ADKU/ResearchGPT_model_dataset huggingface dataset:
A hybrid deep model with cumulative learning for few-shot learning
Authors: Jiehao Liu, Zhao Yang, Liufei Luo, Mingkai Luo, Luyu Hu, Jiahao Li

Few-shot learning (FSL) aims to recognize unseen classes with only a few samples for each class. This challenging research endeavors to narrow the gap between the computer vision technology and the human visual system. Recently, mainstream approaches for FSL can be grouped into meta-learning and classification learning. These two methods train the FSL model from local and global classification viewpoints respectively. In our work, we find the former method can effectively learn transferable knowledge (generalization capacity) with an episodic training paradigm but encounters the problem of slow convergence. The latter method can build an essential classification ability quickly (classification capacity) with a mini-batch training paradigm but easily causes an over-fitting problem. In light of this issue, we propose a hybrid deep model with cumulative learning to tackle the FSL problem by absorbing the advantages of the both methods. The proposed hybrid deep model innovatively integrates meta-learning and classification learning (IMC) in a unified two-branch network framework in which a meta-learning branch and a classification learning branch can work simultaneously. Besides, by considering the different characteristics of the two branches, we propose a cumulative learning strategy to take care of both generalization capacity learning and classification capacity learning in our IMC model training. With the proposed method, the model can quickly build the basic classification capability at the initial stage and continually mine discriminative class information during the remaining training for better generalization. Extensive experiments on CIFAR-FS, FC100, mini-ImageNet and tiered-ImageNet datasets are implemented to demonstrate the promising performance of our method.
Distributed source DOA estimation based on deep learning networks
Authors: Quan Tian, Ruiyan Cai, Gongrun Qiu, Yang Luo

With space electromagnetic environments becoming increasingly complex, the direction of arrival (DOA) estimation based on the point source model can no longer meet the requirements of spatial target location. Based on the characteristics of the distributed source, a new DOA estimation algorithm based on deep learning is proposed. The algorithm first maps the distributed source model into the point source model via a generative adversarial network (GAN) and further combines the subspace-based method to achieve central DOA estimation. Second, by constructing a deep neural network (DNN), the covariance matrix of the received signals is used as the input to estimate the angular spread of the distributed source. The experimental results show that the proposed algorithm can achieve better performance than the existing methods for a distributed source.
Deep learning implementations in mining applications: a compact critical review
Authors: Faris Azhari, Charlotte C. Sennersten, Craig A. Lindley, Ewan Sellers

Deep learning is a sub-field of artificial intelligence that combines feature engineering and classification in one method. It is a data-driven technique that optimises a predictive model via learning from a large dataset. Digitisation in industry has included acquisition and storage of a variety of large datasets for interpretation and decision making. This has led to the adoption of deep learning in different industries, such as transportation, manufacturing, medicine and agriculture. However, in the mining industry, the adoption and development of new technologies, including deep learning methods, has not progressed at the same rate as in other industries. Nevertheless, in the past 5 years, applications of deep learning have been increasing in the mining research space. Deep learning has been implemented to solve a variety of problems related to mine exploration, ore and metal extraction and reclamation processes. The increased automation adoption in mining provides an avenue for wider application of deep learning as an element within a mine automation framework. This work provides a compact, comprehensive review of deep learning implementations in mining-related applications. The trends of these implementations in terms of years, venues, deep learning network types, tasks and general implementation, categorised by the value chain operations of exploration, extraction and reclamation are outlined. The review enables shortcomings regarding progress within the research context to be highlighted such as the proprietary nature of data, small datasets (tens to thousands of data points) limited to single operations with unique geology, mine design and equipment, lack of large scale publicly available mining related datasets and limited sensor types leading to the majority of applications being image-based analysis. Gaps identified for future research and application includes the usage of a wider range of sensor data, improved understanding of the outputs by mining practitioners, adversarial testing of the deep learning models, development of public datasets covering the extensive range of conditions experienced in mines.
Deep learning based active image steganalysis: a review
Authors: Punam Bedi, Anuradha Singhal, Veenu Bhasin

Steganalysis plays a vital role in cybersecurity in today’s digital era where exchange of malicious information can be done easily across web pages. Steganography techniques are used to hide data in an object where the existence of hidden information is also obscured. Steganalysis is the process for detection of steganography within an object and can be categorized as active and passive steganalysis. Passive steganalysis tries to classify a given object as a clean or modified object. Active steganalysis aims to extract more details about hidden contents such as length of embedded message, region of inserted message, key used for embedding, required by cybersecurity experts for comprehensive analysis. Images being a viable source of exchange of information in the era of internet, social media are the most susceptible source for such transmission. Many researchers have worked and developed techniques required to detect and alert about such counterfeit exchanges over the internet. Literature present in passive and active image steganalysis techniques, addresses these issues by detecting and unveiling details of such obscured communication respectively. This paper provides a systematic and comprehensive review of work done on active image steganalysis techniques using deep learning techniques. This review will be helpful to the new researchers to become aware and build a strong foundation of literature present in active image steganalysis using deep learning techniques. The paper also includes various steganographic algorithms, dataset and performance evaluation metrics used in literature. Open research challenges and possible future research directions are also discussed in the paper.
Early detection and prediction of Heart Disease using Wearable devices and Deep Learning algorithms
Authors: S. Sivasubramaniam, S. P. Balamurugan

In this paper, we propose a multimodal deep learning algorithm that combines convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for early detection and prediction of heart disease using data collected from wearable devices. This combined multi-model deep learning algorithm is used to detect the accurate precision and accuracy value. At first, we consider, ECG and PPG signals, which are collected from the dataset. Then, the features from ECG and PPG are extracted using CNN and the accelerometer features are extracted using the LSTM model. The combined features are then classified using hybrid CNN-LSTM network architecture. The algorithm is evaluated using a publicly available benchmark dataset. The model achieved an accuracy of 99.33% in detecting heart disease, outperforming several state-of-the-art deep learning models. In addition, the model can predict the likelihood of developing heart disease with a precision of 99.33%, providing an early warning system for at-risk patients. The results demonstrate the potential of a multimodal approach for early detection and prediction of heart disease using wearable devices and deep learning algorithms.
Deep cross-domain transfer for emotion recognition via joint learning
Authors: Dung Nguyen, Duc Thanh Nguyen, Sridha Sridharan, Mohamed Abdelrazek, Simon Denman, Son N. Tran, Rui Zeng, Clinton Fookes

Deep learning has been applied to achieve significant progress in emotion recognition from multimedia data. Despite such substantial progress, existing approaches are hindered by insufficient training data, leading to weak generalisation under mismatched conditions. To address these challenges, we propose a learning strategy which jointly transfers emotional knowledge learnt from rich datasets to source-poor datasets. Our method is also able to learn cross-domain features, leading to improved recognition performance. To demonstrate the robustness of the proposed learning strategy, we conducted extensive experiments on several benchmark datasets including eNTERFACE, SAVEE, EMODB, and RAVDESS. Experimental results show that the proposed method surpassed existing transfer learning schemes by a significant margin.
Scoring method of English composition integrating deep learning in higher vocational colleges
Authors: Shuo Feng, Lixia Yu, Fen Liu

Along with the progress of natural language processing technology and deep learning, the subjectivity, slow feedback, and long grading time of traditional English essay grading have been addressed. Intelligent English automatic scoring has been widely concerned by scholars. Given the limitations of topic relevance feature extraction methods and traditional automatic grading methods for English compositions, a topic decision model is proposed to calculate the topic relevance score of the topic richness in English composition. Then, based on the Score of Relevance Based on Topic Richness (TRSR) calculation method, an intelligent English composition scoring method combining artificial feature extraction and deep learning is designed. From the findings, the Topic Decision (TD) model achieved the best effect only when it was iterated 80 times. The corresponding accuracy, recall and F1 value were 0.97, 0.93 and 0.95 respectively. The model training loss finally stabilized at 0.03. The Intelligent English Composition Grading Method Integrating Deep Learning (DLIECG) method has the best overall performance and the best performance on dataset P. To sum up, the intelligent English composition scoring method has better effectiveness and reliability.
Deep learning based vessel arrivals monitoring via autoregressive statistical control charts
Authors: Sara El Mekkaoui, Ghait Boukachab, Loubna Benabbou, Abdelaziz Berrado

This paper introduces a methodology for monitoring the vessel arrival process, a critical factor in enhancing maritime operational efficiency. This approach uses deep learning sequence models and Statistical Process Control Charts to track the variability in a vessel arrival process. The proposed solution uses the predictive deep learning model to get a vessel’s estimated time of arrival, produces quality characteristics, and applies statistical control charts to monitor their variability. The paper presents the results of applying the proposed methodology for vessel arrivals at a coal terminal, which demonstrates the effectiveness of the method. By enabling precise monitoring of arrival times, this methodology not only supports efficient ship and port operations planning but also aids in the timely adoption of operational adjustments. This can significantly contribute to operational measures aimed at reducing shipping emissions and optimizing resource utilization.
Single sample face recognition using deep learning: a survey
Authors: Vivek Tomar, Nitin Kumar, Ayush Raj Srivastava

Face recognition has become popular in the last few decades among researchers across the globe due to its applicability in several domains. This problem becomes more challenging when only a single training image is available and is popularly known as single sample face recognition (SSFR) problem. SSFR becomes even more complex when images are captured under varying illumination conditions, different poses, occlusion, and expression. Further, deep learning methods have shown performance at par with humans recently. Due to the emergence of deep learning methods in the last decade, it has been made possible to recognize faces with excellent accuracy even in a single sample scenario. In this paper, we present a comprehensive survey of SSFR using deep learning. We also propose a novel taxonomy and broadly divide these methods into three categories viz. virtual sample generation, feature-based, and hybrid methods. Performance comparison of these methods as reported in the literature has also been performed. Finally, we review publicly available databases used by the researchers and give some important future research directions which will help aspiring researchers in this fascinating area.
RNA contact prediction by data efficient deep learning
Authors: Oskar Taubert, Fabrice von der Lehr, Alina Bazarova, Christian Faber, Philipp Knechtges, Marie Weiel, Charlotte Debus, Daniel Coquelin, Achim Basermann, Achim Streit, Stefan Kesselheim, Markus Götz, Alexander Schug

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps”) as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction.
A review on vision-based deep learning techniques for damage detection in bolted joints
Authors: Zahir Malik, Ansh Mirani, Tanneru Gopi, Mallika Alapati

Bolted connections are widely used in steel structures. Detection of bolt loosening is the prime concern in the bolted joints to avoid sudden failure leading to catastrophe. Loosening of the bolts causes interfacial movement by reducing the pre-torque when subjected to vibrations due to dynamic loads. With the advent of computing capabilities, sensor technologies, and machine learning model accuracy in bolt loosening detection, damage recognition efficiency in bolted joints has increased. Integrating deep learning with machine vision, effective models can be proposed without human interventions. The present paper summarizes the research review on bolt loosening detection using machine vision and deep learning techniques from the past decade.
Real-time thermography for breast cancer detection with deep learning
Authors: Mohammed Abdulla Salim Al Husaini, Mohamed Hadi Habaebi, Md Rafiqul Islam

In this study, we propose a framework that enhances breast cancer classification accuracy by preserving spatial features and leveraging in situ cooling support. The framework utilizes real-time thermography video streaming for early breast cancer detection using Deep Learning models. Inception v3, Inception v4, and a modified Inception Mv4 were developed using MATLAB 2019. However, the thermal camera was connected to a mobile phone to capture images of the breast area for classification of normal and abnormal breast. This study’s training dataset included 1000 thermal images, where a FLIR One Pro thermal camera connected to a mobile device was used for the imaging process. Of the 1000 images obtained, 700 images were considered for the normal breast thermography class while the 300 images were suitable for the abnormal class. We evaluate Deep Convolutional Neural Network models, such as Inception v3, Inception v4, and a modified Inception Mv4. Our results demonstrate that Inception Mv4, with real-time video streaming, efficiently detects even the slightest temperature contrast in breast tissue sequences achieving a 99.748% accuracy in comparison to a 99.712% and 96.8% for Inception v4 and v3, respectively. The use of in situ cooling gel further enhances image acquisition efficiency and detection accuracy. Interestingly, increasing the tumor surface temperature by 0.1% leads to an average 7% improvement in detection and classification accuracy. Our findings support the effectiveness of Inception Mv4 for real-time breast cancer detection, especially when combined with in situ cooling gel and varying tumor temperatures. In conclusion, future research directions should focus on incorporating thermal video clips into the thermal images database, utilizing high-quality thermal cameras, and exploring alternative Deep Learning models for improved breast cancer detection.
How deep learning is complementing deep thinking in ATLAS
Authors: Deepak Kar

ATLAS collaboration uses machine learning (ML) algorithms in many different ways in its physics programme, starting from object reconstruction, simulation of calorimeter showers, signal to background discrimination in searches and measurements, tagging jets based on their origin and so on. Anomaly detection (AD) techniques are also gaining popularity where they are used to find hidden patterns in the data, with lesser dependence on simulated samples as in the case of supervised learning-based methods. ML methods used in detector simulation and in jet tagging in ATLAS will be discussed, along with four searches using ML/AD techniques.
Multiclass skin lesion classification using deep learning networks optimal information fusion
Authors: Muhammad Attique Khan, Ameer Hamza, Mohammad Shabaz, Seifeine Kadry, Saddaf Rubab, Muhammad Abdullah Bilal, Muhammad Naeem Akbar, Suresh Manic Kesavan

A serious, all-encompassing, and deadly cancer that affects every part of the body is skin cancer. The most prevalent causes of skin lesions are UV radiation, which can damage human skin, and moles. If skin cancer is discovered early, it may be adequately treated. In order to diagnose skin lesions with less effort, dermatologists are increasingly turning to machine learning (ML) techniques and computer-aided diagnostic (CAD) systems. This paper proposes a computerized method for multiclass lesion classification using a fusion of optimal deep-learning model features. The dataset used in this work, ISIC2018, is imbalanced; therefore, augmentation is performed based on a few mathematical operations. After that, two pre-trained deep learning models (DarkNet-19 and MobileNet-V2) have been fine-tuned and trained on the selected dataset. After training, features are extracted from the average pool layer and optimized using a hybrid firefly optimization technique. The selected features are fused in two ways: (i) original serial approach and (ii) proposed threshold approach. Machine learning classifiers are used to classify the chosen features at the end. Using the ISIC2018 dataset, the experimental procedure produced an accuracy of 89.0%. Whereas, 87.34, 87.57, and 87.45 are sensitivity, precision, and F1 score respectively. At the end, comparison is also conducted with recent techniques, and it shows the proposed method shows improved accuracy along with other performance measures.
REDQT: a method for automated mobile application GUI testing based on deep reinforcement learning algorithms
Authors: Fengyu Wang, Chuanqi Tao, Jerry Gao

As mobile applications become increasingly prevalent in daily life, the demand for their functionality and reliability continues to grow. Traditional mobile application testing methods, particularly graphical user interface (GUI) testing, face challenges of limited automation and adaptability.Despite the application of various machine learning approaches to GUI testing, enhancing the utilization of limited component samples in complex mobile application environments remains an overlooked issue in many automated testing methods. This study introduces a mobile application testing method based on deep reinforcement learning, aimed at improving performance and adaptability during the testing process. By integrating the feature recognition capabilities of deep learning with the decision-making mechanisms of reinforcement learning, our method can effectively simulate user operations and identify potential application pitfalls. Initially, the study analyzes the limitations of traditional mobile application testing methods and explores the advantages of deep reinforcement learning in handling complex tasks. Subsequently, we present RedqT: an automated mobile application GUI testing method based on a deep reinforcement learning algorithm (REDQ), aimed at enhancing the utilization of application information through the characteristics of the REDQ algorithm. A study testing 18 open-source Android applications on GitHub demonstrated that our method shows promising performance in terms of code coverage and testing speed.
Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
Authors: Sarmad Dashti Latif, Ali Najah Ahmed

As a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resources; they have benefited human cultures in a variety of ways, including enhanced human health, increased food production, water supply for domestic and industrial use, economic growth, irrigation, hydro-power generation, and flood control. This study aims to compare the application of deep learning and conventional machine learning algorithms for predicting daily reservoir inflow. Long short-term memory (LSTM) has been applied as a deep learning algorithm and boosted regression tree (BRT) has been implemented as a machine learning algorithm. Five statistical indices have been selected to evaluate the performance of the proposed models. The selected statistical measurements are mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), Nash Sutcliffe Model Efficiency Coefficient (NSE), and the RMSE-observations standard deviation ratio (RSR). The findings showed that LSTM outperformed BRT with a significant difference in terms of accuracy.
Deep learning enables fast, gentle STED microscopy
Authors: Vahid Ebrahimi, Till Stephan, Jiah Kim, Pablo Carravilla, Christian Eggeling, Stefan Jakobs, Kyu Young Han

STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that restoring STED images with deep learning can mitigate photobleaching and photodamage by reducing the pixel dwell time by one or two orders of magnitude. Our method allows for efficient and robust restoration of noisy 2D and 3D STED images with multiple targets and facilitates long-term imaging of mitochondrial dynamics.