
proposed an unsupervised machine learning strategy for exploring the polypharmacology-based mechanism of
TCM, utilizing hierarchical clustering based on cellular functional similarity, to establish a connection from the
chemical clustering module to cellular function. Meanwhile, FDA-approved drugs against FD were used as references
for the mechanism of action (MoA) of FD. First, according to the compound-compound network built by
the similarity of cellular function of XEFP compounds and FDA-approved FD drugs, the possible therapeutic
function of TCM may represent a known mechanism of FDA-approved drugs. Then, as unsupervised learning,
hierarchical clustering of TCM compounds based on cellular function fingerprint similarity could help to classify
the compounds into several modules with similar therapeutic functions to investigate the polypharmacology
effect of TCM. Furthermore, the integration of quantitative omics data of TCM and approved drugs (from LINCS
datasets) provides more quantitative evidence for TCM therapeutic function consistency with approved drugs. A
spasmolytic activity experiment was launched to confirm vanillic acid activity to repress smooth muscle contraction;
vanillic acid was also predicted to be active compound of XEFP, supporting the accuracy of our strategy.
In summary, the approach proposed in this study provides a new unsupervised learning strategy for polypharmacological
research investigating TCM by establishing a connection between the compound functional
module and drug-activated cellular processes shared with FDA-approved drugs, which may elucidate the unique
mechanism of traditional medicine using FDA-approved drugs as references, facilitate the discovery of potential
active compounds of TCM and provide new insights into complex diseases. Keywords: Polypharmacology | Traditional Chinese medicine | Unsupervised clustering | Cellular function fingerprints | FDA-approved drugs | Functional dyspepsia |
مقاله انگلیسی |
4 |
Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data
شبکه های هیدروکربنی مصنوعی موازی تصادفی تصادفی: پیاده سازی برای یادگیری ماشین تحت نظارت سریع و قوی در داده های با ابعاد بالا-2020 Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures
and mechanisms – have shown improvements in predictive power and interpretability in comparison with
other well-known machine learning models. However, AHN are very time-consuming that are not able to deal
with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon
networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional
data. This training method comprises a population-based meta-heuristic optimization with defined individual
encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a
stochastic learning approach for consuming large data. We conducted three experiments with synthetic and
real data sets to validate the training execution time and performance of the proposed algorithm. Experimental
results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed
of training more than 10, 000???? times in the worst case scenario. Additionally, we present two case studies in
real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities
classification in healthcare monitoring systems (classification problem). These case studies showed that SPEAHN
improves the state-of-the-art machine learning models in both engineering problems. We anticipate our
new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering,
aerospace, and others, in which large amounts of data (e.g. big data) is essential. Keywords: Machine learning | Parallel computing | Extreme learning machines | Stochastic learning | Regression | Classification | Big data |
مقاله انگلیسی |
5 |
A machine learning forensics technique to detect post-processing in digital videos
یک روش پزشکی قانونی برای یادگیری ماشین برای تشخیص پس از پردازش در فیلم های دیجیتال-2020 Technology has brought great benefits to human beings and has served to improve the quality of
life and carry out great discoveries. However, its use can also involve many risks. Examples include
mobile devices, digital cameras and video surveillance cameras, which offer excellent performance and
generate a large number of images and video. These files are generally shared on social platforms and
are exposed to any manipulation, compromising their authenticity and integrity. In a legal process, a
manipulated video can provide the necessary elements to accuse an innocent person of a crime or to
exempt a guilty person from criminal acts. Therefore, it is essential to create robust forensic methods,
which will strengthen the justice administration systems and thus make fair decisions. This paper
presents a novel forensic technique to detect the post-processing of digital videos with MP4, MOV
and 3GP formats. Concretely, detect the social platform and editing program used to execute possible
manipulation attacks. The proposed method is focused on supervised machine learning techniques. To
achieve our goal, we take advantage that the social platforms and editing programs, execute filtering
and compression processes on the videos when they are shared or manipulated. The result of these
transformations leaves a characteristic pattern in the videos that allow us to detect the social platform
or editing program efficiently. Three phases are involved in the method: 1) Dataset preparation; 2) data
features extraction; 3) Supervised model creation. To evaluate the scalability of the technique in real
scenarios, we used a robust, heterogeneous and far superior dataset than that used in the literature. Keywords: Editing programs detection | Machine learning processing | Multimedia container structure | Social networks detection | Video forensics | Video post-processing detection |
مقاله انگلیسی |
6 |
Capacitance Extraction and Power Grid Analysis Using Statistical and AI Methods
استخراج ظرفیت خازنی و تحلیل شبکه برق با استفاده از روشهای آماری و هوش مصنوعی-2020 Capacitance extraction and power grid (PG) analysis
for IC design involve large-scale numerical simulation
problems. As the process technology becomes more complicated
and design margin is shrinking, the capacitance field solver and
power-grid matrix solver with high accuracy and capability for
handing large and complex structure are highly demanded. In
this invited paper, we present recent application of statistical and
AI methods in these two fields. The Markov-chain model and relevant
analysis are presented for developing an efficient technique
for handling conformal dielectrics in the floating random walk
based capacitance extraction. Then, two approaches reducing the
computational cost of a domain decomposition based power-grid
solver are presented. One employs supervised machine learning
while the other is inspired by the A∗-search algorithm. |
مقاله انگلیسی |
7 |
Combining hierarchical clustering approaches using the PCA method
ترکیب روشهای خوشه بندی سلسله مراتبی با استفاده از روش PCA-2019 In expert systems, data mining methods are algorithms that simulate humans’ problem-solving capabil- ities. Clustering methods as unsupervised machine learning methods are crucial approaches to catego- rize similar samples in the same categories. The use of different clustering algorithms to a given dataset produces clusters with different qualities. Hence, many researchers have applied clustering combination methods to reduce the risk of choosing an inappropriate clustering algorithm. In these methods, the out- puts of several clustering algorithms are combined. In these research works, the input hierarchical clus- terings are transformed to descriptor matrices and their combination is achieved by aggregating their descriptor matrices. In previous works, only element-wise aggregation operators have been used and the relation between the elements of each descriptor matrix has been ignored. However, the value of each element of the descriptor matrix is meaningful in comparison with its other elements. The current study proposes a novel method of combining hierarchical clustering approaches based on principle component analysis (PCA). PCA as an aggregator allows considering all elements of the descriptor matrices. In the proposed approach, basic clusters are made and transformed to descriptor matrices. Then, a final ma- trix is extracted from the descriptor matrices using PCA. Next, a final dendrogram is constructed from the matrix that is used to summarize the results of the diverse clustering. The experimental results on popular available datasets show the superiority of the clustering accuracy of the proposed method over basic clustering methods such as single, average and centroid linkage and previously combined hierar- chical clustering methods. In addition, statistical tests show that the proposed method significantly out- performed hierarchical clustering combination methods with element-wise averaging operators in almost all tested datasets. Several experiments have also been conducted which confirm the robustness of the proposed method for its parameter setting. Keywords: Clustering | Hierarchical clustering | Principle component analysis | PCA |
مقاله انگلیسی |
8 |
Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning
شناسایی و تجزیه و تحلیل فنوتیپ های رفتاری در اختلال طیف اوتیسم از طریق یادگیری ماشین بدون نظارت-2019 Background and objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored
potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically
distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale
large enough to robustly examine treatment response across such subgroups. The purpose of the present study
was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and
examine treatment response across the learned phenotypes.
Materials and methods: The present study included a sample of children with ASD (N = 2400), the largest of its
kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic
relationships. Retrospective treatment data were available for a portion of the sample (n =1034). Treatment
response was examined within each subgroup via regression.
Results: The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups
through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique
deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated
response to treatment was found across subtypes, with a substantially higher amount of variance accounted for
due to the homogenization effect of the clustering.
Discussion: The high amount of variance explained by the regression models indicates that clustering provides a
basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These
findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized
intervention based on unsupervised machine learning. Keywords: Machine learning | Autism spectrum disorder | Behavioral phenotypes | Cluster analysis | Treatment response |
مقاله انگلیسی |
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Data mining algorithm for pre-processing biopharmaceutical drug product manufacturing records
الگوریتم داده کاوی برای سوابق تولید دارویی بیوشیمیایی قبل از پردازش-2019 The quality of data plays a crucial role in providing a reliable decision-making process when improving processes and operations under uncertainty. We present a data mining-based algorithm for robustly pre- processing the manufacturing records of biopharmaceutical batch processes. The algorithm can identify the time intervals in which the process is in commercial operation, and can characterize process fail- ures automatically. An approximate string-matching algorithm, a decision tree classifier and a constrained clustering is applied to sequence the raw data, to classify the noise and identify each single batches; fi- nally process failure are characterized. The algorithm was applied to the records of the process named as “cleaning- and sterilizing-in-place”, which is an essential process in manufacturing environment, in a case study. The algorithm was training on state of the art manual pre-processing outcome and was ap- plied reducing the execution time of the activity down to 11.7% while maintaining high data quality and integrity. Keywords: GMP | Noise Filtering | Language recognition | Supervised machine learning | Semi-supervised machine learning | Ishikawa fishbone diagram |
مقاله انگلیسی |
10 |
Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning
پیش بینی نتایج درمان دارویی ضد صرع بیماران مبتلا به صرع تازه کشف شده با یادگیری ماشین-2019 Objective: The objective of this study was to build a supervised machine learning-based classifier, which can accurately
predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.
Methods: We collected information from 287 patients with newly diagnosed epilepsy between 2009 and
2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at
least 3 years. A number of features, including demographic features,medical history, and auxiliary examinations
(electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with
different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission
is further divided into early remission and late remission. Five classical machine learning algorithms,
i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and
trained by our dataset to get classification models.
Results: Our study shows that 1) comparedwith the other four algorithms, the XGBoost algorithmbased machine
learning model achieves the best prediction performance of the AEDtreatment outcomes between remission and
never remission patientswith an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best
discriminative factor for remission and never remission patients is higher number of seizures before treatment
(N3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and
later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the
highest dependence to the categories of early and late remission patients.
Significances: Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment
outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The
classifiers prediction result could help disease guide counseling and eventually improve treatment strategies Keywords: Machine learning | Epilepsy | Epilepsy remission | Antiepileptic drug | Outcome prediction |
مقاله انگلیسی |