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  1. Article ; Online: On solving textual ambiguities and semantic vagueness in MRC based question answering using generative pre-trained transformers.

    Ahmed, Muzamil / Khan, Hikmat / Iqbal, Tassawar / Khaled Alarfaj, Fawaz / Alomair, Abdullah / Almusallam, Naif

    PeerJ. Computer science

    2023  Volume 9, Page(s) e1422

    Abstract: Machine reading comprehension (MRC) is one of the most challenging tasks and active fields in natural language processing (NLP). MRC systems aim to enable a machine to understand a given context in natural language and to answer a series of questions ... ...

    Abstract Machine reading comprehension (MRC) is one of the most challenging tasks and active fields in natural language processing (NLP). MRC systems aim to enable a machine to understand a given context in natural language and to answer a series of questions about it. With the advent of bi-directional deep learning algorithms and large-scale datasets, MRC achieved improved results. However, these models are still suffering from two research issues: textual ambiguities and semantic vagueness to comprehend the long passages and generate answers for abstractive MRC systems. To address these issues, this paper proposes a novel Extended Generative Pretrained Transformers-based Question Answering (ExtGPT-QA) model to generate precise and relevant answers to questions about a given context. The proposed architecture comprises two modified forms of encoder and decoder as compared to GPT. The encoder uses a positional encoder to assign a unique representation with each word in the sentence for reference to address the textual ambiguities. Subsequently, the decoder module involves a multi-head attention mechanism along with affine and aggregation layers to mitigate semantic vagueness with MRC systems. Additionally, we applied syntax and semantic feature engineering techniques to enhance the effectiveness of the proposed model. To validate the proposed model's effectiveness, a comprehensive empirical analysis is carried out using three benchmark datasets including SQuAD, Wiki-QA, and News-QA. The results of the proposed ExtGPT-QA outperformed state of art MRC techniques and obtained 93.25% and 90.52% F1-score and exact match, respectively. The results confirm the effectiveness of the ExtGPT-QA model to address textual ambiguities and semantic vagueness issues in MRC systems.
    Language English
    Publishing date 2023-07-24
    Publishing country United States
    Document type Journal Article
    ISSN 2376-5992
    ISSN (online) 2376-5992
    DOI 10.7717/peerj-cs.1422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A hybrid security system for drones based on ICMetric technology.

    Alheeti, Khattab M Ali / Khaled Alarfaj, Fawaz / Alreshoodi, Mohammed / Almusallam, Naif / Al Dosary, Duaa

    PloS one

    2023  Volume 18, Issue 3, Page(s) e0282567

    Abstract: Recently, the number of drones has increased, and drones' illegal and malicious use has become prevalent. The dangerous and wasteful effects are substantial, and the probability of attacks is very high. Therefore, an anomaly detection and protection ... ...

    Abstract Recently, the number of drones has increased, and drones' illegal and malicious use has become prevalent. The dangerous and wasteful effects are substantial, and the probability of attacks is very high. Therefore, an anomaly detection and protection system are needed. This paper aims to design and implement an intelligent anomaly detection system for the security of unmanned aerial vehicles (UAVs)/drones. The proposed system is heavily based on utilizing ICMetric technology to exploit low-level device features for detection. This technology extracts the accelerometer and gyroscope sensors' bias to create a unique number known as the ICMetric number. Hence, ICMetric numbers represent additional features integrated into the dataset used to detect drones. This study performs the classification using a deep neural network (DNN). The experimental results prove that the proposed system achieves high levels of detection and performance metrics.
    MeSH term(s) Unmanned Aerial Devices ; Probability ; Technology
    Language English
    Publishing date 2023-03-21
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0282567
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Analysis of Privacy-Preserving Edge Computing and Internet of Things Models in Healthcare Domain.

    Almusallam, Naif / Alabdulatif, Abdulatif / Alarfaj, Fawaz

    publication RETRACTED

    Computational and mathematical methods in medicine

    2021  Volume 2021, Page(s) 6834800

    Abstract: The healthcare sector is rapidly being transformed to one that operates in new computing environments. With researchers increasingly committed to finding and expanding healthcare solutions to include the Internet of Things (IoT) and edge computing, there ...

    Abstract The healthcare sector is rapidly being transformed to one that operates in new computing environments. With researchers increasingly committed to finding and expanding healthcare solutions to include the Internet of Things (IoT) and edge computing, there is a need to monitor more closely than ever the data being collected, shared, processed, and stored. The advent of cloud, IoT, and edge computing paradigms poses huge risks towards the privacy of data, especially, in the healthcare environment. However, there is a lack of comprehensive research focused on seeking efficient and effective solutions that ensure data privacy in the healthcare domain. The data being collected and processed by healthcare applications is sensitive, and its manipulation by malicious actors can have catastrophic repercussions. This paper discusses the current landscape of privacy-preservation solutions in IoT and edge healthcare applications. It describes the common techniques adopted by researchers to integrate privacy in their healthcare solutions. Furthermore, the paper discusses the limitations of these solutions in terms of their technical complexity, effectiveness, and sustainability. The paper closes with a summary and discussion of the challenges of safeguarding privacy in IoT and edge healthcare solutions which need to be resolved for future applications.
    MeSH term(s) Cloud Computing ; Computational Biology ; Computer Security ; Delivery of Health Care ; Electronic Health Records ; Humans ; Internet of Things ; Privacy
    Language English
    Publishing date 2021-12-30
    Publishing country United States
    Document type Journal Article ; Retracted Publication
    ZDB-ID 2252430-7
    ISSN 1748-6718 ; 1748-670X ; 1027-3662
    ISSN (online) 1748-6718
    ISSN 1748-670X ; 1027-3662
    DOI 10.1155/2021/6834800
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Users' Rating Predictions Using Collaborating Filtering Based on Users and Items Similarity Measures.

    Nudrat, Sofia / Khan, Hikmat Ullah / Iqbal, Saqib / Talha, Mian Muhammad / Alarfaj, Fawaz Khaled / Almusallam, Naif

    Computational intelligence and neuroscience

    2022  Volume 2022, Page(s) 2347641

    Abstract: The social media has made the world a global world and we, in addition to, as part of physical society, are now part of the virtual society as well. There has been the generation of a large amount of information over the social web. By way of increasing ... ...

    Abstract The social media has made the world a global world and we, in addition to, as part of physical society, are now part of the virtual society as well. There has been the generation of a large amount of information over the social web. By way of increasing online information, new opportunities emerged, and diverse issues have been raised, which have attracted researchers to address these research problems. In this current age, where online business and e-commerce are part of our daily lives, recommender systems (RSs) are very effective for information filtering. RSs play a significant role in our lives by assisting users in recommending items and services what they may be interesting in to purchase or avail. In this research work, our goal is to predict the users' ratings for various items, which are an active research area in collaborative filtering (CF). In this work, we have explored various similarity measures based on user-user and item-item rating predictions on different datasets by applying collaborative filtering approaches. The comparison of item-item and user-user CF algorithms such as user K-Nearest Neighbour using cosine; similarity, Pearson correlation as well as item-based K-NN using these measures with baseline approaches and matrix-based methods such as Matrix factorization (MF), biased MF, and factor wise MF has been carried out. For empirical-based comparison analysis, diverse approaches have been selected such as slope one, random, and global average, and it revealed that item-item K-NN using Pearson correlation has outperformed all other applied approaches. For the experiments, three real world and widely used datasets of MovieLens 1M, CiaoDVD, and MovieLens 100k have been used. The empirical-based results have been evaluated by using standard performance evaluation measures of RMSE and MAE.
    MeSH term(s) Algorithms ; Commerce ; Consumer Behavior ; Humans
    Language English
    Publishing date 2022-07-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2022/2347641
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images.

    Naga Srinivasu, Parvathaneni / Krishna, T Balamurali / Ahmed, Shakeel / Almusallam, Naif / Khaled Alarfaj, Fawaz / Allheeib, Nasser

    Journal of healthcare engineering

    2023  Volume 2023, Page(s) 1566123

    Abstract: Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance ... ...

    Abstract Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.
    MeSH term(s) Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging ; Algorithms ; Diagnosis, Computer-Assisted
    Language English
    Publishing date 2023-01-17
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2545054-2
    ISSN 2040-2309 ; 2040-2295
    ISSN (online) 2040-2309
    ISSN 2040-2295
    DOI 10.1155/2023/1566123
    Database MEDical Literature Analysis and Retrieval System OnLINE

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