ADKU/ResearchGPT_model_dataset huggingface dataset:
Meta-heuristic-based hybrid deep learning model for vulnerability detection and prevention in software system
Authors: Lijin Shaji, R. Suji Pramila

Software vulnerabilities are flaws that may be exploited to cause loss or harm. Various automated machine-learning techniques have been developed in preceding studies to detect software vulnerabilities. This work tries to develop a technique for securing the software on the basis of their vulnerabilities that are already known, by developing a hybrid deep learning model to detect those vulnerabilities. Moreover, certain countermeasures are suggested based on the types of vulnerability to prevent the attack further. For different software projects taken as the dataset, feature fusion is done by utilizing canonical correlation analysis together with Deep Residual Network (DRN). A hybrid deep learning technique trained using AdamW-Rat Swarm Optimizer (AdamW-RSO) is designed to detect software vulnerability. Hybrid deep learning makes use of the Deep Belief Network (DBN) and Generative Adversarial Network (GAN). For every vulnerability, its location of occurrence within the software development procedures and techniques of alleviation via implementation level or design level activities are described. Thus, it helps in understanding the appearance of vulnerabilities, suggesting the use of various countermeasures during the initial phases of software design, and therefore, assures software security. Evaluating the performance of vulnerability detection by the proposed technique regarding recall, precision, and f-measure, it is found to be more effective than the existing methods.
Sentiment analysis of Canadian maritime case law: a sentiment case law and deep learning approach
Authors: Bola Abimbola, Qing Tan, Enrique A. De La Cal Marín

Historical information in the Canadian Maritime Judiciary increases with time because of the need to archive data to be utilized in case references and for later application when determining verdicts for similar cases. However, such data are typically stored in multiple systems, making its reachability technical. Utilizing technologies like deep learning and sentiment analysis provides chances to facilitate faster access to court records. Such practice enhances impartial verdicts, minimizing workloads for court employees, and decreases the time used in legal proceedings for claims during maritime contracts such as shipping disputes between parties. This paper seeks to develop a sentiment analysis framework that uses deep learning, distributed learning, and machine learning to improve access to statutes, laws, and cases used by maritime judges in making judgments to back their claims. The suggested approach uses deep learning models, including convolutional neural networks (CNNs), deep neural networks, long short-term memory (LSTM), and recurrent neural networks. It extracts court records having crucial sentiments or statements for maritime court verdicts. The suggested approach has been used successfully during sentiment analysis by emphasizing feature selection from a legal repository. The LSTM + CNN model has shown promising results in obtaining sentiments and records from multiple devices and sufficiently proposing practical guidance to judicial personnel regarding the regulations applicable to various situations.
Multi-step framework for glaucoma diagnosis in retinal fundus images using deep learning
Authors: Sanli Yi, Lingxiang Zhou

Glaucoma is one of the most common causes of blindness in the world. Screening glaucoma from retinal fundus images based on deep learning is a common method at present. In the diagnosis of glaucoma based on deep learning, the blood vessels within the optic disc interfere with the diagnosis, and there is also some pathological information outside the optic disc in fundus images. Therefore, integrating the original fundus image with the vessel-removed optic disc image can improve diagnostic efficiency. In this paper, we propose a novel multi-step framework named MSGC-CNN that can better diagnose glaucoma. In the framework, (1) we combine glaucoma pathological knowledge with deep learning model, fuse the features of original fundus image and optic disc region in which the interference of blood vessel is specifically removed by U-Net, and make glaucoma diagnosis based on the fused features. (2) Aiming at the characteristics of glaucoma fundus images, such as small amount of data, high resolution, and rich feature information, we design a new feature extraction network RA-ResNet and combined it with transfer learning. In order to verify our method, we conduct binary classification experiments on three public datasets, Drishti-GS, RIM-ONE-R3, and ACRIMA, with accuracy of 92.01%, 93.75%, and 97.87%. The results demonstrate a significant improvement over earlier results.Graphical abstract
A new content-aware image resizing based on Rényi entropy and deep learning
Authors: Jila Ayubi, Mehdi Chehel Amirani, Morteza Valizadeh

One of the most popular techniques for changing the purpose of an image or resizing a digital image with content awareness is the seam-carving method. The performance of image resizing algorithms based on seam machining shows that these algorithms are highly dependent on the extraction of importance map techniques and the detection of salient objects. So far, various algorithms have been proposed to extract the importance map. In this paper, a new method based on Rényi entropy is proposed to extract the importance map. Also, a deep learning network has been used to detect salient objects. The simulator results showed that combining Rényi’s importance map with a deep network of salient object detection performed better than classical seam-carving and other extended seam-carving algorithms based on deep learning.
A deep learning framework for students' academic performance analysis
Authors: Sumati Pathak, Hiral Raja, Sumit Srivastava, Neelam Sahu, Rohit Raja, Amit Kumar Dewangan

Students Performance (SP) analysis is regarded as one of the most important steps in the educational system for supporting students' academic success and the institutions' overall outcomes. Nevertheless, it is tremendously challenging due to the numerous details that many students have. Data Mining (DM) is the most widely used approach for SP prediction that extracts imperative information from a bigger raw data set. Even though there are various DM-centered performance prediction approaches, they all have low accuracy and high training time and don't produce the desired output. This paper proposes a hybrid deep learning framework using Deer Hunting Optimization based Deep Learning Neural Networks (DH-DLNN). A self-structured questionnaire covers all aspects of using information and communication technology, including increased access, knowledge building, learning, performance, motivation, classroom management and interaction, collaborative learning, and satisfaction. Data Cleaning and data conversion preprocess the dataset. The prediction of the student's level is then performed by extracting imperative features from the preprocessed data, followed by feature ranking using entropy calculations. The obtained entropy values are inputted into the DH-DLNN, which predicts the students' academic performance. Finally, the accuracy of the proposed system is evaluated using K-fold cross-validation. The experiment results revealed that DH-DLNN outperforms the other classification approaches with an accuracy of 96.33%.
Application of deep learning and XGBoost in predicting pathological staging of breast cancer MR images
Authors: Yue Miao, Siyuan Tang, Zhuqiang Zhang, Jukun Song, Zhi Liu, Qiang Chen, Miao Zhang

The methods of deep learning and traditional radiomics feature extraction were preliminarily discussed, and a multimodal data prediction model for breast cancer clinical stage was established. The MR images and clinical staging data of breast cancer were obtained from the official websites of the American Cancer Center TCGA and TCIA, respectively, with a total of 139 patient samples. The region of interest was delineated on the enhanced image of breast cancer MR, and then the feature extraction of radiomics and deep learning was performed, and 108 radiomics features and 1024 deep-learning features were extracted for each case. After feature screening and processing, clinical data were integrated, and a machine-learning model was used to predict clinical stage I and non-stage I. Results 26 radiomic features and 12 deep features related to staging were screened out by LASSO algorithm, and a classification model was constructed based on XGBoost machine learning. The patients were predicted with an accuracy rate of 80.00%, and the area under the curve of the receiver operating characteristic curve was 0.833. It is feasible to predict the clinical stage of breast cancer through radiomics and deep-learning feature extraction and machine-learning technology. The classification model based on multimodal data established by using machine-learning classifier can distinguish clinical stage I and non-stage I in breast cancer and have higher accuracy. This study confirms the feasibility and accuracy of combining data from different modalities to contribute to clinical staging prediction. The research contributions include demonstrating the superiority of deep-learning models for feature extraction and classification, as well as highlighting the potential of combining deep learning and traditional machine-learning algorithms for improved classification performance.
Learning key steps to attack deep reinforcement learning agents
Authors: Chien-Min Yu, Ming-Hsin Chen, Hsuan-Tien Lin

Deep reinforcement learning agents are vulnerable to adversarial attacks. In particular, recent studies have shown that attacking a few key steps can effectively decrease the agent’s cumulative reward. However, all existing attacking methods define those key steps with human-designed heuristics, and it is not clear how more effective key steps can be identified. This paper introduces a novel reinforcement learning framework that learns key steps through interacting with the agent. The proposed framework does not require any human heuristics nor knowledge, and can be flexibly coupled with any white-box or black-box adversarial attack scenarios. Experiments on benchmark Atari games across different scenarios demonstrate that the proposed framework is superior to existing methods for identifying effective key steps. The results highlight the weakness of RL agents even under budgeted attacks.
An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images
Authors: Andrew S. Boehringer, Amirhossein Sanaat, Hossein Arabi, Habib Zaidi

PurposeThis study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.MethodsThe publicly available training dataset provided for the 2021 RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge was used in this study, consisting of 1251 multi-institutional, multi-parametric MR images. Post-contrast T1, T2, and T2 FLAIR images as well as ground truth manual segmentation were used as input for the model. The data were split into a training set of 1151 cases and testing set of 100 cases, with the testing set remaining constant throughout. Deep convolutional neural network segmentation models were trained using the NiftyNet platform. To test the viability of active learning in training a segmentation model, an initial reference model was trained using all 1151 training cases followed by two additional models using only 575 cases and 100 cases. The resulting predicted segmentations of these two additional models on the remaining training cases were then addended to the training dataset for additional training.ResultsIt was demonstrated that an active learning approach for manual segmentation can lead to comparable model performance for segmentation of brain gliomas (0.906 reference Dice score vs 0.868 active learning Dice score) while only requiring manual annotation for 28.6% of the data.ConclusionThe active learning approach when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.Critical relevance statementActive learning concepts were applied to a deep learning-assisted segmentation of brain gliomas from MR images to assess their viability in reducing the required amount of manually annotated ground truth data in model training.Key points• This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.• The active learning approach for manual segmentation can lead to comparable model performance for segmentation of brain gliomas.• Active learning when applied to model training can drastically reduce the time and labor spent on preparation of ground truth training data.Graphical Abstract
Efficient shallow learning as an alternative to deep learning
Authors: Yuval Meir, Ofek Tevet, Yarden Tzach, Shiri Hodassman, Ronit D. Gross, Ido Kanter

The realization of complex classification tasks requires training of deep learning (DL) architectures consisting of tens or even hundreds of convolutional and fully connected hidden layers, which is far from the reality of the human brain. According to the DL rationale, the first convolutional layer reveals localized patterns in the input and large-scale patterns in the following layers, until it reliably characterizes a class of inputs. Here, we demonstrate that with a fixed ratio between the depths of the first and second convolutional layers, the error rates of the generalized shallow LeNet architecture, consisting of only five layers, decay as a power law with the number of filters in the first convolutional layer. The extrapolation of this power law indicates that the generalized LeNet can achieve small error rates that were previously obtained for the CIFAR-10 database using DL architectures. A power law with a similar exponent also characterizes the generalized VGG-16 architecture. However, this results in a significantly increased number of operations required to achieve a given error rate with respect to LeNet. This power law phenomenon governs various generalized LeNet and VGG-16 architectures, hinting at its universal behavior and suggesting a quantitative hierarchical time–space complexity among machine learning architectures. Additionally, the conservation law along the convolutional layers, which is the square-root of their size times their depth, is found to asymptotically minimize error rates. The efficient shallow learning that is demonstrated in this study calls for further quantitative examination using various databases and architectures and its accelerated implementation using future dedicated hardware developments.
Crop type classification with hyperspectral images using deep learning : a transfer learning approach
Authors: Usha Patel, Mohib Pathan, Preeti Kathiria, Vibha Patel

Crop classification plays a vital role in felicitating agriculture statistics to the state and national government in decision-making. In recent years, due to advancements in remote sensing, high-resolution hyperspectral images (HSIs) are available for land cover classification. HSIs can classify the different crop categories precisely due to their narrow and continuous spectral band reflection. With improvements in computing power and evolution in deep learning technology, Deep learning is rapidly being used for HSIs classification. However, to train deep neural networks, many labeled samples are needed. The labeling of HSIs is time-consuming and costly. A transfer learning approach is used in many applications where a labeled dataset is challenging. This paper opts for the heterogeneous transfer learning models on benchmark HSIs datasets to discuss the performance accuracy of well-defined deep learning models—VGG16, VGG19, ResNet, and DenseNet for crop classification. Also, it discusses the performance accuracy of customized 2-dimensional Convolutional neural network (2DCNN) and 3-dimensional Convolutional neural network (3DCNN) deep learning models using homogeneous transfer learning models on benchmark HSIs datasets for crop classification. The results show that although HSIs datasets contain few samples, the transfer learning models perform better with limited labeled samples. The results achieved 99% of accuracy for the Indian Pines and Pavia University dataset with 15% of labeled training samples with heterogeneous transfer learning. As per the overall accuracy, homogeneous transfer learning with 2DCNN and 3DCNN models pre-trained on the Indian Pines dataset and adjusted on the Salinas scene dataset performs far better than heterogeneous transfer learning.
Novel deep learning models for yoga pose estimator
Authors: Amira Samy Talaat

Yoga pose recognition and correction are artificial intelligent techniques to provide standardized and appropriate yoga poses. Incorrect yoga poses can cause serious injuries and long-term complications. Analyzing human posture can identify and rectify abnormal positions, improving well-being at home. A posture estimator extracts yoga asana attributes from properly represented images. These extracted features are then utilized directly as inputs for various neural networks and machine learning models. These models serve the purpose of evaluating and predicting the accuracy of specific yoga poses. The objective of this research is to explore multiple methods for classifying yoga poses. The LGDeep model is introduced, which combines a novel residual convolutional neural network with three deep learning approaches: Xception, VGGNet, and SqueezeNet. Additionally, the LGDeep model incorporates feature extraction methods such as LDA and GDA. Experimental results demonstrate that the LGDeep classifier outperforms other approaches and achieves the highest classification accuracy ratio.
GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning
Authors: Haipeng Li, Kunming Luo, Bing Zeng, Shuaicheng Liu

Existing homography and optical flow methods are erroneous in challenging scenes, such as fog, rain, night, and snow because the basic assumptions such as brightness and gradient constancy are broken. To address this issue, we present an unsupervised learning approach that fuses gyroscope into homography and optical flow learning. Specifically, we first convert gyroscope readings into motion fields named gyro field. Second, we design a self-guided fusion module (SGF) to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details. Meanwhile, we propose a homography decoder module (HD) to combine gyro field and intermediate results of SGF to produce the homography. To the best of our knowledge, this is the first deep learning framework that fuses gyroscope data and image content for both deep homography and optical flow learning. To validate our method, we propose a new dataset that covers regular and challenging scenes. Experiments show that our method outperforms the state-of-the-art methods in both regular and challenging scenes. The code and dataset are available at https://github.com/lhaippp/GyroFlowPlus.
An effective facial spoofing detection approach based on weighted deep ensemble learning
Authors: My Abdelouahed Sabri, Assia Ennouni, Abdellah Aarab

Deep learning has seen successful implementation in various domains, such as natural language processing, image classification, and object detection in recent times. In the field of biometrics, deep learning has also been used to develop effective anti-spoofing systems. Facial spoofing, the act of presenting fake facial information to deceive a biometric system, poses a significant threat to the security of face recognition systems. To address this challenge, we propose, in this paper, an effective and robust facial spoofing detection approach based on weighted deep ensemble learning. Our method combines the strengths of two powerful deep learning architectures, DenseNet201 and MiniVGG. The choice of these two architectures is based on a comparative study between DenseNet201, DenseNet169, VGG16, MiniVGG, and ResNet50, where DenseNet201 and MiniVGG obtained the best recall and precision scores, respectively. Our proposed weighted voting ensemble leverages each architecture-specific capabilities to make the final prediction. We assign weights to each classification model based on its performance, which are determined by a mathematical formulation considering the trade-off between recall and precision. To validate the effectiveness of our proposed approach, we evaluate it on the challenging ROSE-Youtu face liveness detection dataset. Our experimental results demonstrate that our proposed method achieves an impressive accuracy rate of 99% in accurately detecting facial spoofing attacks.
Deep active learning with high structural discriminability for molecular mutagenicity prediction
Authors: Huiyan Xu, Yanpeng Zhao, Yixin Zhang, Junshan Han, Peng Zan, Song He, Xiaochen Bo

The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecules. However, experimental mutagenicity testing can be time-consuming and costly. One solution to reduce the annotation cost is active learning, where the algorithm actively selects the most valuable molecules from a vast chemical space and presents them to the oracle (e.g., a human expert) for annotation, thereby rapidly improving the model’s predictive performance with a smaller annotation cost. In this paper, we propose muTOX-AL, a deep active learning framework, which can actively explore the chemical space and identify the most valuable molecules, resulting in competitive performance with a small number of labeled samples. The experimental results show that, compared to the random sampling strategy, muTOX-AL can reduce the number of training molecules by about 57%. Additionally, muTOX-AL exhibits outstanding molecular structural discriminability, allowing it to pick molecules with high structural similarity but opposite properties.
A privacy-preserving approach for detecting smishing attacks using federated deep learning
Authors: Mohamed Abdelkarim Remmide, Fatima Boumahdi, Bousmaha Ilhem, Narhimene Boustia

Smishing is a type of social engineering attack that involves sending fraudulent SMS messages to trick recipients into revealing sensitive information. In recent years, it has become a significant threat to mobile communications. In this study, we introduce a novel smishing detection method based on federated learning, which is a decentralized approach ensuring data privacy. We develop a robust detection model within a federated learning framework based on deep learning methods such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). Our experiments show that the federated learning method using Bi-LSTM achieves an accuracy of 88.78%, highlighting its effectiveness in tackling smishing detection while preserving user privacy. This approach not only offers a promising solution to smishing attacks but also lays the groundwork for future research in mobile security and privacy-preserving machine learning.