Documentos donde el Autor es "Jamal, Muhammad Hasan"

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis. metadata Raza, Imran; Jamal, Muhammad Hasan; Qureshi, Rizwan; Shahid, Abdul Karim; Rojas Vistorte, Angel Olider; Samad, Md Abdus y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, angel.rojas@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis. Scientific Reports, 14 (1). ISSN 2045-2322

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Named Entity Recognition (NER) is a natural language processing task that has been widely explored for different languages in the recent decade but is still an under-researched area for the Urdu language due to its rich morphology and language complexities. Existing state-of-the-art studies on Urdu NER use various deep-learning approaches through automatic feature selection using word embeddings. This paper presents a deep learning approach for Urdu NER that harnesses FastText and Floret word embeddings to capture the contextual information of words by considering the surrounding context of words for improved feature extraction. The pre-trained FastText and Floret word embeddings are publicly available for Urdu language which are utilized to generate feature vectors of four benchmark Urdu language datasets. These features are then used as input to train various combinations of Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), CRF, and deep learning models. The results show that our proposed approach significantly outperforms existing state-of-the-art studies on Urdu NER, achieving an F-score of up to 0.98 when using BiLSTM+GRU with Floret embeddings. Error analysis shows a low classification error rate ranging from 1.24% to 3.63% across various datasets showing the robustness of the proposed approach. The performance comparison shows that the proposed approach significantly outperforms similar existing studies. metadata Khan, Hikmat Ullah; Anam, Rimsha; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Bajwa, Usama Ijaz; Diez, Isabel de la Torre; Silva Alvarado, Eduardo René; Soriano Flores, Emmanuel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, emmanuel.soriano@uneatlantico.es, SIN ESPECIFICAR (2024) A deep learning approach for Named Entity Recognition in Urdu language. PLOS ONE, 19 (3). e0300725. ISSN 1932-6203

Artículo Materias > Biomedicina
Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Objective This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy. Method The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture. Results The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture. Conclusion Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance. metadata Alvi, Sohaib Bin Khalid; Nayyer, Muhammad Ziad; Jamal, Muhammad Hasan; Raza, Imran; de la Torre Diez, Isabel; Rodríguez Velasco, Carmen Lilí; Breñosa, Jose y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carmen.rodriguez@uneatlantico.es, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR (2023) A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation. DIGITAL HEALTH, 9. ISSN 2055-2076

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés Non-word and real-word errors are generally two types of spelling errors. Non-word errors are misspelled words that are nonexistent in the lexicon while real-word errors are misspelled words that exist in the lexicon but are used out of context in a sentence. Lexicon-based lookup approach is widely used for non-word errors but it is incapable of handling real-word errors as they require contextual information. Contrary to the English language, real-word error detection and correction for low-resourced languages like Urdu is an unexplored area. This paper presents a real-word spelling error detection and correction approach for the Urdu language. We develop an extensive lexicon of 593,738 words and use this lexicon to develop a dataset for real-word errors comprising 125562 sentences and 2,552,735 words. Based on the developed lexicon and dataset, we then develop a contextual spell checker that detects and corrects real-word errors. For the real-word error detection phase, word-gram features are used along with five machine learning classifiers, achieving a precision, recall, and F1-score of 0.84,0.79, and 0.81 respectively. We also test the proposed approach with a 40% error density. For real-word error correction, the Damerau-Levenshtein distance is used along with the n-gram model for further ranking of the suggested candidate words, achieving an accuracy of up to 83.67%. metadata Aziz, Romila; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Bajwa, Usama Ijaz; Kuc Castilla, Ángel Gabriel; Uc-Rios, Carlos; Bautista Thompson, Ernesto y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, carlos.uc@unini.edu.mx, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR (2023) Real Word Spelling Error Detection and Correction for Urdu Language. IEEE Access. p. 1. ISSN 2169-3536

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés In the Internet of things (IoT), data packets are accumulated and disseminated across IoT devices without human intervention, therefore the privacy and security of sensitive data during transmission are crucial. For this purpose, multiple routing techniques exist to ensure security and privacy in IoT Systems. One such technique is the routing protocol for low power and lossy networks (RPL) which is an IPv6 protocol commonly used for routing in IoT systems. Formal modeling of an IoT system can validate the reliability, accuracy, and consistency of the system. This paper presents the formal modeling of RPL protocol and the analysis of its security schemes using colored Petri nets that applies formal validation and verification for both the secure and non-secure modes of RPL protocol. The proposed approach can also be useful for formal modeling-based verification of the security of the other communication protocols. metadata Balfaqih, Mohammed; Ahmad, Farooq; Chaudhry, Muhammad Tayyab; Jamal, Muhammad Hasan; Sohail, Muhammad Amar; Gavilanes Aray, Daniel; Masías Vergara, Manuel y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, daniel.gavilanes@uneatlantico.es, manuel.masias@uneatlantico.es, SIN ESPECIFICAR (2023) Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets. PLOS ONE, 18 (8). e0285700. ISSN 1932-6203

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés In the field of natural language processing, machine translation is a colossally developing research area that helps humans communicate more effectively by bridging the linguistic gap. In machine translation, normalization and morphological analyses are the first and perhaps the most important modules for information retrieval (IR). To build a morphological analyzer, or to complete the normalization process, it is important to extract the correct root out of different words. Stemming and lemmatization are techniques commonly used to find the correct root words in a language. However, a few studies on IR systems for the Urdu language have shown that lemmatization is more effective than stemming due to infixes found in Urdu words. This paper presents a lemmatization algorithm based on recurrent neural network models for the Urdu language. However, lemmatization techniques for resource-scarce languages such as Urdu are not very common. The proposed model is trained and tested on two datasets, namely, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU). The datasets are lemmatized with the help of recurrent neural network models. The Word2Vec model and edit trees are used to generate semantic and syntactic embedding. Bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), bidirectional gated recurrent neural network (BiGRNN), and attention-free encoder–decoder (AFED) models are trained under defined hyperparameters. Experimental results show that the attention-free encoder-decoder model achieves an accuracy, precision, recall, and F-score of 0.96, 0.95, 0.95, and 0.95, respectively, and outperforms existing models metadata Hafeez, Rabab; Anwar, Muhammad Waqas; Jamal, Muhammad Hasan; Fatima, Tayyaba; Martínez Espinosa, Julio César; Dzul López, Luis Alonso; Bautista Thompson, Ernesto y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, ulio.martinez@unini.edu.mx, luis.dzul@uneatlantico.es, ernesto.bautista@unini.edu.mx, SIN ESPECIFICAR (2023) Contextual Urdu Lemmatization Using Recurrent Neural Network Models. Mathematics, 11 (2). p. 435. ISSN 2227-7390