ADKU/ResearchGPT_model_dataset huggingface dataset:
Enhanced Hybrid Intrusion Detection System with Attention Mechanism using Deep Learning
Authors: Pundalik Chavan, H. Hanumanthappa, E. G. Satish, Sunil Manoli, S. Supreeth, S. Rohith, H. C. Ramaprasad

The introduction of the Attention mechanism by the Internet of Things—or WSN-IoT—in the sector has greatly enhanced the intrusion detection mechanism capabilities, whereas the deep learning techniques, together with the attention mechanism, enhance the efficacy and efficiency of IDS within WSNs. The proposed “Enhanced Hybrid Intrusion Detection System with Attention Mechanism” (EHID-SCA) underlying insight of this is that the characteristics of WSN data validate this including Convolutional Neural Networks (CNNs) and other deep learning models into an effective and coherent architecture. The design of the deep hybrid network with attention incorporates Channel Attention and Spatial Attention as some of the main components. The model biased capacity is enlarged to choose and stress important spatial and channel information from the sensor input. The method is based on the consideration of these things and it cuts noise. Therefore, the proposed technique can be made to pave the way for enabling the Internet of Things intrusion detection systems to do the automatic extraction of useful information from sensor data through the utilization of deep learning along with the Attention mechanism. The attack might therefore be better situated for mitigation in the network design that allows analysis of the geographical and temporal context of events, which are then more properly termed as intrusion events.
Improving Access Trust in Healthcare Through Multimodal Deep Learning for Affective Computing
Authors: I. Sakthidevi, G. Fathima

In healthcare domain, access trust is of prime importance paramount to ensure effective delivery of medical services. It also fosters positive patient-provider relationships. With the advancement of technology, affective computing has emerged as a promising approach to enhance access trust. It enables systems to understand and respond to human emotions. The research work investigates the application of multimodal deep learning techniques in affective computing to improve access trust in healthcare environment. A novel algorithm, "Belief-Emo-Fusion," is proposed, aiming to enhance the understanding and interpretation of emotions in healthcare. The research conducts a comprehensive simulation analysis, comparing the performance of Belief-Emo-Fusion with existing algorithms using simulation metrics: modal accuracy, ınference time, and F1-score. The study emphasizes the importance of emotion recognition and understanding in healthcare settings. The work highlights the role of deep learning models in facilitating empathetic and emotionally intelligent technologies. By addressing the challenges associated with affective computing, the proposed approach contributes to the development of more effective and reliable healthcare systems. The findings offer valuable insights for researchers and practitioners seeking to leverage deep learning techniques for enhancing trust and communication in healthcare environments.
Grapevine fruits disease detection using different deep learning models
Authors: Om G, Saketh Ram Billa, Vishal Malik, Eslavath Bharath, Sanjeev Sharma

In India, grapes are one of the most important crops for business. Grapes and their byproducts are one of India’s leading exports. The leaves of grapes are susceptible to a variety of diseases. Large-scale production of grapes can be affected by these diseases. The purpose of this research paper is to classify and detect disease on leaves using deep learning before taking a big picture. It describes the advances in detecting leaf disease and shows how to improve results. A total of seven different types of pre-trained deep Convolutional Neural Networks (CNNs) were used for transfer learning: MobileNet, InceptionResNetV2, DenseNet121, InceptionV3, Xception, VGG16 and ResNet101V2. There are 4062 images of grapevine leaves in total, divided into four classes: Leaf Blight, Black Rot, Black Measles, and Healthy. The major challenge was to detect if the leaf was healthy or had been infected, followed by classifying the type of disease of the leaf. In order to learn and detect the disease, these images were trained to seven different transfer learning models. The image classification accuracy obtained is 99.672% by DenseNet121 and it is the highest accuracy obtained compared to any accuracy reported in the literature. It is followed by 99.500% of Xception and 99.345% of VGG16, 99.345% of InceptionV3. Therefore, the proposed research paper can be useful for detecting Grapevine disease at an early stage and preventing its spread. This process can increase the production rate and profit for farmers.
Emotion Detection-Based Video Recommendation System Using Machine Learning and Deep Learning Framework
Authors: Anuja Bokhare, Tripti Kothari

Emotions play an important role in identification of the interest of user with respect to product, technology and other parameter. A human being has so many emotions which is used to express emotions such as anger, surprise, happiness, sadness and fear. In today's fast growing world of technology, there is necessity of expert system which helps to recognize the interest of user based on emotions. These systems would be helpful for various applications. Techniques such as machine learning or deep learning are the emerging ones’ for the same. These are the technologies that have been used in applications like in automatic cars, for music recommendation and movie recommendation. Human emotions cannot be expressed but it can be detected by the help of techniques that is machine learning and deep learning. Current study emphases on human face detection and emotion detection to recommend video which motivates the person for changing the mood. Current system attention is on detection of face and emotion, using Haar cascade and deep face technology. Based on the outcome, an appropriate video is proposed.
Deep learning based System for automatic motorcycle license plates detection and recognition
Authors: Abdolhossein Fathi, Babak Moradi, Iman Zarei, Afshin Shirbandi

Nowadays, by increased the utilization of motorcycle the detection and recognition of its license plate play a very important role in intelligent transportation systems (ITS). ITS can be used for traffic control, violation monitoring, e-payment systems in the toll pay and parking. Several algorithms have been developed for this task and each of them has advantages and disadvantages under different circumstances and situations. By emerging deep learning based methods, they were employed to tackle the issue of automatic license plate detection and recognition. Since the deep learning models need a high volume of data for efficient training, and also each country has its license plate template, at first, it is crucial to collect proper dataset and then trains an efficient model on it. To this end, this research collected and introduced a new dataset, and then, designed a deep learning-based system for automatically detecting and identifying Iranian motorcycle license plates. At first, images that have different dimensions, angles, levels of lighting (daytime and nighttime images), were collected from various cities. Then two datasets for detection and identification are annotated and constructed from these images. Finally for implementing an efficient deep learning-based system, three networks YOLOv8, SSD, and Faster RCNN were investigated for detection and identification of license plates. The obtained results showed that the YOLOv8 network has the best result with 98.5% accuracy in the detection stage and 99% accuracy in the identification stage. The proposed YOLOv8 model was compared with deep learning-based methods and showed better performance on the collected dataset. The collected dataset and the source code of the investigated models are publicly available.
An Adaptive Learning Rate Deep Learning Optimizer Using Long and Short-Term Gradients Based on G–L Fractional-Order Derivative
Authors: Shuang Chen, Changlun Zhang, Haibing Mu

Deep learning model is a multi-layered network structure, and the network parameters that evaluate the final performance of the model must be trained by a deep learning optimizer. In comparison to the mainstream optimizers that utilize integer-order derivatives reflecting only local information, fractional-order derivatives optimizers, which can capture global information, are gradually gaining attention. However, relying solely on the long-term estimated gradients computed from fractional-order derivatives while disregarding the influence of recent gradients on the optimization process can sometimes lead to issues such as local optima and slower optimization speeds. In this paper, we design an adaptive learning rate optimizer called AdaGL based on the Grünwald–Letnikov (G–L) fractional-order derivative. It changes the direction and step size of parameter updating dynamically according to the long-term and short-term gradients information, addressing the problem of falling into local minima or saddle points. To be specific, by utilizing the global memory of fractional-order calculus, we replace the gradient of parameter update with G–L fractional-order approximated gradient, making better use of the long-term curvature information in the past. Furthermore, considering that the recent gradient information often impacts the optimization phase significantly, we propose a step size control coefficient to adjust the learning rate in real-time. To compare the performance of the proposed AdaGL with the current advanced optimizers, we conduct several different deep learning tasks, including image classification on CNNs, node classification and graph classification on GNNs, image generation on GANs, and language modeling on LSTM. Extensive experimental results demonstrate that AdaGL achieves stable and fast convergence, excellent accuracy, and good generalization performance.
Deep learning-based point cloud upsampling: a review of recent trends
Authors: Soonjo Kwon, Ji-Hyeon Hur, Hyungki Kim

Point clouds are acquired primarily using 3D scanners and are used for product inspection and reverse engineering. The quality of the point cloud varies depending on the scanning environment and scanner specifications. The quality of the point cloud has a significant impact on the accuracy of automatic or manual modeling. In response, various point cloud post-processing technologies are being developed. Point cloud upsampling is a technique to improve the resolution of point clouds, and the purpose of upsampling is to generate additional points to express the target object more accurately and in higher detail. This technology is important in areas where high-resolution 3D representation is required, and approaches based on deep learning have been recently gaining attention. Deep learning-based point cloud upsampling research can be classified as surface consolidation or edge consolidation research depending on the target regions to be consolidated, and as supervised or self-supervised learning depending on the type of learning approaches. This study examines the latest research trends in deep learning-based point cloud sampling, analyzes the issues and limitations of each research category, and proposes future research directions.Graphical abstract
Determination of scintillation pixel location through deep learning using a two-layer DOI detector
Authors: Byungdu Jo, Seung-Jae Lee

Small gantries and long, thin scintillation pixels are used in preclinical positron emission tomography, resulting in parallax errors outside the system’s field of view. To solve this problem, a detector for measuring the depth of interaction (DOI) was developed. In addition, conduct of research on methods for DOI measurement through deep learning is underway. In this study, we designed a detector for measurement of DOI, consisting of two layers of scintillation pixel arrays and developed a method for specifying 3-dimensional (3D) position through deep learning. DETECT2000 simulation was performed to assess the 3D-positioning accuracy of the designed detector. Data acquired through DETECT2000 simulation wereused for learning a deep learning model, and assessment of location specification accuracy was performed using data generated at a new location and the deep learning model. According to the result, the 3D-position measurement accuracy was calculated as 94.48% on average.
Sentiment analysis using a deep ensemble learning model
Authors: Muhammet Sinan Başarslan, Fatih Kayaalp

The coronavirus pandemic has kept people away from social life and this has led to an increase in the use of social media over the past two years. Thanks to social media, people can now instantly share their thoughts on various topics such as their favourite movies, restaurants, hotels, etc. This has created a huge amount of data and many researchers from different sciences have focused on analysing this data. Natural Language Processing (NLP) is one of these areas of computer science that uses artificial technologies. Sentiment analysis is also one of the tasks of NLP, which is based on extracting emotions from huge post data. In this study, sentiment analysis was performed on two datasets of tweets about coronavirus and TripAdvisor hotel reviews. A frequency-based word representation method (Term Frequency-Inverse Document Frequency (TF-IDF)) and a prediction-based Word2Vec word embedding method were used to vectorise the datasets. Sentiment analysis models were then built using single machine learning methods (Decision Trees-DT, K-Nearest Neighbour-KNN, Naive Bayes-NB and Support Vector Machine-SVM), single deep learning methods (Long Short Term Memory-LSTM, Recurrent Neural Network-RNN) and heterogeneous ensemble learning methods (Stacking and Majority Voting) based on these single machine learning and deep learning methods. Accuracy was used as a performance measure. The heterogeneous model with stacking (LSTM-RNN) has outperformed the other models with accuracy values of 0.864 on the coronavirus dataset and 0.898 on the Trip Advisor dataset and they have been evaluated as promising results when compared to the literature. It has been observed that the use of single methods as an ensemble gives better results, which is consistent with the literature, which is a step forward in the detection of sentiments through posts. Investigating the performance of heterogeneous ensemble learning models based on different algorithms in sentiment analysis tasks is planned as future work.
Differential testing for machine learning: an analysis for classification algorithms beyond deep learning
Authors: Steffen Herbold, Steffen Tunkel

Differential testing is a useful approach that uses different implementations of the same algorithms and compares the results for software testing. In recent years, this approach was successfully used for test campaigns of deep learning frameworks. There is little knowledge about the application of differential testing beyond deep learning. Within this article, we want to close this gap for classification algorithms. We conduct a case study using Scikit-learn, Weka, Spark MLlib, and Caret in which we identify the potential of differential testing by considering which algorithms are available in multiple frameworks, the feasibility by identifying pairs of algorithms that should exhibit the same behavior, and the effectiveness by executing tests for the identified pairs and analyzing the deviations. While we found a large potential for popular algorithms, the feasibility seems limited because, often, it is not possible to determine configurations that are the same in other frameworks. The execution of the feasible tests revealed that there is a large number of deviations for the scores and classes. Only a lenient approach based on statistical significance of classes does not lead to a huge amount of test failures. The potential of differential testing beyond deep learning seems limited for research into the quality of machine learning libraries. Practitioners may still use the approach if they have deep knowledge about implementations, especially if a coarse oracle that only considers significant differences of classes is sufficient.
Federated Deep Learning for Solving an Image Classification Problem on a Desktop Grid System
Authors: I. I. Kurochkin, A. I. Prun, A. A. Balaev

AbstractThe paper considers the adaptation of federated deep learning on a desktop grid system using the example of an image classification problem. Restrictions are imposed on data transfer between the nodes of the desktop grid only for a part of the dataset. The implementation of federated deep learning on a desktop grid system based on the BOINC platform is considered. Methods for generating local datasets for desktop grid nodes are discussed. The results of numerical experiments are presented.
Deep learning-based PET image denoising and reconstruction: a review
Authors: Fumio Hashimoto, Yuya Onishi, Kibo Ote, Hideaki Tashima, Andrew J. Reader, Taiga Yamaya

This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.
Deep learning algorithms for hedging with frictions
Authors: Xiaofei Shi, Daran Xu, Zhanhao Zhang

This work studies the deep learning-based numerical algorithms for optimal hedging problems in markets with general convex transaction costs. Our main focus is on how these algorithms scale with the length of the trading time horizon. Based on the comparison results of the FBSDE solver by Han, Jentzen, and E (2018) and the Deep Hedging algorithm by Buehler, Gonon, Teichmann, and Wood (2019), we propose a Stable-Transfer Hedging (ST-Hedging) algorithm, to aggregate the convenience of the leading-order approximation formulas and the accuracy of the deep learning-based algorithms. Our ST-Hedging algorithm achieves the same state-of-the-art performance in short and moderately long time horizon as FBSDE solver and Deep Hedging, and generalize well to long time horizon when previous algorithms become suboptimal. With the transfer learning technique, ST-Hedging drastically reduce the training time, and shows great scalability to high-dimensional settings. This opens up new possibilities in model-based deep learning algorithms in economics, finance, and operational research, which takes advantage of the domain expert knowledge and the accuracy of the learning-based methods.
Enhancing trash classification in smart cities using federated deep learning
Authors: Haroon Ahmed Khan, Syed Saud Naqvi, Abeer A. K. Alharbi, Salihah Alotaibi, Mohammed Alkhathami

Efficient Waste management plays a crucial role to ensure clean and green environment in the smart cities. This study investigates the critical role of efficient trash classification in achieving sustainable solid waste management within smart city environments. We conduct a comparative analysis of various trash classification methods utilizing deep learning models built on convolutional neural networks (CNNs). Leveraging the PyTorch open-source framework and the TrashBox dataset, we perform experiments involving ten unique deep neural network models. Our approach aims to maximize training accuracy. Through extensive experimentation, we observe the consistent superiority of the ResNext-101 model compared to others, achieving exceptional training, validation, and test accuracies. These findings illuminate the potential of CNN-based techniques in significantly advancing trash classification for optimized solid waste management within smart city initiatives. Lastly, this study presents a distributed framework based on federated learning that can be used to optimize the performance of a combination of CNN models for trash detection.
Interpreting and generalizing deep learning in physics-based problems with functional linear models
Authors: Amirhossein Arzani, Lingxiao Yuan, Pania Newell, Bei Wang

Although deep learning has achieved remarkable success in various scientific machine learning applications, its opaque nature poses concerns regarding interpretability and generalization capabilities beyond the training data. Interpretability is crucial and often desired in modeling physical systems. Moreover, acquiring extensive datasets that encompass the entire range of input features is challenging in many physics-based learning tasks, leading to increased errors when encountering out-of-distribution (OOD) data. In this work, motivated by the field of functional data analysis (FDA), we propose generalized functional linear models as an interpretable surrogate for a trained deep learning model. We demonstrate that our model could be trained either based on a trained neural network (post-hoc interpretation) or directly from training data (interpretable operator learning). A library of generalized functional linear models with different kernel functions is considered and sparse regression is used to discover an interpretable surrogate model that could be analytically presented. We present test cases in solid mechanics, fluid mechanics, and transport. Our results demonstrate that our model can achieve comparable accuracy to deep learning and can improve OOD generalization while providing more transparency and interpretability. Our study underscores the significance of interpretable representation in scientific machine learning and showcases the potential of functional linear models as a tool for interpreting and generalizing deep learning.
Can supervised deep learning architecture outperform autoencoders in building propensity score models for matching?
Authors: Mohammad Ehsanul Karim

PurposePropensity score matching is vital in epidemiological studies using observational data, yet its estimates relies on correct model-specification. This study assesses supervised deep learning models and unsupervised autoencoders for propensity score estimation, comparing them with traditional methods for bias and variance accuracy in treatment effect estimations.MethodsUtilizing a plasmode simulation based on the Right Heart Catheterization dataset, under a variety of settings, we evaluated (1) a supervised deep learning architecture and (2) an unsupervised autoencoder, alongside two traditional methods: logistic regression and a spline-based method in estimating propensity scores for matching. Performance metrics included bias, standard errors, and coverage probability. The analysis was also extended to real-world data, with estimates compared to those obtained via a double robust approach.ResultsThe analysis revealed that supervised deep learning models outperformed unsupervised autoencoders in variance estimation while maintaining comparable levels of bias. These results were supported by analyses of real-world data, where the supervised model’s estimates closely matched those derived from conventional methods. Additionally, deep learning models performed well compared to traditional methods in settings where exposure was rare.ConclusionSupervised deep learning models hold promise in refining propensity score estimations in epidemiological research, offering nuanced confounder adjustment, especially in complex datasets. We endorse integrating supervised deep learning into epidemiological research and share reproducible codes for widespread use and methodological transparency.