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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]

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]

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]

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]

[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]

• 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]

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]

This article introduces a special issue on hyperspectral remote sensing, which involves advanced satellite sensors that provide data in three categories: hyperspatial, superspectral, and hyperspectral. These data types offer higher resolutions but also present challenges in handling and analysis. The special issue aims to explore the characteristics and limitations of hyperspectral data and their opportunities for advancing remote sensing science. The selected papers in the issue cover various applications and use advanced techniques to provide valuable knowledge in the field. The document discusses three research papers related to remote sensing and hyperspectral data, focusing on mapping building shadows, identifying asphalt pavement aging, and separating wheat crops from similar crops. It emphasizes the potential of hyperspectral data in advancing remote sensing science while acknowledging the challenges associated with data analysis and calibration. [Extracted from the article]

In recent years, multi-focus image fusion has emerged as a prominent area of research, with transformers gaining recognition in the field of image processing. Current approaches encounter challenges such as boundary artifacts, loss of detailed information, and inaccurate localization of focused regions, leading to suboptimal fusion outcomes necessitating subsequent post-processing interventions. To address these issues, this paper introduces a novel multi-focus image fusion technique leveraging the Swin Transformer architecture. This method integrates a frequency layer utilizing Wavelet Transform, enhancing performance in comparison to conventional Swin Transformer configurations. Additionally, to mitigate the deficiency of local detail information within the attention mechanism, Convolutional Neural Networks (CNN) are incorporated to enhance region recognition accuracy. Comparative evaluations of various fusion methods across three datasets were conducted in the paper. The experimental findings demonstrate that the proposed model outperformed existing techniques, yielding superior quality in the resultant fused images. [ABSTRACT FROM AUTHOR]

This paper intends to investigate the risk management of inclusive digital financial platforms. First, it explains the idea of smart cities, their function, and inclusive financial risk control technologies based on big data. The varieties of digital inclusive financial platforms and their risk profiles are next examined. The Back Propagation (BP) neural network is used to build a BP-KMV model based on the KMV model. Finally, utilizing M Company as a case study, this paper uses the BP-KMV model to examine the credit risk and risk management of unlisted enterprises on the digital inclusive financial platform. The results show that of the four unlisted companies, L Company has the greatest default rate (7.35%), while J Company has the lowest default rate (4.82%). The highest research and development (R&D) spending rate is 14.1% for J company, while the highest patent ownership rate is 43.09% for L company. The data demonstrates a negative correlation between the percentage of R&D expenditures and the default rate of unlisted enterprises. In other words, a larger default risk is associated with lower R&D expense rates. Additionally, there is a correlation between patent ownership and default rates that is positive, suggesting that higher patent ownership rates are linked to higher default rates. Additionally, the risk management technologies of M business can complement one another. The theoretical research of comprehensive digital inclusive finance risk control can be enriched by the risk analysis of digital inclusive financial platforms utilizing the BP-KMV model in the context of smart cities. [ABSTRACT FROM AUTHOR]

The man-machine interface encompasses a crucial area-emotion recognition through facial expressions. Despite its significance, emotion recognition faces challenges such as facial accessories, nonuniform illuminations, pose variations, audio speeches, text conversations, and hand and facial gestures. Understanding emotions like happiness, anger, anxiety, joy, and shock, along with their varying degrees and overlaps, is essential for accurate recognition. These nuances, inherent to humans, pose difficulties and costs in achieving standard results through facial recognition. Recognizing someone's mood through facial expression, conversation, voice modulation, and gestures is a skill humans excel at. However, replicating this ability through facial recognition has proven challenging and costly. This paper addresses these challenges by proposing diverse approaches to emotion detection. By exploring various modes, including facial expressions, conversation analysis, voice modulation, and gestures, the paper tackles current research problems and holds practical applications in public experiments and exhaustive sentiment analysis. The paper presents a good combo of various modes of emotion recognition on multiple datasets (tried and tested widely before amalgamating all to produce an excellent optimal result as an output of the model). [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]

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]

Advanced modelling and optimisation techniques have been widely used in recent years to enable intelligent manufacturing and digitalisation of manufacturing processes. In this context, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components. Machine learning methods have already been applied to optimise various individual objectives concerning process characteristics, tool wear, or product quality in machining. However, the overall improvement of the machining process requires multi-objective optimisation approaches, which are rarely considered and implemented. The state-of-the-art in application of various optimisation and artificial intelligence methods for process optimisation in machining operations, including milling, turning, drilling, and grinding, is presented in this paper. The Milling process and deep learning are found to be the most widely researched operation and implemented machine learning technique, respectively. The surface roughness turns out to be the most critical quality measure considered. The different optimisation targets in artificial intelligence applications are elaborated and analysed to highlight the need for a holistic approach that covers all critical aspects of the machining operations. As a result, the key factors for a successful total machining performance improvement are identified and discussed in this paper. The AI methods were investigated and analysed in the frame of the IMPACT project initiated by the CIRP. [ABSTRACT FROM AUTHOR]

This paper focuses on enhancing touch-typing skills through state-of-the-art deep learning technologies. It has been demonstrated that writing posture can be assessed using integrated keyboard detection and hand pose estimation in real-time browser environments. A unique dataset was created for training the object detection model to recognize individual keyboards. The object detection models were trained using various architectures and then assessed for both inference time and accuracy. In addition, a hand pose estimation was implemented to precisely recognize 21 knuckle points of the hand and compute their exact positions. In order to implement object detection and hand pose estimation, a stream transmission of the keyboard and hand scene is required. For this purpose, server-client communication via a QR code connection is implemented to transfer the stream between two mobile devices. Based on these deep learning technologies, a mobile web app was developed that offers 170 learning courses for touch-typing training. With the help of the Typing Learning Academy prototype, a study was conducted as part of this paper to evaluate the usability and utility of the learning app. This research demonstrates the potential for enhancing the development of writing skills through touch typing through the utilization of advanced deep learning technologies. [ABSTRACT FROM AUTHOR]

In the era of 6G, the rapid increase in communication data volume poses higher demands on traditional channel estimation techniques and those based on deep learning, especially when processing large-scale data as their computational load and real-time performance often fail to meet practical requirements. To overcome this bottleneck, this paper introduces quantum computing techniques, exploring for the first time the application of Quantum Generative Adversarial Networks (QGAN) to broadband channel estimation challenges. Although generative adversarial technology has been applied to channel estimation, obtaining instantaneous channel information remains a significant challenge. To address the issue of instantaneous channel estimation, this paper proposes an innovative QGAN with a dualmodule design in the generator. The adversarial loss function and the Mean Squared Error (MSE) loss function are separately applied for the parameter updates of these two modules, facilitating the learning of statistical channel information and the generation of instantaneous channel details. Experimental results demonstrate the efficiency and accuracy of the proposed dual-module QGAN technique in channel estimation on the Pennylane quantum computing simulation platform. This research opens a new direction for physical layer techniques in wireless communication and offers expanded possibilities for the future development of wireless communication technologies. [ABSTRACT FROM AUTHOR]

Consumer feedback is often used for various purposes in many fields. However, traditional paper questionnaires or surveys cannot fully meet the demands for accurately understanding consumers' feelings. Consumers often convey their feelings through their facial expressions, whether consciously or unconsciously. Understanding these feelings can provide very direct and useful feedbacks. Yet, humans may miss those subtle changes because the micro expression is too brief to be captured. Therefore, in this paper. we proposed a deep learning based recognition approach of fucial micro expressions, in which more realistic emotional feedback of users can be extracted. To achieve this goal, we integrated several approaches including: ( 1) using trained face detection model to capture face image from input; (2) training a high accurate 468- point landmark detection model with multiple face dataset. Based on the FACS (Facial Action Coding System) table, we categorized these landmarks into 13 groups of facial regions. These regions with specific emotion labels are used as our target units of AU (Action Unit) detection; (3) training CNN model to detect and analyze AUs from facial landmark data: (4) implying FACS to evaluate the facial expressions and emotions: and (5) using a straightforward GUI plotter to show the digitized emotions. The experiment results show that not only the primaty emotion but also the secondary emotion of users in movies can be detected and evaluated successfully. Therefore, our system has a great potential for obtaining users' feedbacks iii a more accurate and comprehensive manner. [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]

Radiographic testing is the most common method of non-destructive testing to detect discontinuities in industrial piping welds. Human interpretation of radiographic films is time-consuming and requires high degrees of expertise. Despite the significant advancements of deep learning techniques in related fields, such as medical radiography, prior research endeavors focused on weld discontinuities were constrained by the limitations stemming from a lack of training data and their inadequate representation of real-world conditions in the field. This paper introduces a comprehensive system that automatically detects welding zones, assesses film quality, and classifies weld discontinuities for the piping process. The proposed framework demonstrates superior generalization capabilities that bypass a single industry or piping size. The key advantages of our technique lie in its enhanced accuracy, rapid processing, and automatic interpretation of welding films across a wide range of image qualities. Consequently, it achieves remarkable detection and classification accuracy, offering substantial benefits for welding inspection and quality assessment. [ABSTRACT FROM AUTHOR]

Within the realm of sustainable agriculture and the protection of crops, the presence of intruders poses a substantial risk, leading to potential crop damages and injuries. In our paper, we conceptualize intruders as objects. Consequently, the implementation of object detection emerges as a crucial safety and field protection measure, effectively mitigating the adverse consequences of such encounters. While deep learning techniques have proven to yield superior results in object detection, their computational and parameter requirements have posed challenges in their widespread implementation. This research paper presents an alert system AgriGuard equipped with a lightweight object detection model for enhancing security in agriculture fields. The proposed embedded system utilizes EmbdYOLOv3 and TinyEmbdYOLOv3 models, modified versions of YOLOv3 and TinyYOLOv3, respectively, to overcome challenges in object detection, such as occluded or look-alike animals. The system integrates ultrasonic sensors, raspberry pi, real-time object detectors, google firebase, and an android application to detect and alert farmers for unauthorized access. Experimental results show that EmbdYOLOv3 outperforms YOLOv3 by 36.85%, and TinyEmbdYOLOv3 outperforms Tiny-YOLOv3 by 14.48% under the same embedded environment. The study highlights the effectiveness of low-power IoT devices and deep learning techniques in providing robust security solutions for the agriculture industry, offering a potential approach for mitigating crop damages and ensuring field safety. [ABSTRACT FROM AUTHOR]

This document is a foreword for a special issue of the Journal of Information Science & Engineering on the topics of FinTech, RegTech, and Sustainable Finance. The special issue includes papers presented at the 3rd International Conference on FinTech, RegTech, and Sustainable Finance (FRSF2022), as well as open call papers. The ten accepted papers cover various topics such as Artificial Intelligence Finance, Cybersecurity, Digital Currency Analysis, ESG and Sustainable Finance, Financial Big Data Analysis, Fraud and Financial Credit Investigation, Risk Assessment, Financial Forecast Analysis, and Supply Chain Finance. The papers explore innovative solutions and techniques in these areas. The foreword expresses gratitude to the authors, reviewers, and editorial staff for their contributions to the special issue. [Extracted from the article]

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]

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As digital technologies have advanced more rapidly, the number of paper documents recently converted into a digital format has exponentially increased. To respond to the urgent need to categorize the growing number of digitized documents, the classification of digitized documents in real time has been identified as the primary goal of our study. A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement. Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits, which were not conceivable ten years ago. Deep learning aids in comprehending input patterns so that object classes may be predicted. The segmentation process divides the input image into separate segments for a more thorough image study. This study proposes a deep learning-enabled framework for automated document classification, which can be implemented in higher education. To further this goal, a dataset was developed that includes seven categories: Diplomas, Personal documents, Journal of Accounting of higher education diplomas, Service letters, Orders, Production orders, and Student orders. Subsequently, a deep learning model based on Conv2D layers is proposed for the document classification process. In the final part of this research, the proposed model is evaluated and compared with other machine-learning techniques. The results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%, 94.79%, 94.62%, 94.43%, 94.07% in accuracy, precision, recall, F-score, and AUC-ROC, respectively. The achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker. [ABSTRACT FROM AUTHOR]

Cancer is a life-threatening ailment characterized by the uncontrolled proliferation of cells. Breast cancer (BC) represents the most highly infiltrative neoplasms and constitutes the primary cause of mortality in the female population due to cancer-related complications. Consequently, the imperative for early detection and prognosis has emerged as a means to enhance long-term survival rates and mitigate mortality. Emerging artificial intelligence (AI) technologies are being utilized to aid radiologists in the analysis of medical images, resulting in enhanced outcomes for individuals diagnosed with cancer. The purpose of this survey is to examine peer-reviewed computer-aided diagnosis (CAD) systems that have been recently developed and utilize machine learning (ML) and deep learning (DL) techniques for the diagnosis of BC. The survey aims to compare these newly developed systems with previously established methods and provide technical details, as well as the advantages and disadvantages associated with each model. In addition, this paper addresses several unresolved matters, areas of research that require further exploration, and potential avenues for future investigation in the realm of advanced computer-aided design (CAD) models utilized in the interpretation of medical images. Furthermore, the integration of Internet of Things (IoT) in BC research and treatment holds immense significance by facilitating real-time monitoring and personalized healthcare solutions. IoT devices, such as wearable sensors and smart implants, enable continuous data collection, empowering healthcare professionals to track patients' vital signs, response to treatment, and overall health trends, fostering more proactive and tailored approaches to BC management. Moreover, the advent of 5G technology in BC applications promises to revolutionize communication speeds and data transfer, enabling rapid and seamless transmission of large medical datasets. This high-speed connectivity enhances the efficiency of remote diagnostics, telemedicine, and collaborative research efforts, ultimately accelerating the pace of innovation and improving patient outcomes in BC care. The present study aims to examine various classifiers utilized in ML and DL methodologies for the purpose of diagnosing BC. Research findings have demonstrated that DL has superior performance compared to standard ML methods in the context of BC diagnosis, particularly when the dataset is extensive. The existing body of research indicates that there are significant gaps in knowledge that need to be addressed in order to enhance healthcare outcomes in the future. These gaps highlight the pressing need for both practical and scientific research in the field. Finally, IoT and 5G will be how they can be used in order to enhance BC detection, treatment and patient care. • Cancer, characterized by uncontrolled cell proliferation, poses a significant threat to human life. • Breast carcinoma is highly invasive and a leading cause of female cancer-related mortality, emphasizing the need for early detection and prognosis. • Emerging AI technologies are assisting radiologists in analyzing medical images, improving cancer diagnosis outcomes. • This survey focuses on recent computer-aided diagnosis (CAD) systems using machine learning and deep learning for breast carcinoma diagnosis. • The paper identifies unresolved research areas and future investigation prospects in advanced CAD models for medical image interpretation. [ABSTRACT FROM AUTHOR]

This paper presents a novel machine learning (ML) based approach to predict machining cycle (part program running) times for complex 5-axis machining. Typical 5-axis machining toolpaths consist of short-segmented discrete linear cutter location lines (CL-lines) with simultaneously varying tool center point (TCP) and orientation vectors (ORI). As the 5-axis machine tool NC (numerical control) system tries to interpolate such part programs smoothly, the TCP motion decelerates and accelerates repeatedly causing the actual feedrate to fluctuate. The actual observed (resultant) feedrate can be approximately 30 % lower than the user commanded one. Furthermore, if the toolpath requires the 5-axis machine tool to travel closer to its singular point, or if the workpiece placement on the table is not optimal, actual feedrate is even lowered further. This paper presents two ML-based approaches to accurately predict 5-axis machining toolpaths. The first strategy uses a bidirectional long short-term memory (Bi-LSTM) network to model the machine tool behavior and generates a direct final cycle time estimation for any given part program. This approach only uses the toolpath geometry to predict the cycle time, and for training it only needs the "final" cycle times of similar part programs making it practical on today's shop floors. The second strategy requires individual CL-line processing times for training. In return, it can provide highly accurate cycle time predictions. Both strategies can capture the interpolation dynamics of 5-axis machine tool NC systems accurately. Simulation studies and experimental validations are conducted on modern 5-axis machine tools with varying workpiece placements and interpolation parameters. Proposed approaches have shown to predict cycle times with 90–95 % accuracy on real-life complex 5-axis machining toolpaths. [ABSTRACT FROM AUTHOR]

This paper introduces a novel machine learning framework to address the challenge of optimizing literature research by identifying the optimal path. To create dataset and ensure the versatility of the solution for different applications, we developed an online scraping tool designed to extract articles from ResearchGate based on a specific search query. The proposed machine learning model leverages contextual embeddings and graph theory, translating intricate scholarly work into informative steps for one to go wider rather than deeper in their research. By employing a Christofides approximation of the Traveling Salesman Problem algorithm, our model efficiently navigates through more than 1000 article embeddings. We prove that the resulting path not only accelerates the knowledge gaining process, but also evidently diversifies the findings. Moreover, we evaluated multiple PDF reader libraries to arrive at the most suitable one for the purpose. This adaptability allows the framework to be applied not only to scraped articles, but also to those stored as PDF files, giving an option for multiple data sources. In conclusion, this paper presents a transformative approach for literature research optimization, equipping researchers with a potent tool to efficiently explore articles. [ABSTRACT FROM AUTHOR]

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. [ABSTRACT FROM AUTHOR]

Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector. In quantitative genetics prediction of genetic values is a central issue. Wheat is an allohexaploid (AABBDD) with three distinct genomes. The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat, Therefore, genome sequence approaches based on techniques of Artificial Intelligence (AI) are necessary. This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources andmanaging genetic variation in their breeding program, as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints. In this paper, the hyperparameters of the network are optimized in the CNN to decrease the requirement formanual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks (CNN). [ABSTRACT FROM AUTHOR]

In this paper we aim to analyze the influence of Deep Learning (DL) and Neural Networks (NN), on financial forecasting, as a result we have extensively examined research papers and real life implementations of these sophisticated computer models that forecast patterns and movements in financial markets, in the same way we will explore and compare the development of financial forecasting techniques starting from classical methods to the adoption of deep learning models, like Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM). To be more specific, in this article we delve into the abilities of these models to capture non-linear patterns in financial data such as stock market asset prices. We highlight how they excel in some situations compared to classical methods used for predicting the future of a financial asset. We emphasize how these models outperform conventional techniques for projecting the future of a financial asset in some circumstances, we also address the challenges and barriers associated with using these models to forecasting of financial outcomes, some challenges include overfitting, the need for high quality data, and the requirement for large databases. By examining and combining insights from multiple sources, our goal is to present a global perspective on the current state of neural networks and deep learning used for predicting future changes in the financial sector, we aim to identify areas where these models demonstrate some kind of performance while also showing areas that necessitate more research and development. [ABSTRACT FROM AUTHOR]

High-resolution range profile (HRRP) automatic recognition has been widely applied to military and civilian domains. Present HRRP recognition methods have difficulty extracting deep and global information about the HRRP sequence, which performs poorly in real scenes due to the ambient noise, variant targets, and limited data. Moreover,most existingmethods improve the recognition performance by stacking a large number of modules, but ignore the lightweight of methods, resulting in over-parameterization and complex computational effort, which will be challenging to meet the deployment and application on edge devices. To tackle the above problems, this paper proposes an HRRP sequence recognition method based on a lightweight Transformer named RLAT, which consists of rotary position encoding, local-aggregated attention unit (LAU), and lightweight feedforward neural network (LW-FFN). Rotary position encoding is utilized to embed the relative position information for the HRRP sequence. Local aggregation attention unit can effectively aggregate and extract local features by local group linear transformation, and then the self-attention mechanism is adopted for perception and enhancement of global information. Thereby, the enhanced features are extracted by lightweight FFN. In addition, this paper adopts Label Smoothing regularization to add noise to the sample labels, which can improve the generalization performance of themethod. Finally, the effectiveness of the proposed method in real scenes is verified based on the MSTAR dataset, a real-world dataset for radar target recognition. Experimental results show that the proposed method achieves superior recognition performance compared to other remarkable methods and achieves significant generalization performance and robustness under variant sample and limited sample conditions. RLAT achieved an accuracy of 99.86% on the MSTAR standard dataset and 99.73% on the MSTAR variant dataset. In particular, it achieves an accuracy of 95.83% with only 274 training samples. Furthermore, the proposed method is more lightweight, with 90.90% reduction in the number of parameters and 96.70% reduction in the computation compared to the Vanilla Transformer, which facilitates deployment in edge devices. [ABSTRACT FROM AUTHOR]

With the rapid increase in IoT devices and advanced machine learning and deep learning techniques, there has been a growing concern about computational cost and data privacy issues since the data coming from IoT devices is non-independent identically distributed (non-IID). However, the implementation of the federated learning algorithm has proven to be a booster in the performance and a solution to the existing data privacy concerns. This paper gives insight into topics such as Blockchain, Unmanned Aerial Vehicles (UAV), Wireless communication, Vehicular Internet of Things, Healthcare, and Cloud Computing and how they have been implemented and co-related to federated Learning and the application and the emerging use cases in the field of federated learning (FL) with respect to the above-mentioned topics have also been discussed. This paper uniquely shows how federated learning has an edge over the traditional machine learning and deep learning techniques in IoT infrastructure since computing nodes are trained using local models on the devices and then these local models are uploaded to the central global server instead of data directly into a global model on a central server ensuring data privacy. [ABSTRACT FROM AUTHOR]

Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F 1 -Scores between 83.11% and 98.34% in the pre-trained architectures. [Display omitted] • Transfer learning is applied to address the unique characteristics of thermal images for switchgear fault detection. • A thermal image dataset is setup and augmented to improve the classification performance. • Establish standard comparisons among eleven transfer learning architectures. • Achieve rapid and precise classification with the proposed transfer learning architectures. [ABSTRACT FROM AUTHOR]

Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly segment brain tumors using computers because of the mutual infiltration and fuzzy boundary between the focus areas and the normal brain tissue. To address the above issues, a segmentation method which integrates edge features is proposed in this paper. The overall segmentation architecture follows the encoder decoder structure, extracting rich features from the encoder. The first two layers of features are input to the edge attention module, and to extract tumor edge features which are fully fused with the features of the decoder segment. At the same time, an adaptive weighted mixed loss function is introduced to train the network by adaptively adjusting the weights of different loss parts in the training process. Relevant experiments were carried out using the public brain tumor data set. The Dice mean values of the proposed segmentation model in the whole tumor area (WT), the core tumor area (TC), and the enhancing tumor area (ET) reach 91.10%, 87.16%, and 88.86%, respectively, and the mean values of Hausdorff distance are 3.92, 5.12, and 1.92 mm, respectively. The experimental results showed that the proposed method can significantly improve segmentation accuracy, especially the segmentation effect of the edge part. [ABSTRACT FROM AUTHOR]

Pedestrian re-identification technology enables accurate identification of individuals and is widely used in modern intelligent video surveillance systems to aid law enforcement, including criminal apprehension and locating missing persons. However, variations in lighting, background, resolution, and other imaging conditions captured by different cameras create significant challenges in pedestrian feature extraction, often leading to poor recognition accuracy. To overcome these challenges, this paper presents a Multi-resolution Feature Fusion (MRFF) method for pedestrian re-identification, based on the Pedestrian Re-identification Relational Network (RNFPR). This approach incorporates the Coordinate Attention (CA) module into the DenseNet161 network to enhance feature extraction capabilities. Improving the discriminative and recognition accuracy of features requires learning and fusing pedestrian features from multiple low-resolution images. This process enhances the expressive power of feature maps, ultimately improving pedestrian recognition performance. Additionally, this method introduces a multi-resolution feature fusion module that segments and integrates multi-resolution features from image data. This enables the model to effectively combine feature information from various resolution levels, resulting in a more comprehensive feature representation. Experimental results show that the MRFF method achieves a 1.3% increase in mean Average Precision (mAP) and a 1.0% improvement in Rank-1 accuracy on the Market1501 dataset. For the DukeMTMC-reID dataset, it provides a 0.2% increase in mAP and a 0.7% enhancement in Rank-1 accuracy. Consequently, the MRFF approach results in an overall mAP increase of 0.7% on the DukeMTMC-ReID dataset, significantly improving pedestrian gender re-identification accuracy. [ABSTRACT FROM AUTHOR]

The detection algorithms of small objects in remote sensing images is often challenging due to the complex background and limited pixels. This can lead to reduced accuracy in detection and an increased number of missed small objects. So this paper introduces YOLOFNC, an enhanced network based on YOLOv7. To improve the model's ability to capture features of small objects, an enhanced C3-Faster module based on the C3 module is designed and integrated into the YOLOv7 network. This module helps extract more features related to small objects. Additionally, we employ Normalized Wasserstein Distance (NWD) fusion GIoU as a novel loss function to refine the accuracy of network optimization weights and the small object regression framework. Furthermore, a coordinated attention (CA) mechanism is incorporated at strategic locations in the model to reduce redundant information in the feature layer and prevent the loss of important small object features. we conduct comparison experiments between YOLO-FNC and other commonly used object detection algorithms on DIOR, AITOD, and VisDrone datasets. The experimental results show that YOLO-FNC achieves 84.4% mAP on the DIOR dataset, 35.9% mAP on the AI-TOD dataset, and 52.6% mAP on the VisDrone dataset. Compared to YOLOv7 and other remote sensing object detection models, YOLO-FNC demonstrates better performance in object detection. [ABSTRACT FROM AUTHOR]

As a large percentage of faults in transmission lines are single-line-to-ground (SLG) and transient in nature, the use of adaptive single-phase auto-reclosing (ASPAR) schemes is essential. In this paper, a new scheme for ASPAR is proposed to improve the stability and reliability of power transmission lines. The proposed approach distinguishes between the transient and permanent faults by using the feature of Stationary Wavelet Transform (SWT) and Gate Recurrent Unit (GRU) deep artificial neural network. Also, by utilizing another GRU network, the proposed scheme predicts the extinction time of the secondary arc (SA) one power cycle before the complete extinction. This can increase the speed of the faulty phase reclosing in the case of a transient fault. Simulation results carried out on a 400 kV power transmission line in the EMTP-RV and MATLAB software environments illustrate that the F 1 score of the proposed ASPAR in classifying the faults is 99.55 %, and its average error in predicting the extinction time of the SA based on the root mean square error (RMSE) criterion is 0.0557. Also, the superiority and high-quality performance of the proposed protection scheme are demonstrated by comparing the results with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes Scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our Scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that Scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our Scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software. [ABSTRACT FROM AUTHOR]