ADKU/ResearchGPT_model_dataset huggingface dataset:
CNN-Transformer: A deep learning method for automatically identifying learning engagement
Authors: Yan Xiong, Guo Xinya, Junjie Xu

Learning engagement is an essential indication to define students' learning pacification in the class, and its automated identification technique is the foundation for exploring how to effectively explain the motive of learning impact modifications and making intelligent teaching choices. Current research have demonstrated that there is a direct link between learning engagement and emotional investment and behavioural investment, and it is appropriate and required to apply artificial intelligence to perform autonomous assessment. Unfortunately, the number of relevant research is limited, and the features of learning engagement in certain contexts have not been thoroughly examined. In this research, we highlight the features of a particular application scenario of learning engagement: the application scenario of learning engagement has to incorporate both the coarse-grained information of human body position and the fine-grained information of facial expressions. On the basis of this analysis, a fine-grained learning participation recognition model that suppresses background clutter information is presented. This model can effectively extract coarse and fine-grained information to improve the recognition of learning participation in real-world teaching situations. Particularly, the CNN-Transformer model suggested in this study employs CNN to extract fine-grained information of facial expressions and Transformer to recover coarse-grained information of human body position. Simultaneously, we gathered and categorised real teaching data based on the features of learning engagement situations and enhanced the data quality via crowdsourcing and expert verification. The experimental findings indicate that the CNN-Transformer model can accurately predict the learning engagement of unknown participants with a 92.9% rate of accuracy. Comparative trials reveal that the model's recognition impact is much greater than that of other sophisticated deep learning approaches. Our research offers a framework for future work on deep learning approaches in learning engagement settings.
Deep learning based features extraction for facial gender classification using ensemble of machine learning technique
Authors: Fazal Waris, Feipeng Da, Shanghuan Liu

Accurate and efficient gender recognition is an essential for many applications such as surveillance, security, and biometrics. Recently, deep learning techniques have made remarkable advancements in feature extraction and have become extensively implemented in various applications, including gender classification. However, despite the numerous studies conducted on the problem, correctly recognizing robust and essential features from face images and efficiently distinguishing them with high accuracy in the wild is still a challenging task for real-world applications. This article proposes an approach that combines deep learning and soft voting-based ensemble model to perform automatic gender classification with high accuracy in an unconstrained environment. In the proposed technique, a novel deep convolutional neural network (DCNN) was designed to extract 128 high-quality and accurate features from face images. The StandardScaler method was then used to pre-process these extracted features, and finally, these preprocessed features were classified with soft voting ensemble learning model combining the outputs from several machine learning classifiers such as random forest (RF), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), gradient boosting classifier (GBC) and XGBoost to improve the prediction accuracy. The experimental study was performed on the UTK, label faces in the wild (LFW), Adience and FEI datasets. The results attained evidently show that the proposed approach outperforms all current approaches in terms of accuracy across all datasets.
Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays
Authors: Rupali Vyas, Deepak Rao Khadatkar

Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia classification from chest X-ray (CXR) images. We deployed modified VGG19, ResNet50V2, and DenseNet121 models for feature extraction, followed by five machine learning classifiers (logistic regression, support vector machine, decision tree, random forest, artificial neural network). The approach we suggested displayed remarkable accuracy, with VGG19 and DenseNet121 models obtaining 99.98% accuracy when combined with random forest or decision tree classifiers. ResNet50V2 achieved 99.25% accuracy with random forest. These results illustrate the advantages of merging deep learning models with machine learning classifiers in boosting the speedy and accurate identification of pneumonia. The study underlines the potential of DLxMLC systems in enhancing diagnostic accuracy and efficiency. By integrating these models into clinical practice, healthcare practitioners could greatly boost patient care and results. Future research should focus on refining these models and exploring their application to other medical imaging tasks, as well as including explainability methodologies to better understand their decision-making processes and build trust in their clinical use. This technique promises promising breakthroughs in medical imaging and patient management.
Instance segmentation on distributed deep learning big data cluster
Authors: Mohammed Elhmadany, Islam Elmadah, Hossam E. Abdelmunim

Distributed deep learning is a promising approach for training and deploying large and complex deep learning models. This paper presents a comprehensive workflow for deploying and optimizing the YOLACT instance segmentation model as on big data clusters. OpenVINO, a toolkit known for its high-speed data processing and ability to optimize deep learning models for deployment on a variety of devices, was used to optimize the YOLACT model. The model is then run on a big data cluster using BigDL, a distributed deep learning library for Apache Spark. BigDL provides a high-level programming interface for defining and training deep neural networks, making it suitable for large-scale deep learning applications. In distributed deep learning, input data is divided and distributed across multiple machines for parallel processing. This approach offers several advantages, including the ability to handle very large data that can be stored in a distributed manner, scalability to decrease processing time by increasing the number of workers, and fault tolerance. The proposed workflow was evaluated on virtual machines and Azure Databricks, a cloud-based platform for big data analytics. The results indicated that the workflow can scale to large datasets and deliver high performance on Azure Databricks. This study explores the benefits and challenges of using distributed deep learning on big data clusters for instance segmentation. Popular distributed deep learning frameworks are discussed, and BigDL is chosen. Overall, this study highlights the practicality of distributed deep learning for deploying and scaling sophisticated deep learning models on big data clusters.
Deep learning-based personalized learning recommendation system design for "T++" Guzheng Pedagogy
Authors: Xingyue Wang

This study investigates the development and impact of a deep learning-based personalized learning recommendation system designed specifically for 'T++' Guzheng pedagogy. In the realm of music education, particularly in the context of the traditional Chinese Guzheng instrument, technology-driven personalization has the potential to revolutionize learning experiences. The research involves data collection, algorithm development, and integration to create a system that tailors Guzheng learning materials to individual students' skill levels and preferences. Results indicate high recommendation accuracy, increased user satisfaction, and positive engagement rates. This study contributes to the intersection of technology, music education, and cultural preservation, showcasing the promise of personalized learning in the realm of Guzheng pedagogy.
Deep learning in two-dimensional materials: Characterization, prediction, and design
Authors: Xinqin Meng, Chengbing Qin, Xilong Liang, Guofeng Zhang, Ruiyun Chen, Jianyong Hu, Zhichun Yang, Jianzhong Huo, Liantuan Xiao, Suotang Jia

Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials.
Revitalizing Arabic Character Classification: Unleashing the Power of Deep Learning with Transfer Learning and Data Augmentation Techniques
Authors: Marwa Amara, Nadia  Smairi, Sami  Mnasri, Abdelmalek  Zidouri

Deep learning techniques have demonstrated remarkable success in various domains, including character classification tasks. However, the performance of deep learning models heavily relies on the availability of large-annotated datasets. This research work is motivated by the need to overcome the difficulties associated with handwritten Arabic character recognition, and the constraints provided by limited training data. It is also motivated by the need to enhance the model’s generalizability to unknown characteristics and to improve the accuracy of deep learning character classification models. To overcome this limitation, we apply transfer learning and data augmentation strategies to improve the character classification using deep learning. The proposed model transfers knowledge from previously trained models via transfer learning, addresses data scarcity, and reflects generalizable properties. Indeed, we utilize a VGG16-ImageNet transfer learning model, which is systematically enhanced through data augmentation across three distinct models: pre-trained ImageNet weights with a frozen backbone, pre-trained ImageNet weights with a fine-tuned backbone, and initiating with a randomly initialized. In each case, data augmentation plays a critical role. Our experimental results show that better precision and recall values were recorded for most classes in our dataset, which indicates the model’s ability to accurately identify instances of each character. Moreover, when applying our method to the IFHCDB and HACDB datasets, we observed an impressive recognition accuracy of 96.01% and 97.15%, respectively. This clearly indicates that involving transfer learning and data augmentation significantly improves the performance of deep learning models, especially for small size training datasets.
A systematic survey of fuzzy deep learning for uncertain medical data
Authors: Yuanhang Zheng, Zeshui Xu, Tong Wu, Zhang Yi

Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.
Predicting discrete-time bifurcations with deep learning
Authors: Thomas M. Bury, Daniel Dylewsky, Chris T. Bauch, Madhur Anand, Leon Glass, Alvin Shrier, Gil Bub

Many natural and man-made systems are prone to critical transitions—abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for critical transitions by learning generic features of bifurcations from large simulated training data sets. So far, classifiers have only been trained to predict continuous-time bifurcations, ignoring rich dynamics unique to discrete-time bifurcations. Here, we train a deep learning classifier to provide an early warning signal for the five local discrete-time bifurcations of codimension-one. We test the classifier on simulation data from discrete-time models used in physiology, economics and ecology, as well as experimental data of spontaneously beating chick-heart aggregates that undergo a period-doubling bifurcation. The classifier shows higher sensitivity and specificity than commonly used early warning signals under a wide range of noise intensities and rates of approach to the bifurcation. It also predicts the correct bifurcation in most cases, with particularly high accuracy for the period-doubling, Neimark-Sacker and fold bifurcations. Deep learning as a tool for bifurcation prediction is still in its nascence and has the potential to transform the way we monitor systems for critical transitions.
Machine learning and deep learning techniques for poultry tasks management: a review
Authors: Thavamani. Subramani, Vijayakumar. Jeganathan, Sruthi. Kunkuma Balasubramanian

In recent years the poultry production industry has adopted automation with the help of different kinds of technological advancements like verities of monitoring and sensing tools, IoT devices, sensors, monitoring devices, and more. These advanced techniques will offer numerous advantages in poultry product production. Only with human resources based large-scale poultry production is not an easy task because the public health threads from ingesting foods with high antibiotic remains will residues a problem. Sometimes, zoonotic diseases and foodborne diseases present a crucial task to poultry producers. These repeated tasks concern massive and hazardous work accomplished within a suffering work domain, leading to high human health safety, labor cost, and sustainable risk of cross-infection through manufacturing facilities. In recent years, Artificial Intelligence technology has played a vital role in all sectors including poultry production has utilized advanced Machine Learning and Deep Learning technologies which is a subfield of AI, this technology has an acceptable potential to manage numerous challenges in the establishment of information-based farming and various task-handling systems in the poultry production sector. This article comprehensively reviews some recently established machine learning and deep learning algorithms for poultry management tasks like chicken activity monitoring, farm weather monitoring and control, weight prediction, earlier identification of diseased chickens, and more. It is divine that this proposed review work will compose a piece of helpful information for all who are interested in enhancing recognition of the future scope of utilizing the ML and DL techniques in the poultry production sectors.
Explaining deep learning-based leaf disease identification
Authors: Ankit Rajpal, Rashmi Mishra, Sheetal Rajpal, Kavita, Varnika Bhatia, Naveen Kumar

Crop diseases adversely affect agricultural productivity and quality. The primary cause of these diseases is the presence of biotic stresses such as fungi, viruses, and bacteria. Detecting these causes at early stages requires constant monitoring by domain experts. Technological advancements in machine learning and deep learning methods have enabled the automated identification of leaf disease-specific symptoms through image analysis. This paper proposes image-based detection of leaf diseases using various deep learning-based models. The experiment was conducted on the PlantVillage dataset, which consists of 54,305 colour leaf images (healthy and diseased) belonging to 11 crop species categorized into 38 classes. The Inception-ResNet-V2-based model achieved a 10-fold cross-validation accuracy of \(0.9991 \pm 0.002\) outperforming the other deep neural architectures and surpassing the performance of existing models in recent state-of-the-art works. Each underlined model is validated on an independent cohort. The Inception-ResNet-V2-based model achieved the best 10-fold cross-validation accuracy of \(0.9535 \pm 0.041\) and was found statistically significant among other deep learning-based models. However, these deep learning models are considered a black box as their leaf disease predictions are opaque to end users. To address this issue, a local interpretable framework is proposed to mark the superpixels that contribute to identifying leaf disease. These superpixels closely confirmed the annotations of the human expert.
Model-based deep reinforcement learning for accelerated learning from flow simulations
Authors: Andre Weiner, Janis Geise

In recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. Employing simulation-based environments in reinforcement learning enables a priori end-to-end optimization of the control system, provides a virtual testbed for safety-critical control applications, and allows to gain a deep understanding of the control mechanisms. While reinforcement learning has been applied successfully in a number of rather simple flow control benchmarks, a major bottleneck toward real-world applications is the high computational cost and turnaround time of flow simulations. In this contribution, we demonstrate the benefits of model-based reinforcement learning for flow control applications. Specifically, we optimize the policy by alternating between trajectories sampled from flow simulations and trajectories sampled from an ensemble of environment models. The model-based learning reduces the overall training time by up to \(85\%\) for the fluidic pinball test case. Even larger savings are expected for more demanding flow simulations.
Deep learning algorithms applied to computational chemistry
Authors: Abimael Guzman-Pando, Graciela Ramirez-Alonso, Carlos Arzate-Quintana, Javier Camarillo-Cisneros

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.
Deep learning for named entity recognition: a survey
Authors: Zhentao Hu, Wei Hou, Xianxing Liu

Named entity recognition (NER) aims to identify the required entities and their types from unstructured text, which can be utilized for the construction of knowledge graphs. Traditional methods heavily rely on manual feature engineering and face challenges in adapting to large datasets within complex linguistic contexts. In recent years, with the development of deep learning, a plethora of NER methods based on deep learning have emerged. This paper begins by providing a succinct introduction to the definition of the problem and the limitations of traditional methods. It enumerates commonly used NER datasets suitable for deep learning methods and categorizes them into three classes based on the complexity of named entities. Then, some typical deep learning-based NER methods are summarized in detail according to the development history of deep learning models. Subsequently, an in-depth analysis and comparison of methods achieving outstanding performance on representative and widely used datasets is conducted. Furthermore, the paper reproduces and analyzes the recognition results of some typical models on three different types of typical datasets. Finally, the paper concludes by offering insights into the future trends of NER development.