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Named Entity Recognition (NER) in social media has received much research attention in the field of natural language processing (NLP) and information extraction. Research on this topic has grown dramatically in recent years. Hence, one of the objectives of this systematic literature review (SLR) is to present the outline techniques, approaches, and methods used to handle NER on X based on English datasets prepared for WNUT (Workshop on User-generated Text). This study could be used to develop more accurate models in the future. This SLR focuses on articles that had been published over the course of eight years, i.e., from July 2015 to the end of 2023. A total of 67 out of 316 articles published during the period were selected having met the set chosen criteria. Based on the analysis of the selected articles, challenges were identified and discussed. In this SLR, we aim to provide a better understanding of current viewpoints and highlight opportunities for research in NER in User-generated Text specifically for English usage on X. It can aid in identifying named entities, such as names, locations, companies, and groups, within a specific informal social media context like X. This research is notable for being the first systematic review that emphasizes the dearth of NER on X based on English datasets prepared for WNUT.The main contribution of this systematic review is a comprehensive study on NER in X messages for social media, entailing its challenges and opportunities. Moreover, new possible research directions are suggested for the researchers.

The proliferation of Internet of Things (IoT) devices and other IT forms in almost every area of human existence has resulted in an enormous influx of data that must be managed and stored. One viable solution to this issue is to store and handle massive amounts of data in cloud environments. Real-time data analysis has always been critical. However, it becomes even more crucial as technology and the IoT develop, and new applications emerge, such as autonomous cars, smart cities, and IoT devices for healthcare, agriculture, and other industries. Given the massive volume of data, moving to a remote cloud is time-consuming and produces severe network congestion, rendering cloud administration and rapid data processing difficult. Fog computing provides close-to-device processing at the network’s periphery, and fog computing can analyze data in near real-time. However, the increased amount of IoT gadgets and data they produce is a formidable challenge for fog nodes. Task offloading may enhance fog computing by offloading the excess data to other nodes for processing due to the restricted resources in the fog. Management of tasks and resources must be optimized in fog devices. This review article overviews related works on task offloading in IoT-Fog-Cloud Environment. In addition, we discuss about fog networks and Software-defined network (SDN) applications and challenges in fog offloading.

Phishing attacks have become a perilous threat in recent years, which has led to numerous studies to determine the classification technique that best detects these attacks. Several studies have made comparisons using only specific datasets and techniques without including the most crucial aspect, which is the performance evaluation of data changes. Hence, classification techniques cannot be generalized if they only use specific datasets and techniques. Therefore, this research determined the performance of classification techniques on changing data through a subset of schemes in a dataset. It was conducted using unbalanced and balanced phishing datasets, as well as subset schemes in ratios of 90:10, 80:20, 70:30, and 60:40. The thirteen most recent classification techniques used in preliminary phishing studies were compared and evaluated against ten performance measures. The results showed that the proposed schemes successfully uncover the maximum and minimum performance obtained by a classification technique. These comparisons can provide deeper insights into phishing classification techniques than related research.

Phishing has become a serious and concerning problem within the past 10 years, with many reviews describing attack patterns and anticipating different method utilizations. This indicates that the results are still not comprehensive, subsequently leaving a critical gap in phishing reports. Therefore, this study aims to conduct a systematic review, to show a more crucial issue in phishing attacks, namely classification techniques. These issues were categorized into techniques, datasets, performance evaluation, and phishing types. The obtained results are expected to help developers prevent future phishing attacks more effectively, especially in selectively and carefully determining the techniques and evaluations to address specific types of phishing.

A Quranic optical character recognition (OCR) system based on convolutional neural network (CNN) followed by recurrent neural network (RNN) is introduced in this work. Six deep learning models are built to study the effect of different representations of the input and output, and the accuracy and performance of the models, and compare long short-term memory (LSTM) and gated recurrent unit (GRU). A new Quranic OCR dataset is developed based on the most famous printed version of the Holy Quran (Mushaf Al-Madinah), and a page and line-text image with the corresponding labels is prepared. This work’s contribution is a Quranic OCR model capable of recognizing the Quranic image’s diacritic text. A better performance in word recognition rate (WRR) and character recognition rate (CRR) is achieved in the experiments. The LSTM and GRU are compared in the Arabic text recognition domain. In addition, a public database is built for research purposes in Arabic text recognition that contains the diacritics and the Uthmanic script, and is large enough to be used with the deep learning models. The outcome of this work shows that the proposed system obtains an accuracy of 98% on the validation data, and a WRR of 95% and a CRR of 99% in the test dataset.

The provision of resources and services for scientific workflow applications using a multi-cloud architecture and a pay-per-use rule has recently gained popularity within the cloud computing research domain. This is because workflow applications are computation intensive. Most of the existing studies on workflow scheduling in the cloud mainly focus on finding an ideal makespan or cost. Nevertheless, there are other important quality of service metrics that are of critical concern in workflow scheduling such as reliability and resource utilization. In this respect, this paper proposes a new multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS) for scheduling scientific workflow based on particle swarm optimization (PSO) method. The algorithm minimizes cost and makespan while considering reliability constraint. The coding scheme jointly considers task execution location and data transportation order. Simulation experiments reveal that FR-MOS outperforms the basic MOS over the PSO algorithm.

In recent years, a significant boost in data availability for persistent data streams has been observed. These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space. This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams. In this study, we propose a new online method, which is a density grid-based method for data stream clustering. The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality. The method is conducted entirely online and consists of two main phases. The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters. The grid-based method was utilized as an outlier buffer in order to handle multi-density data and noises. The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes. The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality.

Lacunes or lacunar infarcts are small fluid-filled cavities associated with cerebral small vessel disease (cSVD). They contribute to the development of lacunar stroke, dementia, and gait impairment. The identification of lacunes is of great significance in elucidating the pathophysiological mechanism of cSVD. This paper proposes a semi-automated 3D multi-scale residual convolutional network (3D ResNet) for lacunar infarcts detection, which can learn global representations of the anatomical location of lacunes using two multi-scale magnetic resonance image modalities. This process requires minimal user intervention by passing the potential suspicious lacunes into the network. The proposed network is trained, validated, and tested using five-fold cross-validation using data, including 696 lacunes, from 288 subjects. We also present experiments on various combinations of multi-scale inputs and their effect on extracting global context features that directly influence identification performance. The proposed system shows its capability to differentiate between true lacunes and lacune mimics, providing supportive interpretations for neuroradiologists. The proposed 3D multi-scale ResNet identifies lacunar infarcts with a sensitivity of 96.41%, a specificity of 90.92%, an overall accuracy of 93.67%, and an area under the receiver operator characteristic curve (AUC) of 93.67% over all fold tests. The proposed system also achieved a precision of 91.40% and an average number of FPs per subject of 1.32. The system may be feasible for clinical use by supporting decision-making for lacunar infarct detection.

Intracranial artery stenosis is an important public health concern internationally, due to it being one of the major causes of ischemic stroke. In this study, we aim to provide a computer-aided diagnosis algorithm capable of automatically distinguishing between Internal Carotid Artery (ICA) stenosis and normal to minimize the labor-intensiveness of stenosis detection. Using Time-of-Flight Magnetic Resonance Angiography (TOF-MRA), a novel deep learning detection model via 3D Squeeze and Excitation Residual Networks (SE-ResNet) is proposed. Pre-processing of TOF-MRA, data augmentation, training of 3D SE-ResNet, and testing using patch-based and patient-based methods with cross-validation is described. The proposed network using a database consisting of 50 normal cases (ICA-N) and 41 stenosis cases (ICA-S) with grade level of above 30% was evaluated. All 41 ICA-S cases were categorized according to the diameter (D_stenosis) of the artery at the site of the most severe stenosis by expert radiologists, whereas percent stenosis was measured by Warfarin-Aspirin Symptomatic Intracranial Disease (WASID) method. The proposed 3D SE-ResNet was further compared with more conventional networks including 3D ResNet and 3D VGG. The results showed the capability to detect stenosis achieving overall Area Under the Curve (AUC) and accuracies of 0.947 and 91.0% for patch-based and 0.884 and 81.0% for patient-based testing, respectively. In addition, the proposed 3D SE-ResNet achieved better performance against conventional 3D ResNet and 3D VGG with improvement rates of 0.053 and 0.095 for patch-based and 0.053 and 0.065 for patient-based testing in terms of AUC, respectively.