Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the pass...
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Detecting plagiarism in documents is a well-established task in natural language processing (NLP). Broadly, plagiarism detection is categorized into two types (1) intrinsic: to check the whole document or all the passages have been written by a single author;(2) extrinsic: where a suspicious document is compared with a given set of source documents to figure out sentences or phrases which appear in both documents. In the pursuit of advancing intrinsic plagiarism detection, this study addresses the critical challenge of intrinsic plagiarism detection in Urdu texts, a language with limited resources for comprehensive language models. Acknowledging the absence of sophisticated large language models (LLMs) tailored for Urdu language, this study explores the application of various machine learning, deep learning, and language models in a novel framework. A set of 43 stylometry features at six granularity levels was meticulously curated, capturing linguistic patterns indicative of plagiarism. The selected models include traditional machine learning approaches such as logistic regression, decision trees, SVM, KNN, Naive Bayes, gradient boosting and voting classifier, deep learning approaches: GRU, BiLSTM, CNN, LSTM, MLP, and large language models: BERT and GPT-2. This research systematically categorizes these features and evaluates their effectiveness, addressing the inherent challenges posed by the limited availability of Urdu-specific language models. Two distinct experiments were conducted to evaluate the impact of the proposed features on classification accuracy. In experiment one, the entire dataset was utilized for classification into intrinsic plagiarized and non-plagiarized documents. Experiment two categorized the dataset into three types based on topics: moral lessons, national celebrities, and national events. Both experiments are thoroughly evaluated through, a fivefold cross-validation analysis. The results show that the random forest classifier achieved an ex
In applications involving,e.g.,panel data,images,genomics microarrays,etc.,trace regression models are useful *** address the high-dimensional issue of these applications,it is common to assume some sparsity *** the c...
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In applications involving,e.g.,panel data,images,genomics microarrays,etc.,trace regression models are useful *** address the high-dimensional issue of these applications,it is common to assume some sparsity *** the case of the parameter matrix being simultaneously low rank and elements-wise sparse,we estimate the parameter matrix through the least-squares approach with the composite penalty combining the nuclear norm and the *** extend the existing analysis of the low-rank trace regression with *** to exponentialβ-mixing *** explicit convergence rate and the asymptotic properties of the proposed estimator are ***,as well as a real data application,are also carried out for illustration.
In this paper,we study the inverse local times at 0 of one-dimensional reflected diffusions on[0,∞)and establish a comparison principle for these inverse local *** also provide applications to Green function estimate...
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In this paper,we study the inverse local times at 0 of one-dimensional reflected diffusions on[0,∞)and establish a comparison principle for these inverse local *** also provide applications to Green function estimates for non-local operators.
The medical industry generates vast amounts of data suitable for machine learning during patient-clinician interaction in ***,as a result of data protection regulations like the general data protection regulation(GDPR...
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The medical industry generates vast amounts of data suitable for machine learning during patient-clinician interaction in ***,as a result of data protection regulations like the general data protection regulation(GDPR),patient data cannot be shared freely across *** these cases,federated learning(FL)is a viable option where a global model learns from multiple data sites without moving the *** this paper,we focused on random forests(RFs)for its effectiveness in classification tasks and widespread use throughout the medical industry and compared two popular federated random forest aggregation algorithms on horizontally partitioned *** first provided necessary background information on federated learning,the advantages of random forests in a medical context,and the two aggregation algorithms.A series of extensive experiments using four public binary medical datasets(an excerpt of MIMIC III,Pima Indian diabetes dataset from Kaggle,and diabetic retinopathy and heart failure dataset from UCI machine learning repository)were then performed to systematically compare the two on equal-sized,unequal-sized,and class-imbalanced clients.A follow-up investigation on the effects of more clients was also *** finally empirically analyzed the advantages of federated learning and concluded that the weighted merge algorithm produces models with,on average,1.903%higher F1 score and 1.406%higher AUCROC value.
Previous works of negation understanding mainly focus on negation cue detection and scope resolution, without identifying negation subject which is also significant to the downstream tasks. In this paper, we propose a...
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Among various quality assurance activities, process capability indices (PCIs) are recognized as the most effective tools to quantify and evaluate process performance. The one-sided capability index (Formula presented....
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New energy automobile industry plays an important role in building a green, low-carbon and recycling industrial system. In this paper, the prediction simulation training and prediction accuracy comparison study are ca...
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Dengue hemorrhagic fever (DHF) is a serious public health issue worldwide, including Central Java, Indonesia. Several analyses need to be conducted to serve as a reference for the government to take action to reduce t...
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Breast cancer diagnosis via histopathology is clinically important but challenges remain. We develop a Forward Attention-based deep network (FA-VGG16) for classifying breast histopathology images. For binary classific...
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First of all, on the basis of complete lattice, the concept of neutrosophic pseudo-t-norm (NPT) is given. Definitions and examples of representable neutrosophic pseudo-t-norms (RNPTs) are given, while unrepresentable ...
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