Deep Learning Processing Research Articles

Deep learning and time series signal processing for bending detection in mining environment using optical fiber sensor

Optical fiber sensors are widely used to monitor environmental disturbances that may trigger or affect mining landslides, a complex phenomenon that involves various factors and can cause severe damage. In this study, we propose a method to detect the curvature of a bent optical fiber, which affects the optical signals traveling through it, using deep learning and time series signal processing. We use a Mach-Zehnder interferometer (MZI) sensor and a single-mode fiber (SMF) cable to capture the interference signal caused by different bending angles. We apply a transformation random convolutional kernel (TRANCE) to reduce the signal and extract its mean and standard deviation as features. We train a dense neural network (DNN) with three hidden layers to classify the bending events into four categories: low, medium, high, and extreme. We evaluate our method on a dataset of 400 samples and achieve an average accuracy of 99% in the confusion matrix and the fifty times tenfold validation experiments. Our method outperforms other methods that use multimode fiber (MMF), speckle images, and convolutional neural networks (CNNs) in terms of accuracy, speed, and generalization. Our method also has the advantages of using SMF, which enables integrated sensing and communication, and using 1D signals, which are easier to process than 2D images. Our method is suitable for high-speed signals containing information in dangerous environments.

A Fault Detection Robotic Cell Application Based on Deep Learning and Image Processing Hybrid Approach for Quality Control of Automotive Parts

A Fault Detection Robotic Cell Application Based on Deep Learning and Image Processing Hybrid Approach for Quality Control of Automotive Parts

Application of Deep Learning in Sentiment Analysis Based on Movie Review Datasets

Abstract. With the rapid development of the Internet and social networks. user-generated content (UGC) has exploded on the Internet as an important form of user feedback, especially movie reviews, which are of great value to the film industry and market analysis, and can help relevant groups understand the reputation and evaluation of movies. As a natural language processing technology, sentiment analysis can effectively identify and extract emotional biases in text, providing a new perspective and method for sentiment analysis from film criticism. The results show that using deep learning can significantly speed up this work and make sentiment analysis more efficient and faster. The purpose of this article is to explore the application and specific process of deep learning in sentiment analysis of film criticism datasets, to find the most appropriate model by comparing and analyzing multiple models, and to present the rating data of some films from the perspective of display technology.

AI-Based Phishing Detection and Automated Response: A Multi-Channel Security Framework for Modern Communication Platforms

Phishing attacks have evolved in sophistication over the changing digital communication landscape, taking advantage of several channels like social media, SMS, and email to trick people and businesses. This research offers a multi-channel security architecture for automated phishing response and detection that uses cutting-edge artificial intelligence (AI) technology to counteract this ubiquitous threat. The system utilizes automatic reaction mechanisms to mitigate threats in real time and incorporates state-of-the-art AI algorithms to improve the detection of phishing attempts across various communication channels. This study examines recent developments in artificial intelligence (AI) for cybersecurity, emphasizing the use of deep learning, machine learning, and natural language processing in phishing detection and response. It also looks at how phishing techniques have changed over time, how difficult it is to integrate AI across different platforms, and how dangerous it could be for AI systems to come under hostile attack. The report shows the usefulness and practical use of AI-driven solutions with case studies from social media, financial services, and enterprise communication platforms. It also discusses ethical and regulatory issues, highlighting the necessity of adhering to data protection regulations and using AI responsibly. The technological difficulties, potential avenues for future investigation, and prospects for innovation in AI-based phishing detection are covered in the paper's conclusion. With this methodology, cybersecurity researchers and practitioners can benefit from a thorough approach to improving cybersecurity with artificial intelligence.

Mixed Reality-Based Concrete Crack Detection and Skeleton Extraction Using Deep Learning and Image Processing

Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. However, these systems cannot work interactively with human inspectors. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m with the Canny algorithm for real-time concrete crack detection and skeleton extraction with a Microsoft HoloLens 2 MR device. The YOLO v5n demonstrates a superior mean average precision (mAP) 0.5 and speed, while YOLO v5m achieves the highest mAP 0.5 0.95 among the other YOLO v5 structures. The Canny algorithm also outperforms the Sobel and Prewitt edge detectors with the highest F1 score. The developed MR-based system could not only be employed for real-time defect assessment but also be utilized for the automatic recording of the location and other specifications of the cracks for further analysis and future re-inspections.

Personalized Emotion Detection Adapting Models to Individual Emotional Expressions

Emotion recognition from text and speech has become a critical area of research in artificial intelligence (AI), enhancing human-computer interaction across various sectors. This paper explores the methodologies used in emotion recognition, focusing on Natural Language Processing (NLP) for text and acoustic analysis for speech. It reviews key machine learning and deep learning models, including Support Vector Machines (SVM), neural networks, and transformers, and highlights the datasets commonly used in emotion detection studies. The paper also addresses challenges such as multimodal integration, data ambiguity, and ethical considerations like privacy concerns and bias in models. Applications in customer service, healthcare, education, and entertainment are discussed, showcasing the growing importance of emotion recognition in AI- driven systems. Future research directions, including advancements in deep learning, multimodal systems, and real-time processing, are also explored to address existing limitations.

EXPLOITING OPTIMAL SELF-ATTENTION DEEP LEARNING-BASED RECOGNITION OF TEXTUAL EMOTIONS FOR DISABLED PERSONS

A disability is a significant issue that has posed and continues to pose a challenge. Disability is a basis of frustration because it can be observed as a mental, constraint, cognitive, and physical handicap that inhibits the individual’s growth and involvement. Consequently, significant effort has been put into removing these kinds of restrictions. These initiatives address the trouble that disabled people encounter. People with disabilities often need to rely on others to meet their requirements. Machine learning (ML) is excelling in producing smart cities and offering a secure environment for disabled individuals. Emotional detection is an important research domain that can expose many appreciated inputs. Emotion is expressed differently through speech and facial expressions, gestures, and written text. Emotion detection in a text document is fundamentally a content-based classification task, utilizing models from deep learning (DL), complex systems and natural language processing (NLP). This paper presents an Optimal Self-Attention DL-based Recognition of Textual Emotions (OSADL-RTE) technique for Disabled Persons. The presented OSADL-RTE technique focuses on identifying distinct types of emotions in the textual data. As a primary preprocessing step, the OSADL-RTE technique comprises different phases to transform the input in a useful way. For word embedding, the bag of words (BoWs) approach is exploited. The OSADL-RTE technique derives self-attention long short-term memory (SA-LSTM) approach to identify emotions. Lastly, the arithmetic fractals optimization algorithm (AOA) approach correctly tunes the hyperparameter selection of the SA-LSTM approach. The experimental study of the OSADL-RTE approach occurs on the emotion database. The investigational outcome of the OSADL-RTE approach portrayed a superior accuracy outcome of 99.59% over existing methods.

AI based web application for diet planning and recipe generation

Introduces a web-based diet planning system based on machine learning, specifically Random Forests, deep learning and image processing capabilities to offer personalized nutritional input. Based on users' health data, their calorie needs, and food choices, the system is tailored to design diet plans specifically for each user. Finally, the system has functionalities of image processing capabilities: it identifies ingredients that feature in uploaded images of available food and thus comes out with recipes based on whatever is available. The goal of this integration is to make meal planning less painful, especially to individuals who have conditions like diabetes or hypertension, with respect to making healthier diets. Preliminary results demonstrate very good accuracy in recommending diets and identifying ingredients; these findings are positive toward the impact of the entire system in personalized nutrition management.

Image-driven prediction system: Automatic extraction of aggregate gradation of pavement core samples integrating deep learning and interactive image processing framework

Aggregate gradation plays a crucial role in determining the asphalt pavement performance, necessitating assessment post-construction. Additionally, efficient gradation measurement is vital for the design and construction of reclaimed pavement. This study proposes a methodology for predicting gradation based on imagery. Initially, a scanning device is devised to capture unfolded side surface images of pavement core samples. Subsequently, an interactive software named PyImageJ is developed for image post-processing and acquiring aggregate feature information. A morphological model is then introduced to determine the mass ratios among various grading levels of coarse aggregates with nominal particle sizes greater than 2.36 mm. Finally, sequence prediction models are established to forecast the mass ratios among different grading levels of fine aggregates with nominal particle sizes smaller than 2.36 mm. Simple conversion of the mass ratios can yield the gradation. Experimental results indicate that the Long Short-Term Memory (LSTM) network offers more accurate predictions for the mass ratios of fine aggregates in stone mastic asphalt (SMA-13), with an R-squared value of 0.8985. Conversely, one-dimensional convolutional neural networks (1D CNN) are better suited for predicting the fine aggregate mass ratios in asphalt concrete (AC-20 and AC-25), with R-squared values of 0.9010 and 0.8824, respectively. Visualization results demonstrate that the predicted complete gradation closely aligns with the actual values (R-squared > 0.99), opening a new avenue for evaluating pavement quality.

NetraDeep: An Integrated Deep Learning and Image Processing System for Precise Detection of Hard Exudates

Hard exudate (HE) is a common manifestation of various eye diseases, such as diabetic retinopathy (DR), and a prominent cause of vision loss and blindness. Researchers aim to visualize and quantify these exudates using deep learning (DL) and image processing (IP) models from retinal images. However, the requirement for a large number of labelled image datasets for DL models to work on diverse and poor-quality images makes this task challenging. To address this challenge, we introduce NetraDeep, a system that integrates data-driven DL and rule-based IP techniques for exudate segmentation. Our system uses IP models to detect and extract some features and assists DL models in detecting more advanced features and vice versa. The IP models are rule-based and use predefined rules to process images, while the DL models are data-driven and learn from the input data. NetraDeep provides visual and quantitative assessments while mitigating noise and other confounding factors such as artifacts and noise. The training of DL models of this system requires only a limited number of labelled fundus images from publicly available datasets. It provides accurate pixel-wise segmentation results on the public and private image datasets collected from local eye hospitals. Through extensive evaluation, our system achieved remarkable performance, with a dice coefficient of \(0.84\) for the public dataset and a rating of \(9.78\) and \(9.43\) out of 10, as corroborated by two medical experts with experience of more than 20 and 5 years, respectively, for the private image dataset.

AI-Guided Cancer Therapy for Patients with Coexisting Migraines.

Background: Cancer remains a leading cause of death worldwide. Progress in its effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with comorbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become instrumental in the discovery of genetic and molecular biomarkers of cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in comorbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusions: AI holds significant potential to improve the precision and effectiveness of the management and therapy of cancer patients with comorbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for the approach outlined herein.

Emerging Technologies and Applications of AI Chips: Integrating Deep Learning Algorithms with Advanced Hardware Architectures

Due to the ongoing promotion of the latest technological revolution and industrial upgrading, as well as the widespread use of artificial intelligence and deep learning, the intelligent Internet of Things terminal has reached a more advanced stage of development. The combination of training data, model algorithms, and processing power is the basis for advancing artificial intelligence. The semiconductor business plays a crucial role in shaping the future of AI algorithms and the computing power industry. This study explores the progression of artificial intelligence, and essential technologies, and examines the categorization and structure of AI chips. The process of deep learning was then analyzed, demonstrating the application of AI in real life. Next, this study showcases distinct AI chip-related models, specifically designed for the automated implementation of Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). The study then proceeds to examine the unique procedure and the significance of the resulting data. The future of AI was examined in the domains of space exploration, healthcare, and autonomous driving, along with future forecasts.

Revolutionizing Radiology With Artificial Intelligence.

Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.

Impact of Artificial Intelligence on Auditing and the Future of Human Workforce Replacement

The advancements in artificial intelligence (AI) over the recent years have been drastic and stunning, and they have revolutionized the world in such ways as influencing the advancement of other fields such as accounting. This research paper aims to study the deep influence of Artificial Intelligence (AI) on the auditing profession. They plan to assess the strengths and the existing applications to auditing: machine learning, natural language processing, deep learning, and robotic process automation. These technologies allow the auditors to identify issues that they were previously unable to, or it would have taken them so much of their time to be able to locate. The paper also brings out the various risks related to the application of AI to auditing tasks. Some challenges include data quality and its privacy, ethical dilemmas, opaqueness, and regulatory matters on the use of Artificial Intelligence. Also, the soundness of AI choices and the ability to explain such decisions remain an issue, especially with complex financial data that depend on human perception and experience. Additionally, the paper also examines the possibility of AI imposing to replace human auditors in the future while comparing the technological aspects of the audit together with the humanistic aspects.

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

BackgroundRecently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results. The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification.ResultsThe final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607).ConclusionIn this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.

AI-Driven Particulate Matter Estimation Using Urban CCTV: A Comparative Analysis Under Various Experimental Conditions

Despite the high public interest in particulate matter (PM), a key determinant for indoor and outdoor activities, the current PM information provided by monitoring stations (e.g., data per administrative district) is insufficient. This study employed the closed-circuit television (CCTV) cameras densely installed within a city to explore the spatial expansion of PM information. It conducted a comparative analysis of PM estimation effects under diverse experimental conditions based on AI image recognition. It also fills a gap by providing an optimal analysis framework that comprehensively considers the combination of variables, including the sun’s position, day and night settings, and the PM distribution per class. In the deep learning model structure and process comparison experiment, the hybrid DL-ML model using ResNet152 and XGBoost showed the highest predictive power. The classification model was better than the ResNet regression model, and the hybrid DL-ML model with the post-processed XGBoost was better than the single ResNet152 model regarding AI prediction of PM. All four experiments that excluded the nighttime, added the solar incidence angle variable, applied the distribution of PM per class, and removed the outlier removal algorithm showed high predictive power. In particular, the final experiment that satisfied all conditions, including the exclusion of nighttime, addition of solar incidence angle variable, and application of outlier removal algorithm, derived predictive values that are expected to be commercialized.

Efficient labeling of french mammogram reports with MammoBERT

Recent advances in deep learning and natural language processing (NLP) have broadened opportunities for automatic text processing in the medical field. However, the development of models for low-resource languages like French is challenged by limited datasets, often due to legal restrictions. Large-scale training of medical imaging models often requires extracting labels from radiology text reports. Current methods for report labeling primarily rely on sophisticated feature engineering based on medical domain knowledge or manual annotations by radiologists. These methods can be labor-intensive. In this work, we introduce a BERT-based approach for the efficient labeling of French mammogram image reports. Our method leverages both the expansive scale of existing rule-based systems and the precision of radiologist annotations. Our experimental results showcase the superiority of the proposed approach. It was initially fine-tuned on a limited dataset of radiologist annotations. Then, it underwent training on annotations generated by a rule-based labeler. Our findings reveal that our final model, MammoBERT, significantly outperforms the rule-based labeler while simultaneously reducing the necessity for radiologist annotations during training. This research not only advances the state of the art in medical image report labeling but also offers an efficient and effective solution for large-scale medical imaging model development.

Deep Learning and Statistical Process Control Approach to Improve Quality in Immigration Services

Technological advancements have profoundly influenced various sectors, including tourism, by simplifying international travel and increasing the demand for passports. In this context, public feedback on the Surabaya Immigration Office's services, gathered from Google Maps reviews, presents an invaluable dataset for analysis. This study introduces a novel approach to quality management systems in immigration services by applying the Convolutional Long Short-Term Memory (Co-LSTM) for sentiment analysis of public reviews and p-attribute control charts for statistical process control. Focusing on the Surabaya Immigration Office, we analyze public feedback from Google Maps to assess service quality. Our methodology preprocesses and classifies opinion data into sentiment classes, employing Co-LSTM for its superior accuracy over traditional models. The sentiment analysis reveals a predominance of positive over negative reviews, with classification accuracy demonstrating Area Under Curve (AUC) values of 98.61% for training and 85.66% for testing data. Furthermore, the p-attribute control charts are utilized to monitor service defects, identifying areas of uncontrolled variability and suggesting the necessity for service improvement interventions. The study uncovers that the primary public grievance relates to the perceived lack of friendliness and politeness from office staff. By integrating sentiment analysis with statistical process control, this research offers a comprehensive approach for immigration service providers to enhance service quality, respond proactively to public sentiment, and ensure customer satisfaction.

AI in Business and Finance: A Micro-to-Macro Perspective on Its Impact

This comprehensive survey examines the transformative impact of machine learning (ML), deep learning (DL), and natural language processing (NLP) across diverse business and finance sectors. From micro-level operational efficiencies to macro-level strategic decision-making, we delve into the multifaceted applications of AI and its influence on traditional practices. Our analysis highlights the increasing prominence of ML in enhancing decision-making and achieving greater accuracy and efficiency. We also identify key challenges such as data quality, modelinterpretability, and linguistic information processing. By addressing these challenges, we can further unlock the potential of ML in revolutionizing business and finance. We conducted a systematic literature review using the PRISMA technique to identify relevant research published within the last five years. Our findings reveal the growing adoption of ML techniques in various business and finance applications, such as fraud detection, risk assessment, customer relationship management, and algorithmic trading. We identified relevant publications through comprehensive searches in leading academic databases, including Scopus, Web of Science, and Google Scholar. We also discuss the challenges associated with implementing ML models in these domains, including data quality issues, model interpretability, and ethical considerations. Byunderstanding the applications, challenges, and opportunities associated with AI, businesses, policymakers, researchers, and investors can make informed decisions and leverage AI to drive innovation and growth in the business and finance sectors.

An Efficient Ship Detection Method Based on YOLO and Ship Wakes Using High-Resolution Optical Jilin1 Satellite Imagery.

In this study, we propose a practical and efficient scheme for ship detection in remote sensing imagery. Our method is developed using both ship body detection and ship wake detection and combines deep learning and feature-based image processing. A deep convolutional neural network is used to achieve ship body detection, and a feature-based processing method is proposed to detect ship wakes. For better analysis, we model the sea region and evaluate the quality of the image. Generally, the wake detection result is used to assist ship detection and obtain the sailing direction. Conventional methods cannot detect ships that are covered by clouds or outside the image boundary. The method proposed in this paper uses the wake to detect such ships, with a certain level of confidence and low false alarm probability in detection. Practical aspects such as the method's applicability and time efficiency are considered in our method for real applications. We demonstrate the effectiveness of our method in a real remote sensing dataset. The results show that over 93.5% of ships and over 70% of targets with no visible ship body can be successfully detected. This illustrates that the proposed detection framework can fill the gap regarding the detection of sailing ships in a remote sensing image.