ADKU/ResearchGPT_model_dataset huggingface dataset:
Aspect-oriented extraction and sentiment analysis using optimized hybrid deep learning approaches
Authors: Srividya Kotagiri, A. Mary Sowjanya, B. Anilkumar, N Lakshmi Devi

Aspect-oriented extraction involves the identification and extraction of specific aspects, features, or entities within a piece of text. Traditional methods often struggled with the complexity and variability of language, leading to the exploration of advanced deep learning approaches. In the realm of sentiment analysis, the conventional approaches often fall short when it comes to providing a nuanced understanding of sentiments expressed in textual data. Traditional sentiment analysis models often overlook the specific aspects or entities within the text that contribute to the overall sentiment. This limitation poses a significant challenge for businesses and organizations aiming to gain detailed insights into customer opinions, product reviews, and other forms of user-generated content.In this research, we propose an innovative approach for aspect-oriented extraction and sentiment analysis leveraging optimized hybrid deep learning techniques. Our methodology integrates the powerful capabilities of deep learning models with the efficiency of Reptile Search Optimization. Furthermore, we introduce an advanced sentiment analysis framework employing the state-of-the-art Extreme Gradient Boosting Algorithm. The fusion of these techniques aims to enhance the precision and interpretability of aspect-oriented sentiment analysis. The proposed approach first utilizes deep learning architectures to extract and comprehend diverse aspects within textual data. Through the incorporation of Reptile Search Optimization, we optimize the learning process, ensuring adaptability and improved model generalization across various datasets. Subsequently, the sentiment analysis phase employs the robust Extreme Gradient Boosting Algorithm, known for its effectiveness in handling complex relationships and patterns within data. Our experiments, conducted on diverse datasets, demonstrate the superior performance of the proposed methodology in comparison to traditional approaches. The optimized hybrid deep learning approach, coupled with the Reptile Search Optimization and Extreme Gradient Boosting Algorithm, showcases promising results in accurately capturing nuanced sentiments associated with different aspects. This research contributes to the advancement of aspect-oriented sentiment analysis techniques, offering a comprehensive and efficient solution for understanding sentiment nuances in textual data across various domains. The ResNet 50 and EfficientNet B7 architecture of the modified pre-trained model is proposed for the aspect extraction function. The Reptile Search Optimization based Extreme Gradient Boosting Algorithm (RSO-EGBA) is proposed to analyze and predict customer sentiments. The execution of this study is carried out using python software. It has been observed that the overall accuracy of our proposed method is 99.8%, while that of the other state-of-the-art. The overall accuracy of our proposed method shows an increment of 9–16% from that of the state-of-the-art methods.
A review of research on micro-expression recognition algorithms based on deep learning
Authors: Fan Zhang, Lin Chai

Micro-expression is a special kind of human emotion. Due to its characteristics of short time, low intensity, and local region, micro-expression recognition is a difficult task. At the same time, it is a natural, spontaneous, and unconcealable emotion that can well convey a person's actual psychological state and, therefore, has certain research value and practical significance. This paper focuses on micro-expression recognition in the field of deep learning through the survey and understanding of existing micro-expression recognition research, as well as grasping the research trend, for the previous literature on micro-expression review ignored the handcrafted features as an important part of the micro-expression recognition framework, and at the same time lacked the analysis of the various enhancement processing, a new micro-expression recognition framework based on deep learning is proposed. The model is designed from the perspective of modularity and streaming data. On the other hand, unlike the previous process of feeding the data directly into the network for training and recognition, the handcrafted features are used as the initial encoding of the micro-expression recognition data, followed by the training and learning of the deep model and at the same time the modular embedding approach is used to incorporate the feature enhancement module, and finally the classification and recognition. The article provides a detailed summary and analysis of each part of the whole framework and a comprehensive introduction to the current problems, experimental protocols, evaluation metrics, and application areas. Finally, it summarizes and gives possible future research directions. Therefore, this paper provides a comprehensive summary and analysis of micro-expression recognition in deep learning so that the related personnel can have a new understanding of the development of this field. On the other hand, it proposes a new recognition framework that also provides a reference for the researchers' later research.
HDLP: air quality modeling with hybrid deep learning approaches and particle swam optimization
Authors: Elmenawy Osman, C. Banerjee, Ajeet Singh Poonia

Predicting air pollution in cities has become an important tool for preventing its negative impacts. Therefore, citizens should be aware of air quality level, especially for individuals suffering from diseases caused by air pollutants. Collective efforts from researchers, environmental institutions, governments, industrial companies, and policy makers are shaping the future of the Air Quality Index (AQI) to effectively address severe air pollution in urban areas. Many air quality prediction models have been introduced in the literature; modern advances in deep learning techniques are promising more precise prediction results and data integration. The aim of this paper is to review methods, contributions, findings, limitations, and gaps in predicting air quality index and PM2.5 concentrations using a hybrid deep learning approach. A literature review led researchers to propose a Hybrid Deep Learning model with Particle Swarm Optimization (HDLP) which combines CNN, LSTM, and PSO. Algorithm, first Discrete Wavelet Transform (DWT) is used to solve the air pollution signal, then it is fed to CNN-LSTM neural network for PSO optimization to get the final prediction result. Optimized parameters input models are trained on the original data. In addition to the beneficial assessment cycle, to outperform current models.
Research progress in water quality prediction based on deep learning technology: a review
Authors: Wenhao Li, Yin Zhao, Yining Zhu, Zhongtian Dong, Fenghe Wang, Fengliang Huang

Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.
Implementing a Hierarchical Deep Learning Approach for Simulating Multilevel Auction Data
Authors: Igor Sadoune, Marcelin Joanis, Andrea Lodi

We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of DGM as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI.
Beschleunigte muskuloskeletale Magnetresonanztomographie mit Deep-Learning-gestützter Bildrekonstruktion bei 0,55 T–3 T
Authors: Jan Vosshenrich, Jan Fritz

Klinisches/methodisches ProblemDie Magnetresonanztomographie (MRT) ist ein zentraler Bestandteil der muskuloskeletalen Diagnostik. Lange Akquisitionszeiten können jedoch zu Einschränkungen in der klinischen Praxis führen.Radiologische StandardverfahrenDie MRT hat sich aufgrund des hohen Auflösungsvermögens und Signal-zu-Rausch-Verhältnisses (SNR) sowie des exzellenten Weichteilkontrastes als Modalität der Wahl in der Diagnostik von Verletzungen und Erkrankungen des muskuloskeletalen Systems etabliert.Methodische InnovationenKontinuierliche Weiterentwicklungen in der Hard- und Softwaretechnologie haben eine bildqualitäts- und genauigkeitsneutrale Beschleunigung von 2D-Turbo-Spin-Echo(TSE)-Sequenzen um den Faktor 4 ermöglicht. Kürzlich vorgestellte, auf Deep Learning (DL) basierende Bildrekonstruktionsalgorithmen helfen, die Abhängigkeit zwischen SNR, räumlicher Auflösung und Akquisitionszeit weiter zu minimieren und erlauben die Anwendung höherer Beschleunigungsfaktoren.LeistungsfähigkeitDie kombinierte Anwendung fortschrittlicher Beschleunigungstechniken und DL-basierter Bildrekonstruktion birgt enormes Potenzial, um die Effizienz, den Patientenkomfort und die Zugänglichkeit der muskuloskeletalen MRT bei gleichbleibend hoher diagnostischer Genauigkeit zu maximieren.BewertungDL-rekonstruierte beschleunigte MRT-Untersuchungen haben ihre Praxisreife und ihren Mehrwert innerhalb kürzester Zeit unter Beweis gestellt. Aktuelle wissenschaftliche Erkenntnisse legen nahe, dass das Potenzial dieser Technologie noch nicht ausgeschöpft ist.Empfehlung für die PraxisBeschleunigte MRT-Untersuchungen mit DL-gestützter Bildrekonstruktion können zuverlässig in der Primärdiagnostik und Verlaufskontrolle muskuloskeletaler Fragestellungen eingesetzt werden.
Fluid dynamic control and optimization using deep reinforcement learning
Authors: Innyoung Kim, Donghyun You

This paper presents a review of recent research on applying deep reinforcement learning in fluid dynamics. Reinforcement learning is a technique in which the agent autonomously learns optimal action strategies while interacting with the environment, mimicking human learning mechanisms. Combined with artificial intelligence technology, it is providing a new direction in fluid dynamic control and optimization, which were challenging due to the nonlinear and high-dimensional characteristics of the fluid. In the section on fluid dynamic control, control strategies for drag reduction and research on controlling biological motion are reviewed. The optimization section focuses on shape optimization and automation of computational fluid dynamics. Current challenges and possible future developments are also described.Graphical Abstract
Deep-learning based artificial intelligence tool for melt pools and defect segmentation
Authors: Amra Peles, Vincent C. Paquit, Ryan R. Dehoff

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.
Multi-echelon inventory optimization using deep reinforcement learning
Authors: Kevin Geevers, Lotte van Hezewijk, Martijn R. K. Mes

This paper studies the applicability of a deep reinforcement learning approach to three different multi-echelon inventory systems, with the objective of minimizing the holding and backorder costs. First, we conduct an extensive literature review to map the current applications of reinforcement learning in multi-echelon inventory systems. Next, we apply our deep reinforcement learning method to three cases with different network structures (linear, divergent, and general structures). The linear and divergent cases are derived from literature, whereas the general case is based on a real-life manufacturer. We apply the proximal policy optimization (PPO) algorithm, with a continuous action space, and show that it consistently outperforms the benchmark solution. It achieves an average improvement of 16.4% for the linear case, 11.3% for the divergent case, and 6.6% for the general case. We explain the limitations of our approach and propose avenues for future research.
An Analysis of Plant Diseases on Detection and Classification: From Machine Learning to Deep Learning Techniques
Authors: P. K. Midhunraj, K. S. Thivya, M. Anand

Plants are acknowledged as being crucial because they are the main source of human energy generation due to their nutritional, therapeutic, and other benefits. Therefore, it is necessary to increase crop productivity. One of these significant factors contributing to reduced agricultural yields is the prevalence of bacterial, fungal, and viral illnesses. Applying techniques for plant disease identification can stop and treat these diseases. So, numerous machine learning (ML) and deep learning (DL) methods were created and tested by researchers to identify plant diseases. Therefore, this study gives a detailed discussion of the various research studies conducted in plant disease detection utilizing ML and DL-based techniques. This review offers research advancements in plant disease recognition from ML to DL techniques. Additionally, many datasets about plant diseases are thoroughly examined. It also addresses the difficulties and issues with the current systems.
The Mori–Zwanzig formulation of deep learning
Authors: Daniele Venturi, Xiantao Li

We develop a new formulation of deep learning based on the Mori–Zwanzig (MZ) formalism of irreversible statistical mechanics. The new formulation is built upon the well-known duality between deep neural networks and discrete dynamical systems, and it allows us to directly propagate quantities of interest (conditional expectations and probability density functions) forward and backward through the network by means of exact linear operator equations. Such new equations can be used as a starting point to develop new effective parameterizations of deep neural networks and provide a new framework to study deep learning via operator-theoretic methods. The proposed MZ formulation of deep learning naturally introduces a new concept, i.e., the memory of the neural network, which plays a fundamental role in low-dimensional modeling and parameterization. By using the theory of contraction mappings, we develop sufficient conditions for the memory of the neural network to decay with the number of layers. This allows us to rigorously transform deep networks into shallow ones, e.g., by reducing the number of neurons per layer (using projection operators), or by reducing the total number of layers (using the decay property of the memory operator).
XDeMo: a novel deep learning framework for DNA motif mining using transformer models
Authors: Rajashree Chaurasia, Udayan Ghose

Motivation: Recognizing and studying DNA patterns is crucial for improving knowledge of illnesses, cell function, and gene control. Motifs determine which transcription factor a protein may bind to, leading to a better unraveling of gene expression. Advancements in the fields of deep learning and high-throughput sequencing have made possible the exploration of motif discovery anew, with greater accuracy and performance. Methodology: In this paper, a novel deep learning framework (XDeMo – Transformer-based Deep Motifs) for DNA motif mining using Transformer models is proposed. Furthermore, a hybrid encoding scheme is also introduced, called ‘blended’ encoding specifically designed for use with deep learning transformer models that are trained using DNA sequences. Results: Our proposed transformer-based framework for DNA motif discovery augmented by blended encoding outperforms many state-of-the-art deep learning models on many baseline performance metrics when trained on the standard datasets. Our models demonstrated robust performance in predicting motifs with high discriminative power, precision, recall, and F1 score. Conclusion: The model’s ability to capture intricate sequence patterns and long-range dependencies led to the discovery of biologically meaningful motifs that were verified from known transcription factor binding motif databases. This shows that our novel framework can be effectively used to find DNA motifs and therefore, aid in further downstream analyses for biomedical and biotechnological applications.
DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology
Authors: Lingbo Jin, Yubo Tang, Jackson B. Coole, Melody T. Tan, Xuan Zhao, Hawraa Badaoui, Jacob T. Robinson, Michelle D. Williams, Nadarajah Vigneswaran, Ann M. Gillenwater, Rebecca R. Richards-Kortum, Ashok Veeraraghavan

Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.
Identification of rural courtyards’ utilization status using deep learning and machine learning methods on unmanned aerial vehicle images in north China
Authors: Maojun Wang, Wenyu Xu, Guangzhong Cao, Tao Liu

The issue of unoccupied or abandoned homesteads (courtyards) in China emerges given the increasing aging population, rapid urbanization and massive rural-urban migration. From the aspect of rural vitalization, land-use planning, and policy making, determining the number of unoccupied courtyards is important. Field and questionnaire-based surveys were currently the main approaches, but these traditional methods were often expensive and laborious. A new workflow is explored using deep learning and machine learning algorithms on unmanned aerial vehicle (UAV) images. Initially, features of the built environment were extracted using deep learning to evaluate the courtyard management, including extracting complete or collapsed farmhouses by Alexnet, detecting solar water heaters by YOLOv5s, calculating green looking ratio (GLR) by FCN. Their precisions exceeded 98%. Then, seven machine learning algorithms (Adaboost, binomial logistic regression, neural network, random forest, support vector machine, decision trees, and XGBoost algorithms) were applied to identify the rural courtyards’ utilization status. The Adaboost algorithm showed the best performance with the comprehensive consideration of most metrics (Accuracy: 0.933, Precision: 0.932, Recall: 0.984, F1-score: 0.957). Results showed that identifying the courtyards’ utilization statuses based on the courtyard built environment is feasible. It is transferable and cost-effective for large-scale village surveys, and may contribute to the intensive and sustainable approach to rural land use.