ADKU/ResearchGPT_model_dataset huggingface dataset:
A deep learning approach for host-based cryptojacking malware detection
Authors: Olanrewaju Sanda, Michalis Pavlidis, Nikolaos Polatidis

With the continued growth and popularity of blockchain-based cryptocurrencies there is a parallel growth in illegal mining to earn cryptocurrency. Since mining for cryptocurrencies requires high computational resource; malicious actors have resorted to using malicious file downloads and other methods to illegally use a victim’s system to mine for cryptocurrency without them knowing. This process is known as host-based cryptojacking and is gradually becoming one of the most popular cyberthreats in recent years. There are some proposed traditional machine learning methods to detect host-based cryptojacking but only a few have proposed using deep-learning models for detection. This paper presents a novel approach, dubbed CryptoJackingModel. This approach is a deep-learning host-based cryptojacking detection model that will effectively detect evolving host-based cryptojacking techniques and reduce false positives and false negatives. The approach has an overall accuracy of 98% on a dataset of 129,380 samples and a low performance overhead making it highly scalable. This approach will be an improvement of current countermeasures for detecting, mitigating, and preventing cryptojacking.
Pneumonia detection based on RSNA dataset and anchor-free deep learning detector
Authors: Linghua Wu, Jing Zhang, Yilin Wang, Rong Ding, Yueqin Cao, Guiqin Liu, Changsheng Liufu, Baowei Xie, Shanping Kang, Rui Liu, Wenle Li, Furen Guan

Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due to the maturity and cost reduction of chest X-ray technology, and with the development of artificial intelligence technology, pneumonia identification based on deep learning and chest X-ray has attracted attention from all over the world. Although the feature extraction capability of deep learning is strong, existing deep learning object detection frameworks are based on pre-defined anchors, which require a lot of tuning and experience to guarantee their excellent results in the face of new applications or data. To avoid the influence of anchor settings in pneumonia detection, this paper proposes an anchor-free object detection framework and RSNA dataset based on pneumonia detection. First, a data enhancement scheme is used to preprocess the chest X-ray images; second, an anchor-free object detection framework is used for pneumonia detection, which contains a feature pyramid, two-branch detection head, and focal loss. The average precision of 51.5 obtained by Intersection over Union (IoU) calculation shows that the pneumonia detection results obtained in this paper can surpass the existing classical object detection framework, providing an idea for future research and exploration.
Comparative Study for Optimized Deep Learning-Based Road Accidents Severity Prediction Models
Authors: Hussam Hijazi, Karim Sattar, Hassan M. Al-Ahmadi, Sami El-Ferik

Road traffic accidents remain a major cause of fatalities and injuries worldwide. Effective classification of accident type and severity is crucial for prompt post-accident protocols and the development of comprehensive road safety policies. This study explores the application of deep learning techniques for predicting crash injury severity in the Eastern Province of Saudi Arabia. Five deep learning models were trained and evaluated, including various variants of feedforward multilayer perceptron, a back-propagated artificial neural network (ANN), an ANN with radial basis function (RPF), and tabular data learning network (TabNet). The models were optimized using Bayesian optimization (BO) and employed the synthetic minority oversampling technique (SMOTE) for oversampling the training dataset. While SMOTE enhanced balanced accuracy for ANN with RBF and TabNet, it compromised precision and increased recall. The results indicated that oversampling techniques did not consistently improve model performance. Additionally, significant features were identified using least absolute shrinkage and selection operator (LASSO) regularization, feature importance, and permutation importance. The results indicated that oversampling techniques did not consistently improve model performance. While SMOTE enhanced balanced accuracy for ANN with RBF and TabNet, it compromised precision and increased recall. The study's findings emphasize the consistent significance of the 'Number of Injuries Major' feature as a vital predictor in deep learning models, regardless of the selection techniques employed. These results shed light on the pivotal role played by the count of individuals with major injuries in influencing the severity of crash injuries, highlighting its potential relevance in shaping road safety policy development.
Blockchain-based multi-diagnosis deep learning application for various diseases classification
Authors: Hakima Rym Rahal, Sihem Slatnia, Okba Kazar, Ezedin Barka, Saad Harous

Misdiagnosis is a critical issue in healthcare, which can lead to severe consequences for patients, including delayed or inappropriate treatment, unnecessary procedures, psychological distress, financial burden, and legal implications. To mitigate this issue, we propose using deep learning algorithms to improve diagnostic accuracy. However, building accurate deep learning models for medical diagnosis requires substantial amounts of high-quality data, which can be challenging for individual healthcare sectors or organizations to acquire. Therefore, combining data from multiple sources to create a diverse dataset for efficient training is needed. However, sharing medical data between different healthcare sectors can be problematic from a security standpoint due to sensitive information and privacy laws. To address these challenges, we propose using blockchain technology to provide a secure, decentralized, and privacy-respecting way to share locally trained deep learning models instead of the data itself. Our proposed method of model ensembling, which combines the weights of several local deep learning models to build a single global model, that enables accurate diagnosis of complex medical conditions across multiple locations while preserving patient privacy and data security. Our research demonstrates the effectiveness of this approach in accurately diagnosing three diseases (breast cancer, lung cancer, and diabetes) with high accuracy rates, surpassing the accuracy of local models and building a multi-diagnosis application.
Deep learning-based question answering: a survey
Authors: Heba Abdel-Nabi, Arafat Awajan, Mostafa Z. Ali

Question Answering is a crucial natural language processing task. This field of research has attracted a sudden amount of interest lately due mainly to the integration of the deep learning models in the Question Answering Systems which consequently power up many advancements and improvements. This survey aims to explore and shed light upon the recent and most powerful deep learning-based Question Answering Systems and classify them based on the deep learning model used, stating the details of the used word representation, datasets, and evaluation metrics. It aims to highlight and discuss the currently used models and give insights that direct future research to enhance this increasingly growing field.
Deep Learning Architecture for Computer Vision-based Structural Defect Detection
Authors: Ruoyu Yang, Shubhendu Kumar Singh, Mostafa Tavakkoli, M. Amin Karami, Rahul Rai

Structural health monitoring (SHM) refers to the implementation of a damage detection strategy for structures. Fault occurrence in these structural systems during the operation is inevitable. Efficient, fast, and precise health monitoring methods are required to proactively perform the necessary repairs and maintenance on time before it is too late. The current structural health monitoring methods involve physically attached sensors or non-contact vision-based vibration measurements. However, these methods have significant drawbacks due to the low spatial resolution, weight influence on the lightweight structure, and time/labor consumption. Recently, computer-vison-based deep learning methods like convolutional neural network (CNN) and fully convolutional neural network (FCN) have been applied for defect detection and localization, which address the aforementioned problems and obtain high accuracy. This paper proposes a novel hybrid deep learning architecture comprising CNN and temporal convolutional networks (CNN-TCN) for the computer vision-based defect detection task. Various beam samples, consisting of five different materials and various structural defects, were used to evaluate the proposed deep learning algorithms’ performance. The proposed deep learning methods treat each pixel of the video frame like a sensor to extract valuable features for defect detection. Through empirical results, we demonstrate that this ’pixel-sensor’ approach is more efficient and accurate and can achieve a better defect detection performance on different beam samples compared with the current state-of-the-art approaches, including CNN-long short-term memory (LSTM), CNN-bidirectional long short-term memory (BiLSTM), multi-scale CNN-LSTM, and CNN-gated recurrent unit(GRU) methods.
Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service
Authors: Mohammad Shahin, F. Frank Chen, Ali Hosseinzadeh, Neda Zand

Accurate detection of possible machine failure allows manufacturers to identify potential fault situations in processes to avoid downtimes caused by unexpected tool wear or unacceptable workpiece quality. This paper aims to report the study of more than 20 fault detection models using machine learning (ML), deep learning (DL), and deep hybrid learning (DHL). Predicting how the system could fail based on certain features or system settings (input variables) can help avoid future breakdowns and minimize downtime. The effectiveness of the proposed algorithms was experimented with a synthetic predictive maintenance dataset published by the School of Engineering of the University of Applied Sciences in Berlin, Germany. The fidelity of these algorithms was evaluated using performance measurement values such as accuracy, precision, recall, and the F-score. Final results demonstrated that deep forest and gradient boosting algorithms had shown very high levels of average accuracy (exceeded 90%). Additionally, the multinomial logistic regression and long short-term memory-based algorithms have shown satisfactory average accuracy (above 80%). Further analysis of models suggests that some models outperformed others. The research concluded that, through various ML, DL, and DHL algorithms, operational data analytics, and health monitoring system, engineers could optimize maintenance and reduce reliability risks.
DVNE-DRL: dynamic virtual network embedding algorithm based on deep reinforcement learning
Authors: Xiancui Xiao

Virtual network embedding (VNE), as the key challenge of network resource management technology, lies in the contradiction between online embedding decision and pursuing long-term average revenue goals. Most of the previous work ignored the dynamics in Virtual Network (VN) modeling, or could not automatically detect the complex and time-varying network state to provide a reasonable network embedding scheme. In view of this, we model a network embedding framework where the topology and resource allocation change dynamically with the number of network users and workload, and then introduce a deep reinforcement learning method to solve the VNE problem. Further, a dynamic virtual network embedding algorithm based on Deep Reinforcement Learning (DRL), named DVNE-DRL, is proposed. In DVNE-DRL, VNE is modeled as a Markov Decision Process (MDP), and then deep learning is introduced to perceive the current network state through historical data and embedded knowledge, while utilizing reinforcement learning decision-making capabilities to implement the network embedding process. In addition, we improve the method of feature extraction and matrix optimization, and consider the characteristics of virtual network and physical network together to alleviate the problem of redundancy and slow convergence. The simulation results show that compared with the existing advanced algorithms, the acceptance rate and average revenue of DVNE-DRL are increased by about 25% and 35%, respectively.
Deep learning-based 1-D magnetotelluric inversion: performance comparison of architectures
Authors: Mehdi Rahmani Jevinani, Banafsheh Habibian Dehkordi, Ian J. Ferguson, Mohammad Hossein Rohban

The study compares the three deep learning approaches and assesses their relative performance solving the 1-D magnetotellurics (MT) inverse problem. MT data from a 1-D geothermal-type structure are used as an example to examine Variational Autoencoder (VAE), Residual Network (Res-Net), and U-Net architectures, adapted for 1-D MT inversion. Root Mean Square Error (RMSE) and Pearson correlation coefficient are applied as misfit measure and similarity criterion, and box plot tools are used to parameterize individual model parameters. The results show that the U-Net provides the most successful recovery of the 1-D resistivity models, even though all three approaches can produce accurate inversions of MT data. To investigate applicability of results to real data sets, the models performance are examined for the case of data containing noise. Three deep learning algorithms are robust with respect to data noise, although the U-Net is relatively superior. The study results provide a platform for more complex magnetotelluric inverse problems and ones involving real data sets.
Automatic segmentation of inconstant fractured fragments for tibia/fibula from CT images using deep learning
Authors: Hyeonjoo Kim, Young Dae Jeon, Ki Bong Park, Hayeong Cha, Moo-Sub Kim, Juyeon You, Se-Won Lee, Seung-Han Shin, Yang-Guk Chung, Sung Bin Kang, Won Seuk Jang, Do-Kun Yoon

Orthopaedic surgeons need to correctly identify bone fragments using 2D/3D CT images before trauma surgery. Advances in deep learning technology provide good insights into trauma surgery over manual diagnosis. This study demonstrates the application of the DeepLab v3+ -based deep learning model for the automatic segmentation of fragments of the fractured tibia and fibula from CT images and the results of the evaluation of the performance of the automatic segmentation. The deep learning model, which was trained using over 11 million images, showed good performance with a global accuracy of 98.92%, a weighted intersection over the union of 0.9841, and a mean boundary F1 score of 0.8921. Moreover, deep learning performed 5–8 times faster than the experts’ recognition performed manually, which is comparatively inefficient, with almost the same significance. This study will play an important role in preoperative surgical planning for trauma surgery with convenience and speed.
Smart city urban planning using an evolutionary deep learning model
Authors: Mansoor Alghamdi

Following the evolution of big data collection, storage, and manipulation techniques, deep learning has drawn the attention of numerous recent studies proposing solutions for smart cities. These solutions were focusing especially on energy consumption, pollution levels, public services, and traffic management issues. Predicting urban evolution and planning is another recent concern for smart cities. In this context, this paper introduces a hybrid model that incorporates evolutionary optimization algorithms, such as Teaching–learning-based optimization (TLBO), into the functioning process of neural deep learning models, such as recurrent neural network (RNN) networks. According to the achieved simulations, deep learning enhanced by evolutionary optimizers can be an effective and promising method for predicting urban evolution of future smart cities.
Recent advances in deep learning models: a systematic literature review
Authors: Ruchika Malhotra, Priya Singh

In recent years, deep learning has evolved as a rapidly growing and stimulating field of machine learning and has redefined state-of-the-art performances in a variety of applications. There are multiple deep learning models that have distinct architectures and capabilities. Up to the present, a large number of novel variants of these baseline deep learning models is proposed to address the shortcomings of the existing baseline models. This paper provides a comprehensive review of one hundred seven novel variants of six baseline deep learning models viz. Convolutional Neural Network, Recurrent Neural Network, Long Short Term Memory, Generative Adversarial Network, Autoencoder and Transformer Neural Network. The current review thoroughly examines the novel variants of each of the six baseline models to identify the advancements adopted by them to address one or more limitations of the respective baseline model. It is achieved by critically reviewing the novel variants based on their improved approach. It further provides the merits and demerits of incorporating the advancements in novel variants compared to the baseline deep learning model. Additionally, it reports the domain, datasets and performance measures exploited by the novel variants to make an overall judgment in terms of the improvements. This is because the performance of the deep learning models are subject to the application domain, type of datasets and may also vary on different performance measures. The critical findings of the review would facilitate the researchers and practitioners with the most recent progressions and advancements in the baseline deep learning models and guide them in selecting an appropriate novel variant of the baseline to solve deep learning based tasks in a similar setting.
Deep Learning Based Alzheimer Disease Diagnosis: A Comprehensive Review
Authors: S. Suganyadevi, A. Shiny Pershiya, K. Balasamy, V. Seethalakshmi, Saroj Bala, Kumud Arora

Dementia encompasses a range of cognitive disorders, with Alzheimer’s Disease being the utmost widespread and devastating. AD gradually erodes memory and daily functioning through the progressive deterioration of brain cells. It poses a significant global health challenge, necessitating early identification and intervention. Detecting AD at its onset holds immense potential to predict future health outcomes for individuals. By harnessing the power of artificial intelligence and leveraging MRI scans, we could utilize advanced technology to not only classify AD patients but also predict the likelihood of them developing this life-altering condition. This paper delves into the latest advancements in Deep Learning techniques and their functions in image analysis in medical field. Its primary goals are to elucidate the intricacies of medical image processing and to elucidate and implement key findings and recommendations from recent research.
Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?
Authors: Sermin Can, Ömer Türk, Muhammed Ayral, Günay Kozan, Hamza Arı, Mehmet Akdağ, Müzeyyen Yıldırım Baylan

IntroductionWe aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations.Material methodA total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group. The diagnoses of the patients were confirmed histopathologically. Two CT images from all the patients in each group were used in the study. The CT images were classified using ResNet50, NASNetMobile, and DenseNet121 architecture input.ResultsThe classification accuracies obtained with ResNet50, DenseNet121, and NASNetMobile were 92.5%, 90.62, and 87.5, respectively.ConclusionDeep learning is a useful diagnostic tool in diagnosing cervical lymphadenopathy. In the near future, many diseases could be diagnosed with deep learning models without radiologist interpretations and invasive examinations such as histopathological examinations. However, further studies with much larger case series are needed to develop accurate deep-learning models.
Deep Learning Models for Diagnosis of Schizophrenia Using EEG Signals: Emerging Trends, Challenges, and Prospects
Authors: Rakesh Ranjan, Bikash Chandra Sahana, Ashish Kumar Bhandari

Schizophrenia (ScZ) is a chronic neuropsychiatric disorder characterized by disruptions in cognitive, perceptual, social, emotional, and behavioral functions. In the traditional approach, the diagnosis of ScZ primarily relies on the subject’s response and the psychiatrist’s experience, making it highly subjective, prejudiced, and time-consuming. In recent medical research, incorporating deep learning (DL) into the diagnostic process improves performance by reducing inter-observer variation and providing qualitative and quantitative support for clinical decisions. Compared with other modalities, such as magnetic resonance images (MRI) or computed tomography (CT) scans, electroencephalogram (EEG) signals give better insights into the underlying neural mechanisms and brain biomarkers of ScZ. Deep learning models show promising results but the utilization of EEG signals as an effective biomarker for ScZ is still under research. Numerous deep learning models have recently been developed for automated ScZ diagnosis with EEG signals exclusively, yet a comprehensive assessment of these approaches still does not exist in the literature. To fill this gap, we comprehensively review the current advancements in deep learning-based schizophrenia diagnosis using EEG signals. This review is intended to provide systematic details of prominent components: deep learning models, ScZ EEG datasets, data preprocessing approaches, input data formulations for DL, chronological DL methodology advancement in ScZ diagnosis, and design trends of DL architecture. Finally, few challenges in both clinical and technical aspects that create hindrances in achieving the full potential of DL models in EEG-based ScZ diagnosis are expounded along with future outlooks.
GMPP-NN: a deep learning architecture for graph molecular property prediction
Authors: Outhman Abbassi, Soumia Ziti, Meryam Belhiah, Souad Najoua Lagmiri, Yassine Zaoui Seghroucheni

The pharmacy industry is highly focused on drug discovery and development for the identification and optimization of potential drug candidates. One of the key aspects of this process is the prediction of various molecular properties that justify their potential effectiveness in treating specific diseases. Recently, graph neural networks have gained significant attention, primarily due to their strong suitability for predicting complex relationships that exist between atoms and other molecular structures. GNNs require significant depth to capture global features and to allow the network to iteratively aggregate and propagate information across the entire graph structure. In this research study, we present a deep learning architecture known as a graph molecular property prediction neural network. which combines MPNN feature extraction with a multilayer perceptron classifier. The deep learning architecture was evaluated on four benchmark datasets, and its performance was compared to the smiles transformer, fingerprint to vector, deeper graph convolutional networks, geometry-enhanced molecular, and atom-bond transformer-based message-passing neural network. The results showed that the architecture outperformed the other models using the receiver operating characteristic area under the curve metric. These findings offer an exciting opportunity to enhance and improve molecular property prediction in drug discovery and development.