Concept Of Deep Learning Research Articles

Comprehensive Study on Scoring and Grading Systems for Predicting the Severity of Knee Osteoarthritis.

Knee Osteoarthritis (KOA) is a degenerative joint ailment characterized by cartilage loss, which can be seen using imaging modalities and converted into imaging features. The older population is the most affected by knee OA, which affects 16% of people worldwide who are 15 years of age and older. Due to cartilage tissue degradation, primary knee OA develops in older people. In contrast, joint overuse or trauma in younger people can cause secondary knee OA. Early identification of knee OA, according to research, may be a successful management tactic for the condition. Scoring scales and grading systems are important tools for the management of knee osteoarthritis as they allow clinicians to measure the progression of the disease's severity and provide suggestions on suitable treatment at identified stages. The comprehensive study reviews various subjective and objective knee evaluation scoring systems that effectively score and grade the KOA based on where defects or changes in articular cartilage occur. Recent studies reveal that AI-based approaches, such as that of DenseNet, integrating the concept of deep learning for scoring and grading the KOA, outperform various state-of-the-art methods in order to predict the KOA at an early stage.

Research on Complex Data Mining Analysis and Pattern Recognition Based on Deep Learning

This paper systematically investigates and discusses the application of deep learning in complex data mining analysis and pattern recognition. Firstly, it introduces the basic concepts of deep learning and commonly used models, including artificial neural networks, convolutional neural networks, and recurrent neural networks. Then, it elaborates on the application methods and techniques of deep learning in mining different types of complex data (such as images, text, time series, etc.), and explores the latest research progress in the field of pattern recognition. Furthermore, it analyzes the challenges faced by deep learning in practical applications, such as data scarcity and model generalization capabilities, and proposes future development trends and research directions. Finally, it summarizes the research content and significance of this paper, emphasizing the importance and application prospects of deep learning in the field of complex data mining and pattern recognition.

AI Enabled Fire-Detecting Drone Using Deep Learning Model

Wildfires pose significant threats to both human lives and the environment, necessitating advanced technological solutions for early detection and rapid response. This project aims to design, develop, and implement an autonomous drone system equipped with cutting-edge fire detection technology leveraging deep learning concepts. The system's primary objective is to swiftly identify and locate wildfires, enabling timely intervention and mitigation efforts to enhance public safety and environmental protection. The proposed drone system integrates state-of-the-art deep learning algorithms, allowing it to analyze real-time aerial imagery for signs of fire outbreaks with high accuracy and efficiency. Through the utilization of convolutional neural networks(CNNs) trained on vast datasets of wildfire images, the system can recognize distinct patterns and characteristics associated with flames, smoke, and heat sources amidst varying environmental conditions. Key components of the autonomous drone system include advanced sensors such as infrared cameras and multispectral imaging devices, facilitating enhanced detection capabilities across different spectrums. The integration of these sensors enables the system to detect wildfires even in challenging scenarios such as dense foliage or low visibility conditions. Upon detection of potential wildfire activity, the autonomous drone employs intelligent navigation algorithms to swiftly navigate to the identified location for closer inspection and assessment. Real-time data transmission capabilities enable seamless communication with ground-based emergency response teams, providing critical information for prompt decision-making and deployment of firefighting resources.

DEEP LEARNING IN THE ERA OF BIG DATA: FOUNDATIONS, ADVANCES, APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS

Deep learning (DL) has revolutionized machine learning, especially in the era of big data, by autonomously learning hierarchical representations from vast datasets. This paper offers a comprehensive overview of DL, covering foundational concepts, recent advances, practical applications, challenges, and future directions. It traces DLs historical roots from the perceptron model to contemporary deep neural networks, highlighting its resurgence in the early 21st century due to computational advancements and large-scale datasets. The paper discusses DLs objectives, including methodologies overview, challenges analysis, and future trends exploration. Foundational DL concepts are explained, including artificial neurons, activation functions, and network architectures like feedforward, convolutional, and recurrent neural networks. Recent advancements such as transfer learning, reinforcement learning, meta-learning, and self-supervised learning are explored, enhancing model performance across domains like computer vision, natural language processing, and robotics. Applications of DL in healthcare, finance, automotive, and more are detailed with real-world examples. The paper also analyzes challenges like data scarcity, computational complexity, and ethical considerations, proposing strategies for mitigation. Future directions in DL, including explainable AI and ethical considerations, are discussed, emphasizing DLs transformative impact and its role in driving innovation in big data analytics.

Vehicle Detection and Tracking Techniques Based on Deep Learning in Road Traffic Surveillance

This article discusses the application of deep learning in vehicle detection and tracking technology, elaborating on the basic concepts of deep learning and its advantages in vehicle target detection. Deep learning models such as Convolutional Neural Networks (CNNs) overcome the reliance on manual feature engineering by automatically learning image features. The article focuses on two deep learning detection frameworks, Faster R-CNN and YOLO. The former combines region proposal networks with region classification networks to achieve end-to-end optimization, while the latter transforms the detection task into a regression problem, enabling real-time detection in a single forward pass. Regarding vehicle tracking, the article explores the challenges of multi-object tracking such as occlusion, cross-movement, and the tracking requirements of different vehicle types. Deep learning applications in this field, such as the DeepSORT and Tracktor algorithms, combine CNNs, RNNs, and traditional tracking methods to achieve feature learning, historical state modeling, and probabilistic reasoning. Performance evaluation is discussed in terms of metrics like IoU, precision, recall, and F1 Score, comparing and analyzing the performance of different algorithms in vehicle detection and tracking tasks. Lastly, the article discusses the balance between real-time and accuracy in deep learning-based vehicle detection and tracking technology in road traffic monitoring, as well as its significant role in traffic accident warning and management.

Research on the Construction of Deep Learning-oriented College English Blended Gold Courses in the Context of New Liberal Arts

Comprehensively promoting the construction of new liberal arts is an inevitable requirement for the education and teaching reform of liberal arts majors in colleges and universities in the new era. Different from the traditional liberal arts, the new liberal arts can better serve the national development and regional social progress. Based on the background of the new liberal arts, this paper discusses its new requirements for College English. By investigating the current situation of College English blended teaching, the paper finds the deficiencies, and combines the concept of deep learning to mainly analyze the necessity of applying it to college English teaching. On this basis, it puts forward new initiatives for building the deep learning-oriented blended College English gold courses based on the new liberal arts from five aspects, namely, teaching environment, teaching resources, teaching process, assessment system, and teacher literacy, so as to promote the connotative and innovative development of the College English course and achieve the goal of all-round education.

Plant disease recognition using residual convolutional enlightened Swin transformer networks

Agriculture plays a pivotal role in the economic development of a nation, but, growth of agriculture is affected badly by the many factors one such is plant diseases. Early stage prediction of these disease is crucial role for global health and even for game changers the farmer’s life. Recently, adoption of modern technologies, such as the Internet of Things (IoT) and deep learning concepts has given the brighter light of inventing the intelligent machines to predict the plant diseases before it is deep-rooted in the farmlands. But, precise prediction of plant diseases is a complex job due to the presence of noise, changes in the intensities, similar resemblance between healthy and diseased plants and finally dimension of plant leaves. To tackle this problem, high-accurate and intelligently tuned deep learning algorithms are mandatorily needed. In this research article, novel ensemble of Swin transformers and residual convolutional networks are proposed. Swin transformers (ST) are hierarchical structures with linearly scalable computing complexity that offer performance and flexibility at various scales. In order to extract the best deep key-point features, the Swin transformers and residual networks has been combined, followed by Feed forward networks for better prediction. Extended experimentation is conducted using Plant Village Kaggle datasets, and performance metrics, including accuracy, precision, recall, specificity, and F1-rating, are evaluated and analysed. Existing structure along with FCN-8s, CED-Net, SegNet, DeepLabv3, Dense nets, and Central nets are used to demonstrate the superiority of the suggested version. The experimental results show that in terms of accuracy, precision, recall, and F1-rating, the introduced version shown better performances than the other state-of-art hybrid learning models.

Deep Learning in Single-cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and heterogeneous and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.

Assessing the impact of marine renewable energy in Portugal: an analysis based on ACO-TCN-attention

As the global demand for renewable energy continues to increase, marine renewable energy has attracted much attention as a potential source of clean energy. As a country with rich marine resources, Portugal’s marine environment is of great significance to the development of marine energy. However, the current impact assessment of marine renewable energy projects has shortcomings such as incomplete understanding of ecosystems, incomplete consideration of fishery resources and socioeconomic impacts, lack of accuracy, and failure to consider geographical differences, thus lacking comprehensiveness and accuracy. To this end, we propose the ACO-TCN-Attention model to address these shortcomings in current impact assessments of marine renewable energy projects. The goal of this model is to provide a more comprehensive, precise and nuanced analysis to better understand the impacts of these projects on ecosystems, socio-economics and local communities. “ACO-TCN-Attention” is a model architecture that combines multiple machine learning and deep learning concepts. It includes three main parts: Ant Colony Optimization (ACO), Temporal Convolutional Network (TCN) and Attention mechanism. The ant colony optimization model simulates the behavior of ants and is used to optimize the operating strategies of marine renewable energy projects. Temporal Convolutional Network specializes in processing time series data and improves the prediction accuracy of the model. The attention mechanism allows the model to dynamically focus on the pieces of information that are most important for the current task. Extensive experimental evaluation shows that our method performs well on multiple datasets, significantly outperforming other models. This research is of great significance as it provides new methods and tools for improving the environmental impact assessment of marine renewable energy projects. By understanding the potential impacts of projects more accurately, we can better balance the relationship between the development of renewable energy and environmental protection, supporting the achievement of the Sustainable Development Goals. This research also provides useful guidance and reference for future research and practice in the field of marine energy.

Identifying implant abutments and fixtures by application of deep learning concept through artificial intelligence – A review

Artificial intelligence (AI) is an evolving and assuring approach in healthcare and has begun to improve the dental science ever since. A subgroup of AI called deep learning (DL) related convolutional neural network algorithm have showed significant applications in computer based tasks and have been shown to be fairly applicable for dental image recognition and treatment planning analysis. This article aims to review the adaptation of artificial intelligence in identifying implant systems and abutments using deep learning concept.

Deep Learning in Dental Radiographic Imaging

Deep learning algorithms are becoming more prevalent in dental research because they are utilized in everyday activities. However, dental researchers and clinicians find it challenging to interpret deep learning studies. This review aimed to provide an overview of the general concept of deep learning and current deep learning research in dental radiographic image analysis. In addition, the process of implementing deep learning research is described. Deep-learning-based algorithmic models perform well in classification, object detection, and segmentation tasks, making it possible to automatically diagnose oral lesions and anatomical structures. The deep learning model can enhance the decision-making process for researchers and clinicians. This review may be useful to dental researchers who are currently evaluating and assessing deep learning studies in the field of dentistry.

인공지능 기반 심리상담에 활용되는 딥러닝 기법 고찰

Objectives This study aims to review and summarize deep learning techniques that have been developed to date and to discuss how they can be applied in practice to artificial intelligence-based psychology counseling robots.
 Methods To examine the latest deep learning techniques utilized in artificial intelligence psychological counseling, an extensive literature search was conducted for research papers and relevant books from both domestic and in-ternational sources in the fields of emotion analysis, facial expression generation, and text generation.. For do-mestic literature, databases such as RISS and KISS were utilized, and for international literature, research paper search sites including ERIC (ProQuest), JSTOR, and Google Scholar were employed.
 Results When applying artificial intelligence in psychological counseling, the prominent technology of interest in-volves recognizing emotions from the interlocutor's facial expressions and generating similar expressions, or rec-ognizing emotions from sentences spoken by the interlocutor and generating responsive sentences. The study in-troduced the concepts of machine learning and deep learning and explained the workings of deep learning techni-ques, such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), Transformer, BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pretrained Transformer), and GAN (Generative Adversarial Network). These techniques are founded on the princi-ples derived from human brain functioning. Particularly, emotion recognition programs through image analysis have predominantly relied on CNN, while research in the realm of language has advanced with the utilization of GPT-2, LSTM, and similar methods.
 Conclusions Among the existing deep learning techniques, the recognition of emotions from the interlocutor's fa-cial expressions and the generation of similar expressions, or the recognition of emotions from sentences spoken by the interlocutor and the generation of responsive sentences, are the technologies predominantly under scrutiny for their application in artificial intelligence-based psychological counseling. Nevertheless, rule-based chatbots continue to dominate the landscape. This paper explored the merits and demerits of AI-based counseling em-ploying such deep learning techniques. Looking ahead, there is a need for the advancement of technology that en-ables natural interactions, coupled with the essential verification of its effectiveness.

A Deep Learning Framework to Identify Remedial Action Schemes Against False Data Injection Cyberattacks Targeting Smart Power Systems

This article proposes aremedial actionscheme (RAS) based on the concept of deep learning to mitigate the impacts of false data injection (FDI) cyberattacks on smart power systems. As a prerequisite of such a RAS, power system operator is being in attacker's shoe to scrutinize different scenarios of cyberattacks. In design of the RAS, long short-term memory (LSTM) cells have been integrated into a deep recurrent neural network (DRNN) to effectively process the data of an intelligent archive framework (IAF), identifying the proper reaction mechanisms. Power flow analysis has been considered to examine the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interdependence</i> between transmission/distribution sectors to react to the cyberattacks for which similar <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pre-investigated</i> remedial actions have not been saved in the IAF. Effectiveness of the proposed RAS is validated on two IEEE transmission/distribution systems, where consequences of FDI cyberattacks are reduced by 30% in case of experiencing attacks, which are not pre-investigated by system operator.

Convolutional neural network-based techniques and error level analysis for image tamper detection

&lt;span&gt;Photographs are the foremost powerful and trustworthy media of expression. At present, digital pictures not only serve forged information but also disseminate deceptive information. Users and experts with various objectives edit digital photographs. Images are frequently used as proof of reality or fact, therefore fake news or any publication that makes use of photos that have been altered in any way has a larger chance of deceiving readers. There is a need for a high-resolution image analysis model that processes individual pixels in images and a substantial amount of diverse image data, to detect image falsification. Convolutional neural network (CNN) with error level analysis (ELA) adopted in this research is found to be an ideal deep learning concept for detecting image manipulation. The model exhibited a validation accuracy of 99.6%, 99.7%, and 99.4% for CASIA V1.0, CASIA V2.0 and MICC datasets respectively. The accuracy for handmade tampered images was found to be 99.2%.&lt;/span&gt;

College Students ' Ecological Environment Moral Education From the Perspective of Ecological Civilization

This article discusses the ecological environment moral education of college students based on deep learning. This article first points out the concept of deep learning and the relationship between ecological civilization and college students' ecological environment morality. Then, it expounds the latest concepts and achievements of ecological civilization in China, analyzes the deficiencies and reasons of current college students' ecological moral education, and clearly points out the significance of strengthening college students' ecological moral education based on in-depth learning. This article focuses on the main contents and principles of setting suggestions of the ecological environment moral education for college students. Finally, it gives the countermeasures for the ecological environment moral education of college students based on deep learning. It focuses on the theoretical realization of college students' ecological environmental moral education from the perspective of deep learning, aiming at promoting the construction of ecological civilization.

Deep learning assisted pressure measurements using femtosecond laser-induced grating scattering technique

The femtosecond laser-induced grating scattering (fs-LIGS) technique has recently been developed and applied for temperature and pressure measurements. In this work, we combined deep learning with the fs-LIGS technique to predict the gas-phase pressure from raw signals without data post-processing. Two deep learning models, a fully connected neural network and a convolutional neural network, were trained to master the hidden relationship between the features of the raw signal traces and the corresponding pressure under which the signal was recorded. Accurate pressure predictions by both models were achieved as mean percentage errors in model-predicted pressures compared to actual values within −4%–2%. These results suggest the feasibility of combining the deep-learning concept with the fs-LIGS technique for instantaneous pressure determination. Given the proper training of the models, this strategy could be extended to the simultaneous measurement of multiple thermodynamic quantities in real-time combustion and reacting flow diagnostics.

Accelerated Pure Shift NMR Spectroscopy with Deep Learning.

Pure shift nuclear magnetic resonance (NMR) spectroscopy presents a promising solution to provide sufficient spectral resolution and has been increasingly applied in various branches of chemistry, but the optimal resolution is generally accompanied by long experimental times. We present a proof of concept of deep learning for fast, high-quality, and reliable pure shift NMR reconstruction. The deep learning (DL) protocol allows one to eliminate undersampling artifacts, distinguish peaks with close chemical shifts, and reconstruct high-resolution pure shift NMR spectroscopy along with accelerated acquisition. More meaningfully, the lightweight neural network delivers satisfactory reconstruction performance on personal computers by several hundred simulated data learning, which somewhat lifts the prohibiting demand for a large volume of real training samples and advanced computing hardware generally required in DL projects. Additionally, an M-to-S strategy applicable to common DL cases is further exploited to boost the network generalization capability. As a result, this study takes a meaningful step toward deep learning protocols for broad chemical applications.

English Reading Teaching in Senior High School from the Perspective of Deep Learning

The introduction of the concept of deep learning has led to a change in the way of learning, and a new teaching concept has been constructed from the aspects of learning content and overall understanding. In English, students must construct a comprehensive, detailed and systematic knowledge structure when reading and master language under the premise of deep thinking, and then absorb and internalize it through dialectical thinking. Practice shows that the combination of deep learning and senior high school English reading teaching conforms to the law of English teaching development, and is also an effective strategy to optimize and upgrade English reading teaching. Therefore, on the basis of summarizing the connotation and characteristics of deep learning, the author put forward targeted suggestions according to the existing problems.

A Real-time Environmental Air Pollution Predictor Model Using Dense Deep Learning Approach in IoT Infrastructure

&lt;p&gt;With the technical advancements in Deep Learning (DL), it is probable to construct the predictor model for monitoring and controlling pollution from real-time data. Here, IoT techniques are used for sensing the emission rate from various factors and the predictor model is constructed using the available data, for instance, carbon monoxide prediction. Modern sensors are embedded to evaluate the level of pollutants and using these modern techniques, the source of emission rate is identified and notified to the specific environment. Deep learning concepts are used for predicting the pollution level based on the current and previous data attained from the sensors. Here, we have implemented a learning solution to predict carbon monoxide concentration hourly using the novel Dense Residual Convolutional Network Model with Bi-LSTM (Bidirection-Long Short Term Memory) with the spatial and temporal features by integrating the features of the present and previous pollutant data. The side output from the residual network model is used to evaluate prediction quality. The performance is compared with existing approaches like standard LSTM, CNN, pre-trained network model, etc. The experimentation is done in a Python environment, and the proposed model facilitates more prediction accuracy for the pollutants CO,SO_2,O_3 and NO_2 than other conventional network models and establishes a better trade-off.&lt;/p&gt;&#x0D;

Exploring the connection between deep learning and learning assessments: a cross-disciplinary engineering education perspective

It is widely accepted that student learning is significantly affected by assessment methods, but a concrete relationship has not been established in the context of multidisciplinary engineering education. Students make a physiological investment and internalize learning (deep learning) if they see high value in their learning. They persist despite challenges and take delight in accomplishing their work. As student deep learning is affected by the assessment system, it is important to explore the relationship between assessment systems and factors affecting deep learning. This study identifies the factors associated with deep learning and examines the relationships between different assessment systems those factors. A conceptual model is proposed, and a structured questionnaire was designed and directed to 600 Queensland University of Technology (QUT) multidisciplinary engineering students, with 243 responses received. The gathered data were analyzed using both SPSS and SEM. Exploratory factor analysis revealed that deep learning is strongly associated with learning environment and course design and content. Strong influence of both summative and formative assessment on learning was established in this study. Engineering educators can facilitate deep learning by adopting both assessment types simultaneously to make the learning process more effective. The proposed theoretical model related to the deep learning concept can support the key practices and modern learning methodologies currently adopted to enhance the learning and teaching process.