ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep learning-based solution for smart contract vulnerabilities detection
Authors: Xueyan Tang, Yuying Du, Alan Lai, Ze Zhang, Lingzhi Shi

This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.
Image deep learning in fault diagnosis of mechanical equipment
Authors: Chuanhao Wang, Yongjian Sun, Xiaohong Wang

With the development of industry, more and more crucial mechanical machinery generate wildness demand of effective fault diagnosis to ensure the safe operation. Over the past few decades, researchers have explored and developed a variety of approaches. In recent years, fault diagnosis based on deep learning has developed rapidly, which achieved satisfied results in the filed of mechanical equipment fault diagnosis. However, there is few review to systematically summarize and sort out these special image deep learning methods. In order to fill this gap, this paper concentrates on reviewing comprehensively the development of special image deep learning for mechanical equipment fault diagnosis in past 5 years. In general, a typical image fault diagnosis based on fault image deep learning generally consists of data acquisition, signal processing, model construction, feature learning and decision-making. Firstly, the method of signal preprocessing is introduced, and several common methods of converting signals into images are briefly compared and analyzed. Then, the principles and variants of deep learning models are expounded. Further more, the difficulties and challenges encountered at this stage are summarized. Last but not least, the future development and potential trends of the work are concluded, and it hoped that this work will facilitate and inspire further exploration for researchers in this area.
A deep learning framework for non-functional requirement classification
Authors: Kiramat Rahman, Anwar Ghani, Sanjay Misra, Arif Ur Rahman

Analyzing, identifying, and classifying nonfunctional requirements from requirement documents is time-consuming and challenging. Machine learning-based approaches have been proposed to minimize analysts’ efforts, labor, and stress. However, the traditional approach of supervised machine learning necessitates manual feature extraction, which is time-consuming. This study presents a novel deep-learning framework for NFR classification to overcome these limitations. The framework leverages a more profound architecture that naturally captures feature structures, possesses enhanced representational power, and efficiently captures a broader context than shallower structures. To evaluate the effectiveness of the proposed method, an experiment was conducted on two widely-used datasets, encompassing 914 NFR instances. Performance analysis was performed on the applied models, and the results were evaluated using various metrics. Notably, the DReqANN model outperforms the other models in classifying NFR, achieving precision between 81 and 99.8%, recall between 74 and 89%, and F1-score between 83 and 89%. These significant results highlight the exceptional efficacy of the proposed deep learning framework in addressing NFR classification tasks, showcasing its potential for advancing the field of NFR analysis and classification.
Deep Learning-Enabled Image Classification for the Determination of Aluminum Ions
Authors: Ce Wang, Zhaoliang Wang, Yifei Lu, Tingting Hao, Yufang Hu, Sui Wang, Zhiyong Guo

AbstractIn this work, an image classification based on deep learning for quantitative field determination of aluminum ions (Al3+) was developed. Carbon quantum dots with yellow fluorescence were synthesized by a one-pot hydrothermal method which could specifically recognize Al3+ and produce enhanced green fluorescence. Using the convolutional neural network model in deep learning, an image classification was constructed to classify Al3+ samples at different concentrations. Then, a fitting method for classification information was proposed for the first time which could convert discontinuous, semi-quantitative concentration classification information into continuous, quantitative, and accurate concentration information. Recoveries of 92.0–110.3% in the concentration range of 0.3–320 μM were obtained with a lower limit of detection of 0.3 μM, exhibiting excellent accuracy and sensitivity. It could be completed in 2 min simply without requiring large equipment. Thus, the deep learning-enabled image classification paves a new way for the determination of metal ions.
A comparative analysis of classical machine learning and deep learning techniques for predicting lung cancer survivability
Authors: Shigao Huang, Ibrahim Arpaci, Mostafa Al-Emran, Serhat Kılıçarslan, Mohammed A. Al-Sharafi

Lung cancer, one of the deadliest forms of cancer, can significantly improve patient survival rates by 60–70% if detected in its early stages. The prediction of lung cancer patient survival has grown to be a popular area of research among medical and computer science experts. This study aims to predict the survival period of lung cancer patients using 12 demographic and clinical features. This is achieved through a comparative analysis between traditional machine learning and deep learning techniques, deviating from previous studies that primarily used CT or X-ray images. The dataset included 10,001 lung cancer patients, and the data attributes involved gender, age, race, T (tumor size), M (tumor dissemination to other organs), N (lymph node involvement), Chemo, DX-Bone, DX-Brain, DX-Liver, DX-Lung, and survival months. Six supervised machine learning and deep learning techniques were applied, including logistic-regression (Logistic), Bayes classifier (BayesNet), lazy-classifier (LWL), meta-classifier (AttributeSelectedClassifier (ASC)), rule-learner (OneR), decision-tree (J48), and deep neural network (DNN). The findings suggest that DNN surpassed the performance of the six traditional machine learning models in accurately predicting the survival duration of lung cancer patients, achieving an accuracy rate of 88.58%. This evidence is thought to assist healthcare experts in cost management and timely treatment provision.
A comprehensive review of deep learning power in steady-state visual evoked potentials
Authors: Z. T. Al-Qaysi, A. S. Albahri, M. A. Ahmed, Rula A. Hamid, M. A. Alsalem, O. S. Albahri, A. H. Alamoodi, Raad Z. Homod, Ghadeer Ghazi Shayea, Ali M. Duhaim

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, includes intelligent decision-making tools for evaluation and benchmarking. Rather than just finding a comparable or similar analogy, this framework is intended to help guide future research and pragmatic applications, and to determine which SSVEP-based BCI applications have succeeded at responsibility for what they set out with.
Quantum deep learning-based anomaly detection for enhanced network security
Authors: Moe Hdaib, Sutharshan Rajasegarar, Lei Pan

Identifying and mitigating aberrant activities within the network traffic is important to prevent adverse consequences caused by cyber security incidents, which have been increasing significantly in recent times. Existing research mainly focuses on classical machine learning and deep learning-based approaches for detecting such attacks. However, exploiting the power of quantum deep learning to process complex correlation of features for anomaly detection is not well explored. Hence, in this paper, we investigate quantum machine learning and quantum deep learning-based anomaly detection methodologies to accurately detect network attacks. In particular, we propose three novel quantum auto-encoder-based anomaly detection frameworks. Our primary aim is to create hybrid models that leverage the strengths of both quantum and deep learning methodologies for efficient anomaly recognition. The three frameworks are formed by integrating the quantum autoencoder with a quantum one-class support vector machine, a quantum random forest, and a quantum k-nearest neighbor approach. The anomaly detection capability of the frameworks is evaluated using benchmark datasets comprising computer and Internet of Things network flows. Our evaluation demonstrates that all three frameworks have a high potential to detect the network traffic anomalies accurately, while the framework that integrates the quantum autoencoder with the quantum k-nearest neighbor yields the highest accuracy. This demonstrates the promising potential for the development of quantum frameworks for anomaly detection, underscoring their relevance for future advancements in network security.
An enhanced deep learning approach for vascular wall fracture analysis
Authors: Alexandros Tragoudas, Marta Alloisio, Elsayed S. Elsayed, T. Christian Gasser, Fadi Aldakheel

This work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response.
Deep Learning-Based Image and Video Inpainting: A Survey
Authors: Weize Quan, Jiaxi Chen, Yanli Liu, Dong-Ming Yan, Peter Wonka

Image and video inpainting is a classic problem in computer vision and computer graphics, aiming to fill in the plausible and realistic content in the missing areas of images and videos. With the advance of deep learning, this problem has achieved significant progress recently. The goal of this paper is to comprehensively review the deep learning-based methods for image and video inpainting. Specifically, we sort existing methods into different categories from the perspective of their high-level inpainting pipeline, present different deep learning architectures, including CNN, VAE, GAN, diffusion models, etc., and summarize techniques for module design. We review the training objectives and the common benchmark datasets. We present evaluation metrics for low-level pixel and high-level perceptional similarity, conduct a performance evaluation, and discuss the strengths and weaknesses of representative inpainting methods. We also discuss related real-world applications. Finally, we discuss open challenges and suggest potential future research directions.
A pedagogical study on promoting students' deep learning through design-based learning
Authors: Chunmeng Weng, Congying Chen, Xianfeng Ai

This paper illustrates the design-based learning (DBL) approach to promoting the deep learning of students and improving the quality of teaching in engineering design education. We performed three aspects of research with students in a typical educational activity. The first study investigated students' deep learning before and after the DBL approach, both in terms of deep learning status and deep learning ability. The second study examined the effectiveness of the DBL approach by comparative research of a control class (traditional teaching method) and an experimental class (DBL method). The third study examined students' evaluations of the DBL approach. It is approved that the DBL approach has distinctively stimulated the students' motivation to learn, making them more actively engaged in study. The students' higher-order thinking and higher-order capabilities are enhanced, such as critical thinking ability and problem-solving ability. At the same time, they are satisfied with the DBL approach. These findings suggest that the DBL approach is effective in promoting students' deep learning and improving the quality of teaching and learning.
Brain tumour detection using machine and deep learning: a systematic review
Authors: Novsheena Rasool, Javaid Iqbal Bhat

Brain tumors rank as the 1oth leading cause of mortality worldwide, accounting for 85% to 95% of all primary nervous system malignancies. The prevalence of this life-threatening disease is steadily increasing worldwide, highlighting the urgent need for an early and precise diagnosis. Timely identification is critical for initiating effective treatment and improving patient survival chances. Delayed diagnosis significantly elevates the risk of mortality. However, the heterogeneous nature of tumor cells poses challenges for radiologists, making manual diagnosis from magnetic resonance imaging (MRI) images time-consuming and complex. Machine learning (ML) and deep learning (DL) have become useful tools in medical image analysis. These techniques facilitate the automated extraction of intricate patterns and features from MRI images, thereby facilitating a more accurate and efficient tumor diagnosis. Furthermore, these algorithms have demonstrated the capability to handle the intricacy and variability of brain tumor characteristics, thereby improving the diagnostic process. A range of deep learning-based algorithms have been utilized to detect brain tumors, yielding impressive results. The purpose of this paper is to provide an exhaustive examination of the latest techniques used for diagnosing brain tumors from MRI imaging, utilizing machine and deep learning technologies. Moreover, it seeks to outline potential avenues for future exploration within this field. The profound insights gleaned from this comprehensive review are poised to offer invaluable guidance and support to both researchers and medical professionals in the healthcare industry.
Deep learning-based streamflow prediction for western Himalayan river basins
Authors: Tabasum Majeed, Riyaz Ahmad Mir, Rayees Ahmad Dar, Mohd Anul Haq, Shabana Nargis Rasool, Assif Assad

Accurate streamflow (Qflow) forecasting plays a pivotal role in water resource monitoring and management, presenting a complex challenge for water managers and engineers. Effective streamflow prediction enables the optimized operation of water resource systems in alignment with technological, financial, ethical, and political objectives. Traditional data-driven models like the Autoregressive model and Autoregressive Moving Average model are widely used for water resource management. However, these models show limitations in handling intricate nonlinear hydrological phenomena. To address these limitations, Deep Learning models emerge as promising alternatives, given their inherent ability to handle nonlinearity. Nonlinearity in time series modeling is formidable due to factors like long-term trends, seasonal variations, cyclical oscillations, and external disturbances. This study proposes a deep neural network architecture based on Neural Basis Expansion Analysis for Time Series (N-BEATS) to predict the daily Qflow of western Himalayan river basins. The study employs datasets collected from the Rampur station of the Satluj basin and the Pandoh and Manali stations of the Beas basin. The experimental results unequivocally demonstrate the superiority of the proposed deep neural network model over benchmarked conventional deep learning models such as Long Short-Term Memory, Feedforward Neural Network, Gated Recurrent Unit, and Recurrent Neural Network. The proposed deep neural network model achieves remarkable accuracy, exhibiting a root mean square error below 0.05 m3/s when comparing actual and predicted Qflow values across all datasets. Consequently, the proposed deep neural network model based on N-BEATS emerges as an efficient and invaluable solution for precise Qflow prediction, empowering efficient water resource management and control. The results suggest that the proposed model can serve for streamflow prediction and water management in Himalayan river basins.
Supervised deep learning for content-aware image retargeting with Fourier Convolutions
Authors: MohammadHossein Givkashi, MohammadReza Naderi, Nader Karimi, Shahram Shirani, Shadrokh Samavi

Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, normal convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model’s ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach.
Identifying keystone species in microbial communities using deep learning
Authors: Xu-Wen Wang, Zheng Sun, Huijue Jia, Sebastian Michel-Mata, Marco Tulio Angulo, Lei Dai, Xuesong He, Scott T. Weiss, Yang-Yu Liu

Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.