ADKU/ResearchGPT_model_dataset huggingface dataset:
CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage
Authors: Xianjing Zhao, Bijing Zhou, Yong Luo, Lei Chen, Lequn Zhu, Shixin Chang, Xiangming Fang, Zhenwei Yao

ObjectivesTo predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images.MethodsA retrospective, bi-center study of ICH patients was conducted. Firstly, a custom 3D convolutional model was built for predicting the functional outcome of ICH patients based on CT scans from randomly selected ICH patients in H training dataset collected from H hospital. Secondly, clinical data and radiological features were collected at admission and the Extreme Gradient Boosting (XGBoost) algorithm was used to establish a second model, named the XGBoost model. Finally, the Convolution model and XGBoost model were fused to build the third “Fusion model.” Favorable outcome was defined as modified Rankin Scale score of 0–3 at discharge. The prognostic predictive accuracy of the three models was evaluated using an H test dataset and an external Y dataset, and compared with the performance of ICH score and ICH grading scale (ICH-GS).ResultsA total of 604 patients with ICH were included in this study, of which 450 patients were in the H training dataset, 50 patients in the H test dataset, and 104 patients in the Y dataset. In the Y dataset, the areas under the curve (AUCs) of the Convolution model, XGBoost model, and Fusion model were 0.829, 0.871, and 0.905, respectively. The Fusion model prognostic performance exceeded that of ICH score and ICH-GS (p = 0.043 and p = 0.045, respectively).ConclusionsDeep learning models have good accuracy for predicting functional outcome of patients with spontaneous intracerebral hemorrhage.Clinical relevance statementThe proposed deep learning Fusion model may assist clinicians in predicting functional outcome and developing treatment strategies, thereby improving the survival and quality of life of patients with spontaneous intracerebral hemorrhage.Key Points• Integrating clinical presentations, CT images, and radiological features to establish deep learning model for functional outcome prediction of patients with intracerebral hemorrhage.• Deep learning applied to CT images provides great help in prognosing functional outcome of intracerebral hemorrhage patients.• The developed deep learning model performs better than clinical prognostic scores in predicting functional outcome of patients with intracerebral hemorrhage.
Enhanced variational mode decomposition with deep learning SVM kernels for river streamflow forecasting
Authors: Subramaniam Nachimuthu Deepa, Narayanan Natarajan, Mohanadhas Berlin

The present scenario of global climatic change challenges the sustainability and existence of water bodies around the globe. Due to which, it is always important and necessary to forecast the streamflow of rivers with respect to natural precipitation process. In this research study, novel enhanced variational mode decomposition (EVMD) with deep support vector machine (DSVM) kernels is proposed to perform forecasting of river streamflow. The developed computational intelligent machine learning model is a hybrid combination of the new enhanced VMD and the novel deep SVM kernels that is trained suitably to forecast the streamflow with respect to their deep learning layers. Initially, singular spectrum analysis (SSA) is employed for noise removal and the enhanced VMD with its features of decomposing and extracting more prominent features from the data are hybridized with the deep SVM kernel models to predict the streamflow of the considered Cahaba River data sets. Deterministic grey wolf optimizer (DGWO) is modelled in this research paper to fine tune the parameters of the deep SVM model. Previous prediction techniques modelled had difficulties in respect of local and global minima occurrences, stagnation, delayed and premature convergence and so on. Hence, in this study, the hybrid deep learning model forecasted the streamflow for the considered data sets and its superiority was validated with the comparative analysis with the previous forecasting techniques adopted. The developed forecasting model shall be used by the hydrologists for predicting the daily streamflow with the highest prediction accuracy rate of 97.54% with respect to training process and in case of testing mechanism the prediction accuracy rate is 96.47% This 97.54% of training prediction accuracy rate confirms the effectiveness of modelled new deep SVM algorithm for the streamflow forecasting of hydrologists.
Sound signal analysis in Japanese speech recognition based on deep learning algorithm
Authors: Yang Xiaoxing

As an important carrier of information, since sound can be collected quickly and is not limited by angle and light, it is often used to assist in understanding the environment and creating information. Voice signal recognition technology is a typical speech recognition application. This article focuses on the voice signal recognition technology around various deep learning models. By using deep learning neural networks with different structures and different types, information and representations related to the recognition of sound signal samples can be obtained, so as to further improve the detection accuracy of the sound signal recognition detection system. Based on this, this paper proposes an enhanced deep learning model of multi-scale neural convolutional network and uses it to recognize sound signals. The CCCP layer is used to reduce the dimensionality of the underlying feature map, so that the units captured in the network will eventually have internal features in each layer, thereby retaining the feature information to the maximum extent, which will form a convolutional multi-scale model in network deep learning Neurons. Finally, the article discusses the related issues of Japanese speech recognition on this basis. This article first uses the font (gra-phonem), that is, all these Japanese kana and common Chinese characters, using a total of 2795 units for modeling. There is a big gap between the experiment and the (BiLSTM-HMM) system. In addition, when Japanese speech is known, it is incorporated into the end-to-end recognition system to improve the performance of the Japanese speech recognition system. Based on the above-mentioned deep learning and sound signal analysis experiments and principles, the final effect obtained is better than the main effect of the Japanese speech recognition system of the latent Markov model and the long–short memory network, thus promoting its development.
SEFWaM–deep learning based smart ensembled framework for waste management
Authors: Sujal Goel, Anannya Mishra, Garima Dua, Vandana Bhatia

Waste generation has seen a significant surge in the last decade, presenting an urgent need for efficient and sustainable waste management strategies. The mounting piles of landfill waste underscore the criticality of segregating recyclable from non-recyclable waste, a measure that could alleviate numerous global environmental challenges. The increasing necessity to differentiate waste into specific categories such as paper, plastic, and metal is becoming evident; these categories demand distinct disposal and recycling methods. Addressing this automatic detection and segregation issue calls for an automated garbage classification system, achievable through computer vision. This paper introduces a deep learning and computer vision-based Smart Ensembled Framework for Waste Management (SEFWaM) to categorize garbage into various classes for improved waste management. This innovative model employs a fusion of transfer learning—a deep learning technique and boosting to devise an ensembled approach for efficient waste classification. Our proposed approach, trained on the Trashnet 2.0 dataset, has demonstrated superior performance over competing algorithms in terms of Weighted Macro Precision (WMP), Weighted Macro Recall (WMR), Weighted Macro F1-Score (WMF), and test accuracy. The proposed SEFWaM model achieves an accuracy of 94.2% for the considered dataset, proving its superior efficiency over other deep learning-based models. This research thereby contributes to a pressing environmental issue by offering an automated, efficient solution for waste management.
Graph-ensemble fusion for enhanced IoT intrusion detection: leveraging GCN and deep learning
Authors: Kajol Mittal, Payal Khurana Batra

The proliferation of Internet of Things (IoT) applications has heightened the vulnerability of information security, making it susceptible to attacks that may lead to the compromise of sensitive data. Intrusion Detection System (IDS) is deployed in IoT networks for the detection of attacks and to ensure the security of information. In previous works, the IDS datasets suffer from an imbalanced distribution of data about attacks and, the flow of packets in IDS which hinders the ability of deep learning models for potent and coherent classification. With the emergence of graph convolution neural network (GCN), a new sub-field of deep learning models, the structure of graphs can be leveraged to represent the data effectively. IDS datasets typically consist of flow records of data which can naturally be represented as graph structures capturing both edge features and network topology information for classification of attacks. Hence, in this paper, a novel GCN-Ensemble fusion model is proposed for enhanced IoT IDS. There are three stages in this proposed model: (1) Data processing and attribute graph generation, (2) Feature engineering and (3) Classification. The flow attributes of data packets in IDS datasets are represented as the edges and the corresponding varying attacks as nodes of the attribute graph. Here the GCN model is leveraged for feature engineering of the IDS dataset. Further, a novel Ensemble of Convolution Neural Networks is proposed for the classification task. The evaluation of the proposed model encompasses the utilization of four distinct datasets, namely BoT-IoT, ToN-IoT, CIC-IDS2018, and NF UQ NIDS. In the BoT-IoT dataset, the proposed model demonstrates superior performance compared to state-of-art models like Deep learning and Graph neural network (GNN), achieving accuracy improvements of 3.16 and 0.91%, respectively. The observed superior performance of the model in comparison to the baseline models serves to emphasize its potential to augment IoT network security.
Information set supported deep learning architectures for improving noisy image classification
Authors: Saurabh Bhardwaj, Yizhi Wang, Guoqiang Yu, Yue Wang

Deep learning models have been widely used in many supervised learning applications. However, these models suffer from overfitting due to various types of uncertainty with deteriorating performance when facing data biases, class imbalance, or noise propagation. The Information-Set Deep learning (ISDL) architectures with four variants are developed by integrating information set theory and deep learning principles to address the critical problem of the absence of robust deep learning models. There is a description of the ISDL architectures, learning algorithms, and analytic workflows. The performance of the ISDL models and standard architectures is evaluated using a noise-corrupted benchmark dataset. The experimental results show that the ISDL models can efficiently handle noise-dominated uncertainty and outperform peer architectures.
Octorotor flight control system design with stochastic optimal tuning, deep learning and differential morphing
Authors: Oguz Kose

In this paper, simultaneous longitudinal and lateral flight control is investigated for an octorotor by using stochastic optimal tuning and deep learning under differential morphing. Octorotor models for differential morphing were drawn in SOLIDWORKS drawing program. Arm lengths are randomly estimated in the algorithm. Moments of inertia changing according to morphing ratios are estimated with deep neural network. In addition, the proportional–integral–derivative controller coefficients required for both longitudinal and lateral flight according to the morphing ratios are estimated by simultaneous perturbation stochastic approximation. Considering the design performance criteria, 49.95% improvement was achieved in the total cost. The estimation of unknown parameters by optimization method and deep learning was tested in simulations, and the octorotor successfully followed the given reference angle.
Development of a remote music teaching system based on facial recognition and deep learning
Authors: Ning Zhang, Huizhong Wang

With the continuous progress of computer and network technology, teaching methods and educational models are also constantly evolving and improving. The development of facial recognition technology has brought new opportunities and challenges to the development of educational theory and systems. This article establishes a remote music teaching system based on facial recognition and deep learning technology. The system adopts the Java EE framework structure and deep learning technology. By conducting deep learning and training on a large amount of facial data, we can identify students' facial expressions and emotional states, thereby better understanding their learning status and needs. At the same time, the system also supports multiple teaching modes and interactive methods, providing teachers and students with a more convenient and efficient teaching management and learning experience. Subsequently, this article evaluated and explored the effectiveness of the remote music teaching system through a questionnaire survey. The results show that most students believe that the system can help them better master basic music knowledge and professional skills, improve learning effectiveness and achieve learning goals. The use of the system can also stimulate students' interest in music learning, providing new ways and means for teaching.
Advances in Deep Learning Techniques for Short-term Energy Load Forecasting Applications: A Review
Authors: Radhika Chandrasekaran, Senthil Kumar Paramasivan

Today, the majority of the leading power companies place a significant emphasis on forecasting the electricity load in the balance of power and administration. Meanwhile, since electricity is an integral component of every person’s contemporary life, energy load forecasting is necessary to afford the energy demand required. The expansion of the electrical infrastructure is a key factor in increasing sustainable economic growth, and the planning and control of the utility power system rely on accurate load forecasting. Due to uncertainty in energy utilization, forecasting is turning into a complex task, and it makes an impact on applications that include energy scheduling and management, price forecasting, etc. The statistical methods involving time series for regression analysis and machine learning techniques have been used in energy load forecasting extensively over the last few decades to precisely predict future energy demands. However, they have some drawbacks with limited model flexibility, generalization, and overfitting. Deep learning addresses the issues of handling unstructured and unlabeled data, automatic feature learning, non-linear model flexibility, the ability to handle high-dimensional data, and simultaneous computation using GPUs efficiently. This paper investigates factors influencing energy load forecasting, then discusses the most commonly used deep learning approaches in energy load forecasting, as well as evaluation metrics to evaluate the performance of the model, followed by bio-inspired algorithms to optimize the model, and other advanced technologies for energy load forecasting. This study discusses the research findings, challenges, and opportunities in energy load forecasting.
Advancing differential diagnosis: a comprehensive review of deep learning approaches for differentiating tuberculosis, pneumonia, and COVID-19
Authors: Kajal Kansal, Tej Bahadur Chandra, Akansha Singh

In the realm of medical diagnostics, particularly in differential diagnosis, where differentiating between illnesses or ailments with comparable symptoms is essential, deep learning has gained importance. Recent developments in deep learning have demonstrated considerable promise for revolutionizing medical diagnostics by using the ability of artificial intelligence (AI) to accurately interpret radiological images. We examine the most cutting-edge deep learning techniques currently being utilized for the differential diagnosis of tuberculosis, pneumonia, and COVID-19 in this in-depth review. The study presents an in-depth critical review of several SOTA (state-of-the-art) studies used for differential diagnosis of different respiratory abnormalities like TB, Pneumonia, and COVID-19. In addition, an overview of various approaches, datasets employed in each method, various diagnosis tests, used assessment measures, and obtained performance is summarized and comprehensively compared to assist future research. We suggest a pathway for future research and development of deep learning solutions for differential diagnosis by critically analyzing the current literature and outlining the limitations and potential in this sector.
Molecular design with automated quantum computing-based deep learning and optimization
Authors: Akshay Ajagekar, Fengqi You

Computer-aided design of novel molecules and compounds is a challenging task that can be addressed with quantum computing (QC) owing to its notable advances in optimization and machine learning. Here, we use QC-assisted learning and optimization techniques implemented with near-term QC devices for molecular property prediction and generation tasks. The proposed probabilistic energy-based deep learning model trained in a generative manner facilitated by QC yields robust latent representations of molecules, while the proposed data-driven QC-based optimization framework performs guided navigation of the target chemical space by exploiting the structure–property relationships captured by the energy-based model. We demonstrate the viability of the proposed molecular design approach by generating several molecular candidates that satisfy specific property target requirements. The proposed QC-based methods exhibit an improved predictive performance while efficiently generating novel molecules that accurately fulfill target conditions and exemplify the potential of QC for automated molecular design, thus accentuating its utility.
ConvMixer deep learning model for detection of pneumonia disease using chest X-ray images
Authors: Ankit Chaudhary, Sushil Kumar Saroj

Pneumonia is a common and fatal disease in children nowadays. It infects the lungs resulting in difficulties in breathing. Severe cases of it may lead to death. Therefore, early and accurate detection of pneumonia disease is essential. There are existing various methods for the detection of pneumonia disease today. Deep learning methods are considered more effective for this. We have applied a novel deep learning model i.e. the ConvMixer model for the detection of pneumonia disease. The model replaces traditional convolutional layers with a mixer of channels and spatial dimensions. By mixing channels and spatial dimensions, it reduces the number of parameters and computations required for processing each layer leading to improved efficiency. We have applied the model to the large numbers of chest X-ray images that are publicly available on Kaggle, provided by Guangzhou Women and Children’s Medical Centre in Guangzhou. The model has achieved the highest accuracy of 95.11%. It has also been evaluated for precision, recall, and f1-score parameters.
Research on Real-time Detection of Stacked Objects Based on Deep Learning
Authors: Kaiguo Geng, Jinwei Qiao, Na Liu, Zhi Yang, Rongmin Zhang, Huiling Li

Deep Learning has garnered significant attention in the field of object detection and is widely used in both industry and everyday life. The objective of this study is to investigate the applicability and targeted improvements of Deep Learning-based object detection in complex stacked environments. We analyzed the limitations in practical applications under such conditions, pinpointed the specific problems, and proposed corresponding improvement strategies. First, the study provided an overview of recent advancements in mainstream one-stage object detection algorithms, which included Anchor-based, Anchor-free, and Transformer-based architectures. The high real-time performance of these algorithms holds particular significance in practical engineering applications. It then looked at relevant technologies in three emerging research areas: Parts Recognition, Intelligent Driving, and Agricultural Picking. The study summarized existing limitations in real-time object detection within complex stacked environments and provided a comprehensive analysis of prevalent improvement strategies such as multi-level feature fusion, knowledge distillation, and hyperparameter optimization. Finally, after analyzing the performance of recent advanced one-stage algorithms on official datasets, this paper conducted empirical tests on a self-constructed industrial stacked dataset with algorithms of different structure and analyzed the experimental results in detail. A comprehensive analysis shows that Deep Learning-based object detection algorithms offer extensive applicability in complex stacked environments. In addressing diverse target sizes, overlapping occlusions, real-time constraints, and the need for lightweight solutions in complex stacked environments, each improvement strategy has its own advantages and limitations. Selecting and integrating appropriate enhancement strategies is critical and typically requires holistic evaluation, tailored to specific application contexts and challenges.
Characterization of fault-karst reservoirs based on deep learning and attribute fusion
Authors: Zhipeng Gui, Junhua Zhang, Yintao Zhang, Chong Sun

The identification of fault-karst reservoir is crucial for the exploration and development of fault-controlled oil and gas reservoirs. Traditional methods primarily rely on well logging and seismic attribute analysis for karst cave identification. However, these methods often lack the resolution needed to meet practical demands. Deep learning methods offer promising solutions by effectively overcoming the complex response characteristics of seismic wave fields, owing to their high learning capabilities. Therefore, this research proposes a method for fault-karst reservoir identification. Initially, a comparative analysis between the improved U-Net++ network and traditional deep convolutional networks is conducted to select appropriate training parameters for separate training of karst caves and faults. Subsequently, the trained models are applied to actual seismic data to predict karst caves and faults within the research area, followed by attribute fusion to acquire data on fault-karst reservoirs. The results indicate that: (1) The proposed method effectively identifies karst caves and faults, outperforming traditional seismic attribute and coherence methods in terms of identification accuracy, and slightly surpassing U-Net and FCN; (2) The fusion of predicted karst caves and faults yields clear delineation of the relationship between top karst caves and bottom fractures within the research area. In summary, the proposed method for fault-karst reservoirs identification and characterization provides valuable insights for the exploration and development of fault-controlled oil and gas reservoirs in the region.