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In recent years, the research pertaining to speech recognition technology in the Kazakh language has gained significant importance. This is due to the increasing demand for natural language processing applications in the region where Kazakh is predominantly spoken. Thus, there exists an urgent requirement for precise and dependable speech recognition systems. The research study examines the application of sophisticated deep learning methodologies, such as Natural Language Processing (NLP) and Hidden Markov Model (HMM), in facilitating speech recognition for the Kazakh language. Additionally, the investigation delves into how various techniques, including data preprocessing, acoustic modeling, and language modeling, can aid in devising effective speech recognition systems. The article deliberates on the feasible uses of speech recognition technology in the geographic area where Kazakh language is spoken and outlines its future research prospects. The investigation underscores the significance of persistent inquiry in this realm to confront distinctive obstacles encountered in creating speech recognition systems for languages with restricted resources. [ABSTRACT FROM AUTHOR]

The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models. [Display omitted] [ABSTRACT FROM AUTHOR]

• We propose a semi-automatic annotation pipeline to obtain high-quality annotations for scientific papers. • We use a transformer-based generative model to generate both high quality layouts of papers and their annotations. • We populate layout regions with synthetically generated content exploiting a layout-agnostic technique. [Display omitted] Document layout analysis is an important task to extract information from scientific literature. Deep-learning solutions for document layout analysis require large collections of training data that are not always available. We generate a large number of synthetic pages to subsequently train a neural network to perform document object detection. The proposed pipeline allows users to deal with less common layouts for which it is not easy to find large annotated datasets. High-quality annotations for a small collection of papers are obtained through a semi-automatic approach. Then, a generative model, based on LayoutTransformer, is used to generate plausible layouts that are subsequently populated with random information to perform data augmentation. We evaluate the proposed method considering scientific articles with two different types of layouts: double and single columns. For double-column papers, we improve detection by 1% starting from 385 manually annotated scientific articles. For single-column papers, we improve detection by 49% starting from 218 articles. [ABSTRACT FROM AUTHOR]

Accurate shelf-life estimation of food products has the potential to improve the safety, reliability, and sustainability of food supply chains. Here, a scalable system for accurately indicating the freshness-related information of salmon for consumers was developed and characterized. The system comprises two parts, an optically changeable colorimetric sensor by dye incorporated with sol-gel functionalized paper sensor and a deep convolutional neural network (DCNN)-based freshness estimation system based on images. The paper sensor was produced based on paper and immersed, coated, or printed with (3-aminopropyl) triethoxysilane (APTMS) and tetraethyl orthosilicate (TEOS) sol-gel particles and indicators (e.g., pH indicator, ammonia gas indicator, oxidation indicator, and other volatile organic compounds indicators). After being augmented by machine learning, the limit of detection reaches as low as 17.1 ppm for ammonia detection and the accuracy achieved up to 99.2% for salmon freshness estimation, which satisfied the application in food supply chains. The system balanced the trade-off between detection sensitivity, stability, and cost with the ability for scalable batch production. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection. This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1–42 (Aβ 1–42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1–42, particularly in areas with low resources and limited communication infrastructure. The schematic diagram of the overview of an offline deep learning-assisted smartphone-based paper-based microfluidic platform for screening of Alzheimer's disease. [Display omitted] • Deep learning-assisted smartphone-based platform enables offline detection. • Fully automated microfluidic paper analysis devices (μPADs). • Smartphone-based platform for detection of biomarker of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

Microfluidic technology facilitates high-throughput generation of time series data for biological and medical studies. Deep learning enables accurate, predictive analysis and proactive decision-making based on autonomous recognition of intricate pattern hidden in series. In this work, we first devised a paper-based microfluidic system for portable nucleic acid amplification test with economic energy consumption. Then, we employed Graph Neural Network (GNN), distinguished by its non-Euclidean data structure tailored for deep learning, with spatio-temporal attention mechanism to perform near-sensor predictive analysis of the on-chip reaction. Our findings demonstrated that the novel GNN model can provide accurate predictions of positive outcomes at the early stages of the reaction using less than one-third of the total reaction time. Then, the deep learning model trained by on-chip data was subsequently applied to more than 900 clinical plots. Generalization of the GNN model was successfully validated across different detection methods, diverse types of datasets and time series with variable length. Accuracy, sensitivity and specificity of the predictive approach were 96.5 %, 94.3 % and 99.0 % by utilizing the early half of reaction information. Finally, we compared the GNN model with various deep learning models. Despite differences in the prediction of negative samples among various models were minute, GNN obviously offered overall superior performance. This work ignites a cutting-edge application of deep learning in point-of-care and near-sensor tests. By harnessing the power of body area networks and edge/fog computing, our approach unlocks promising possibilities in diverse fields like healthcare and instrument science. [Display omitted] • Paper devices for economic on-chip amplification. • GNN analyzes data generated in paper devices and predicts reaction results quickly. • The deep learning model can work across platforms, methods & datasets. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep learning enabled predictive nucleic acid amplification tests analysis. • On-site fluorescence signal processing powered by cloud computing. • Accurate prediction can be obtained using the early 22.5% data. • Approach can be universally extended to other areas of biomedical research. Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dynamics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research. [ABSTRACT FROM AUTHOR]

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots. [ABSTRACT FROM AUTHOR]

• A text-content based approach that can reveal the scholarly impact of papers without human domain-specific knowledge. • Modeling a paper's characteristic by leveraging the spatial relations in the representation space. • The simulations based on regression model could reproduce the process of scholarly impact generation. • Introducing the large-scale Pre-Trained Model to address a classical problem in bibliometrics. Citation and citation-based metrics are traditionally used to quantify the scholarly impact of scientific papers. However, for documents without citation data, i.e., newly published papers, the citation-based metrics are not available. By leveraging deep representation techniques, we propose a text-content based approach that may reveal the scholarly impact of papers without human domain-specific knowledge. Specifically, a large-scale Pre-Trained Model (PTM) with 110 million parameters is utilized to automatically encode the paper into the vector representation. Two indicators, τ (Topicality) and σ (Originality) , are then proposed based on the learned representations. These two indicators leverage the spatial relations of paper representations in the semantic space to capture the impact-related characteristics of a scientific paper. Extensive experiments have been conducted on a COVID-19 open research dataset with 1,056,660 papers. The experimental results demonstrate that the deep representation learning method can better capture the scientific content in the published literature; and the proposed indicators are positively and significantly associated with a paper's potential scholarly impact. In the multivariate regression analysis for the potential impact of a paper, the coefficients of σ and τ are 5.4915 (P < 0.001) and 6.6879 (P < 0.001) for next 6 months prediction, 12.9964 (P < 0.001) and 13.8678 (P < 0.001) for next 12 months prediction. The proposed framework may facilitate the study of how scholarly impact is generated, from a textual representation perspective. [ABSTRACT FROM AUTHOR]

• Qualitative and quantitative detection of amine gases could be achieved by CSA. • A visible detection of beef freshness using the amine-responsive CSA was proposed. • ResNet34 had the best performance for beef freshness detection based on CSA. • T-SNE could further visualize and understand the classification process of DL. This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t -distributed stochastic neighbour embedding (t -SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness. [ABSTRACT FROM AUTHOR]

Pathogens cause various infectious diseases and high morbidity and mortality which is a global public health threat. The highly sensitive and specific detection is of significant importance for the effective treatment and intervention to minimise the impact. However, conventional detection methods including culture and molecular method gravely depend on expensive equipment and well-trained skilled personnel, limiting in the laboratory. It remains challenging to adapt in resource-limiting areas, e.g., low and middle-income countries (LMICs). To this end, low-cost, rapid, and sensitive detection tools with the capability of field testing e.g., a portable device for identification and quantification of pathogens, has attracted increasing attentions. Recently, paper-based microfluidic analytical devices (μPADs) have shown a promising tool for rapid and on-site diagnosis, providing a cost-effective and sensitive analytical approach for pathogens detection. The fast turn-round data collection may also contribute to better understanding of the risks and insights on mitigation method. In this paper, critical developments of μPADs for in-field detection of pathogens both for clinical diagnostics and environmental surveillance are reviewed. The future development, and challenges of μPADs for rapid and onsite detection of pathogens are discussed, including using the cross-disciplinary development with, emerging techniques such as deep learning and Internet of Things (IoT). [Display omitted] • The state-of-the-art on μPAD is critically reviewed. • μPAD enables onsite detections of biochemical markers from molecules to organisms. • Insights is provided highlighting on multidisciplinary aspects. [ABSTRACT FROM AUTHOR]

With the latest development in information technology, smart campuses have become a topic that attracts attention in the world of education, especially in higher education. The smart campus has the potential to change the way of managing, accessing facil- ities, and teaching and learning process. The smart campus can collaborate various latest technologies such as edge computing, blockchain, deep learning, virtual reality, and other trend recent technologies. Therefore, this research is a guideline for developing an effective and efficient smart campus system with the latest information and communication technology. This paper explains smart campus technology, features, architecture that have been implemented in real life. Thirty-two papers will be analyzed using the Systematic Literature Review (SLR) method. Hopefully, this guideline can be a valuable reference for smart campus designers, researchers, and practitioners interested in developing innovative and efficient smart campus systems. [ABSTRACT FROM AUTHOR]

Municipal papers and polyethylene terephthalate (PET) wastes are primary constituents of municipal solid waste (MSW) which necessitates effective disposal. Co-pyrolysis is an efficient transformation technology for the production of valuable gas and liquid fuels. Therefore, co-pyrolysis characteristics, kinetics, gas and liquid products of cellulose and PET mixtures were investigated in a two-stage reactor by multiple characterizations coupling with deep learning approach. The results indicated that there exhibited positive interaction for cellulose and PET co-pyrolysis in terms of mass difference (δ m) and mass loss rate difference (δ r) between experimental and calculated values. The thermal decomposition process and synergistic effects (δ m and δ r) were accurately predicted by deep learning approaches (R 2 =0.9823–0.9993) using temperature, catalyst, mixing ratio and heating rate variables. Feature importance revealed that the temperature, catalyst, heating rate and mixing ratio were in the descending order to affect the remaining mass from TG curves. And the temperature played a predominant role in synergistic effects. The Flynn-Wall-Ozawa (FWO) method was more suitable for fitting the pyrolysis process to obtain activation energy (E a) with higher R 2 (0.94714–0.99640) compared with Kissinger-Akahira-Sunose (KAS) method. When the mixing ratio of cellulose and PET was 1:1, the E a was significantly reduced to 80.73 KJ/mol, and further reduced to 73.04 KJ/mol by introducing the catalyst, as compared to E a of individual cellulose (178.95 KJ/mol) and PET (198.34 KJ/mol). Co-pyrolysis of cellulose and PET led to a reduction of carbohydrates, furans, alcohols, ketones, and phenols, while the content of acids was remarkably increased which was consistent with the observation in FTIR that the absorption peak of C＝O became sharp and strong. The introduction of ZSM-11 resulted in an increase in aromatics and a decrease in acids content owing to aromatization and deoxygenation. This work is expected to offer beneficial guidance for the efficient disposal of MSW and the application of deep learning methods. [Display omitted] • The interactive pathway for catalytic co-pyrolysis of cellulose and PET was proposed. • The activation energy reached the lowest 73.04 KJ/mol when the mixing ratio was 1:1. • Optimized convolutional neural network predicted synergistic effects with R 2 > 0.99. • Feature importance for TG was temperature > catalyst > heating rate > mixing ratio. [ABSTRACT FROM AUTHOR]

During global outbreaks such as COVID-19, regular nucleic acid amplification tests (NAATs) have posed unprecedented burden on hospital resources. Data of traditional NAATs are manually analyzed post assay. Integration of artificial intelligence (AI) with on-chip assays give rise to novel analytical platforms via data-driven models. Here, we combined paper microfluidics, portable optoelectronic system with deep learning for SARS-CoV-2 detection. The system was quite streamlined with low power dissipation. Pixel by pixel signals reflecting amplification of synthesized SARS-CoV-2 templates (containing ORF1ab, N and E genes) can be real-time processed. Then, the data were synchronously fed to the neural networks for early prediction analysis. Instead of the quantification cycle (C q) based analytics, reaction dynamics hidden at the early stage of amplification curve were utilized by neural networks for predicting subsequent data. Qualitative and quantitative analysis of the 40-cycle NAATs can be achieved at the end of 22nd cycle, reducing time cost by 45%. In particular, the attention mechanism based deep learning model trained by microfluidics-generated data can be seamlessly adapted to multiple clinical datasets including readouts of SARS-CoV-2 detection. Accuracy, sensitivity and specificity of the prediction can reach up to 98.1%, 97.6% and 98.6%, respectively. The approach can be compatible with the most advanced sensing technologies and AI algorithms to inspire ample innovations in fields of fundamental research and clinical settings. [Display omitted] • Paper device with deep learning enable portable and rapid nucleic acid amplification. • Accurate prediction can be obtained using partly gathered real-time data. • Deep learning models used by on-chip tests were well-suited to clinical study. • Approach can inspire ample innovations in fields of analytical science. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology, the number of academic papers published each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the standard for evaluation and decision-making of them, such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very critical. The most common standard for measuring the quality of academic papers is the number of citation counts of them, as this indicator is widely used in the evaluation of scientific publications. It also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. To improve the effective of citation counts prediction, we try to solve the citation counts prediction problem from the perspective of information cascade prediction and take advantage of deep learning techniques. Thus, we propose an end-to-end deep learning framework (DeepCCP), consisting of graph structure representation and recurrent neural network modules. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and outputs the citation counts of the corresponding paper after a period of time. It only exploits the structure and temporal information of the citation network, and does not require other additional information. According to experiments on two real academic citation datasets, DeepCCP is shown superior to the state-of-the-art methods in terms of the accuracy of citation count prediction. [ABSTRACT FROM AUTHOR]

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs. [Display omitted] • This deep learning-assisted smartphone platform is unaffected by ambient lighting. • A fully automated "image in, answer out" operation fashion. • A 2048 custom image dataset is used to test 4 mainstream deep learning algorithms. • GoogLeNet provides >97% accuracy in quantitative rabbit IgG testing. • The area under the curve (AUC) is 4% higher than that of conventional methods. [ABSTRACT FROM AUTHOR]

Water holding capacity (WHC) plays an important role when obtaining a high-quality pork meat. This attribute is usually estimated by pressing the meat and measuring the amount of water expelled by the sample and absorbed by a filter paper. In this work, we used the Deep Learning (DL) architecture named U-Net to estimate water holding capacity (WHC) from filter paper images of pork samples obtained using the press method. We evaluated the ability of the U-Net to segment the different regions of the WHC images and, since the images are much larger than the traditional input size of the U-Net, we also evaluated its performance when we change the input size. Results show that U-Net can be used to segment the external and internal areas of the WHC images with great precision, even though the difference in the appearance of these areas is subtle. [ABSTRACT FROM AUTHOR]

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper's ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies. • The proposed framework can generate a comprehensive introduction based on users' brief inputs. • This paper demonstrates a three-testing-threshold approach to check the content quality. • The introduction generated through this method has less than 3% similarity to the original text by comparison. [ABSTRACT FROM AUTHOR]

Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or absence of hemagglutination in the sample zones after immobilizing the antibodies targeting each Rh antigen. The PAD was optimized in terms of filter paper type, antibodies, and distance of the visualization zone. In this study, the optimal conditions were Whatman filter paper Grade 4; anti-D, –C, -E, -c, and -e antibodies; RBC suspension of 30%; and a visualization zone of 1 cm above the sample zone. The accuracy of simultaneously phenotyping the five Rh RBC antigens in the blood samples (n = 4692) was 99.19%, comparable with the accuracy of the gold-standard tube method used by blood bank laboratories in several regions of Thailand. Furthermore, decision making based on this method can be assisted by deep learning. After implementing a two-stage objective detection algorithm (YOLO v4-tiny) and classification model (DenseNet-201), the ambiguous images (n = 48) were interpreted with 100% accuracy. The PAD integrated with customized-region convolutional neural networks can reduce the interpretation discrepancies in RBC antigen phenotyping in any laboratory. [Display omitted] • Five Rh antigens typing PADs combined with deep learning algorithm for an assisted human-decision. • Rh antigens typing PADs were tested with real blood samples and compared with the standard tube method. • Dataset of photographs following Rh PADs testing were trained and evaluated by the in-house deep learning platform CiRA CORE. • Misinterpretation caused by weak or ambiguous reactions was solved by the CiRA CORE platform. [ABSTRACT FROM AUTHOR]

A paper-based colourimetric assay for the detection of alanine transaminase has been developed. In the presence of alanine transaminase, 2,4-dinitrophenyl hydrazine changes to pyruvate hydrazone leading to a colour change from pale yellow to dark yellow. Reaction conditions were optimized using absorption spectroscopic studies. Hydrophobic patterns on the Whatman chromatographic paper were created by wax printing, and the reagents were drop cast at the reagent zone. On the paper device, the intensity of the yellow colour increases with ALT concentration in the range of 20–140 U/L in human serum. For the quantification of ALT, coloured images were captured using a digital camera and were processed with Image J software. The machine learning approach was also explored for the ALT analysis by training with colour images of the paper device and testing using a cross-validation procedure. The results obtained with real clinical samples on the paper device showed good accuracy of less than 5% relative error with the clinical lab results. Furthermore, the paper device shows high selectivity to ALT in the presence of various interfering species in blood serum with a sensitivity of 0.261 a.u/(U/L), a detection limit of 4.12 U/L, and precise results with an RSD of less than 7%. For the testing of whole blood, a plasma separation membrane was integrated with the patterned paper. [Display omitted] • Colourimetric detection of ALT on paper strip using 2,4-DNPH. • Negligible interference from other biomolecules. • Tested with serum and ALT spiked serum samples. • Plasma separation membrane incorporated strip used for testing of blood samples. • Quantification of ALT on paper strip using ImageJ and Machine learning approach. [ABSTRACT FROM AUTHOR]

• Methodology to automatically generate geo-located waste container maps. • Use of Computer Vision algorithms to detect waste containers. • Automatic division into locations with and without containers in city maps. • Robust model with consistent performance disregarding the container type. • System evaluated in eleven Spanish cities with an average accuracy of 89%. This paper describes the scientific achievements of a collaboration between a research group and the waste management division of a company. While these results might be the basis for several practical or commercial developments, we here focus on a novel scientific contribution: a methodology to automatically generate geo-located waste container maps. It is based on the use of Computer Vision algorithms to detect waste containers and identify their geographic location and dimensions. Algorithms analyze a video sequence and provide an automatic discrimination between images with and without containers. More precisely, two state-of-the-art object detectors based on deep learning techniques have been selected for testing, according to their performance and to their adaptability to an on-board real-time environment: EfficientDet and YOLOv5. Experimental results indicate that the proposed visual model for waste container detection is able to effectively operate with consistent performance disregarding the container type (organic waste, plastic, glass and paper recycling,...) and the city layout, which has been assessed by evaluating it on eleven different Spanish cities that vary in terms of size, climate, urban layout and containers' appearance. [ABSTRACT FROM AUTHOR]

Developing monitoring system for paper industrial wastewater treatment system is an important route for wastewater reuse and recycling from wastewater, which are regarded as effective way for cleaner production. A novel hybrid deep leaning CLSTMA model, which based on sequential fusion convolutional neural network (CNN), long short term memory (LSTM) and attention mechanism (AM), was developed to monitor the water quality in a full-scale paper industrial wastewater treatment system for energy conservation and emissions reduction. The hybrid CLSTMA model for predicting water quality of paper industrial wastewater treatment system was divided into three steps: spatial information fusion by using CNN module, temporal information fusion by using LSTM module and variable weighted calculation by using AM module. Compare with other models (CNN, LSTM and CLSTM models), RMSE of CLSTMA model for the effluent chemical oxygen demand (COD eff) reduced by 23.3–31.55%, MAE of CLSTMA model reduced by 38.89–74.50%, R of CLSTMA model increased by 8.29–11.86%. For the effluent suspended solids (SS eff), compared with CNN and LSTM models, RMSE of CLSTMA model reduced by 10.26% and 9.92%, MAE of CLSTMA model reduced by 5.37% and 3.44%, R of CLSTMA model increased by 15.13% and 37.21%, respectively. While, R of CLSTMA was consistent with CLSTM model, but RMSE and MAE of CLSTMA model reduced by 16.07% and 7.49% than the CLSTM model. Simulation results demonstrate that the proposed CLSTMA model has a great potential in monitoring paper industrial wastewater treatment system for cleaner production. [Display omitted] • A new deep learning model was proposed for real-time estimation of water quality. • The new model was based on convolutional neural network, long-term short-term memory and attention mechanism. • The new model can guarantee wastewater reuse and reduce the wastewater treatment cost. [ABSTRACT FROM AUTHOR]

• This paper utilizes novel connected vehicle (CV) emulated data to predict real-time crash potential for arterials. • The CV emulated data are flexible to obtain. Different speed-related variables are estimated based on the CV emulated data to depict continuous traffic conditions • The results proved the feasibility of using CV emulated data for real-time crash potential prediction • The proposed methods can be applied to other types of vehicles Real-time crash potential prediction could provide valuable information for Active Traffic Management Systems. Fixed infrastructure-based vehicle detection devices were widely used in the previous studies to obtain different types of data for crash potential prediction. However, it was difficult to obtain data in large range through these devices due to the costs of installation and maintenance. This paper introduced a novel connected vehicle (CV) emulated data for real-time crash potential prediction. Different from the fixed devices' data, CV emulated data have high flexibility and can be obtained continuously with relatively low cost. Crash and CV emulated data were collected from two urban arterials in Orlando, USA. Crash data were archived by the Signal for Analytics system (S4A), while the CV emulated data were obtained through the data collection API with a high frequency. Different data cleaning and preparation techniques were implemented, while various speed-related variables were generated from the CV emulated data. A Long Short-term Memory (LSTM) neural network was trained to predict the crash potential in the next 5−10 min. The results from the model illustrated the feasibility of using a novel CV emulated data to predict real-time crash potential. The average and 50th percentile speed were the two most important variables for the crash potential prediction. In addition, the proposed LSTM outperformed Bayesian logistics regression and XGBoost in terms of sensitivity, Area under Curve (AUC), and false alarm rate. With the rapid development of the connected vehicle systems, the results from this paper can be extended to other types of vehicles and data, which can significantly enhance traffic safety. [ABSTRACT FROM AUTHOR]

Emotional intelligence (EI) involves recognizing, regulating, and understanding emotions in oneself and others, enabling effective relationship management. Emotional Intelligence acts as a differentiator in both personal and professional lives. Nevertheless, many people lack the ability of conveying and interpreting human emotions efficiently due to which they fall short in their social and professional lives. However, this can be improved if people know their Emotional Quotient (EQ) score and work towards improving it by implementing certain skills. Although traditional methods for measuring EQ exist, majority of them are manual and are built on self-efficacy based questionnaire which are often susceptible to faking. In order to overcome these lacuna, this paper proposes a digital approach that can be used for an automatic evaluation of EQ of a person via audio visual signal input. The paper devises four major factors self-awareness, self-regulation, self-motivation and social relationships with their weightages that can be utilised for automatic evaluation through the use of technology. The paper also proposes quantifiable parameters for measuring these factors. The proposed criteria were developed with the emergence of deep learning and its applicability in assessing a person's EQ in mind. [ABSTRACT FROM AUTHOR]

The integration of deep learning techniques from the field of image generation into digital rock analysis has resulted in substantial advancements. However, generating high-quality and highly controllable heterogeneous digital rocks still presents challenges and necessities. This paper reports a digital rock reconstruction method using a multi-condition denoising diffusion probabilistic model (MCDDPM). This method has the diffusion and denoising processes inherent in the diffusion model, ensuring high-quality reconstructed samples. Meanwhile, various physical property parameters are embedded as conditions in the network layers of the model at multiple scales, enhancing the conditional control ability of this method. We compared the porosity, pore size distribution, and two-point correlation coefficient under the same conditions, as well as the evaluation indicators of different methods. The comparative results provide convincing evidence for the accuracy of our proposed digital rock reconstruction method. In addition, studies on the control effect of different conditions on reconstructed samples also demonstrate the effectiveness of our strategy. Therefore, MCDDPM can reconstruct reasonable heterogeneous digital rocks based on combinations of physical property conditions, which provides effective technical support for expanding the library of heterogeneous digital rock samples and even achieving zero-shot learning of digital rocks. • The real numerical rock possesses strong heterogeneity, and the method proposed in this paper can effectively reconstruct heterogeneous digital rock. • Compared to other deep learning network models, the reconstruction effect of the method proposed in this paper is at a high level. • The method we propose supports multiple physical property conditions as input. [ABSTRACT FROM AUTHOR]

An Election is a method of selection of individuals to hold the public office in democracy. Ballot is basically a piece of paper that is used to cast vote during election. In ballot paper voting system each voter uses a ballot paper which is not shared and basically it is a paper printed with the name and symbols of the candidates. The Electronic Voting Machine is basically a memory recorder which records the vote casted by the voters. In this paper, main advantages of E-voting systems for country is highlighted. For constructing E-voting systems, every countries need to do great attention to Verification and Validation requirements. In this research, E-voting scheme with face recognition using deep learning technique is proposed. The process of casting vote is accomplished by blockchain technology and blind signature mechanism. The main objective of the proposed scheme is to explore the positive effects of security and safety in online voting system. • The Election is a method of selection of individuals to hold the public office in democracy. • The Electronic Voting Machine is basically a memory recorder which records the vote casted by the voters. • The aim of the research is to E-voting scheme with face recognition using deep learning technique is proposed. • The main objective of the proposed scheme is to explore the positive effects of security and safety in online voting system. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFCs) offer high energy conversion rate and low pollution, while low durability of PEMFCs hinder the further development. Accurately predicting degradation trends and remaining useful life (RUL) are crucial to timely detecting problem and improving PEMFCs durability. In this paper, an enhanced transfer learning (Add-TL) based on adder network (AdderNet) and deep domain confusion is proposed for the prediction of PEMFCs. To reinforce the ability of extracting key features from extensive data, AdderNet with attention mechanism is applied. Furthermore, the fused deep domain confusion employs existing data to achieve higher prediction accuracy. The experimental results demonstrate that Add-TL outperforms other classical methods in predicting degradation trend, the optimal adjusted R-square (R2) values for the two datasets are 0.99879 and 0.99906, respectively. When Add-TL is applied to RUL prediction, the accuracy scores for two datasets reach 0.995, which further validating the applicability of Add-TL in PEMFCs prediction. • AdderNet applied for the first time to predict degradation trends and RUL of PEMFCs. • The method proposed in this paper integrates deep learning and transfer learning. • The incorporation of transfer learning the connections between data, aiming to improve the accuracy of RUL prediction. • Add-TL predicted static and dynamic conditions, providing a comprehensive validation. • Add-TL achieves best performance when training on datasets with varying lengths. [ABSTRACT FROM AUTHOR]

Conditional computing processes an input using only part of the neural network's computational units. Learning to execute parts of a deep convolutional network by routing individual samples has several advantages: This can facilitate the interpretability of the model, reduce the model complexity, and reduce the computational burden during training and inference. Furthermore, if similar classes are routed to the same path, that part of the network learns to discriminate between finer differences and better classification accuracies can be attained with fewer parameters. Recently, several papers have exploited this idea to select a particular child of a node in a tree-shaped network or to skip parts of a network. In this work, we follow a Trellis-based approach for generating specific execution paths in a deep convolutional neural network. We have designed routing mechanisms that use differentiable information gain-based cost functions to determine which subset of features in a convolutional layer will be executed. We call our method Conditional Information Gain Trellis (CIGT). We show that our conditional execution mechanism achieves comparable or better model performance compared to unconditional baselines, using only a fraction of the computational resources. We provide our code and model checkpoints used in the paper at: https://github.com/ufukcbicici/cigt/tree/prl/prl_scripts. • We introduce Conditional Information Gain Trellis (CIGT) for conditional computing. • We derive the CIGT loss function based on classification and information gain losses. • CIGT performs better or comparably using a fraction of the computational resources. • We give tests on MNIST, Fashion MNIST, and CIFAR 10, showing CIGT compares favorably. • Supplementary materials show that semantically similar classes are grouped together. [ABSTRACT FROM AUTHOR]

Offshore wind-hydrogen systems operate in harsh marine environments for extended periods, posing risks of low accessibility and high failure rates. This paper proposes a data-driven fault detection framework for offshore wind-hydrogen systems, aiming to promptly detect potential faults based on the system's physical parameters. The research establishes a comprehensive model based on the operating principles of offshore wind-hydrogen systems. The study uses fault injection techniques to simulate common partial faults in offshore wind-hydrogen systems and collects relevant physical parameters (such as wind turbine power generation, energy storage battery charging/discharging current, and electrolyzer hydrogen production rate). A fault detection model was constructed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enable precise detection of potential faults in the system. The established model provides theoretical support for designing, optimizing, and operating offshore wind-hydrogen systems, reducing reliance on actual experiments and lowering project risks and costs. Introducing artificial faults can evaluate the system's performance in different fault scenarios. This paper proposes a CNN-BiLSTM fault detection method that achieves automatic feature extraction and high-precision classification of time-series fault data, confirming the practicality and feasibility of offshore wind-hydrogen systems. This investigation realizes the integration of the offshore wind-hydrogen system model and deep learning, as well as a fully automated learning process from data to fault detection. By calculating and comparing results, it is verified that the framework has a high accuracy of 96.9%. This innovative research offers new methods and perspectives for enhancing the reliability and efficiency of fault detection in offshore wind-hydrogen systems. • A comprehensive multi-system simulation model for offshore wind-hydrogen systems is established. • Fault injection is employed to acquire data samples for offshore wind-hydrogen systems. • Utilizing the CNN-BiLSTM method, a high-accuracy deep learning fault detection model is crafted. • A simulation model-based data-driven deep learning framework for fault detection is devised in this study. [ABSTRACT FROM AUTHOR]

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection. • HO management in HetNets is challenging due to network densification and dynamic environmental behaviors. • Existing HO decision algorithms are unable to efficiently ensure a high-quality user experience in HetNets. • The paper introduces a robust and data-driven HO decision model aimed at improving HO performance in HetNets. • To address the limitations of the traditional approach, a data-driven HO decision model is proposed based on LSTM. • The proposed algorithm's advantages and disadvantages are assessed based on the simulation results. [ABSTRACT FROM AUTHOR]

This paper proposes a Generalized Multilevel B-spline Approximation (GMBA) method, which addresses scattered data interpolation problems in image processing. Mathematically, the GMBA provides a better solution for the B-spline control lattice by superimposing identical level B-splines compared with traditional Multilevel B-spline Approximation (MBA). Specifically, the GMBA allows the spacing of next control lattice to be subdivided arbitrarily or remained unchanged, which is determined by a predefined spacing set or the current error level. These improvements bring higher approximation accuracy and more flexibility for algorithm design to avoid over-fitting. In this paper, basic GMBA algorithm and its refined algorithm are compiled for image processing. Finally, six relevant cases are involved to test the GMBA, including surface approximation, image enlargement, image completion, and Salt-and-Pepper (SAP) noise removal. The experimental results show that the GMBA has better performance than the MBA in surface approximation and image processing, performs comparatively fast with the best performance on more than half of the standard test images compared with traditional algorithms, and has partially better performance even than deep learning algorithms. The GMBA can effectively recover meaningful details in images contaminated with even extremely high SAP noise level (up to 99%). • We give a generalized multilevel B-spline approximation (GMBA) method. • GMBA allows the control spacing to be subdivided arbitrarily or remained unchanged. • GMBA offers higher approximation accuracy and greater flexibility than traditional MBA. • GMBA provides superior performance and less run time than many of the state-of-the-art methods for SAP noise removal. • GMBA can recovery meaningful detail at noise levels as high as 99% for SAP noise removal. [ABSTRACT FROM AUTHOR]

3D printed ceramic parts often have defects due to their inherent brittleness. These defects are of various scale sizes. Detecting these defects, especially with multiple sizes, is a challenging task in the field of detection. Furthermore, there is a lack of surface defect real-time detection systems suitable for industrial applications in this field. Based on this, this paper makes two contributions: the design and development of a real-time defect detection system, and the proposal of a multiscale defect detection method for 3D printed ceramic surfaces within this system. Firstly, the paper establishes the overall structure of the real-time detection system for surface defects on 3D printed ceramic components, and describes the hardware and software components of the system. On this basis, a dataset of ceramic surface defect images is collected and constructed. Then, experimental analyses point out the shortcomings of the You Only Look Once version-5 (YOLOv5) model for multiscale defect detection. To address the shortcomings, the YOLOv5 model is optimized from three aspects, resulting in the Deep separable convolution + residual network-SKNetwork-Efficient Channel Attention Network-YOLOv5 (DepRes-SK-ECA-YOLOv5) model for multiscale defect detection. This model improves the ability to extract and fuse features of defects at different scales. The experimental results show that the DepRes-SK-ECA-YOLOv5 model can achieve 93.5 %, 91.6 %, 94.3 %, and 0.198 s for Precision, Recall, mAP, and Speed for the test set, respectively. Finally, the paper designs the workflow for the system software. The system hardware and system software are integrated to form a real-time detection system. The performance of the detection system is verified through experiments. [ABSTRACT FROM AUTHOR]

The impact of air pollution on public health is substantial, and accurate long-term predictions of air quality are crucial for early warning systems to address this issue. Air quality prediction has drawn significant attention, bridging environmental science, statistics, and computer science. This paper presents a comprehensive review of the current research status and advances in air quality prediction methods. Deep learning, a novel machine learning approach, has demonstrated remarkable proficiency in identifying complex, nonlinear patterns in air quality data, yet its application in air quality prediction is still relatively nascent. This paper also conducts a systematic analysis and summarizes how cutting-edge deep learning models are applied in air quality prediction. Initially, the historical evolution of air quality prediction methods and datasets is presented. This is followed by an examination of conventional air quality prediction techniques. A thorough comparative analysis of progress made with both traditional and deep learning-based prediction methods is provided. This review particularly focuses on three aspects: temporal modeling, spatiotemporal modeling, and attention mechanisms. Finally, emerging trends in the field of air quality prediction are identified and discussed. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) and automated robotics will profoundly influence space operations. By utilising machine learning and deep learning approaches, AI-enabled systems may accomplish tasks as well as improve their own performance. These capabilities are useful in the often-remote settings of outer space and will grow in value as automated space operations become more widespread. As AI extends throughout the space domain, automated algorithms will take on many of the roles that have historically been handled by humans. Artificial intelligence is progressing from theory to implementation in the space environment by exposing new satellites and orbital autonomous vehicles to new data. Even though all initial computational parameters are provided, such systems' outputs can be very unpredictable, putting people, property, and the environment at risk. This paper investigates the application of United Nations space treaties, selected regional AI regulations, and various 'soft-law' instruments and industry initiatives focusing on responsible AI system development to space-based AI systems. Following that, reforms are proposed to clarify the practical relationship between AI systems and the international legal regime that governs space, as well as a 'bottom-up' regulatory approach to better facilitate the future development of regulation governing the use of AI by the global space sector. While this work does not purport to provide a conclusive resolution to these multifaceted matters, its objective is to underscore significant obstacles that arise at the convergence of space law and AI, serving as a preliminary foundation for subsequent discussions on this issue. • Advances in AI and automated robotics will have a profound impact on space operations. Automated algorithms will take on roles traditionally handled by humans as AI becomes more widespread in space. • However, the unpredictable outputs of AI systems can put people, property, and the environment at risk, raising questions about liability. • The paper investigates the application of UN space treaties, regional AI regulations, and industry initiatives to space-based AI systems. • Reforms are proposed to clarify the relationship between AI systems and the international legal regime governing space. • A 'bottom-up' regulatory approach is suggested to facilitate future regulation of AI in the global space sector. [ABSTRACT FROM AUTHOR]

Nowadays, road safety is a major concern for public authorities on national, secondary and classified roads in developing countries. This paper looks at the problem of non-compliance with road signs and the deteriorating state of roads, which are the cause of many accidents. Road users sometimes miss roadside signs because their attention is focused on the road. At other times, they regularly miss certain road markings or road deformations in unfamiliar areas, which is dangerous for travelers. Ideally, therefore, drivers should be able to be alerted to road signs or deformations from a distance without having to divert their attention. This would make it possible to avoid accidents, especially on national roads between regions of a country and between countries in sub-Saharan Africa. In this paper, we propose a model for the detection and recognition of road signs and deformations using deep learning. The dataset for this model will consist of a hybrid dataset that includes Kaggle data and data from AGEROUTE (Autonomous Agency for Works and Road Management) in Senegal. In addition, the proposed model is trained on two datasets; one for training 80% and the other for testing 20%. Experimental results showed that the proposed model achieved an accuracy of 95.34% for the datasets. [ABSTRACT FROM AUTHOR]

The paper presented introduces an innovative accident prevention system that employs deep learning models and sensors to identify and alert drivers who lose concentration while driving for a variety of reasons. The system targets drowsiness, cell phone use, and alcohol consumption, which are among the leading causes of automobile accidents. The proposed solution employs neural networks to identify specific patterns associated with driver inattention and generates an audible alert to refocus the driver's attention on the road. In addition, the system utilizes alcohol-detection sensors, as alcohol consumption is another significant cause of accidents. This system seeks to reduce the number of casualties by enhancing road safety considering the high number of daily collisions, which are primarily caused by speeding. The paper describes the design, architecture, and implementation of the system on a modest scale to provide a lifesaving, cost-effective solution. [ABSTRACT FROM AUTHOR]

Air writing is a unique method that allows individuals to freely write or draw in the air without the need for sensor devices. One of the significant applications is aimed at addressing the challenges faced by dyslexic children, who often struggle with reading, writing, and spelling. Currently, there is a lack of tailored applications to guide dyslexic children through their learning process. This paper presents a groundbreaking solution designed to assist dyslexic individuals in mastering the art of writing alphabets, digits, and words. Using various hand gestures, children can effortlessly write any character, digit, or word on a virtual canvas and memorize them easily through the hand movements. This approach not only makes learning enjoyable but also boosts a child's confidence. Additionally, the application allows children to unleash their imagination by creating doodles. While existing air writing methods have limitations, such as reliance on devices and restricting content to digits and characters within predefined boundaries. These limitations have been overcome in our proposed approach. Leveraging afordable technologies, our system caters to the needs of dyslexic learners and serves as an interactive tool to enhance their writing skills. It also provides a means of communication for the deaf and mute community. Utilizing cutting-edge technologies such as machine learning, deep learning, computer vision, and web development, we have developed a practical application that seamlessly tracks finger movements and gestures, capturing trajectories and images for classification. Our model exhibits remarkable performance, achieving a training accuracy rate of 98.57% for digit recognition, 98.80% for character recognition, and 97.09% for doodle recognition. This paper contributes valuable insights into the development of an effective and engaging tool to improve learning skills for students, particularly dyslexic children, while also addressing communication barriers for the deaf and mute community. Our approach opens up new possibilities for inclusive and accessible learning experiences. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) continues to be a prominent issue in global public health, emphasizing the need for precise and timely detection. This paper employs a deep learning (DL) approach to introduce an extensive methodology for categorizing histopathology images associated with breast cancer into automated binary classifications. The proposed framework architecture is validated on the standard database which is accessible to the public, called Breast Cancer Histopathological Database (BreakHis). Data augmentation techniques are employed for the pre-processing stage. This paper uses the DenseNet 121 pre-trained model for feature extraction and fully connected layers (FCL) to fine-tune the model further. In this experiment, the highest accuracy of 96.09% is observed with the 100X. The experimental results showed an improvement in accuracy for all the magnification factors compared to the existing works. [ABSTRACT FROM AUTHOR]

Fake news generation and propagation is a major challenge of the digital age, resulting in various social impacts namely bandwagon, validity, echo chamber effects, deceiving the public with spams, misinformation, malicious content and many more. The widespread proliferation of fake news not only fosters misinformation but also undermines the credibility of news sources. The veracity of the information is a major concern at all the stages of generation, publication, and propagation. To comprehend the critical need for addressing this pervasive problem, this research paper presents a framework for automatic detection of fake news using a knowledge-based approach. An automatic fact checking mechanism is applied using concepts of Information Retrieval (IR), Natural Language Processing (NLP) and Graph theory. The knowledge base is generated using Twitter dataset, which basically contains four attributes: Subject-Predicate-Object (SPO) triplet, SPO sentiment polarity, SPO occurrence, and topic modeling. These attributes serve as pivotal indicators for the development of a knowledge base, subsequently employed to detect prevalent patterns and traits linked to deceptive or false information. We have employed Named Entity Recognition (NER) model to extract SPO triples and Latent Dirichlet Allocation (LDA) for topic modeling, thereby contributing to knowledge base generation. To evaluate the efficacy and efficiency of our proposed model, we utilize deep learning algorithms like RNN, GRU, LSTM, GPT-3 and BERT Transformer providing an acceptable level of accuracy. This research paper delivers valuable insights into addressing the proliferation of fake news on Twitter, employing data-driven approaches and advanced deep learning algorithms. [ABSTRACT FROM AUTHOR]

The integration of deep learning, computer vision, and advanced algorithms has ushered in a transformative era in the prediction of human driving behavior, consequently revolutionizing road safety. This paper focuses on an innovative convergence of technology that addresses critical issues like driver fatigue and distracted driving by automatically identifying and categorizing diverse driving behaviors. Neural network architectures, such as VGG16, AlexNet, and ResNet are described in this paper that have propelled accuracy in behavior classification to remarkable levels. However, the quest for safer roads remains ongoing, with promising avenues lying ahead. First and foremost, the creation of extensive, diverse, and meticulously annotated datasets is paramount. These datasets serve as the bedrock upon which future models can be trained, enhancing their robustness and generalizability across a spectrum of driving scenarios. Real-time models represent another pivotal frontier. These models hold the potential to provide timely interventions and support systems for drivers, thus preventing accidents proactively. The exploration of hybrid techniques that amalgamate the strengths of various neural network architectures presents an exciting avenue, promising to further push the boundaries of prediction accuracy. Furthermore, this paper also discusses the fusion of multi-modal data, encompassing sensor data from IoT and smartphone devices, that holds immense promise. This holistic approach promises a more comprehensive understanding of driver behavior by integrating diverse data sources, ultimately contributing to the creation of safer road environments.In this research paper, we also explore these cutting-edge developments in deep learning and computer vision, emphasizing technical novelty and innovation. Through an interdisciplinary approach, we envision a future where the synergy of technology, data, and human behavior leads to a substantial reduction in road accidents and improved road safety. [ABSTRACT FROM AUTHOR]

This paper presents a deep learning-based model developed to accurately classify Parkinson's disease patients using 3D MRI scans and data augmentation techniques. The study aimed to address the research question of whether this approach could effectively detect structural changes indicative of Parkinson's disease and enhance diagnostic accuracy. The model utilized 3D brain MRI scans from the midbrain region and leveraged the domain of data augmentation to expand the dataset and improve generalizability. The testing accuracy of the model was evaluated with and without data augmentation to further understand it's applicability. The paper demonstrated the effectiveness of the proposed model in classifying Parkinson's disease patients from healthy controls, achieving a testing accuracy of up to 90.13% with data augmentation. The incorporation of data augmentation led to a significant 15.53% increase in accuracy compared to the model without augmentation. The utilization of 3D MRI scans allowed for a more comprehensive assessment of structural changes associated with the disease, and it is intuitive to believe that shuch efficient predictions can facilitate earlier treatment and better patient outcomes. [ABSTRACT FROM AUTHOR]

Brain diseases are a growing concern in healthcare, with an estimated 50 million people worldwide living with dementia and 15 million new cases each year. This research paper explores the significance of early identification and diagnosis in mitigating the impact of brain diseases on individuals and society at large. Moreover, the study delves into the current state of diagnostic practices, highlighting the limitations and emerging opportunities in the field. The paper concludes by advocating for increased research, improved access to advanced medical technologies, and enhanced training for healthcare professionals to address the growing burden of brain diseases effectively. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is characterized by low mood, loss of interest and even suicidal ideation. Electroencephalogram based diagnosis of a variety of neurological conditions has been conducted in recent years using modern neurocomputing and deep learning approaches. Privacy is a high concern for medical data over distributed training scenarios. Hence, this paper aims to develop an EEG-based privacy-preserved system to identify MDD through the Federated Learning (FL) approach. In this study, training for 3-channel EEG-based MDD screening is introduced over FL using deep learning (DL) algorithms such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and One Dimensional Convolutional Neural Network (1D-CNN). A privacy-preserving solution for MDD patients utilizing FL is advantageous because clinical data is sensitive and most people do not want to share their personal information. Hence, the analysis of the comparative results for multiple applicable strategies including Independent and Identically Disturbed (IID) data, non-IID data, and algorithms provide proof that FL can be used to train DL models. The MODMA dataset of 3-channel EEG with 26 MDD and 29 non-MDD individuals is used in this paper. Future study on MDD has several potential areas of use and these FL approaches can also be coupled with new technologies. [ABSTRACT FROM AUTHOR]

Improving generalization ability in multi-robot formation can reduce repetitive training and calculation. In this paper, we study the multi-robot formation problem with the ability to generalize the target position. Since the generalization ability of neural network is directly proportional to spatial dimension, we adopt the strategy of using different networks to solve different objectives, so that the network learning can focus on the learning of one objective to obtain better performance. In addition, this paper presents a distributed deep reinforcement learning method based on soft actor–critic algorithm for solving multi-robot formation problem. At the same time, the formation evaluation assignment function is designed to adapt to distributed training. Compared with the original algorithm, the improved algorithm can get higher reward cumulative values. The experimental results show that the proposed algorithm can better maintain the desired formation in the moving process, and the rotation design in the reward function makes the multi-robot system have better flexibility in formation. The comparison of control signal curve shows that the proposed algorithm is more stable. At the end of the experiments, the universality of the proposed algorithm in formation maintenance and formation variations is demonstrated. [ABSTRACT FROM AUTHOR]