ADKU/ResearchGPT_model_dataset huggingface dataset:
Transfer learning for emotion detection in conversational text: a hybrid deep learning approach with pre-trained embeddings
Authors: Sheetal Kusal, Shruti Patil, Jyoti Choudrie, Ketan Kotecha, Deepali Vora

Understanding the emotions and sentiments from conversations has relevance in many application areas. Specifically, conversational agents, question-answering systems, or areas where natural language inference is used. Therefore, techniques to detect emotions from conversations have become the need of the moment. The convolutional network and recurrent networks have shown different capabilities in text representation. This work proposes a hybrid deep learning network based on the convolutional-recurrent network used to detect the emotions of people based on conversational text. A convolutional network has the ability to capture local patterns and relationships and is inherently shift-invariant. At the same time, the recurrent network captures long-range dependencies in sequential information. This work also utilises the power of transfer learning by employing pre-trained embeddings from Neural Network Language Model models. These pre-trained representations, generated from vast text corpora, encode rich semantic information about words. This study investigates a novel approach towards text-based emotion detection using pre-trained Neural Network Language Model embeddings with hybrid convolutional-recurrent architecture. The proposed hybrid experimental setup has been evaluated on the Empathetic Dialogues dataset and contrasted with the state-of-the-art works. A comparative analysis reveals that the proposed Convolutional Neural Network with a Bidirectional Gated Recurrent Unit hybrid approach with Neural Network Language Model embeddings achieves superior performance and accuracy.
Deep-learning model for evaluating histopathology of acute renal tubular injury
Authors: Thi Thuy Uyen Nguyen, Anh-Tien Nguyen, Hyeongwan Kim, Yu Jin Jung, Woong Park, Kyoung Min Kim, Ilwoo Park, Won Kim

Tubular injury is the most common cause of acute kidney injury. Histopathological diagnosis may help distinguish between the different types of acute kidney injury and aid in treatment. To date, a limited number of study has used deep-learning models to assist in the histopathological diagnosis of acute kidney injury. This study aimed to perform histopathological segmentation to identify the four structures of acute renal tubular injury using deep-learning models. A segmentation model was used to classify tubule-specific injuries following cisplatin treatment. A total of 45 whole-slide images with 400 generated patches were used in the segmentation model, and 27,478 annotations were created for four classes: glomerulus, healthy tubules, necrotic tubules, and tubules with casts. A segmentation model was developed using the DeepLabV3 architecture with a MobileNetv3-Large backbone to accurately identify the four histopathological structures associated with acute renal tubular injury in PAS-stained mouse samples. In the segmentation model for four structures, the highest Intersection over Union and the Dice coefficient were obtained for the segmentation of the “glomerulus” class, followed by “necrotic tubules,” “healthy tubules,” and “tubules with cast” classes. The overall performance of the segmentation algorithm for all classes in the test set included an Intersection over Union of 0.7968 and a Dice coefficient of 0.8772. The Dice scores for the glomerulus, healthy tubules, necrotic tubules, and tubules with cast are 91.78 ± 11.09, 87.37 ± 4.02, 88.08 ± 6.83, and 83.64 ± 20.39%, respectively. The utilization of deep learning in a predictive model has demonstrated promising performance in accurately identifying the degree of injured renal tubules. These results may provide new opportunities for the application of the proposed methods to evaluate renal pathology more effectively.
Deep Learning–Assisted Identification of Femoroacetabular Impingement (FAI) on Routine Pelvic Radiographs
Authors: Michael K. Hoy, Vishal Desai, Simukayi Mutasa, Robert C. Hoy, Richard Gorniak, Jeffrey A. Belair

To use a novel deep learning system to localize the hip joints and detect findings of cam-type femoroacetabular impingement (FAI). A retrospective search of hip/pelvis radiographs obtained in patients to evaluate for FAI yielded 3050 total studies. Each hip was classified separately by the original interpreting radiologist in the following manner: 724 hips had severe cam-type FAI morphology, 962 moderate cam-type FAI morphology, 846 mild cam-type FAI morphology, and 518 hips were normal. The anteroposterior (AP) view from each study was anonymized and extracted. After localization of the hip joints by a novel convolutional neural network (CNN) based on the focal loss principle, a second CNN classified the images of the hip as cam positive, or no FAI. Accuracy was 74% for diagnosing normal vs. abnormal cam-type FAI morphology, with aggregate sensitivity and specificity of 0.821 and 0.669, respectively, at the chosen operating point. The aggregate AUC was 0.736. A deep learning system can be applied to detect FAI-related changes on single view pelvic radiographs. Deep learning is useful for quickly identifying and categorizing pathology on imaging, which may aid the interpreting radiologist.
A deep learning-based framework for road traffic prediction
Authors: Redouane Benabdallah Benarmas, Kadda Beghdad Bey

Due to the exponential rise in the number of vehicles and road segments in cities, traffic prediction becomes more difficult, necessitating the application of sophisticated algorithms such as deep learning (DL). The models used in the literature provide accurate predictions for specific cases when the data flow is properly prepared. However, in complex situations, these approaches fail, and thus, the prediction must be developed through a process rather than a prediction calculation method. In addition to using a pure and robust DL prediction model, an efficient approach could be built by taking into account two other factors, namely the relationships between road segments and the amount and quality of the training data. The main goal of our research is to develop a three-stage framework for road traffic prediction based on statistical and deep learning modules. First, a cross-correlation prediction with a Long Short-Term Memory model (LSTM) is implemented to predict the influential road segments; second, a deep generative model (DGM)-based data augmentation is used to improve the data of the related segments; and third, we adapt a Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) architecture, to the resulting data to implement the prediction module. The framework components are trained and validated using the 6th Beijing road traffic dataset.
Deep learning for Arabic healthcare: MedicalBot
Authors: Mohammed Abdelhay, Ammar Mohammed, Hesham A. Hefny

Since the COVID-19 pandemic, healthcare services, particularly remote and automated healthcare consultations, have gained increased attention. Medical bots, which provide medical advice and support, are becoming increasingly popular. They offer numerous benefits, including 24/7 access to medical counseling, reduced appointment wait times by providing quick answers to common questions or concerns, and cost savings associated with fewer visits or tests required for diagnosis and treatment plans. The success of medical bots depends on the quality of their learning, which in turn depends on the appropriate corpus within the domain of interest. Arabic is one of the most commonly used languages for sharing users’ internet content. However, implementing medical bots in Arabic faces several challenges, including the language’s morphological composition, the diversity of dialects, and the need for an appropriate and large enough corpus in the medical domain. To address this gap, this paper introduces the largest Arabic Healthcare Q &A dataset, called MAQA, consisting of over 430,000 questions distributed across 20 medical specializations. Furthermore, this paper adopts three deep learning models, namely LSTM, Bi-LSTM, and Transformers, for experimenting and benchmarking the proposed corpus MAQA. The experimental results demonstrate that the recent Transformer model outperforms the traditional deep learning models, achieving an average cosine similarity of 80.81% and a BLeU score of 58%.
Deep learning pose detection model for sow locomotion
Authors: Tauana Maria Carlos Guimarães de Paula, Rafael Vieira de Sousa, Marisol Parada Sarmiento, Ton Kramer, Edson José de Souza Sardinha, Leandro Sabei, Júlia Silvestrini Machado, Mirela Vilioti, Adroaldo José Zanella

Lameness affects animal mobility, causing pain and discomfort. Lameness in early stages often goes undetected due to a lack of observation, precision, and reliability. Automated and non-invasive systems offer precision and detection ease and may improve animal welfare. This study was conducted to create a repository of images and videos of sows with different locomotion scores. Our goal is to develop a computer vision model for automatically identifying specific points on the sow's body. The automatic identification and ability to track specific body areas, will allow us to conduct kinematic studies with the aim of facilitating the detection of lameness using deep learning. The video database was collected on a pig farm with a scenario built to allow filming of sows in locomotion with different lameness scores. Two stereo cameras were used to record 2D videos images. Thirteen locomotion experts assessed the videos using the Locomotion Score System developed by Zinpro Corporation. From this annotated repository, computational models were trained and tested using the open-source deep learning-based animal pose tracking framework SLEAP (Social LEAP Estimates Animal Poses). The top-performing models were constructed using the LEAP architecture to accurately track 6 (lateral view) and 10 (dorsal view) skeleton keypoints. The architecture achieved average precisions values of 0.90 and 0.72, average distances of 6.83 and 11.37 in pixel, and similarities of 0.94 and 0.86 for the lateral and dorsal views, respectively. These computational models are proposed as a Precision Livestock Farming tool and method for identifying and estimating postures in pigs automatically and objectively. The 2D video image repository with different pig locomotion scores can be used as a tool for teaching and research. Based on our skeleton keypoint classification results, an automatic system could be developed. This could contribute to the objective assessment of locomotion scores in sows, improving their welfare.
Low Dose CT Image Reconstruction Using Deep Convolutional Residual Learning Network
Authors: Shalini Ramanathan, Mohan Ramasundaram

Image reconstruction from computed tomography measurement is formulated as a thought-provoking statistical inverse problem. Deep learning algorithms are best for ill-posed statistical inverse problems that presently achieve state-of-art reconstruction results. The challenging task is to lower the potentially harmful radiation a patient is exposed to during the CT scan. In recently available CT Scanners, Low-Dose CT (LDCT) reconstruction is presented with a post-processing approach, which uses deep learning-based medical image reconstruction methods to reduce the dose level without compromising the image quality. Therefore, this paper proposes a deep learning-based post-processing method called Deep Convolutional Neural Network with Residual Learning (DCNN-RL). The method trains the network on a newly available low-dose CT benchmark dataset (LoDoPaB-CT). It also enables to compare with other benchmark CT datasets such as AAPM LDCT and COVIDx-CT. The proposed architecture optimizes the filtering part to minimize the error function. It learns the parameters of the residual network via numerous training to maximize the efficiency of production. This paper compares noise methods on DCNN-RL using various LDCT datasets of the same domain (human being's chest CT scan) to analyze the image quality. The experiment findings suggest that the Adagrad optimizer is the best for LDCT images. Gaussian noise with a minor variance outperforms the medical image reconstruction task. Here, it has been demonstrated that this approach with these benchmark datasets drastically improves the medical CT image quality, shown through qualitative and quantitative outcomes.
Transforming clinical virology with AI, machine learning and deep learning: a comprehensive review and outlook
Authors: Abhishek Padhi, Ashwini Agarwal, Shailendra K. Saxena, C. D. S. Katoch

In the rapidly evolving field of clinical virology, technological advancements have always played a pivotal role in driving transformative changes. This comprehensive review delves into the burgeoning integration of artificial intelligence (AI), machine learning, and deep learning into virological research and practice. As we elucidate, these computational tools have significantly enhanced diagnostic precision, therapeutic interventions, and epidemiological monitoring. Through in-depth analyses of notable case studies, we showcase how algorithms can optimize viral genome sequencing, accelerate drug discovery, and offer predictive insights into viral outbreaks. However, with these advancements come inherent challenges, particularly in data security, algorithmic biases, and ethical considerations. Addressing these challenges head-on, we discuss potential remedial measures and underscore the significance of interdisciplinary collaboration between virologists, data scientists, and ethicists. Conclusively, this review posits an outlook that anticipates a symbiotic relationship between AI-driven tools and virology, heralding a new era of proactive and personalized patient care.
A hyperspectral deep learning attention model for predicting lettuce chlorophyll content
Authors: Ziran Ye, Xiangfeng Tan, Mengdi Dai, Xuting Chen, Yuanxiang Zhong, Yi Zhang, Yunjie Ruan, Dedong Kong

BackgroundThe phenotypic traits of leaves are the direct reflection of the agronomic traits in the growth process of leafy vegetables, which plays a vital role in the selection of high-quality leafy vegetable varieties. The current image-based phenotypic traits extraction research mainly focuses on the morphological and structural traits of plants or leaves, and there are few studies on the phenotypes of physiological traits of leaves. The current research has developed a deep learning model aimed at predicting the total chlorophyll of greenhouse lettuce directly from the full spectrum of hyperspectral images.ResultsA CNN-based one-dimensional deep learning model with spectral attention module was utilized for the estimate of the total chlorophyll of greenhouse lettuce from the full spectrum of hyperspectral images. Experimental results demonstrate that the deep neural network with spectral attention module outperformed the existing standard approaches, including partial least squares regression (PLSR) and random forest (RF), with an average R2 of 0.746 and an average RMSE of 2.018.ConclusionsThis study unveils the capability of leveraging deep attention networks and hyperspectral imaging for estimating lettuce chlorophyll levels. This approach offers a convenient, non-destructive, and effective estimation method for the automatic monitoring and production management of leafy vegetables.
A structural reliability analysis method under non-parameterized P-box based on double-loop deep learning models
Authors: Hao Hu, Minya Deng, Weichuan Sun, Jinwen Li, Huichao Xie, Haibo Liu

Structural reliability analysis, when accounting for non-parameterized probability box (P-box) uncertainty, typically entails multiple calls to performance functions and poses significant computational hurdles, largely attributable to its inherently nested double-loop structure. Therefore, this paper proposes a new reliability analysis method tailored for structures with uncertain parameters represented using non-parameterized P-boxes. This method leverages double-loop deep learning models to efficiently calculate both the upper and lower bounds of the failure probability. In the development phase of the double-loop deep learning model, an active learning function is devised that integrates the local prediction uncertainty of the deep learning model, based on the K-fold cross-validation principle with the proximity of training samples to candidate sample points. Different stopping criteria are formulated at distinct stages of the model construction process. Firstly, within the inner loop, a deep learning model is established to represent the original performance function in relation to the input parameters. Secondly, based on the inner-loop deep learning model for the performance function, an outer-loop deep learning model is established for the auxiliary response function corresponding to the P-box bound curves of the performance function response with respect to standard uniform distribution variables. Thirdly, utilizing the outer-loop deep learning approximate model, the Monte Carlo simulation technique is employed to compute the upper and lower bounds of the structural failure probability. Finally, the effectiveness of the proposed method is validated through the investigation of two numerical examples and a practical engineering problem. The influence of parameters in the active learning function, threshold values for stopping criteria, and the number of sample points on the computational results is deliberated.
A comprehensive survey on deep-learning-based visual captioning
Authors: Bowen Xin, Ning Xu, Yingchen Zhai, Tingting Zhang, Zimu Lu, Jing Liu, Weizhi Nie, Xuanya Li, An-An Liu

Generating a description for an image/video is termed as the visual captioning task. It requires the model to capture the semantic information of visual content and translate them into syntactically and semantically human language. Connecting both research communities of computer vision (CV) and natural language processing (NLP), visual captioning presents the big challenge to bridge the gap between low-level visual features and high-level language information. Thanks to recent advances in deep learning, which are widely applied to the fields of visual and language modeling, the visual captioning methods depending on the deep neural networks has demonstrated state-of-the-art performances. In this paper, we aim to present a comprehensive survey of existing deep learning-based visual captioning methods. Relying on the adopted mechanism and technique to narrow the semantic gap, we divide visual captioning methods into various groups. Representative categories in each group are summarized, and their strengths and limitations are discussed. The quantitative evaluations of state-of-the-art approaches on popular benchmark datasets are also presented and analyzed. Furthermore, we provide the discussions on future research directions.
Thermoplastic waste segregation classification system using deep learning techniques
Authors: M. Monica Subashini, R. S. Vignesh

This research proposes a deep learning-based system, named deep CNN architecture, for the automated classification of the plastic resin in plastic waste. The system aims to detect and recognize objects such as drinking water bottles, detergent bottles, squeezable bottles, and plastic plates, and segregate them into PET, PE-HD, PE-LD, and other resin categories. The process involves capturing input images through a camera and using deep learning or traditional algorithms to detect and recognize the objects by comparing them with a trained database containing labeled objects. Unrecognized objects are dynamically trained, labeled, and updated in the database. The proposed system is implemented using Python, a versatile open-source programming language. Python’s functional and aspect-oriented programming paradigms are leveraged to develop the models. The performance of the proposed architecture is evaluated against existing works, demonstrating a classification accuracy of 92.66% according to experimental results.
Deep learning-based power usage effectiveness optimization for IoT-enabled data center
Authors: Yu Sun, Yanyi Wang, Gaoxiang Jiang, Bo Cheng, Haibo Zhou

The proliferation of data centers is driving increased energy consumption, leading to environmentally unacceptable carbon emissions. As the use of Internet-of-Things (IoT) techniques for extensive data collection in data centers continues to grow, deep learning-based solutions have emerged as attractive alternatives to suboptimal traditional methods. However, existing approaches suffer from unsatisfactory performance, unrealistic assumptions, and an inability to address practical data center optimization. In this paper, we focus on power usage effectiveness (PUE) optimization in IoT-enabled data centers using deep learning algorithms. We first develop a deep learning-based PUE optimization framework tailored to IoT-enabled data centers. We then formulate the general PUE optimization problem, simplifying and specifying it for the minimization of long-term energy consumption in chiller cooling systems. Additionally, we introduce a transformer-based prediction network designed for energy consumption forecasting. Subsequently, we transform this formulation into a Markov decision process (MDP) and present the branching double dueling deep Q-network. This approach effectively tackles the challenges posed by enormous action spaces within MDP by branching actions into sub-actions. Extensive experiments conducted on real-world datasets demonstrate the exceptional performance of our algorithms, excelling in prediction precision, optimization convergence, and optimality while effectively managing a substantial number of actions on the order of \(10^{13}\).
Question classification task based on deep learning models with self-attention mechanism
Authors: Subhash Mondal, Manas Barman, Amitava Nag

Question classification (QC) is a process that involves classifying questions based on their type to enable systems to provide accurate responses by matching the question type with relevant information. To understand and respond to natural language questions posed by humans, machine learning models or systems must comprehend the type of information requested, which can often be inferred from the structure and wording of the question. The high dimensionality and sparse nature of text data lead to challenges for text classification. These tasks can be improved using deep learning (DL) approaches to process complex patterns and features within input data. By training on large amounts of labeled data, deep learning algorithms can automatically extract relevant features and representations from text, resulting in more accurate and robust classification. This study utilizes a dataset comprising 5452 instances of questions and six output labels and uses two different word embedding techniques, like GloVe and Word2Vec, tested on the dataset using three deep learning models, LSTM, BiLSTM, and GRU, followed by a convolution layer. Additionally, a self-attention layer is included, which helps the model to focus on more relevant information when making predictions. Finally, an analytical discussion of the proposed models and their performance results provide insight into how GloVe and Word2Vec perform on the above-mentioned models. The GloVe embedding outperforms by achieving 97.68% accuracy and a moderate loss of 16.98 with the GRU model.