ADKU/ResearchGPT_model_dataset huggingface dataset:
OSS reliability assessment method based on deep learning and independent Wiener data preprocessing
Authors: Yoshinobu Tamura, Shoichiro Miyamoto, Lei Zhou, Adarsh Anand, P. K. Kapur, Shigeru Yamada

The fault big data sets of many open source software (OSS) are recorded on the bug tracking systems. In the past, we have proposed the effort assessment method under the assumption that the fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. The past research in terms of the effort assessment method of OSS is based on the effort data sets. On the other hand, we propose the deep learning approach to the OSS fault big data. In the past, the existing method without Wiener process cannot estimate within the range of existing data only. The proposed method assumes that the fault detection process follows the Wiener process such as the imperfect debugging and Markov property. Thereby, the proposed method can estimate the exceeding values by adding the white noise based on the Wiener process. Then, the proposed method make it possible for the OSS managers to assess the values exceeding from the existing data. Then, we show several reliability assessment measures based on the fault modification time based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper.
A comprehensive review of deep learning approaches for group activity analysis
Authors: Gang Zhang, Yang Geng, Zhao G. Gong

The study of group activity analysis has garnered significant attention. Group activity offers a unique perspective on the relationships between individuals, providing insights that individual and crowd activities may not reveal. This paper aims to contribute to the existing body of knowledge by providing a comprehensive review of the methods employed in utilizing deep learning for the analysis of group activity. The review encompasses an overview of various methodologies for group detection, segmentation, feature extraction, and description. Additionally, it delves into the classification, recognition, and prediction of group activities, including crowd trajectory prediction. The representation of crowd activity patterns and labels, along with an exploration of datasets for crowd activity analysis, is also included. Ultimately, the paper concludes with a discussion of potential future research directions in the field. By offering a comprehensive review of the advancements in group activity analysis through the lens of deep learning, this paper aims to provide researchers with a better understanding of the field's progress, thereby contributing to the continued development of this area of study.
Fire Hawks Optimizer with hybrid deep learning driven fall detection on multimodal sensor data
Authors: K. Durga Bhavani, M. Ferni Ukrit

Falls are the main factor contributing to nonfatal and fatal injuries among older persons. Fall detection on sensor data uses different sensors to identify when an individual has fallen. The data from these sensors can be examined to determine if a fall event has occurred. This technology is highly capable of improving the well-being and safety of persons, particularly older adults, by automatically identifying falls and alerting emergency services or caregivers. Deep learning-based fall detection on sensor data is a significant area of research and development that mainly focuses on utilizing deep learning methods for identifying and predicting falls based on data gathered from different sensors. This study presents a novel approach for fall detection utilizing the Fire Hawks Optimizer with hybrid deep learning technique on multimodal sensor data. The proposed methodology integrates deep learning algorithms with the Fire Hawks Optimizer metaheuristic to enhance fall detection accuracy. The technique preprocesses input data using standard scaling and employs a convolutional-recurrent Hopfield neural network model for fall detection and classification. Hyperparameter tuning using the Fire Hawks Optimizer further improves detection outcomes. Experimental evaluation conducted on the KFall dataset demonstrates the effectiveness of the proposed approach. Key metrics including accuracy, precision, recall, F-score, and Matthews correlation coefficient are utilized for evaluation. Results indicate superior performance of the method compared to existing fall detection approaches, with accuracies exceeding 99% on both training and testing sets. Visual representations such as confusion matrices, precision-recall curves, and ROC curves further validate the method's robustness. The proposed model offers significant advancements in fall detection accuracy, making it a promising solution for ensuring the safety and well-being of individuals, particularly older adults.
Anthropogenic fingerprints in daily precipitation revealed by deep learning
Authors: Yoo-Geun Ham, Jeong-Hwan Kim, Seung-Ki Min, Daehyun Kim, Tim Li, Axel Timmermann, Malte F. Stuecker

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1,2,3,4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)5 with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations6. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.
Optimization of deep learning models: benchmark and analysis
Authors: Rasheed Ahmad, Izzat Alsmadi, Mohammad Al-Ramahi

Model optimization in deep learning (DL) and neural networks is concerned about how and why the model can be successfully trained towards one or more objective functions. The evolutionary learning or training process continuously considers the dynamic parameters of the model. Many researchers propose a deep learning-based solution by randomly selecting a single classifier model architecture. Such approaches generally overlook the hidden and complex nature of the model’s internal working, producing biased results. Larger and deeper NN models bring many complexities and logistic challenges while building and deploying them. To obtain high-quality performance results, an optimal model generally depends on the appropriate architectural settings, such as the number of hidden layers and the number of neurons at each layer. A challenging and time-consuming task is to select and test various combinations of these settings manually. This paper presents an extensive empirical analysis of various deep learning algorithms trained recursively using permutated settings to establish benchmarks and find an optimal model. The paper analyzed the Stack Overflow dataset to predict the quality of posted questions. The extensive empirical analysis revealed that some famous deep learning algorithms such as CNN are the least effective algorithm in solving this problem compared to multilayer perceptron (MLP), which provides efficient computing and the best results in terms of prediction accuracy. The analysis also shows that manipulating the number of neurons alone at each layer in a network does not influence model optimization. This paper’s findings will help to recognize the fact that future models should be built by considering a vast range of model architectural settings for an optimal solution.
Classification and detection of natural disasters using machine learning and deep learning techniques: A review
Authors: Kibitok Abraham, Moataz Abdelwahab, Mohammed Abo-Zahhad

For efficient disaster management, it is essential to identify and categorize natural disasters. The classical approaches and current technological advancements for identifying, categorizing, and reducing the harmful effects of natural catastrophes are discussed in this review article. They include human observation and reporting, satellite images, seismology, radar, infrared imagery, and sonar. The article explores natural disasters’ challenges and harmful effects and their mitigation measures. The article explains the benefits and drawbacks of published approaches and emphasizes how they may be used to identify many kinds of natural catastrophes, including earthquakes, floods, wildfires, and hurricanes. Discussions on current technological advancements, including machine and deep learning applications, that can potentially increase the precision and efficiency of natural disaster detection and classification are presented. Overall, the review article emphasizes the significance of continuing research and improving current techniques to increase communities’ and countries’ resilience and preparedness for natural disasters. Moreover, future directions and suggestions to stakeholders in disaster management are highlighted.
Literature survey on deep learning methods for liver segmentation from CT images: a comprehensive review
Authors: Kumar S. S., Vinod Kumar R. S.

Segmentation of the liver from computed tomography images is an essential and critical task in medical image analysis, with significant implications for liver disease diagnosis and treatment. Deep learning techniques have emerged as a powerful tool in this domain, offering unprecedented accuracy and robustness. This literature survey paper provides a comprehensive overview of deep learning techniques for segmentation of liver from CT images, aiming to synthesize recent advancements, identify key contributions, and address challenges in this rapidly evolving field. The survey covers various deep learning architectures, including convolution neural networks, U-Net, attention mechanisms, generative adversarial neural networks and transformer models, highlighting their strengths and weaknesses. Evaluation metrics and benchmark datasets commonly used for performance assessment are discussed in this survey. Furthermore, the survey delves into the challenges and limitations of deep learning methods, including interpretability, model robustness, and ethical considerations. The survey concludes by summarizing key findings, highlighting advancements, and outlining future research directions, such as interpretable models, ethical considerations, and bridging the gap between research and clinical implementation. This literature survey serves as a valuable reference for researchers, healthcare professionals, and developers in their pursuit of accurate liver segmentation and advancing medical image analysis.
Evaluating the impact of reinforcement learning on automatic deep brain stimulation planning
Authors: Anja Pantovic, Caroline Essert

PurposeTraditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many parameters are subject to limitations, especially in terms of sensitivity to local optima, and tend to be replaced by machine learning approaches. This paper explores the feasibility of using deep reinforcement learning (DRL) in this context, starting with the single-electrode use-case of deep brain stimulation (DBS).MethodsWe propose a DRL approach based on deep Q-learning where the states represent the electrode trajectory and associated information, and actions are the possible motions. Deep neural networks allow to navigate the complex state space derived from MRI data. The chosen reward function emphasizes safety and accuracy in reaching the target structure. The results were compared with a reference (segmented electrode) and a conventional technique.ResultsThe DRL approach excelled in navigating the complex anatomy, consistently providing safer and more precise electrode placements than the reference. Compared to conventional techniques, it showed an improvement in accuracy of 2.3% in average proximity to obstacles and 19.4% in average orientation angle. Expectedly, computation times rose significantly, from 2 to 18 min.ConclusionOur investigation into DRL for DBS electrode trajectory planning has showcased its promising potential. Despite only delivering modest accuracy gains compared to traditional methods in the single-electrode case, its relevance for problems with high-dimensional state and action spaces and its resilience against local optima highlight its promising role for complex scenarios. This preliminary study constitutes a first step toward the more challenging problem of multiple-electrodes planning.
Deep learning for determining the difficulty of endodontic treatment: a pilot study
Authors: Hamed Karkehabadi, Elham Khoshbin, Nikoo Ghasemi, Amal Mahavi, Hossein Mohammad-Rahimi, Soroush Sadr

BackgroundTo develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs.MethodsA dataset of 1,386 periapical radiographs was compiled from two clinical sites. Two dentists and two endodontists annotated the radiographs for difficulty using the “simple assessment” criteria from the American Association of Endodontists’ case difficulty assessment form in the Endocase application. A classification task labeled cases as “easy” or “hard”, while regression predicted overall difficulty scores. Convolutional neural networks (i.e. VGG16, ResNet18, ResNet50, ResNext50, and Inception v2) were used, with a baseline model trained via transfer learning from ImageNet weights. Other models was pre-trained using self-supervised contrastive learning (i.e. BYOL, SimCLR, MoCo, and DINO) on 20,295 unlabeled dental radiographs to learn representation without manual labels. Both models were evaluated using 10-fold cross-validation, with performance compared to seven human examiners (three general dentists and four endodontists) on a hold-out test set.ResultsThe baseline VGG16 model attained 87.62% accuracy in classifying difficulty. Self-supervised pretraining did not improve performance. Regression predicted scores with ± 3.21 score error. All models outperformed human raters, with poor inter-examiner reliability.ConclusionThis pilot study demonstrated the feasibility of automated endodontic difficulty assessment via deep learning models.
Application Level Resource Scheduling for Deep Learning Acceleration on MPSoC
Authors: Cong Gao, Sangeet Saha, Xuqi Zhu, Hongyuan Jing, Klaus D. McDonald-Maier, Xiaojun Zhai

Deep Neutral Networks (DNNs) have been widely used in many applications, such as self-driving cars, natural language processing (NLP), image classification, visual object recognition, and so on. Field-programmable gate array (FPGA) based Multiprocessor System on a Chip (MPSoC) is recently considered one of the popular choices for deploying DNN models. However, the limited resource capacity of MPSoC imposes a challenge for such practical implementation. Recent studies revealed the trade-off between the “resources consumed" vs. the “performance achieved". Taking a cue from these findings, we address the problem of efficient implementation of deep learning into the resource-constrained MPSoC in this paper, where each deep learning network is run with different service levels based on resource usage (where a higher service level implies higher performance with increased resource consumption). To this end, we propose a heuristic-based strategy, Application Wise Level Selector (AWLS), for selecting service levels to maximize the overall performance subject to a given resource bound. AWLS can achieve higher performance within a constrained resource budget under various simulation scenarios. Further, we verify the proposed strategy using an AMD-Xilinx Zynq UltraScale+ XCZU9EG SoC. Using a framework designed to deploy multi-DNN on multi-DPUs (Deep Learning Units), it is proved that an optimal solution is achieved from the algorithm, which obtains the highest performance (Frames Per Second) using the same resource budget.
Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding
Authors: Zheng Wang, Chong Wang, Li Peng, Kaibin Lin, Yang Xue, Xiao Chen, Linlin Bao, Chao Liu, Jianglin Zhang, Yang Xie

This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the International Skin Imaging Collaboration (ISIC) dataset, spanning 2016–2020, encompassing a variety of skin lesions. Our approach integrates deep learning with a comprehensive radiomics analysis, utilizing a vast array of quantitative image features to precisely quantify skin lesion patterns. The dataset includes cases of three, four, and eight different skin lesion types. Our methodology was benchmarked against seven classification methods from the ISIC 2020 challenge and prior research using a binary decision framework. The proposed hybrid model demonstrated superior performance in distinguishing benign from malignant lesions, achieving area under the receiver operating characteristic curve (AUROC) scores of 99%, 95%, and 96%, and multiclass decoding AUROCs of 98.5%, 94.9%, and 96.4%, with sensitivities of 97.6%, 93.9%, and 96.0% and specificities of 98.4%, 96.7%, and 96.9% in the internal ISIC 2018 challenge, as well as in the external Jinan and Longhua datasets, respectively. Our findings suggest that the integration of radiomics and deep learning, utilizing dermoscopic images, effectively captures the heterogeneity and pattern expression of skin lesions.
Fusing deep learning features for parameter identification of a stochastic airfoil system
Authors: Jing Feng, Xiaolong Wang, Qi Liu, Yong Xu, Jürgen Kurths

This work proposes a data-driven parameter identification approach for a two-degree-of-freedom airfoil system with cubic nonlinearity and stochasticity, where the random turbulent flow is quantified by non-Gaussian Lévy colored noise. The joint identification of the parameters controlling the flow velocity, airfoil geometry and structural stiffness is shaped as a unified machine learning task that includes three stages. (1) The first stage extracts local deep learning features from measurement data. (2) Next, the local features are fused to construct fixed-length global features representing the whole sample trajectory. (3) The global features are mapped to the parameter estimates and the accuracy indicators for uncertainty quantification. The numerical studies show that the obtained parameter estimation neural network can identify the system parameters from a sample trajectory with partially observed state measurements, namely, system parameters can be fully identified if only one or two of the pitch and plunge degrees of freedom are available. The intermediate deep features extracted by the PENN are compact representations of the stochastic system, as they carry key information of the system parameters. Suitable rules for information fusion are further designed, adapting the PENN to identify the system parameters from multiple short trajectories or time-varying parameters from a sample trajectory. The results suggest that the proposed deep learning approach is a flexible and versatile computation device for information extraction and fusion from limited data of stochastic nonlinear systems.
Biologically informed deep learning for explainable epigenetic clocks
Authors: Aurel Prosz, Orsolya Pipek, Judit Börcsök, Gergely Palla, Zoltan Szallasi, Sandor Spisak, István Csabai

Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.
Remote Cardiac System Monitoring Using 6G-IoT Communication and Deep Learning
Authors: Abdulbasid S. Banga, Mohammed M. Alenazi, Nisreen Innab, Mansor Alohali, Fahad M. Alhomayani, Mohammad H. Algarni, Taoufik Saidani

Remote patient monitoring has recently been popularised due to advanced technological innovations. The advent of the Sixth Generation Internet of Things (6G-IoT) communication technology, combined with deep learning algorithms, presents a groundbreaking opportunity for enhancing remote cardiac system monitoring. This paper proposes an innovative framework leveraging the ultra-reliable, low-latency communication capabilities of 6G-IoT to transmit real-time cardiac data from wearable devices directly to healthcare providers. Integrating deep learning models facilitates the accurate analysis and prediction of cardiac anomalies, significantly improving traditional monitoring systems. Our methodology involves the deployment of cutting-edge wearable sensors capable of capturing high-fidelity cardiac signals. These signals are transmitted via 6G-IoT networks, ensuring minimal delay and maximum reliability. Upon receiving the data, a densely connected deep neural network with an optimised swish activation function—designed explicitly for cardiac anomaly detection is employed to analyse the data in real-time. These algorithms are trained on vast datasets to recognise patterns indicative of potential cardiac issues, allowing immediate intervention when necessary. The proposed system’s efficacy is validated through extensive testing in simulated environments, demonstrating its ability to accurately detect and swiftly predict a wide range of cardiac conditions. Moreover, implementing 6G-IoT communication ensures the system's scalability and adaptability to future technological advancements.