Deep Learning Processing Research Articles

Variational encoding based on factorized temporal-channel fusion and feature fusion for interpretable remaining useful life prediction

The Prognostics Health Management (PHM) of modern equipment typically employs Remaining Useful Life (RUL) prediction to assess health status. Existing mainstream RUL prediction methods have improved prediction performance by adopting data-driven approaches based on deep learning. However, most methods overlook the impact of noise interference caused by the redundancy of sensor data across channels, leading to a decrease in RUL prediction performance. Furthermore, these methods lack transparency and interpretability, which are crucial for maintenance personnel in accurately diagnosing the degradation process of equipment RUL. Therefore, we propose a Factorized temporal-channel fusion and Feature fusion based Variational Encoding (FFVE) for interpretable RUL prediction. By utilizing the factorization operation, we construct the Factorized Temporal-Channel Fusion (FTCF) block to learn temporal and channel dependencies, thereby reducing redundancy between channels. Through the feature fusion operations which mix original information with extracted feature information, we augment the original information that is lost during the deep network learning process, consequently avoiding performance degradation caused by increasing network depth. Through the above encoding process, sensor data is effectively compressed into a 3D latent space for predicting and interpreting the equipment degradation process. Extensive experiments were conducted on two datasets of the C-MAPSS turbofan aircraft engine and the NASA lithium-ion battery. The results demonstrate that our method outperforms state-of-the-art methods in terms of prediction accuracy. Additionally, our study provides a novel visual means of assessing turbofan engine health status, elucidating the engine RUL degradation process learned by our method.

Yolov3 Tabanlı Atık Tespit Sistemleri

Today, the increase in the number of people, advances in industry and technology cause an increase in the number of wastes generated with the acceleration of production. It is important for the future of our country and the world that these wastes are more easily identified and recycled. In the process of recycling wastes, the classification of wastes as well as their collection requires costly energy and manpower. Wastes are basically separated into paper, plastic, glass and metal. Various studies have been carried out to complete these processes in a shorter and easier way with technologies such as artificial intelligence, deep learning and image processing. In this study, a dataset of paper, plastic and food and beverage wastes that are common in the environment was created. In this dataset, paper cups, plastic water bottles and fast food wastes were detected from different locations in nature and photographed. These images were labeled and trained and tested with YoloV3 in deep learning algorithms. In addition, in order to compare the performance of the new dataset, studies were conducted on a global dataset used in the literature. As a result of the studies, it was observed that it was successful in classifying the newly created dataset and the global dataset.

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people’s lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthcare organizations. To detect COVID-19 infections, medical imaging techniques, including computed tomography (CT) scan images and X-ray images, are considered some of the helpful medical tests that healthcare providers carry out. However, in addition to the difficulty of segmenting contaminated areas from CT scan images, these approaches also offer limited accuracy for identifying the virus. Accordingly, this paper addresses the effectiveness of using deep learning (DL) and image processing techniques, which serve to expand the dataset without the need for any augmentation strategies, and it also presents a novel approach for detecting COVID-19 virus infections in lung images, particularly the infection prediction issue. In our proposed method, to reveal the infection, the input images are first preprocessed using a threshold then resized to 128 × 128. After that, a density heat map tool is used for coloring the resized lung images. The three channels (red, green, and blue) are then separated from the colored image and are further preprocessed through image inverse and histogram equalization, and are subsequently fed, in independent directions, into three separate U-Nets with the same architecture for segmentation. Finally, the segmentation results are combined and run through a convolution layer one by one to get the detection. Several evaluation metrics using the CT scan dataset were used to measure the performance of the proposed approach in comparison with other state-of-the-art techniques in terms of accuracy, sensitivity, precision, and the dice coefficient. The experimental results of the proposed approach reached 99.71%, 0.83, 0.87, and 0.85, respectively. These results show that coloring the CT scan images dataset and then dividing each image into its RGB image channels can enhance the COVID-19 detection, and it also increases the U-Net power in the segmentation when merging the channel segmentation results. In comparison to other existing segmentation techniques employing bigger 512 × 512 images, this study is one of the few that can rapidly and correctly detect the COVID-19 virus with high accuracy on smaller 128 × 128 images using the metrics of accuracy, sensitivity, precision, and dice coefficient.

The impact of architecturally qualified data in deep learning methods for the automatic generation of social housing layouts

The objective of this work is to explore the impact of the data in the automatic generation of social housing layouts with different deep learning models. The design of Social Housing is a subfield of architecture that necessitates, from the architect, a high degree of precision in refining layouts to effectively address both the programmatic requirements and the aspiration to attain the utmost architectural quality to guarantee inhabitants comfort. This dual challenge stands to gain significantly from the incorporation of data-driven generative processes bolstered by machine learning techniques in general and deep learning ones in particular. The most widely used dataset for data-driven layout generation with deep learning methods is RPLAN, a generic floor plan dataset available online. Although it contains a significant amount of plans, it lacks any information about the architectural quality criteria used for the selection of the data. We conducted an initial analysis of the architectural consistency and key quality features of RPLAN. Upon finding several issues that raise doubts about its suitability for training social housing generation models, we proceeded to create PUBLICPLAN, a new dataset based on proposals submitted and evaluated in public architecture competitions in Spain over the past three years. It comprises 2446 distinct layouts extracted from 1279 proposals in Catalonia and the Balearic Islands. All of these layouts demonstrate high architectural standards. Then, we retrained the three most advanced existing models for generating interior plans using PUBLICPLAN and compared their performance to the same models trained with different subsets of RPLAN. Once the models were trained, we conducted several experiments comparing different models and data subsets and evaluating the impact of the quantity and quality of the data on the models' performance. Those experiments involved over two hundred architects and architecture students. We also examined the plausibility of the generated plans being synthetic with a survey asking to distinguish between human and machine generated layout plans. Our results showed that PUBLICPLAN had a significant impact compared to the alternatives with a similar number of cases. In the examination of the feasibility of human-generated plans, over 48.43% of architects and architecture students identified machine-generated plans as being of human origin. Moreover, in qualitative experiments conducted with an equivalent dataset, our qualified dataset PUBLICPLAN demonstrated a significantly superior human rating (72.13%) when evaluated by human assessors, as compared to models trained with RPLAN. This highlights the importance of using qualified datasets in deep learning for the generation of social housing layouts. We also identified challenges and priorities in the research field, both in the deep learning process and architectural design practices.

Dementia Detection from Speech: What If Language Models Are Not the Answer?

A growing focus among scientists has been on researching the techniques of automatic detection of dementia that can be applied to the speech samples of individuals with dementia. Leveraging the rapid advancements in Deep Learning (DL) and Natural Language Processing (NLP), these techniques have shown great potential in dementia detection. In this context, this paper proposes a method for dementia detection from the transcribed speech of subjects. Unlike conventional methods that rely on advanced language models to address the ability of the subject to make coherent and meaningful sentences, our approach relies on the center of focus of the subjects and how it changes over time as the subject describes the content of the cookie theft image, a commonly used image for evaluating one’s cognitive abilities. To do so, we divide the cookie theft image into regions of interest, and identify, in each sentence spoken by the subject, which regions are being talked about. We employed a Long Short-Term Memory (LSTM) neural network to learn different patterns of dementia subjects and control ones and used it to perform a 10-fold cross validation-based classification. Our experimental results on the Pitt corpus from the DementiaBank resulted in a 82.9% accuracy at the subject level and 81.0% at the sample level. By employing data-augmentation techniques, the accuracy at both levels was increased to 83.6% and 82.1%, respectively. The performance of our proposed method outperforms most of the conventional methods, which reach, at best, an accuracy equal to 81.5% at the subject level.

A locally weighted, correlated subdomain adaptive network employed to facilitate transfer learning

Domain adaptation has found extensive applications in diverse fields, including transfer learning, deep learning, and image processing, to effectively address the challenge of mismatched data distributions. Nevertheless, persistent challenges such as limited transfer data and the occurrence of negative transfer continue to exist. These issues not only increase the complexity of model learning but also affect the accuracy of the transfer results. To tackle these challenges, this paper introduces a novel approach called the Related Weighted Subdomain Adaptive Network (RWSAN). This network constructs related subdomain clusters in the source domain to encompass a wider spectrum of associated information. It aims to align target domain subdomains with the related subdomain clusters in the source domain as closely as possible, thus increasing the number of transferable samples from the source domain and solving insufficient transferable samples in some adaptations. To address the issue of negative transfer arising from irrelevant subdomains in the source domain during the transfer process, a related weighted maximum mean discrepancy method is introduced. This method optimizes transfer weights, minimizes the feature distribution disparity between the target domain and the source domain, and mitigates the negative impact of irrelevant subdomains on the target domain, thereby enhancing the accuracy of domain adaptation during transfer. To evaluate the proposed RWSAN method, comprehensive performance tests were conducted using widely used red-green-blue (RGB) and hyperspectral image (HIS) image datasets. The results demonstrate that the RWSAN method effectively resolves insufficient transfer data and negative transfer, proving its high reliability. Additionally, it exhibits excellent performance in error analysis, network convergence analysis, data visualization, and distribution difference.

Beyond Polarity: The Potential Applications and Impacts of Sentiment Analysis and Emotion Detection

Opinion mining and emotion detection are two important techniques in natural language processing that have gained significant attention in recent years. Opinion mining is the process of identifying and extracting subjective information from text, such as opinions, attitudes, and emotions, while emotion detection is the process of identifying and extracting emotions from text. These techniques have a wide range of applications in various domains, including social media analysis, customer feedback analysis, and product reviews. This paper provides an overview of opinion mining and emotion detection techniques in natural language processing. We discuss the various approaches and methods used in opinion mining and emotion detection, including machine learning, deep learning, and natural language processing techniques. We also explore the challenges and limitations of these techniques, including the subjectivity of language, cultural differences, and the lack of labeled data. Furthermore, we examine the current state of the art in opinion mining and emotion detection, highlighting recent research and developments in these areas. We also discuss the potential applications of these techniques in various domains, including marketing, healthcare, and social media analysis. Overall, this paper provides a comprehensive overview of opinion mining and emotion detection in natural language processing. It provides insights into the methods, challenges, and potential applications of these techniques, and highlights the importance of these techniques in understanding and analyzing subjective information in text.

A real-world evaluation of the diagnostic accuracy of radiologists using positive predictive values verified from deep learning and natural language processing chest algorithms deployed retrospectively.

This diagnostic study assessed the accuracy of radiologists retrospectively, using the deep learning and natural language processing chest algorithms implemented in Clinical Review version 3.2 for: pneumothorax, rib fractures in digital chest X-ray radiographs (CXR); aortic aneurysm, pulmonary nodules, emphysema, and pulmonary embolism in CT images. The study design was double-blind (artificial intelligence [AI] algorithms and humans), retrospective, non-interventional, and at a single NHS Trust. Adult patients (≥18 years old) scheduled for CXR and CT were invited to enroll as participants through an opt-out process. Reports and images were de-identified, processed retrospectively, and AI-flagged discrepant findings were assigned to two lead radiologists, each blinded to patient identifiers and original radiologist. The radiologist's findings for each clinical condition were tallied as a verified discrepancy (true positive) or not (false positive). The missed findings were: 0.02% rib fractures, 0.51% aortic aneurysm, 0.32% pulmonary nodules, 0.92% emphysema, and 0.28% pulmonary embolism. The positive predictive values (PPVs) were: pneumothorax (0%), rib fractures (5.6%), aortic dilatation (43.2%), pulmonary emphysema (46.0%), pulmonary embolus (11.5%), and pulmonary nodules (9.2%). The PPV for pneumothorax was nil owing to lack of available studies that were analysed for outpatient activity. The number of missed findings was far less than generally predicted. The chest algorithms deployed retrospectively were a useful quality tool and AI augmented the radiologists' workflow. The diagnostic accuracy of our radiologists generated missed findings of 0.02% for rib fractures CXR, 0.51% for aortic dilatation, 0.32% for pulmonary nodule, 0.92% for pulmonary emphysema, and 0.28% for pulmonary embolism for CT studies, all retrospectively evaluated with AI used as a quality tool to flag potential missed findings. It is important to account for prevalence of these chest conditions in clinical context and use appropriate clinical thresholds for decision-making, not relying solely on AI.

Enhancing local citation recommendation with recurrent highway networks and SciBERT-based embedding

When writing academic papers, referencing statements, claims and previous studies is always an important activity. However, it is considered challenging for scientists to find relevant and appropriate scientific articles which are closely related to their current works in order to reference them in their research. As a consequence of the rapid growth in scientific papers being published every year, researchers might easily get overwhelmed within a huge number of resources. One way to help them find the desired references more easily is to use context-aware citation recommendation. The citation recommender system can automatically provide a list of suitable papers as references based on specified inputs which reflect the researchers’ interests. Among the outstanding achievements of deep learning and natural language processing in recent years, the utilization of deep neural learning architectures have supported to address the problem of citation recommendation. As the result, the neural citation recommendation area has received much attention from the academic community, with the aim of enhancing the precision and correctness of the results of existing citation recommendation systems. Following this research direction, in our paper we present a novel context-aware citation recommendation model, called RHN-DualLCR (Recurrent Highway Networks – Dual Local Citation Recommendation), which integrates Recurrent Highway Networks (RHN), an improved model of the original Bidirectional Long Short-Term Memory (BiLSTM) architecture, and uses a SciBERT-based (Science text of Bidirectional Encoder Representations from Transformers) embedding layer to build up the efficiency of the state-of-the-art local citation recommendation model, which enriches context representation with global information. Our research demonstrates its originality and relevance because we used one of the latest achievements of deep models (RHN model) and natural language processing (SciBERT) applied to the citation recommendation problem. We have conducted experiments on the RHN-DualLCR model on 3 widely known datasets for the citation recommendation problem: ACL-200 (Association for Computational Linguistics), ACL-600 and RefSeer, and also used 2 common evaluation standards Mean Reciprocal Rank (MRR) and the Recall@K (R@K for short) to evaluate the performance of our model. Experimental results show that our proposed model is 3% to 16% better than the original models or state-of-the-art models.

A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations

Given the abundance of images related to operations that are being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof of concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal up-front technological investment. The codes and data are made publicly available online through the INFORMS Journal on Computing GitHub site. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0260 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0260 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Towards Industry 4.0: Color-Based Object Sorting Using a Robot Arm and Real-Time Object Detection

With the introduction of Industry 4.0, automation and robotics have made great strides, enabling 
 enterprises to improve their manufacturing processes for increased productivity and efficiency. This project 
 introduces a novel method for implementing Industry 4.0 concepts through color-based object sorting employing 
 a robot arm with real-time object identification capabilities. Creating a reliable and effective system that can 
 automatically categorize items based on their color properties is the main goal of this project. To enable seamless 
 object recognition and manipulation in real time, the suggested system integrates robotic manipulation with 
 computer vision algorithms. The system makes use of a convolutional neural network (CNN) for precise object 
 detection, using recent advancements in deep learning and image processing, allowing the robot arm to interact 
 with a variety of items effectively. The training phase and the sorting phase are the two key phases of the 
 approach. The CNN model is trained on a sizable dataset of labeled objects during the training phase to recognize 
 various colors and forms. In order for the robotic arm to recognize things as they go along the conveyor belt and 
 sort them into predetermined bins according to their respective colors, the trained model must be integrated with 
 the robotic arm during the sorting phase. Several experiments are carried out with various lighting setups and 
 object arrangements to evaluate the performance of the suggested system. The outcomes show how well the 
 system performs in terms of exact object detection and reliable sorting. The system's capacity to effectively 
 handle a variety of objects and adapt to changing environmental conditions further emphasizes its suitability for 
 use in actual industrial scenarios. This project has important ramifications for the manufacturing sector, enabling 
 improved automation capabilities and cost-efficiency. An important step towards implementing Industry 4.0 
 principles is the seamless integration of color-based object sorting and real-time object detection using a robotic 
 arm. This will allow industries to optimize their production processes, minimize human intervention, and 
 increase overall productivity. Further developments in robotics and computer vision are anticipated to push the 
 limits of automation and open the door for more advanced and intelligent industrial systems as technology 
 develops.

Scaffolded Simulation in Psychiatric Mental Health Nursing Education

Objectives: The purpose of this study was to investigate the impacts of using scaffolded clinical simulations on nursing students’ confidence in clinical reasoning, clinical judgment, and critical thinking skills. Next, we also attempted to gain insights into students’ perceptions of the benefits of scaffolded clinical simulations. Method: We used a mixed-methods research design to investigate the impacts of using scaffolded clinical simulations on 133 second-semester baccalaureate nursing students’ confidence in clinical reasoning, clinical judgment, and critical thinking skills. Results: Findings from this study indicate that students who perceived the benefits of scaffolded simulation activities (i.e., peer observations, debriefings, and self-reflections) were more likely to engage in this deep learning process, which in turn led to their higher confidence in clinical reasoning, clinical judgment, and critical thinking skills. Conclusions: This study demonstrates that scaffolded simulations in psychiatric clinical settings, coupled with the novice to expert model in nursing education, are a valuable tool for preparing nursing students for the rigors of clinical practice, despite decreased in-person client experiences in nursing programs. Implications: This study has implications for designing and implementing scaffolded clinical simulations that foster clinical judgment and help students perform tasks with which they are already familiar, while new responsibilities are introduced throughout the semester.

Artificial intelligence in sleep medicine: Present and future.

Artificial intelligence (AI) has impacted many areas of healthcare. AI in healthcare uses machine learning, deep learning, and natural language processing to analyze copious amounts of healthcare data and yield valuable outcomes. In the sleep medicine field, a large amount of physiological data is gathered compared to other branches of medicine. This field is primed for innovations with the help of AI. A good quality of sleep is crucial for optimal health. About one billion people are estimated to have obstructive sleep apnea worldwide, but it is difficult to diagnose and treat all the people with limited resources. Sleep apnea is one of the major contributors to poor health. Most of the sleep apnea patients remain undiagnosed. Those diagnosed with sleep apnea have difficulty getting it optimally treated due to several factors, and AI can help in this situation. AI can also help in the diagnosis and management of other sleep disorders such as insomnia, hypersomnia, parasomnia, narcolepsy, shift work sleep disorders, periodic leg movement disorders, etc. In this manuscript, we aim to address three critical issues about the use of AI in sleep medicine: (1) How can AI help in diagnosing and treating sleep disorders? (2) How can AI fill the gap in the care of sleep disorders? and (3) What are the ethical and legal considerations of using AI in sleep medicine?

Applications of Information Technology in Building Carbon Flow

The construction industry, as one of the three major carbon emission (CE) industries, accounts for about 39% of the global CE. Thus, approaches for energy saving and emission reduction (ES/ER) cannot be delayed. With the advent of the Industry 4.0 era, information technology (IT) is used to investigate CE in the construction industry, which provides great convenience for measuring and calculating building carbon emissions (BCE) and proposing effective ES/ER measures. However, limited studies have provided a holistic overview of the application of IT in BCE. To fill this gap, this study searched related articles and screened 170 relevant papers. Based on the characteristics of the literature, building carbon flow (BCF) was defined. Based on scientometric analysis and network mapping analysis, combined with quantitative and qualitative analysis methods, the functions, advantages, and limitations of IT in each stage of BCF research were reviewed. Finally, the research trends and future research directions of IT in the BCF were discussed. Specifically, the building information model technology penetrates the whole process of BCF research, deep learning and artificial intelligence have great potential in BCF research, and multi-information technology integration will become the focus of subsequent research in the construction industry.

U-Net-RCB7: Image Segmentation Algorithm

The incidence of skin cancer is increasing. Early detection of cases of skin cancer is vital for treatment. Recently, computerized methods have been widely used in cancer diagnosis. These methods have important advantages such as no human error, short diagnosis time, and low cost. We can segment skin cancer images using deep learning and image processing. Properly segmented images can help doctors predict the type of skin cancer. However, skin images can contain noise such as hair. These noises affect the accuracy of segmentation. In our study, we created a noise dataset. It contains 3000 images and masks. We performed noise removal and lesion segmentation by utilizing the ISIC and PH2. We have developed a new deep learning model called U-Net-RCB7. U-Net-RCB7 contains EfficientNetB7 as the encoder and ResNetC before the last layer. This paper uses a modified U-Net model. Images were divided into 36 layers to prevent loss of pixel values in the images. As a result, noise removal and lesion segmentation were 96% and 98.36% successful, respectively.

Differentiable-Decision-Tree-Based Neural Turing Machine Model Integrated Into FDTD for Implementing EM Problems

As known to all, problems consume more and more computational resources, for example, memory and time, with the gradual enhancements of both the size of the physical domain and complexity in numerical methods. Computational efficiencies have to be obviously influenced, especially for the finite-difference time-domain (FDTD) solver due to its inflexibility in discretization principle. To further overcome this problem, the neural turing machine (NTM) model, based on the differentiable decision tree (DDT), is chosen and incorporated to improve the efficiencies during the FDTD solver in our work. The DDT-based NTM model has the remarkable advantages of both trees and neural networks, which can better explain for the numerical data. Meanwhile, it has full differentiability through neural networks and can be trained by the powerful deep-learning processes. The proposed DDT-based NTM model could not only enhance both accuracy and efficiency but also successfully integrate into the FDTD solver to implement the microstrip transmission line.

Automated Plant Disease Diagnosis Using Deep Learning Model

With the world's population growing, it is vital that food crops and medicinal plants are required to be developed in large quantity. A model that can help us understand the process more comprehensively will be explored. Based on a thorough understanding, we are exploring a model that will enable us to extract the precise information and in-depth knowledge we require. There are many plants that contract lethal diseases every year, including food plants. This reduces their output rates. We must employ automated methods to locate the issue inside the facility if we do not want manufacturing prices to rise. The detection of plant diseases can be automated through robotics and image processing. As a result of recent technological advancements, we now have the ability to simplify our artwork. A deep learning and image processing approach can increase the efficiency of detection processes. A great deal of progress has been made in diagnosing plant diseases. An artificial intelligence model was trained to assist in diagnosing flora disorders. Based on the photo information (leaf), the model can be controlled. Identifying the problem and discovering the condition in this study, we propose the use of computer vision technology combined with fuzzy logic to detect and grade leaf diseases. GLCM is performed to extract texture features, and fuzzy logic is applied to grade the disease. K-means clustering is applied for determining defected areas; GLCM is used to determine defected areas; and fuzzy logic is used for diagnosing diseases. About 70% of classifications are accurate according to the model. By using Speed Up Robust Features (SURF), DENSE, and Bag of Visual Words (BOVW) in addition to the global features, the accuracy of the system can be enhanced. A treatment program will help people better understand the illness if one is offered.

Smart Crosswalk: Machine Learning and Image Processing based Pedestrian and Vehicle Monitoring System

The conventional pedestrian crossing system's shortcomings require urgent reform to enhance the safety of pedestrians and improve urban mobility. Issues such as insufficient time for pedestrians to cross, prolong waiting times, neglection of emergency vehicles, and the absence of effective 24/7 response mechanisms at traditional crosswalks present significant safety concerns in urban areas. Our primary intention is to develop a cutting-edge pedestrian crossing system that relies on deep learning and image processing technologies as its foundation. This research addresses to innovate an advanced smart crosswalk consisting of four essential components: a real-time Pedestrian Detection and Priority System customized for individuals with special needs, a responsive system for detecting road conditions, vehicle availability and speed near crosswalks, a real-time Emergency Vehicle Detection and Priority System strengthened by rigorous verification procedures, and a robust framework for identifying pedestrian accidents and violations of crosswalk rules. The entire system has been meticulously designed not only to enhance pedestrian safety by identifying potential dangers but also to optimize traffic flow. In essence, it aims to provide an improved pedestrian crossing experience characterized by increased safety and efficiency.

In-line sorting system with battery detection capabilities in e-waste using combination of X-ray transmission scanning and deep learning

In the recycling process of e-waste, fires caused by the accidental crushing of batteries are a serious problem. Currently, the presence of batteries in e-waste is estimated based on the experience of the staff involved, which lacks speed and accuracy. To solve this problem, an in-line sorting system that can detect batteries by using a combination of X-ray scanning and deep learning was developed. The novel and unique feature of this system is its three-stage deep learning processing. First, the type of e-waste item is estimated from X-ray transmission images. Second, the batteries are detected by networks pre-trained specifically for the estimated item types. And third, batteries overlooked in the image process are detected by a follow-up network trained by a variety of situations. Through a validation study, it was confirmed that the program achieved high accuracy (0.967 for trained e-waste categories and 0.770 for untrained), surpassing a comparative program with a single deep learning network (0.902 for trained e-waste categories and 0.716 for untrained).

From Nanobots to Neural Networks: Multifaceted Revolution of Artificial Intelligence in Surgical Medicine and Therapeutics.

This comprehensive exploration unveils the transformative potential of Artificial Intelligence (AI) within medicine and surgery. Through a meticulous journey, we examine AI's current applications in healthcare, including medical diagnostics, surgical procedures, and advanced therapeutics. Delving into the theoretical foundations of AI, encompassing machine learning, deep learning, and Natural Language Processing (NLP), we illuminate the critical underpinnings supporting AI's integration into healthcare. Highlighting the symbiotic relationship between humans and machines, we emphasize how AI augments clinical capabilities without supplanting the irreplaceable human touch in healthcare delivery. Also, we'd like to briefly mention critical findings and takeaways they can expect to encounter in the article. A thoughtful analysis of the economic, societal, and ethical implications of AI's integration into healthcare underscores our commitment to addressing critical issues, such as data privacy, algorithmic transparency, and equitable access to AI-driven healthcare services. As we contemplate the future landscape, we project an exciting vista where more sophisticated AI algorithms and real-time surgical visualizations redefine the boundaries of medical achievement. While acknowledging the limitations of the present research, we shed light on AI's pivotal role in enhancing patient engagement, education, and data security within the burgeoning realm of AI-driven healthcare.