19 hasil untuk "Mohammad Hossein Moattar"
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Artikel Jurnal
Missing value imputation for breast cancer diagnosis data using tensor factorization improved by enhanced reduced adaptive particle swarm optimization
Cancer refers to a disease in which a group of cells show uncontrolled growth, invasion and metastasis. Data mining and machine learning are common approaches for clinical diagnosis. An important issue in this filed is that these data often include missing value which reduces the diagnostic…
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Artikel Jurnal
Deep aspect extraction and classification for opinion mining in e‐commerce applications using convolutional neural network feature extraction followed by long short term memory attention model
Abstract Users of e‐commerce websites review different aspects of a product in the comment section. In this research, an approach is proposed for opinion aspect extraction and recognition in selling systems. We have used the users' opinions from the Digikala website (www.Digikala.com), which is an…
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Artikel Jurnal
A Robust Voice Activity Detection Based on Short Time Features of Audio Frames and Spectral Pattern of Vowel Sounds
This paper presents a set of voice activity detection (V AD) methods, that are easy to implement, robust against noise, and appropriate for real-time applications. The common characteristic is the use of a voting paradigm in all the proposed methods. In these methods, the decision on the voice…
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Artikel Jurnal Teks lengkap
X-Net: Hybrid Transformer–CNN Framework with Multi-Aspect Attention for Precise Colon Polyp Segmentation
Accurate localization of colon polyps plays a crucial role in the early diagnosis of colon cancer. In this paper, we propose a novel network architecture, X-Net, which integrates the Pyramid Vision Transformer (PVT) and EfficientNet-B0. We selected EfficientNet-B0 due to its optimized design and…
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Artikel Jurnal
Lightweight hybrid adaptive weighted ensemble integrating fine-tuned SBERT with lexical and statistical models for robust steam review sentiment classification
Abstract User reviews on gaming platforms contain valuable indicators of satisfaction, but sentiment classification remains challenging due to slangs and domain-specific terminology. In this paper a lightweight hybrid ensemble is proposed for Steam review sentiment analysis that integrates three…
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Artikel Jurnal
Improved cost-sensitive representation of data for solving the imbalanced big data classification problem
Abstract Dimension reduction is a preprocessing step in machine learning for eliminating undesirable features and increasing learning accuracy. In order to reduce the redundant features, there are data representation methods, each of which has its own advantages. On the other hand, big data with…
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Artikel Jurnal
A hybrid Bayesian network and tensor factorization approach for missing value imputation to improve breast cancer recurrence prediction
Data mining and machine learning approaches can be used to predict breast cancer recurrence. However, real datasets often include missing values for various reasons. In this paper, a hybrid imputation method is proposed with respect to the dependency between the attributes and the type of…
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Artikel Jurnal
A multi-manifold learning based instance weighting and under-sampling for imbalanced data classification problems
Abstract Under-sampling is a technique to overcome imbalanced class problem, however, selecting the instances to be dropped and measuring their informativeness is an important concern. This paper tries to bring up a new point of view in this regard and exploit the structure of data to decide on the…
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Artikel Jurnal Teks lengkap
SGRiT: Non-Negative Matrix Factorization via Subspace Graph Regularization and Riemannian-Based Trust Region Algorithm
Non-negative Matrix Factorization (NMF) has gained popularity due to its effectiveness in clustering and feature selection tasks. It is particularly valuable for managing high-dimensional data by reducing dimensionality and providing meaningful semantic representations. However, traditional NMF…
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Artikel Jurnal
Carpet Back Sizing Quality Assessment by Measuring the Amount of Resin Using Image Processing and Machine Learning Approaches
The mechanical properties of the carpet, such as dimensional stability, bending stiffness, handle and creeping on the surface during use, have a direct relationship with the amount of resin applied to the back of the carpet in the sizing process. In today’s factories, the optimal amount of resin…
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Artikel Jurnal
Dissimilarity space reinforced with manifold learning and latent space modeling for improved pattern classification
Abstract Dissimilarity representation plays a very important role in pattern recognition due to its ability to capture structural and relational information between samples. Dissimilarity space embedding is an approach in which each sample is represented as a vector based on its dissimilarity to…
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Artikel Jurnal Teks lengkap
Supervised Density-Based Metric Learning Based on Bhattacharya Distance for Imbalanced Data Classification Problems
Learning distance metrics and distinguishing between samples from different classes are among the most important topics in machine learning. This article proposes a new distance metric learning approach tailored for highly imbalanced datasets. Imbalanced datasets suffer from a lack of data in the…
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Artikel Jurnal
Secure electronic health record access control via blockchain, dual-attribute encryption, and large language model-based attribute extraction
Abstract Access control and data privacy are two of the main necessities in managing electronic health records (EHRs) across distributed domain. There are privacy gaps that expose EHRs to risks like unauthorized access by unaffiliated medical personnel. Traditional attribute-based encryption (ABE)…
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Artikel Jurnal
A Hybrid Framework for Stock Price Forecasting Using Metaheuristic Feature Selection Approaches and Transformer Models Enhanced by Temporal Embedding and Attention Pruning
ABSTRACT Accurately predicting stock prices remains a major challenge in financial analytics due to the complexity and noise inherent in market data. Feature selection plays a critical role in improving both computational efficiency and predictive performance. In this study, we introduce a novel…
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Artikel Jurnal
Accurate and robust ambiguity detection in software requirements documents using a GloVe-BiLSTM deep learning framework with data augmentation
Abstract Requirements engineering is an important part of software production, maintenance, and development. Requirements engineering documents are typically written in natural language, which is prone to uncertainties, and identification of these ambiguities is essential for successful software…
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Artikel Jurnal Teks lengkap
Reducing The Computational Complexity of Fuzzy Identity-Based Encryption from Lattice
—In order to provide access control on encrypted data, Attribute-based encryption (ABE) defines each user using a set of attributes. Fuzzy identity-based encryption (FIBE) is a variant of ABE that allows for a threshold access structure for users. To address the potential threat posed by future…
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Artikel Jurnal
Innovative blockchain and NFT solutions for enhanced security and interoperability in EHR Management: A review
Abstract Electronic Health Record (EHR) management is one of the challenging problems in digital healthcare and is related to several issues such as data security, privacy, scalability, interoperability, and ownership which are very crucial for reliable exchange of information. This review…
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Artikel Jurnal
Blockchain technology and raft consensus for secure physician prescriptions and improved diagnoses in electronic healthcare systems
Abstract With electronic healthcare systems undergoing rapid change, optimizing the crucial process of recording physician prescriptions is a task with major implications for patient care. The power of blockchain technology and the precision of the Raft consensus algorithm are combined in this…
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Artikel Jurnal
Precise staging of diabetic retinopathy through machine learning analysis of leakage source characteristics: A Radiomics-driven approach integrating OCT-A and FFA
Introduction: Evaluating retinal fundus image for diabetic retinopathy (DR) assessment is used to reduce the risk of blindness among diabetic patients. To do this, DR staging is one of the challenging tasks in screening DR that includes the assessment of disease severity or progression. We…