ADKU/ResearchGPT_model_dataset huggingface dataset:
IRADA: integrated reinforcement learning and deep learning algorithm for attack detection in wireless sensor networks
Authors: Vandana Shakya, Jaytrilok Choudhary, Dhirendra Pratap Singh

Wireless Sensor Networks (WSNs) play a vital role in various applications, necessitating robust network security to protect sensitive data. Intrusion Detection Systems (IDSs) are crucial for preserving the integrity, availability, and confidentiality of WSNs by detecting and countering potential attacks. Despite significant research efforts, the existing IDS solutions still suffer from challenges related to detection accuracy and false alarms. To address these challenges, in this paper, we propose a Bayesian optimization-based Deep Learning (DL) model. However, the proposed optimized DL model, while showing promising results in enhancing security, encounters challenges such as data dependency, computational complexity, and the potential for overfitting. In the literature, researchers have employed Reinforcement Learning (RL) to address these issues. However, it also introduces its own concerns, including exploration, reward design, and prolonged training times. Consequently, to address these challenges, this paper proposes an Innovative Integrated RL-based Advanced DL Algorithm (IRADA) for attack detection in WSNs. IRADA leverages the convergence of DL and RL models to achieve superior intrusion detection performance. The performance analysis of IRADA reveals impressive results, including accuracy (99.50%), specificity (99.94%), sensitivity (99.48%), F1-Score (98.26%), Kappa statistics (99.42%), and area under the curve (99.38%). Additionally, we analyze IRADA’s robustness against adversarial attacks, ensuring its applicability in real-world security scenarios.
Machine learning vs deep learning in stock market investment: an international evidence
Authors: Jing Hao, Feng He, Feng Ma, Shibo Zhang, Xiaotao Zhang

Machine learning and deep learning are powerful tools for quantitative investment. To examine the effectiveness of the models in different markets, this paper applies random forest and DNN models to forecast stock prices and construct statistical arbitrage strategies in five stock markets, including mainland China, the United States, the United Kingdom, Canada and Japan. Each model is applied to the price of major stock indices constituting stocks in these markets from 2005 to 2020 to construct a long-short portfolio with 20 selected stocks by the model. The results show that the a particular model obtains significantly different profits in different markets, among which DNN has the best performance, especially in the Chinese stock market. We find that DNN models generally perform better than other machine learning models in all markets.
Surface wave inversion with unknown number of soil layers based on a hybrid learning procedure of deep learning and genetic algorithm
Authors: Zan Zhou, Thomas Man-Hoi Lok, Wan-Huan Zhou

Surface wave inversion is a key step in the application of surface waves to soil velocity profiling. Currently, a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization variable. However, an improper selection of the number of layers may lead to an incorrect shear wave velocity profile. In this study, a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil layers. First, a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer number. Then, the shear-wave velocity profile is determined by a genetic algorithm with the known layer number. By applying this procedure to both simulated and real-world cases, the results indicate that the proposed method is reliable and efficient for surface wave inversion.
An Extensive Review on Deep Learning and Machine Learning Intervention in Prediction and Classification of Types of Aneurysms
Authors: Renugadevi Ammapalayam Sinnaswamy, Natesan Palanisamy, Kavitha Subramaniam, Suresh Muthusamy, Ravita Lamba, Sreejith Sekaran

Aneurysm (Rupture of blood vessels) may happen in the cerebrum, abdominal aorta and thoracic aorta of humans, which has a high fatal rate. The advancement of the artificial technologies specifically machine learning algorithms and deep learning models have attempted to predict the aneurysm, which may reduce the death rate. The main objective of this paper is to provide the review of various algorithms and models for the early prediction of the various types of aneurysms. The focused literature review was conducted from the preferred journals from 2007 to 2022 on various parameters such as way of collecting images, the techniques used, number of images used in data set, performance metrics and future work. The summarized overview of advances in prediction of aneurysms using the machine learning algorithms from non linear kernel support regression algorithm to 3D Unet architecture of deep learning models starting from CT scan images to final performance analysis in prediction. The range of sensitivity, specificity and area under receiving operating characteristic was from 0. 7 to 1 for the abdominal aortic aneurysm detection, intracranial aneurysm detection. The thoracic aortic aneurysm was not concentrated much in the literature review, so the prediction of thoracic aortic aneurysm using machine learning as well as deep learning model is recommended.
Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies
Authors: Mehran Radak, Haider Yabr Lafta, Hossein Fallahi

BackgroundBreast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer.PurposeIn this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography. We discuss the use of five popular machine learning techniques, including Nearest Neighbor, SVM, Naive Bayesian Network, DT, and ANN, as well as deep learning architectures and convolutional neural networks.ConclusionOur review finds that machine learning and deep learning techniques have achieved high accuracy rates in breast cancer classification and diagnosis across various medical imaging modalities. Furthermore, these techniques have the potential to improve clinical decision-making and ultimately lead to better patient outcomes.
SAKMR: Industrial control anomaly detection based on semi-supervised hybrid deep learning
Authors: Shijie Tang, Yong Ding, Meng Zhao, Huiyong Wang

With the advent of Industry 4.0, industrial control systems (ICS) are more and more closely connected with the Internet, leading to a rapid increase in the types and quantities of security threats that arise from ICS. Anomaly detection is an effective defense measure against attacks. At present, it is the main trend to use hybrid deep learning methods to realize ICS anomaly detection. However, we found that many ICS anomaly detection methods based on hybrid deep learning adopt phased learning, in which each phase is optimized separately with optimization goals deviating from the overall goal. In view of this issue, we propose an end-to-end anomaly detection method SAKMR based on hybrid deep learning. Our method uses radial basis function network (RBFN) to realize K-means clustering, and combines it with stacked auto-encoder (SAE), which is conducive to defining reconstruction error and clustering error into an objective function to ensure joint optimization of feature extraction and classification. Experiments were conducted on the commonly used KDDCUP99 and SWAT datasets. The results show that SAKMR is effective in detecting abnormal industrial control data and outperforms the baseline methods on multiple performance indicators such as F1-Measure.
Deep learning for the harmonization of structural MRI scans: a survey
Authors: Soolmaz Abbasi, Haoyu Lan, Jeiran Choupan, Nasim Sheikh-Bahaei, Gaurav Pandey, Bino Varghese

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements.
PolySeg Plus: Polyp Segmentation Using Deep Learning with Cost Effective Active Learning
Authors: Abdelrahman I. Saad, Fahima A. Maghraby, Osama Badawy

A deep convolution neural network image segmentation model based on a cost-effective active learning mechanism is proposed and named PolySeg Plus. It is intended to address polyp segmentation with a lack of labeled data and a high false-positive rate of polyp discovery. In addition to applying active learning, which assisted in labeling more image samples, a comprehensive polyp dataset formed of five benchmark datasets was generated to increase the number of images. To enhance the captured image features, the locally shared feature method is used, which utilizes the power of employing neighboring features together with one another to improve the quality of image features and overcome the drawbacks of the Conditional Random Features method. Medical image segmentation was performed using ResUNet++, ResUNet, UNet++, and UNet models. Gaussian noise was removed from the images using a gaussian filter, and the images were then augmented before being fed into the models. In addition to optimizing model performance through hyperparameter tuning, grid search is used to select the optimum parameters to maximize model performance. The results demonstrated a significant improvement and applicability of the proposed method in polyp segmentation when compared to state-of-the-art methods on the datasets CVC-ClinicDB, CVC-ColonDB, ETIS Larib Polyp DB, KVASIR-SEG, and Kvasir-Sessile, with Dice coefficients of 0.9558, 0.8947, 0.7547, 0.9476, and 0.6023, respectively. Not only did the suggested method improve the dice coefficients on the individual datasets, but it also produced better results on the comprehensive dataset, which will contribute to the development of computer-aided diagnosis systems.
Deep learning large-scale drug discovery and repurposing
Authors: Min Yu, Weiming Li, Yunru Yu, Yu Zhao, Lizhi Xiao, Volker M. Lauschke, Yiyu Cheng, Xingcai Zhang, Yi Wang

Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochondrial phenotypes. By temporally imaging mitochondrial morphology and membrane potential, we established a pipeline for monitoring time-resolved mitochondrial images, resulting in a dataset comprising 570,096 single-cell images of cells exposed to 1,068 United States Food and Drug Administration-approved drugs. A deep learning model named MitoReID, using a re-identification (ReID) framework and an Inflated 3D ResNet backbone, was developed. It achieved 76.32% Rank-1 and 65.92% mean average precision on the testing set and successfully identified the MOAs for six untrained drugs on the basis of mitochondrial phenotype. Furthermore, MitoReID identified cyclooxygenase-2 inhibition as the MOA of the natural compound epicatechin in tea, which was successfully validated in vitro. Our approach thus provides an automated and cost-effective alternative for target identification that could accelerate large-scale drug discovery and repurposing.
CoVSeverity-Net: an efficient deep learning model for COVID-19 severity estimation from Chest X-Ray images
Authors: Sagar Deep Deb, Rajib Kumar Jha, Rajnish Kumar, Prem S. Tripathi, Yash Talera, Manish Kumar

PurposeCOVID-19 is not going anywhere and is slowly becoming a part of our life. The World Health Organization declared it a pandemic in 2020, and it has affected all of us in many ways. Several deep learning techniques have been developed to detect COVID-19 from Chest X-Ray images. COVID-19 infection severity scoring can aid in establishing the optimum course of treatment and care for a positive patient, as all COVID-19 positive patients do not require special medical attention. Still, very few works are reported to estimate the severity of the disease from the Chest X-Ray images. The unavailability of the large-scale dataset might be a reason.MethodsWe aim to propose CoVSeverity-Net, a deep learning-based architecture for predicting the severity of COVID-19 from Chest X-ray images. CoVSeverity-Net is trained on a public COVID-19 dataset, curated by experienced radiologists for severity estimation. For that, a large publicly available dataset is collected and divided into three levels of severity, namely Mild, Moderate, and Severe.ResultsAn accuracy of 85.71% is reported. Conducting 5-fold cross-validation, we have obtained an accuracy of 87.82 ± 6.25%. Similarly, conducting 10-fold cross-validation we obtained accuracy of 91.26 ± 3.42. The results were better when compared with other state-of-the-art architectures.ConclusionWe strongly believe that this study has a high chance of reducing the workload of overworked front-line radiologists, speeding up patient diagnosis and treatment, and easing pandemic control. Future work would be to train a novel deep learning-based architecture on a larger dataset for severity estimation.
A comparison between machine and deep learning models on high stationarity data
Authors: Domenico Santoro, Tiziana Ciano, Massimiliano Ferrara

Advances in sensor, computing, and communication technologies are enabling big data analytics by providing time series data. However, conventional models struggle to identify sequence features and forecast accuracy. This paper investigates time series features and shows that some machine learning algorithms can outperform deep learning models. In particular, the problem analyzed concerned predicting the number of vehicles passing through an Italian tollbooth in 2021. The dataset, composed of 8766 rows and 6 columns relating to additional tollbooths, proved to have high stationarity and was treated through machine learning methods such as support vector machine, random forest, and eXtreme gradient boosting (XGBoost), as well as deep learning through recurrent neural networks with long short-term memory (RNN-LSTM) cells. From the comparison of these models, the prediction through the XGBoost algorithm outperforms competing algorithms, particularly in terms of MAE and MSE. The result highlights how a shallower algorithm than a neural network is, in this case, able to obtain a better adaptation to the time series instead of a much deeper model that tends to develop a smoother prediction.
Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning
Authors: Gregory Holste, Evangelos K. Oikonomou, Bobak J. Mortazavi, Zhangyang Wang, Rohan Khera

BackgroundAdvances in self-supervised learning (SSL) have enabled state-of-the-art automated medical image diagnosis from small, labeled datasets. This label efficiency is often desirable, given the difficulty of obtaining expert labels for medical image recognition tasks. However, most efforts toward SSL in medical imaging are not adapted to video-based modalities, such as echocardiography.MethodsWe developed a self-supervised contrastive learning approach, EchoCLR, for echocardiogram videos with the goal of learning strong representations for efficient fine-tuning on downstream cardiac disease diagnosis. EchoCLR pretraining involves (i) contrastive learning, where the model is trained to identify distinct videos of the same patient, and (ii) frame reordering, where the model is trained to predict the correct of video frames after being randomly shuffled.ResultsWhen fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improves classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) over other transfer learning and SSL approaches across internal and external test sets. When fine-tuning on 10% of available training data (519 studies), an EchoCLR-pretrained model achieves 0.72 AUROC (95% CI: [0.69, 0.75]) on LVH classification, compared to 0.61 AUROC (95% CI: [0.57, 0.64]) with a standard transfer learning approach. Similarly, using 1% of available training data (53 studies), EchoCLR pretraining achieves 0.82 AUROC (95% CI: [0.79, 0.84]) on severe AS classification, compared to 0.61 AUROC (95% CI: [0.58, 0.65]) with transfer learning.ConclusionsEchoCLR is unique in its ability to learn representations of echocardiogram videos and demonstrates that SSL can enable label-efficient disease classification from small amounts of labeled data.
Detecting defects in PCB manufacturing: an exploration using Yolov8 deep learning
Authors: Weifeng LI

Detecting defects in automated inspection systems for Printed Circuit Board (PCB) manufacturing stands as a critical endeavor for ensuring product quality. Despite numerous methodologies explored in the literature, recent advancements highlight the superiority of deep learning techniques in defect identification, driven by their remarkable accuracy surpassing conventional methods. However, the persistent challenge of achieving higher accuracy rates and real-time processing capabilities persists, motivating the need for innovative solutions. In response, this study introduces a novel approach utilizing the Yolov8 architecture for PCB defect detection, addressing the aforementioned research challenge. Leveraging a custom dataset tailored explicitly for this purpose, our method showcases remarkable performance, with Yolov8x emerging as the top-performing model, achieving a notable F1 Score of 98% and a high mean Average Precision (mAP) of 98.9%. This study’s novelty lies in its comprehensive evaluation of deep learning models specifically tailored for PCB defect detection, offering promising prospects for enhancing precision, non-destructiveness, and real-time capabilities in manufacturing processes.
Deep learning in medical image super resolution: a review
Authors: Hujun Yang, Zhongyang Wang, Xinyao Liu, Chuangang Li, Junchang Xin, Zhiqiong Wang

Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies reconstructing corresponding high-resolution (HR) images from observed low-resolution (LR) images or image sequences. In recent years, significant breakthroughs in SR based on deep learning have been made, and many advanced results have been achieved. However, there is a lack of review literature that summarizes the field’s current state and provides an outlook on future developments. Therefore, we provide a comprehensive summary of the literature on medical image SR (MedSR) based on deep learning since 2018 in five aspects: (1) The SR problem of medical images is described, and the methods of image degradation are summarized. (2) We divide the existing studies into three categories: two-dimensional image SR (2DISR), three-dimensional image SR (3DISR), and video SR (VSR). Each category is subdivided. We analyze the network structure and method characteristics of typical methods. (3) Existing SR reconstruction quality evaluation metrics are presented in detail. (4) The application of MedSR methods based on deep learning is discussed. (5) We discuss the challenges of this phase and point out valuable research directions.