ADKU/ResearchGPT_model_dataset huggingface dataset:
Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey
Authors: Mohammed A. A. Al-qaness, Jie Zhu, Dalal AL-Alimi, Abdelghani Dahou, Saeed Hamood Alsamhi, Mohamed Abd Elaziz, Ahmed A. Ewees

In medical imaging, the last decade has witnessed a remarkable increase in the availability and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant advancement in deep learning techniques, noted for their escalating accuracy. These developments have catalyzed a surge in the application of deep learning in various medical studies, particularly in detecting and classifying lung diseases. This study delves into an extensive compilation of over 200 studies from the recent five years (2018–2023), employing advanced machine learning, including deep learning methodologies to analyze CXR images. Our exploration is twofold: it categorizes these studies based on the methods used and the types of lung diseases addressed. It also presents an in-depth examination of the current limitations and prospective trajectories in this rapidly evolving field. Our findings underscore the transformative impact and continual progress of deep learning models in enhancing the accuracy and efficiency of lung disease detection using CXR images. This survey culminates by emphasizing the critical need for further technological advancement in this domain, aiming to bridge gaps in healthcare provision and improve patient outcomes. The overarching goal is to pave the way for more precise, efficient, and accessible diagnostic tools in the battle against lung diseases, reinforcing the indispensable role of technology in modern healthcare.
Comparative analysis of deep learning models for dysarthric speech detection
Authors: P. Shanmugapriya, V. Mohan

Dysarthria is a speech communication disorder that is associated with neurological impairments. To detect this disorder from speech, we present an experimental comparison of deep models developed based on frequency domain features. A comparative analysis of deep models is performed in the detection of dysarthria using scalogram of dysarthric speech. Also, it can assist physicians, specialists, and doctors based on the results of its detection. Since dysarthric speech signals have segments of breathy and semi-whispery, experiments are performed only on the frequency-domain representation of speech signals. Time-domain speech signal is transformed into a 2-D scalogram image through wavelet transformation. Then, the scalogram images are applied to pre-trained convolutional neural networks. The layers of pre-trained networks are tuned for our scalogram images through transfer learning. The proposed method of applying the scalogram images as input to pre-trained CNNs is evaluated on the TORGO database and the classification performance of these networks is compared. In this work, AlexNet, GoogLeNet, ResNet 50 and two pre-trained sound CNNs, namely VGGish and YAMNET are considered deep models of pre-trained convolutional neural networks. The proposed method of using pre-trained and transfer learned CNN with scalogram image feature achieved better accuracy when compared to other machine learning models in the dysarthria detection system.
Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn’s Disease and Intestinal Tuberculosis
Authors: Ming Cheng, Hanyue Zhang, Wenpeng Huang, Fei Li, Jianbo Gao

This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn’s disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on the validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learning (DL) radiomics features were extracted from the arterial and venous phases images, respectively. The interobserver consistency analysis, Spearman’s correlation, univariate analysis, and the least absolute shrinkage and selection operator (LASSO) regression were used to select features. Based on extracted multi-phase radiomics features, six logistic regression models were finally constructed. The diagnostic performances of different models were compared using ROC analysis and Delong test. The arterial-venous combined deep learning radiomics model for differentiating between CD and ITB showed a high prediction quality with AUCs of 0.885, 0.877, and 0.800 in SMOTE dataset, validation dataset one, and validation dataset two, respectively. Moreover, the deep learning radiomics model outperformed the handcrafted radiomics model in same phase images. In validation dataset one, the Delong test results indicated that there was a significant difference in the AUC of the arterial models (p = 0.037), while not in venous and arterial-venous combined models (p = 0.398 and p = 0.265) as comparing deep learning radiomics models and handcrafted radiomics models. In our study, the arterial-venous combined model based on deep learning radiomics analysis exhibited good performance in differentiating between CD and ITB.
Guarding Against the Unknown: Deep Transfer Learning for Hardware Image-Based Malware Detection
Authors: Zhangying He, Houman Homayoun, Hossein Sayadi

Malware is increasingly becoming a significant threat to computing systems, and detecting zero-day (unknown) malware is crucial to ensure the security of modern systems. These attacks exploit software security vulnerabilities that are not documented or known in the detection mechanism’s database, making it particularly a pressing challenge to address. In recent times, there has been a shift in focus by security researchers toward the architecture of underlying processors. They have suggested implementing hardware-based malware detection (HMD) countermeasures to address the shortcomings of software-based detection methods. HMD techniques involve applying standard machine learning (ML) algorithms to low-level events of processors that are gathered from hardware performance counter (HPC) registers. While these techniques have shown promising results for detecting known malware, accurately recognizing zero-day malware remains an unsolved issue in the existing HPC-based detection methods. Our comprehensive analysis has revealed that standard ML classifiers are ineffective in identifying zero-day malware traces using HPC events. In response, we propose Deep-HMD, a multi-level intelligent and flexible approach based on deep neural network and transfer learning, for accurate zero-day malware detection using image-based hardware events. Deep-HMD first converts HPC-based malware and benign data into images, and subsequently employs a lightweight deep transfer learning methodology to obtain a high malware detection performance for both known and unknown test scenarios. To conduct a thorough analysis, three deep learning-based and nine standard ML algorithms are implemented and evaluated for hardware-based malware detection. The experimental results indicate that our proposed image-based malware detection solution achieves superior performance compared to all other methods, with a 97% detection performance (measured by F-measure and area under the curve) for run-time zero-day malware detection utilizing soley the top four performance counter events. Specifically, our novel approach outperforms the binarized MLP by 16% and the best classical ML algorithm by 18% in F-measure, while maintaining a minimal false positive rate and without incurring any hardware redesign overhead.
Deep learning of causal structures in high dimensions under data limitations
Authors: Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee

Causal learning is a key challenge in scientific artificial intelligence as it allows researchers to go beyond purely correlative or predictive analyses towards learning underlying cause-and-effect relationships, which are important for scientific understanding as well as for a wide range of downstream tasks. Here, motivated by emerging biomedical questions, we propose a deep neural architecture for learning causal relationships between variables from a combination of high-dimensional data and prior causal knowledge. We combine convolutional and graph neural networks within a causal risk framework to provide an approach that is demonstrably effective under the conditions of high dimensionality, noise and data limitations that are characteristic of many applications, including in large-scale biology. In experiments, we find that the proposed learners can effectively identify novel causal relationships across thousands of variables. Results include extensive (linear and nonlinear) simulations (where the ground truth is known and can be directly compared against), as well as real biological examples where the models are applied to high-dimensional molecular data and their outputs compared against entirely unseen validation experiments. These results support the notion that deep learning approaches can be used to learn causal networks at large scale.
Deep learning-based biometric cryptographic key generation with post-quantum security
Authors: Oleksandr Kuznetsov, Dmytro Zakharov, Emanuele Frontoni

In contemporary digital security systems, the generation and management of cryptographic keys, such as passwords and pin codes, often rely on stochastic random processes and intricate mathematical transformations. While these keys ensure robust security, their storage and distribution necessitate sophisticated and costly mechanisms. This study explores an alternative approach that leverages biometric data for generating cryptographic keys, thereby eliminating the need for complex storage and distribution processes. The paper investigates biometric key generation technologies based on deep learning models, specifically utilizing convolutional neural networks to extract biometric features from human facial images. Subsequently, code-based cryptographic extractors are employed to process the primary extracted features. The performance of various deep learning models and the extractor is evaluated by considering Type 1 and Type 2 errors. The optimized algorithm parameters yield an error rate of less than \(10\%\), rendering the generated keys suitable for biometric authentication. Additionally, this study demonstrates that the application of code-based cryptographic extractors provides a post-quantum level of security, further enhancing the practicality and effectiveness of biometric key generation technologies in modern information security systems. This research contributes to the ongoing efforts towards secure, efficient, and user-friendly authentication and encryption methods, harnessing the power of biometric data and deep learning techniques.
Connecting national flags – a deep learning approach
Authors: Theofanis Kalampokas, Dimitrios Mentizis, Eleni Vrochidou, George A. Papakostas

National flags are the most recognizable symbols of the identity of a country. Similarities between flags may be observed due to cultural, historical, or ethical connections between nations, because they may be originated from the same group of people, or due to unrelated sharing of common symbols and colors. Although the fact that similar flags exist is indisputable, this has never been quantified. Quantifying flags’ similarities could provide a useful body of knowledge for vexillologists and historians. To this end, this work aims to develop a supporting tool for the scientific study of nations’ history and symbolisms, through the quantification of the varying degrees of similarity between their flags, by considering three initially stated hypotheses and by using a novel feature inclusion (FI) measure. The proposed FI measure aims to objectively quantify the overall similarity between flags based on optical multi-scaled features extracted from flag images. State-of-the-art deep learning models built for other applications tested their capability for the first time for the problem under study by using transfer learning, towards calculating the FI measure. More specifically, FI was quantified by six deep learning models: Yolo (V4 and V5), SSD, RetinaNet, Fast R-CNN, FCOS and CornerNet. Flags’ images dataset included flags of 195 nations officially recognized by the United Nations. Experimental results reported maximum feature inclusion between flags of up to 99%. The extracted degrees of similarity were subsequently justified with the help of the Vexillology scientific domain, to support research findings and to raise questions for further investigation. Experimental results reveal that the proposed approach and FI measure are reliable and able to serve as a supporting tool to social sciences for knowledge extraction and quantification.
Secure Communications with THz Reconfigurable Intelligent Surfaces and Deep Learning in 6G Systems
Authors: Ajmeera Kiran, Abhilash Sonker, Sachin Jadhav, Makarand Mohan Jadhav, Janjhyam Venkata Naga Ramesh, Elangovan Muniyandy

In anticipation of the 6G era, this paper explores the integration of terahertz (THz) communications with Reconfigurable Intelligent Surfaces (RIS) and deep learning to establish a secure wireless network capable of ultra-high data rates. Addressing the non-convex challenge of maximizing secure energy efficiency, we introduce a novel deep learning framework that employs a variety of neural network architectures for optimizing RIS reflection and beamforming. Our simulations, set against scenarios with varying eavesdropper cooperation, confirm the efficacy of the proposed solution, achieving 97% of the optimal performance benchmarked against a genie-aided model. This research underlines a significant advancement in 6G network security, potentially influencing future standards and laying the groundwork for practical deployment, thereby marking a milestone in the convergence of THz technology, intelligent surfaces, and AI for future-proof secure communications.
A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging
Authors: Dai Kusumoto, Takumi Akiyama, Masahiro Hashimoto, Yu Iwabuchi, Toshiomi Katsuki, Mai Kimura, Yohei Akiba, Hiromune Sawada, Taku Inohara, Shinsuke Yuasa, Keiichi Fukuda, Masahiro Jinzaki, Masaki Ieda

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.
Deep learning based water leakage detection for shield tunnel lining
Authors: Shichang Liu, Xu Xu, Gwanggil Jeon, Junxin Chen, Ben-Guo He

Shield tunnel lining is prone to water leakage, which may further bring about corrosion and structural damage to the walls, potentially leading to dangerous accidents. To avoid tedious and inefficient manual inspection, many projects use artificial intelligence (AI) to detect cracks and water leakage. A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this paper. Our proposal includes a ConvNeXt-S backbone, deconvolutional-feature pyramid network (D-FPN), spatial attention module (SPAM). and a detection head. It can extract representative features of leaking areas to aid inspection processes. To further improve the model’s robustness, we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training samples. Validation experiments are performed, achieving the average precision (AP) score of 56.8%, which outperforms previous work by a margin of 5.7%. Visualization illustrations also support our method’s practical effectiveness.
HCCNet Fusion: a synergistic approach for accurate hepatocellular carcinoma staging using deep learning paradigm
Authors: Devi Rajeev, S. Remya, Anand Nayyar

Hepatocellular carcinoma (HCC) stands as the second most prevalent cancer and a leading cause of cancer-related mortality globally, necessitating precise diagnostic and prognostic methodologies. The study introduces an innovative approach centered around the HCCNet Fusion model; a robust integration of advanced deep-learning techniques designed to elevate the accuracy of HCC stage recognition. Leveraging the synergies between the VGG16 architecture and U-Net and incorporating sophisticated data pre-processing methods such as Otsu’s binary thresholding and marker-based watershed segmentation, this approach aims to strengthen the precision of HCC stage identification. Furthermore, transfer learning plays a pivotal role in HCCNet Fusion, enabling the models to integrate knowledge from diverse medical image settings through pre-trained weights from VGG16 and U-Net architectures. This strategic integration demonstrates the efficacy of advanced deep learning strategies in addressing intricate medical challenges, and outperforms conventional methods with a remarkable accuracy rate of 95%, underscoring the potential of cutting-edge deep learning techniques in medical diagnostics. The evaluation and validation of the proposed HCCNet Fusion model demonstrate its strong performance across many metrics like AUC ROC, Loss, Accuracy, Precision, Recall, and F1. Additionally, a comparative study was done against well-known methods, including CNN, Inception ResNetV2, VGG16, Inception V3, EfficientNet-B0, and ResNet50 and the results state that the proposed system not only advances HCC detection but also sets a pattern for leveraging state-of-the-art methodologies in addressing complex medical issues.
Visual sentiment analysis using data-augmented deep transfer learning techniques
Authors: Haoran Hong, Waneeza Zaheer, Aamir Wali

The use of visual content to express emotions on social media platforms has become increasingly popular. Visual sentiment analysis can be used to understand the sentiment conveyed by the users using images. Compared to text, visual sentiment analysis is a challenging task since images are a more condensed form of data, have ambiguity and do not have explicit textual clues. Recently, a few studies used deep transfer learning techniques for visual sentiment analysis but the results reported can be significantly improved. In this research paper, we introduce a novel architecture that combines data augmentation and transfer learning. Our approach involves feature fusion of the pretrained VGG16 and MobileNetV1 models, followed by fine-tuning using an SVM classifier using augmented training data. For evaluation, we used two image datasets. To augment these datasets, we apply various techniques. The proposed model is also compared with three other transfer techniques, as well as four machine learning models (excluding SVM). VGG16+MobileNetV1-SVM has the best accuracy of 96% and recall of 99% for both datasets. Compared to other studies that also employed the same dataset, the proposed model produced the best results.
Deep Learning Methods for Binding Site Prediction in Protein Structures
Authors: E. P. Geraseva

AbstractThis work is an overview of deep machine learning methods aimed at predicting binding sites in protein structures. Several classes of methods are selected: prediction of binding sites for small molecules, proteins, and nucleic acids. For each class, various approaches to prediction are considered (prediction of binding atoms, residues, surfaces, pockets). Specifics of feature selection and neural network architectures inherent to each class and approach are highlighted, and an attempt is made to explain these specifics and foresee the further direction of their development.
Deep learning for tumor margin identification in electromagnetic imaging
Authors: Amir Mirbeik, Negar Ebadi

In this work, a novel method for tumor margin identification in electromagnetic imaging is proposed to optimize the tumor removal surgery. This capability will enable the visualization of the border of the cancerous tissue for the surgeon prior or during the excision surgery. To this end, the border between the normal and tumor parts needs to be identified. Therefore, the images need to be segmented into tumor and normal areas. We propose a deep learning technique which divides the electromagnetic images into two regions: tumor and normal, with high accuracy. We formulate deep learning from a perspective relevant to electromagnetic image reconstruction. A recurrent auto-encoder network architecture (termed here DeepTMI) is presented. The effectiveness of the algorithm is demonstrated by segmenting the reconstructed images of an experimental tissue-mimicking phantom. The structure similarity measure (SSIM) and mean-square-error (MSE) average of normalized reconstructed results by the DeepTMI method are about 0.94 and 0.04 respectively, while that average obtained from the conventional backpropagation (BP) method can hardly overcome 0.35 and 0.41 respectively.
High-dimensional stochastic control models for newsvendor problems and deep learning resolution
Authors: Jingtang Ma, Shan Yang

This paper studies continuous-time models for newsvendor problems with dynamic replenishment, financial hedging and Stackelberg competition. These factors are considered simultaneously and the high-dimensional stochastic control models are established. High-dimensional Hamilton-Jacobi-Bellman (HJB) equations are derived for the value functions. To circumvent the curse of dimensionality, a deep learning algorithm is proposed to solve the HJB equations. A projection is introduced in the algorithm to avoid the gradient explosion during the training phase. The deep learning algorithm is implemented for HJB equations derived from the newsvendor models with dimensions up to six. Numerical outcomes validate the algorithm’s accuracy and demonstrate that the high-dimensional stochastic control models can successfully mitigate the risk.