Search

The use of Artificial Intelligence (AI) to preserve and promote cultural heritage has experienced significant growth in recent years. Among the various areas of cultural heritage, numismatics have emerged as a particularly promising field where we can develop AI solutions. Numismatics refers to the study of coins, tokens, paper money, and medals, which play a critical role in understanding human history and culture. However, there are still limited resources available to help researchers and collectors in the identification of coins. This is due to the vast number of coins in circulation, which presents a significant challenge in developing smart tools for classification tasks. This paper aims to provide a contribution to this setting. In particular, we start by creating a new dataset called EURO-Coin, which consists of images showing the side of coins with reliefs and is designed to facilitate the training and testing of AI models for euro coin classification. Then, we propose two approaches that leverage Convolutional Neural Networks and self-attention layers to classify the country and value of the coins. In our experiments, we obtain an accuracy of 86.9% for country classification and an accuracy of 96.4% for value classification. Finally, we conduct an ablation study to evaluate the impact of the preprocessing activities and attention layers in our approaches. [ABSTRACT FROM AUTHOR]

Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. The comparison result of the proposed algorithm with other algorithms proves that EDOLSCA also has advantages in heat exchanger optimal design. Finally, the experimental results confirm the efficiency of the proposed scheme in real-world urban scenarios; The last paper by Lidong Zhang et al. on "Elite and dynamic opposite learning enhanced sine cosine algorithm for application to plat-fin heat exchangers design problem" proposes a novel variant of sine and cosine algorithm named EDOLSCA enhanced by dynamic opposite learning algorithm and the elite strategy. [Extracted from the article]

The model integrates artificial intelligence (AI) and big data analytics, utilizing IoMT devices for data acquisition and Hadoop ecosystem for managing big data. The field of medical diagnosis is currently undergoing a remarkable transformation with the emergence of artificial intelligence (AI) techniques, particularly deep learning and big data analytics. By harnessing the power of deep learning and big data analytics, AI-based e-diagnosis has the potential to revolutionize healthcare delivery. [Extracted from the article]

COVID-19 outbreak has caused a devastating impact on the daily lives of people, public health, and economic progress of infected countries. It has become a leading cause of substantial mortality and morbidity around the world. The emergence of new variants of virus has posed severe challenges for humanitarian society. Information and Communication Technology (ICT) has played a vital role in this pandemic and offered various promising innovations to control its dissemination. The current research study presents a scientometric analysis on the literature of ICT-assisted COVID-19 research. In this paper, ICT has been classified into six major categories; artificial intelligence and medical imaging, mobile technology, ubiquitous computing, big data analytics, social media platforms, and printing technology. It extensively examines the role of these technologies in COVID-19 by applying various empirical approaches such as co-citation analysis, publication and citation behavior analysis, participating nations, and knowledge mapping of scientific literature using visualization tool CiteSpace. Furthermore, it provides a visual approach to identify developing paths, evolution trends, research hotspots, cluster analysis, and potential future directions in medical informatics. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and artificial intelligence today, human–computer interaction based solely on computer software operations is far from being able to meet human requirements for computer use. People are looking forward to a more convenient and fast human–computer interface. This paper studies the expression dynamic capture and 3D animation generation methods based on deep learning. For facial expression dynamic capture, this paper proposes a facial feature extraction algorithm based on deep learning and uses SVM technology for feature classification. For 3D animation, C++ and OpenGL are used for rendering simulation. The experimental results show that the face detection algorithm proposed in this paper has good performance in both accuracy and speed. It can realize real-time detection of face regions in video images. [ABSTRACT FROM AUTHOR]

Today's factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company's existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries. [ABSTRACT FROM AUTHOR]

Data missing has always occurred in data processing. To solve this problem, researchers have improved the process methods of the missing data with diverse strategies, which range from directly deleting missing data samples to using artificial intelligence technology to filling in incomplete data. The processing methods of the missing data have been improved. Generative adversarial imputation network (GAIN) is a kind of neural network which has an excellent performance in missing data imputation. A number of publications that research and cite the GAIN model show a significant growth trend after GAIN was proposed in 2018. GAIN has been studied and improved by many scholars in their specific fields. However, few studies have systematically surveyed the GAIN model's development trends on missing data from its birth to the present, which result in a lack of comprehensive information about GAINs general performance in different fields. In this review, we summarize the development of the GAIN model in missing data imputation from 2018 to 2022. Based on the WOS database, 32 publications are selected according to the PRISMA statement. The outcome of this paper is from the following aspects: (1) analyzing the publication information and application fields quantitatively; (2) expounding the GAIN-based models, classification, and research trends; (3) elaborating the model attributes and missing data mechanism; and (4) summarizing the existing issues and proposing the future directions. Above all, this paper can help scholars gain further insight into the missing data issues and better understand the optimized directions of GAIN models. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to clarify the importance of explainability in the crop recommendation process and provide insights on how Explainable Artificial Intelligence (XAI) can be incorporated into existing models successfully. The objective is to increase the definition and transparency of the recommendations implemented by AI in smart agriculture, leading to a detailed analysis of the synchronization between crop recommendation systems and XAI that informs decisions as it has sustainable knowledge and practices in modern agriculture. It reviews state-of-the-art XAI techniques such as local interpretable model-agnostic interpretation (LIME), SHapley interpretation additive approach (SHAP), integrated gradients (IG), and level-wise relevance propagation (LRP). It focuses on interpretable models and critical features analysis, and XAI methods are discussed in terms of their applications, critical features, and definitions. The paper found that XAI methods such as LIME and SHAP can make AI-driven crop recommendation systems more transparent and reliable. Graphical techniques such as dependency plots, summary plots, waterfall graphs, and decision plots effectively analyze feature importance. The paper includes counterfactual explanations using dice ml and hearing with advanced techniques combining IG and LRP to provide in-depth narrative model behavior. The novelty of this study lies in a detailed investigation of how XAI can be incorporated into crop recommendation systems to address the "black box" nature of AI models. It uses a unique XAI technique and model approach to make AI-driven recommendations more meaningful and practical for farmers. The proposed systems and techniques are designed to consume agriculture, addressing the specific needs of intelligent systems, making this research a significant contribution to agricultural AI. [ABSTRACT FROM AUTHOR]

Demand response, and artificial intelligence integration with it, have a considerable effect in optimizing energy consumption, grid stability, and promoting sustainable energy practices. Consequently, this paper presents NeuroQuMan, a comprehensive methodology for simulating demand response using a three-Qubit quantum neural network (QNN) model. NeuroQuMan integrates quantum computing and machine learning techniques to accurately predict demand based on user reaction time. The methodology encompasses an advanced structure that includes data preprocessing, three-Qubit quantum device initialization, quantum circuit definition, user decision-making, QNN predictions, loss calculations, and visualization. During the tests, NeuroQuMan achieved considerable performance values of metrics, with RMSPE of 5.41%, MAPE of 4.43%, as well as MAE of 0.37, RMSE of 0.45, and MSE of 0.21, respectively. These metrics manifest the accuracy and effectiveness of NeuroQuMan in predicting demand response. By the side of future perspectives of the work, it explores the application of advanced quantum techniques to further enhance prediction accuracy. NeuroQuMan represents the potential of quantum computing in addressing demand response challenges and provides a pathway toward more resilient and intelligent energy management systems. The findings and framework presented in this paper are utilized to advance the field of demand response and quantum-based energy management techniques using a three-Qubit structure. [ABSTRACT FROM AUTHOR]

The fundamental objective of a modern power system lies in ensuring reliable and effective energy access for its customers. The assessment and determination of optimal operating conditions for power systems involve the utilization of the optimal power flow (OPF) tool. By considering critical factors such as generator power, bus voltages, and line power flow limits while satisfying the power balance equations, the OPF tool enables the identification of the most favorable configuration for efficient power system operation. Traditional optimization methods have limitations in addressing complex power system problems due to poor convergence and long computational times. As a result, computational intelligence tools have gained popularity in recent years. These tools are versatile and enable efficient solution of power system problems by effectively handling qualitative constraints. This paper presents a well-organized and comprehensive review of the algorithms used in power system optimization in the existing literature, encompassing the most recent developments in the field. Specifically, it examines the application of various population-based artificial intelligence techniques that have gained widespread adoption over the past decade (2012–2022). The aim of these techniques is to resolve an OPF problem. This paper organizes the reviewed papers into various types of population-based metaheuristic algorithms, each one implemented sequentially to deal with the OPF problem in the same chronological order in which they appeared in the literature. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system performance and reliability can be improved through the detection of defects in PV modules and the evaluation of their effects on system operation. In this paper, a novel system is proposed to detect and classify defects based on electroluminescence (EL) images. This system is called Fault Detection and Classification (FDC) and splits into four modules, which are (1) Image Preprocessing Module (IPM), (2) Feature Extraction Module (FEM), (3) Feature Selection Module (FSM), and (4) Classification Module (CM). In the first module (i.e., IPM), the EL images are preprocessed to enhance the quality of the images. Next, the two types of features in these images are extracted and fused together through FEM. Then, during FSM, the most important and informative features are extracted from these features using a new feature selection methodology, namely, Feature Selection-based Chaotic Map (FS-CM). FS-CM consists of two stages: filter stage using chi-square to initially select the most effective features and a modified selection stage using an enhanced version of Butterfly Optimization Algorithm (BOA). In fact, BOA is a popular swarm-based metaheuristic optimization algorithm that has only recently found success. While BOA has many benefits, it also has some drawbacks, including a smaller population and an increased likelihood of getting stuck in a local optimum. In this paper, a new methodology is proposed to improve the performance of BOA, called chaotic-based butterfly optimization algorithm. Finally, these selected features are used to feed the proposed classification model through CM. During CM, Hybrid Classification Model (HCM) is proposed. HCM consists of two stages, which are binary classification stage using Naïve Bayes (NB) and multi-class classification stage using enhanced multi-layer perceptron. According to the experimental results, the proposed system FDC outperforms the most recent methods. FDC introduced 98.2%, 89.23%, 87.2%, 87.9%, 87.55%, and 88.20% in terms of accuracy, precision, sensitivity, specificity, g-mean, and f-measure in the same order. [ABSTRACT FROM AUTHOR]

Broad-XAI moves away from interpreting individual decisions based on a single datum and aims to provide integrated explanations from multiple machine learning algorithms into a coherent explanation of an agent's behaviour that is aligned to the communication needs of the explainee. Reinforcement Learning (RL) methods, we propose, provide a potential backbone for the cognitive model required for the development of Broad-XAI. RL represents a suite of approaches that have had increasing success in solving a range of sequential decision-making problems. However, these algorithms operate as black-box problem solvers, where they obfuscate their decision-making policy through a complex array of values and functions. EXplainable RL (XRL) aims to develop techniques to extract concepts from the agent's: perception of the environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and objectives. This paper aims to introduce the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI. CXF is designed to incorporate many standard RL extensions and integrated with external ontologies and communication facilities so that the agent can answer questions that explain outcomes its decisions. This paper aims to: establish XRL as a distinct branch of XAI; introduce a conceptual framework for XRL; review existing approaches explaining agent behaviour; and identify opportunities for future research. Finally, this paper discusses how additional information can be extracted and ultimately integrated into models of communication, facilitating the development of Broad-XAI. [ABSTRACT FROM AUTHOR]

In the paper titled "A prediction-based cycle life test optimization method for cross-formula batteries using instance transfer and variable-length-input", Ma et al. deal with the prediction of the useful life of lithium-ion power-grade batteries. The practice of maintenance and asset management has been among the key beneficiaries of the industrial transition towards digitalisation. Data-driven machine learning approaches for critical assets can rarely benefit from the availability of data which cover a sufficient range of operating conditions and faults. [Extracted from the article]

High-resolution remote sensing images have become mainstream remote sensing data, but there is an obvious "salt and pepper phenomenon" in the existing semantic segmentation methods of high-resolution remote sensing images. The purpose of this paper is to propose an improved deep convolutional neural network based on HRNet and PSPNet to segment and realize deep scene analysis and improve the pixel-level semantic segmentation representation of high-resolution remote sensing images. Based on hierarchical multiscale segmentation technology research, the main method is multiband segmentation; the vegetation, buildings, roads, waters and bare land rule sets in the experimental area are established, the classification is extracted, and the category is labeled at each pixel in the image. Using the image classification network structure, different levels of feature vectors can be used to meet the judgment requirements. The HRNet and PSPNet algorithms are used to analyze the scene and obtain the category labels of all pixels in an image. Experiments have shown that artificial intelligence uses the pyramid pooling module in the classification and recognition of CCF satellite images. In the context of integrating different regions, PSPNet affects the region segmentation accuracy. FCN, DeepLab and PSPNet are now the best methods and achieve 98% accuracy. However, the PSPNet object recognition algorithm has better advantages in specific areas. Experiments show that this method has high segmentation accuracy and good generalization ability and can be used in practical engineering. [ABSTRACT FROM AUTHOR]

Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation algorithms without using cloud computing may be required to be implemented in X-ray imaging systems. This paper aims to investigate lightweight deep learning methods for dental X-ray image segmentation for the purpose of deployment on edge devices, such as dental X-ray imaging systems. A novel lightweight neural network scheme using knowledge distillation is proposed in this paper. The proposed lightweight method and a number of existing lightweight deep learning methods were trained on a panoramic dental X-ray image data set. These lightweight methods were evaluated and compared by using several accuracy metrics. The proposed lightweight method only requires 0.33 million parameters ( ∼ 7.5 megabytes) for the trained model, while it achieved the best performance in terms of IoU (0.804) and Dice (0.89) comparing to other lightweight methods. This work shows that the proposed method for dental X-ray image segmentation requires small memory storage, while it achieved comparative performance. The method could be deployed on edge devices and could potentially assist clinicians to alleviate their daily workflow and improve the quality of their analysis. [ABSTRACT FROM AUTHOR]

Historical architecture has different special styles attributed to each era, dynasty, or region. These styles are common features such as geometric properties, ratios, scales, colors, and artistic techniques. Historical geometric ornaments have an enormous capability for classification based on their geometric characteristics. Smart pattern recognition allows researchers to classify huge databases of heritage for useful internet searches. So, our main goal in this paper is to implement the detection of categories in geometric patterns for classification and documentation in which by the photography of ornaments in every monument, the type of patterns and the number of every type of pattern would be estimated quickly. Furthermore, due to occurring deterioration in these patterns, our method also contributes to recognizing the deteriorated patterns. When we encounter numerous pieces of deteriorated patterns, manual recognition in order to reassemble and reconstruct is usually impossible or time-consuming. With the aid of artificial intelligence, in this paper, our aim is to seek to solve the automatic recognition of historical geometric patterns, even patterns having deterioration as an occlusion via image processing and machine learning methods. A challenging issue that researchers would tackle in detecting historical geometric pattern's types is the variety in geometric textures, especially when they have occlusion such as deterioration. This issue leads to limited success in classifying via extracting only one feature. The other issue is that the extracted feature must be invariant to the transformation, such as scale, rotation, and noise variation. To cope with the challenges mentioned above and accurately classify, we plan to use the fusion method based on extracting global and local features. So, the features extracted from images in this research are based on local and global. In other words, the proposed fusion strategy lies both in feature and decision level, but the core is the proposed three combination methods in fusion decision methods. In this method, the dataset is composed of four main Persian geometric pattern types: Tond dah, Kond tablghenas, Hashtva 4 lengeh, and HashtvatablKond. So, the model will be trained by extracting global and local features of the images separately. Random forest, as the prevalent machine learning algorithm, is proposed for training data and predicting the class of input images. Finally, the probability of prediction for random forest classifiers is fused by the Decision Templates (DT) combiner, Naïve Bayes (NB) combiner, and Dempster–Shafer combination methods. In comparison with the individual classifier accuracy results of 80% and 85% for global and local features respectively, our proposed approach achieves an improved accuracy of 90%, 88%, and 90% in three fusion decision methods including DT combiner, NB combiner, and Dempster–Shafer combination methods. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a hybrid bio-inspired algorithm which takes the merits of whale optimization algorithm (WOA) and adaptive particle swarm optimization (APSO). The proposed algorithm is referred as the hybrid WOA_APSO algorithm. We utilize a convolutional neural network (CNN) for classification purposes. Extensive experiments are performed to evaluate the performance of the proposed model. Here, pre-processing and segmentation are performed on 120 lung CT images for obtaining the segmented tumored and non-tumored region nodule. The statistical, texture, geometrical and structural features are extracted from the processed image using different techniques. The optimized feature selection plays a crucial role in determining the accuracy of the classification algorithm. The novel variant of whale optimization algorithm and adaptive particle swarm optimization, hybrid bio-inspired WOA_APSO, is proposed for selecting optimized features. The feature selection grouping is applied by embedding linear discriminant analysis which helps in determining the reduced dimensions of subsets. Twofold performance comparisons are done. First, we compare the performance against the different classification techniques such as support vector machine, artificial neural network (ANN) and CNN. Second, the computational cost of the hybrid WOA_APSO is compared with the standard WOA and APSO algorithms. The experimental result reveals that the proposed algorithm is capable of automatic lung tumor detection and it outperforms the other state-of-the-art methods on standard quality measures such as accuracy (97.18%), sensitivity (97%) and specificity (98.66%). The results reported in this paper are encouraging; hence, these results will motivate other researchers to explore more in this direction. [ABSTRACT FROM AUTHOR]

This paper investigates how to solve image classification with Hopfield neural networks (HNNs) and oscillatory neural networks (ONNs). This is a first attempt to apply ONNs for image classification. State-of-the-art image classification networks are multi-layer models trained with supervised gradient back-propagation, which provide high-fidelity results but require high energy consumption and computational resources to be implemented. On the contrary, HNN and ONN networks are single-layer, requiring less computational resources, however, they necessitate some adaptation as they are not directly applicable for image classification. ONN is a novel brain-inspired computing paradigm that performs low-power computation and is attractive for edge artificial intelligence applications, such as image classification. In this paper, we perform image classification with HNN and ONN by exploiting their auto-associative memory (AAM) properties. We evaluate precision of HNN and ONN trained with state-of-the-art unsupervised learning algorithms. Additionally, we adapt the supervised equilibrium propagation (EP) algorithm to single-layer AAM architectures, proposing the AAM-EP. We test and validate HNN and ONN classification on images of handwritten digits using a simplified MNIST set. We find that using unsupervised learning, HNN reaches 65.2%, and ONN 59.1% precision. Moreover, we show that AAM-EP can increase HNN and ONN precision up to 67.04% for HNN and 62.6% for ONN. While intrinsically HNN and ONN are not meant for classification tasks, to the best of our knowledge, these are the best-reported precisions of HNN and ONN performing classification of images of handwritten digits. [ABSTRACT FROM AUTHOR]

Choosing the optimal portfolio is an ongoing challenging research area and a complex process involving selecting the best investment plan according to various factors, such as investor preferences for expected return, risk, and duration of investments. Although various methods have been presented so far, they failed to obtain a holistic approach to the existing data, and the need for a comprehensive mechanism based on the investor's time preferences is felt. In this paper, by considering the fundamental characteristics, technical indicators, time-series data, and budget constraints, we developed a comprehensive and time-varying methodology to forecast stock prices and form an optimal portfolio. The proposed method consists of recurrent neural networks and multi-objective mathematical programming (MOMP). In this regard, the bidirectional long short-term memory model is adopted and optimized by the particle swarm optimization (PSO) algorithm, called PSO-BiLSTM. Furthermore, the hybrid MOMP models are developed based on long-, mid-, and short-term strategies to provide the optimal portfolio of the stocks with investment constraints. The main objectives of this research were to address the following issues: (1) developing a precise and efficient model to forecast the stocks prices, taking account of fundamental characteristics, technical indicators, time-series data appropriate to the period considered by the investor, (2) providing an optimized time-varying portfolio through developing the hybrid MOMP models, and generally (3) proposing a holistic step-by-step methodology considering three groups of market data and deep learning to apply investment constraints as well as investor's time preferences in the process of building more realistic portfolios. The results highlight that the tuned PSO-BiLSTM method performs better than the conventional methods in all three constructed models using fundamental characteristics, technical indicators, and time-series data. Compared to the conventional methods, the proposed methodology outperforms in generalization power, is more precise in forecasting prices, and provides portfolios with more profit. [ABSTRACT FROM AUTHOR]

The usage of intelligent IoT devices is exponentially rising, and so the possibility of attacks in the IoT surfaces. The deep leaning algorithms are competent for directing the sanctuary investigation of IoT systems but have not upgraded the analysis of potential attacks in IoT. This paper aims to advance deep learning methods to create upgraded security strategies for IoT frameworks quickly. The study of the IoT security threats identified with inalienable or recently presented risks is done. Also, this paper does a quick examination of different possible attack surfaces for the IoT framework, and the potential risks identified with each character. The systematic survey of deep learning methods for IoT security and the existence of the chances, focal points, and weaknesses of every strategy opens the door significant for future research. [ABSTRACT FROM AUTHOR]

Automatic detection of falls is extremely important, especially in the remote monitoring of elderly people, and will become more and more critical in the future, given the constant increase in the number of older adults. Within this framework, this paper deals with the task of evaluating several artificial intelligence techniques to automatically distinguish between different activities of daily living (ADLs) and different types of falls. To do this, UniMiB SHAR, a publicly available data set containing instances of nine different ADLs and of eight kinds of falls, is considered. We take into account five different classes of classification algorithms, namely tree-based, discriminant-based, support vector machines, K-nearest neighbors, and ensemble mechanisms, and we consider several representatives for each of these classes. These are all the classes contained in the Classification Learner app contained in MATLAB, which serves as the computational basis for our experiments. As a result, we apply 22 different classification algorithms coming from artificial intelligence under a fivefold cross-validation learning strategy, with the aim to individuate which the most suitable is for this data set. The numerical results show that the algorithm with the highest classification accuracy is the ensemble based on subspace as the ensemble method and on KNN as learner type. This shows an accuracy equal to 86.0%. Its results are better than those in the other papers in the literature that face this specific 17-class problem. [ABSTRACT FROM AUTHOR]

With the introduction of ChatGPT, the public's perception of AI-generated content has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects. [ABSTRACT FROM AUTHOR]

In medical practice, all decisions, as for example the diagnosis based on the classification of images, must be made reliably and effectively. The possibility of having automatic tools helping doctors in performing these important decisions is highly welcome. Artificial Intelligence techniques, and in particular Deep Learning methods, have proven very effective on these tasks, with excellent performance in terms of classification accuracy. The problem with such methods is that they represent black boxes, so they do not provide users with an explanation of the reasons for their decisions. Confidence from medical experts in clinical decisions can increase if they receive from Artificial Intelligence tools interpretable output under the form of, e.g., explanations in natural language or visualized information. This way, the system outcome can be critically assessed by them, and they can evaluate the trustworthiness of the results. In this paper, we propose a new general-purpose method that relies on interpretability ideas. The approach is based on two successive steps, the former being a filtering scheme typically used in Content-Based Image Retrieval, whereas the latter is an evolutionary algorithm able to classify and, at the same time, automatically extract explicit knowledge under the form of a set of IF-THEN rules. This approach is tested on a set of chest X-ray images aiming at assessing the presence of COVID-19. [ABSTRACT FROM AUTHOR]

The eighth paper by Suvra Jyoti Choudhury and Nikhil R. Pal proposes three methods using neural networks and evidential reasoning for the classification of incomplete dataset. The system is realized by combining the activated sludge model, convolutional neural network, and long short-term memory neural networks implementing both knowledge and data-driven modelling. The hybridization of fuzzy inference systems with related techniques, such as neural networks, granular computing and genetic algorithms, can synergize the fuzzy reasoning process with the connectionist structure, information granules and evolving parameter learning. [Extracted from the article]

Deep neural network is an important computer operating system in China. It has made great breakthroughs in target recognition, image classification and other fields. It plays a key role in multi-system integration and intelligent manufacturing industry, such as training and testing. The purpose of this paper is to study the multi-system fusion of deep neural network and its application in intelligent manufacturing industry. By setting up experiments, the multi-system fusion of neural network is carried out, combining with big data and artificial intelligence to verify the efficient operation of neural network in multi-system fusion. Using the methods of mathematical analysis and big data fitting, the collected data are classified, the experimental data are collected and then analyzed. In this paper, the reliability of this method is verified by research; the application of multi-system fusion based on deep neural network in intelligent manufacturing industry can effectively integrate all systems, improve the comprehensive working efficiency of each system by about 20% and improve the multi-system fusion of deep neural network and its application in intelligent manufacturing industry by about 15%. Thus, as the terminal of the system, the deep neural network plays the leading role of multiple systems. It can effectively integrate the various systems and improve the comprehensive work efficiency of each system, which has guiding significance for the development of industry. [ABSTRACT FROM AUTHOR]

With the advancement in modern imaging techniques like CT scan, MRI, PET scan etc., a vast amount of data is generated every day in the field of healthcare. Big data contains hidden information, which necessitates the development of intelligent systems to analyze it and extract relevant information, allowing for accurate and cost-effective decisions in the medical field. By utilizing the untapped potential of the big data available in the medical field, very precise models can be developed for the medical diagnosis of retinal diseases. Optical coherence tomography (OCT) is a non-invasive imaging test that captures different, distinctive layers of the retina and optic nerve in a living eye to map and measure their thickness, that helps diagnose various retinal disorders. With the advancement of the application of deep learning-based techniques in the field of medical sciences, the use of convolutional neural network (CNN) based approaches for disease detection is gaining popularity. While the manual examination of 3D OCT images for the diagnosis of retinal disorders requires extensive time and expert intervention, the use of CNNs provides an effective automated option that provides results with higher accuracy while also reducing the time involved in the overall process. In this paper, we have implemented the aforementioned idea by proposing OCT-CNN, a CNN architecture, that automatically classifies retinal OCT images and identifies potential disorders in a living eye. Several techniques have been employed to enhance the performance of the proposed approach, including digital enhancement of the images, dropout regularization, adaptive learning rates, and early stopping of training to attain optimal performance. The performance of the proposed OCT-CNN is evaluated on the UCSD dataset against several popular deep CNN architectures and existing state-of-the-art approaches to automatic retinal OCT classification. The proposed OCT-CNN attains the best performance on all evaluated metrics, pushing the classification accuracies to 99.28% on CNV, 99.9% on DME, 99.38% on DRUSEN, and 100% on NORMAL images, indicating its superiority over existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm. [ABSTRACT FROM AUTHOR]

The spreading of machine learning (ML) and deep learning (DL) methods in different and critical application domains, like medicine and healthcare, introduces many opportunities but raises risks and opens ethical issues, mainly attaining to the lack of transparency. This contribution deals with the lack of transparency of ML and DL models focusing on the lack of trust in predictions and decisions generated. In this sense, this paper establishes a measure, namely Congruity, to provide information about the reliability of ML/DL model results. Congruity is defined by the lattice extracted through the formal concept analysis built on the training data. It measures how much the incoming data items are close to the ones used at the training stage of the ML and DL models. The general idea is that the reliability of trained model results is highly correlated with the similarity of input data and the training set. The objective of the paper is to demonstrate the correlation between the Congruity and the well-known Accuracy of the whole ML/DL model. Experimental results reveal that the value of correlation between Congruity and Accuracy of ML model is greater than 80% by varying ML models. [ABSTRACT FROM AUTHOR]

Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is an approach that has been proposed to combine the advantages of distributed vector representations and symbolic structured data representations in Artificial Intelligence, Machine Learning, and Pattern Recognition problems. HDC/VSA operate with hypervectors, i.e., brain-like distributed representations of large fixed dimension. The key problem of HDC/VSA is how to transform data of various types into hypervectors. In this paper, we propose a novel approach for the formation of hypervectors of spatial objects, such as images, that provides both an equivariance with respect to the shift of objects and preserves the similarity of objects described by similar features at nearby positions. In contrast to known hypervector formation methods, we represent the features by compositional hypervectors and exploit permutations of hypervectors for representing the position of features. We experimentally explored the proposed approach in some tasks that exploit various descriptions of two-dimensional (2D) images. In terms of standard accuracy measures such as error rate or mean average precision, our results are on a par or better than those of other methods and are obtained without feature learning. The proposed techniques were designed for the HDC/VSA model known as Sparse Binary Distributed Representations. However, they can be adapted to hypervectors in formats of other HDC/VSA models, as well as for representing spatial objects other than 2D images. [ABSTRACT FROM AUTHOR]

Recent progress in the area of modern technologies confirms that information is not only a commodity but can also become a tool for competition and rivalry among governments and corporations, or can be applied by ill-willed people to use it in their hate speech practices. The impact of information is overpowering and can lead to many socially undesirable phenomena, such as panic or political instability. To eliminate the threats of fake news publishing, modern computer security systems need flexible and intelligent tools. The design of models meeting the above-mentioned criteria is enabled by artificial intelligence and, above all, by the state-of-the-art neural network architectures, applied in NLP tasks. The BERT neural network belongs to this type of architectures. This paper presents Transformer-based hybrid architectures applied to create models for detecting fake news. [ABSTRACT FROM AUTHOR]

The proposed paper introduces a new optimization algorithm inspired by nature called the polar fox optimization algorithm (PFA). This algorithm addresses the herd life of polar foxes and especially their hunting method. The polar fox jumping strategy for hunting, which is performed through high hearing power, is mathematically formulated and implemented to perform optimization processes in a wide range of search spaces. The performance of the polar fox algorithm is tested with 14 classic benchmark functions. To provide a comprehensive comparison, all 14 test functions are expanded, shifted, rotated and combined for this test. For further testing, the recent CEC 2021 test's complex functions are studied in the unimodal, basic, hybrid and composition modes. Finally, the rate of convergence and computational time of PFA are also evaluated by several changes with other algorithms. Comparisons show that PFA has numerous benefits over other well-known meta-heuristic algorithms and determines the solutions with fewer control parameters. So it offers competitive and promising results. In addition, this research tests PFA performance with 6 different challenging engineering problems. Compared to the well-known meta-artist methods, the superiority of the PFA is observed from the experimental results of the proposed algorithm in real-world problem-solving. The source codes of the PFA are publicly available at https://github.com/ATR616/PFA. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the method of solving partial differential equations (PDEs) using artificial neural network (ANN) structures, which have been actively applied in artificial intelligence models. The ANN model for solving PDEs offers the advantage of providing explicit and continuous solutions. However, the ANN model for solving PDEs cannot construct a conventionally solvable linear system with known matrix solvers; thus, computational speed could be a significant concern. We study the implementation of the multigrid method, developing a general concept for a coarse-grid correction method to be integrated into the ANN-PDE architecture, with the goal of enhancing computational efficiency. By developing a reduced form of the multigrid method for ANN, we demonstrate that it can be interpreted as an equivalent representation of the classic multigrid expansion. We validated the applicability of the proposed method through rigorous experiments, which included analyzing loss decay and the number of iterations along with improvements in terms of accuracy, speed, and complexity. We accomplished this by employing the gradient descent method and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method to update the gradients while solving the given ANN systems of PDEs. [ABSTRACT FROM AUTHOR]

Saliency maps are input-resolution matrices used for visualizing local interpretations of image classifiers. Their pixel values reflect the importance of corresponding image locations for the model's decision. Despite numerous proposals on how to obtain such maps, their evaluation remains an open question. This paper presents a carefully designed experimental procedure along with a set of quantitative interpretation evaluation metrics that rely solely on the original model behavior. Previously noticed evaluation biases have been attenuated by separating locations with high and low values, considering the full saliency map resolution, and using classifiers with diverse accuracies and all the classes in the dataset. We used the proposed evaluation metrics to compare and analyze seven well-known interpretation methods. Our experiments confirm the importance of object background as well as negative saliency map pixels, and we show that the scale of their impact on the model is comparable to that of positive ones. We also demonstrate that a good class score interpretation does not necessarily imply a good probability interpretation. DeepLIFT and LRP- ϵ methods proved most successful altogether, while Grad-CAM and Ablation-CAM performed very poorly, even in the detection of positive relevance. The retention of positive values alone in the latter two methods was responsible for the inaccurate detection of irrelevant locations as well. [ABSTRACT FROM AUTHOR]

Ensuring environmental safety and regulatory compliance at Department of Energy (DOE) sites demands an efficient and reliable detection system for low-level nuclear waste (LLW). Unlike existing methods that rely on human effort, this paper explores the integration of computer vision algorithms to automate the identification of such waste across DOE facilities. We evaluate the effectiveness of multiple algorithms in classifying nuclear waste materials and their adaptability to newly emerging LLW. Our research introduces and implements five state-of-the-art computer vision models, each representing a different approach to the problem. Through rigorous experimentation and validation, we evaluate these algorithms based on performance, speed, and adaptability. The results reveal a noteworthy trade-off between detection performance and adaptability. YOLOv7 shows the best performance and requires the highest effort to detect new LLW. Conversely, OWL-ViT has lower performance than YOLOv7 and requires minimal effort to detect new LLW. The inference speed does not strongly correlate with performance or adaptability. These findings offer valuable insights into the strengths and limitations of current computer vision algorithms for LLW detection. Each developed model provides a specialized solution with distinct advantages and disadvantages, empowering DOE stakeholders to select the algorithm that aligns best with their specific needs. [ABSTRACT FROM AUTHOR]

Current maintenance models lack the technological capabilities to generate key performance indicators that optimize both critical equipment behavior and the surrounding processes. Artificial intelligence offers powerful tools for predicting and interpreting sensor data collected from such equipment, enabling continuous improvement. This paper proposes a tool that leverages deep learning to predict the remaining useful life (RUL) of a large-scale mining crusher. Additionally, the model incorporates result interpretation algorithms to analyze both training cycles and subsequent production cycles. This analysis not only identifies a process "fingerprint" but also recommends adjustments to the crusher system within the ongoing maintenance plan. By employing a dense neural network and interpretation algorithms, the proposed tool predicts the current crusher cycle's RUL and compares its interpretation graphs to the process fingerprint. This comparison identifies discrepancies, which in turn inform maintenance recommendations tailored to specific crusher components. [ABSTRACT FROM AUTHOR]

Predicting seismic events has long been a huge challenge due to intricate nature of the underlying occurrence mechanisms. The traditional approach to estimating recurrence intervals is highly reliant on prior knowledge and expert experience, which often leads to considerable error margins. Recent advances in high-performance monitoring systems and artificial intelligence (AI) achieving great successes in various areas have renewed interest in identifying failure precursors and predicting seismic events. In this paper, we propose a genetic algorithm wrapped stochastic configuration networks (GAWSCN) approach to explore seismic physics for time-to-failure prediction. Specifically, stochastic configuration networks (SCN), a novel randomized learning paradigm, is utilized to learn essential seismic information from acoustic emission signals. To further enhance the generalization capability and alleviate overfitting, a regularization technique is imposed during the SCN training to establish a robust seismic prediction model. In particular, a genetic algorithm is integrated with SCN to optimize the selection of pertinent seismic features and further improve prediction performance. The experimental results demonstrate that our GAWSCN method outperforms other machine learning methods across evaluation metrics. This study offers promising insights for geophysical hazards assessment. [ABSTRACT FROM AUTHOR]

Recent years have seen an unprecedented increase in fire incidents, resulting in severe damage to forest regions, loss of human and animal lives, and unwarranted displacement of people. Owing to these issues, artificial intelligence-based fire detection systems have been developed. These systems mostly work with camera-based inputs for fire detection and alarm generation. As fire-smoke can be seen from a distance, methods for smoke detection have also been deployed for identifying fire incidents. But detecting smoke in an outdoor environment can be challenging due to weather conditions such as haze, fog, or clouds. These challenges have led to advancements in the field, and various deep learning architectures have been suggested over the years for fire-smoke detection in normal and foggy or cloudy weather conditions. Many existing methods are either computationally expensive and demand high memory, or if they are lightweight, they suffer from lower smoke detection accuracy and high false alarms. In the present paper, a dual-channel vision transformer model 'SmokeViT' is proposed for detecting fire-smoke in an outdoor environment. These channels are interlaced with a convolutional neural network to improve the feature learning capability of the model. 'SmokeViT' shows a remarkable accuracy of more than 99% and false alarm rate below 0.20% on two publicly available smoke datasets, outperforming six state-of-the-art methods. The model architecture has only 0.4 million parameters and 0.17 giga floating-point operations, making it suitable for deployment on resource-constrained surveillance devices. [ABSTRACT FROM AUTHOR]

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model's performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection. [ABSTRACT FROM AUTHOR]

We aim to investigate how closely neural networks (NNs) mimic human thinking. As a step in this direction, we study the behavior of artificial neuron(s) that fire most when the input data score high on some specific emergent concepts. In this paper, we focus on music, where the emergent concepts are those of rhythm, pitch and melody as commonly used by humans. As a black box to pry open, we focus on Google's MusicVAE, a pre-trained NN that handles music tracks by encoding them in terms of 512 latent variables. We show that several hundreds of these latent variables are "irrelevant" in the sense that can be set to zero with minimal impact on the reconstruction accuracy. The remaining few dozens of latent variables can be sorted by order of relevance by comparing their variance. We show that the first few most relevant variables, and only those, correlate highly with dozens of human-defined measures that describe rhythm and pitch in music pieces, thereby efficiently encapsulating many of these human-understandable concepts in a few nonlinear variables. [ABSTRACT FROM AUTHOR]

The fashion industry's traditional price-setting methods, based on historical sales and Fashion Week trends, are inadequate in the digital era. Rapid changes in collections and consumer preferences necessitate advanced Artificial Intelligence (AI) techniques. These AI methods should analyze data from various sources, including social media and e-commerce, to predict future fashion trends and prices. In this paper, we propose, apply, and assess a data analytics approach, i.e., FashionXpert, employing several image processing and machine learning techniques in an AI pipeline for garment price prediction. It integrates various heterogeneous data sources (e.g., textual and image data from e-stores, brand websites, and social media) to obtain more consistent, accurate, and beneficial information. We evaluated its effectiveness with an industrial data set obtained by a fashion search tool from the electronic commerce sites of clothing brands. FashionXpert predicted garment prices with an average Mean Absolute Error (MAE) of 15.31 EUR on a data set that has a standard deviation of 72.99 EUR. [ABSTRACT FROM AUTHOR]

In recent years, with the change of lifestyle in Europe and America, the incidence of breast cancer in Chinese women is increasing. In order to find the model of breast cancer image screening and diagnosis with higher accuracy and better classification performance, this paper mainly constructs the breast cancer CT image detection model and the breast cancer screening model based on the convolution and deconvolution neural network (CDNN) through the convolution neural network (CNN). In this paper, the fuzzy C-means clustering algorithm (FCM) is used to improve and optimize the image of breast cancer, and the experimental results are analyzed. The optimized kernel fuzzy C-means clustering algorithm was tested on a common dataset to segment the region of interest more accurately. Our experiments show that the new deep learning model of this paper improves the automatic classification performance of breast cancer. In this paper, the research results of deep learning are applied to the medical field, and a new method based on CNN model for breast cancer screening and diagnosis is proposed, which provides a new idea for improving the artificial intelligence assisted medical diagnosis method. [ABSTRACT FROM AUTHOR]

This paper proposes the neural network (NN) predictive controller that combines the advantages of NN and predictive control for the automatic voltage regulator (AVR). The NN predictive controller is suggested as a new intelligence controller rather than the conventional controllers for the AVR. This is the first application of the NN predictive controller for AVR. There are five parameters of the NN predictive controller which need a proper tuning to get a good performance by using the NN predictive controller. In recent papers, the parameters of NN predictive controller are typically set by trial and error or by the designer's expertise. The imperialist competitive algorithm (ICA) is introduced in this paper as a new artificial intelligence technique instead of the trial-and-error or the designer's expertise methods to get the optimal parameters of NN predictive controller in order to overcome the deviations of the voltage. The performance of the designed NN predictive controller based on the ICA is compared with the designed NN predictive controller based on the genetic algorithm and the conventional proportional–integral–derivative controller based on Ziegler–Nichols technique. The comparison emphasizes the superiority of the suggested NN predictive controller based on the ICA. [ABSTRACT FROM AUTHOR]

The paper is a comprehensive review of the current research trends in the area of Arabic language especially state-of-the-art approaches to highlight the current status of diverse research aspects of that area to facilitate the adaption and extension of previous systems into new applications and systems. The Arabic language has deep, widespread and unexplored scope to research although the tremendous effort and researches that had been done previously. Modern state-of-the-art methods and approaches with fewer errors are required according to the high speed of hardware and technology development. The focus of this article will be on the offline Arabic handwritten text recognition as it is one of the most important topics in the Arabic scope. The main objective of this paper is critically analyzing the current researches to identify the problem areas and challenges faced by the previous researchers. This identification is intended to provide many recommendations for future advances in the area. It also compares and contrasts technical challenges, methods and the performances of handwritten text recognition previous researches works. It summarizes the critical problems and enumerates issues that should be considered when addressing these tasks. It also shows some of the Arabic datasets that can be used as inputs and benchmarks for training, testing and comparisons. Finally, it provides a fundamental comparison and discussion of some of the remaining open problems and trends in that field. [ABSTRACT FROM AUTHOR]

The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis, but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison with the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity. [ABSTRACT FROM AUTHOR]

Healthcare professionals, patients, and other stakeholders have been storing medical prescriptions and other relevant reports electronically. These reports contain the personal information of the patients, which is sensitive data. Therefore, there exists a need to store these records in a decentralized model (using IPFS and Ethereum decentralized application) to provide data and identity protection. Many patients recurrently visit doctors and undergo treatments while receiving different prescriptions and reports. In case of an emergency, the doctors and attendants may need and benefit from the patients' medical history. However, they are unable to go through medical history and a wide range of previous reports and prescriptions due to time constraints. In this paper, we propose an AI-assisted blockchain-based framework in which the stored medical records (handwritten prescriptions, printed prescriptions, and printed reports) are stored and processed using various AI techniques like optical character recognition (OCR) to form a single patient medical history report. The report concisely presents only the crucial information for convenience and perusal and is stored securely over a decentralized blockchain network for later use. [ABSTRACT FROM AUTHOR]

Process monitoring of industrial production has always been one of the main concerns of process industry systems. As artificial intelligence booms, fault detection and diagnosis via deep learning has been widely used in industrial process monitoring, such as chemical process monitoring flow. Whereas, the current deep framework is difficult to simultaneously reconcile the relationship between different variables and time series caused by the increasing complexity of industrial systems, which reduces the accuracy of fault detection and diagnosis. Considering the significant time series processing potential of long short-term memory (LSTM) networks, this paper proposes a novel two-view LSTM variational auto-encoder, aliased as TVAE. First, the running data in period T is obtained through sliding window, then it utilizes two-view embedding to compress time series information and capture the correlation between time variables, after that it is judged whether it is fault data according to the anomaly score of the variational auto-encoder. Finally, since the encoding process makes the time-dependent and variable dependent features more obvious, the fault characteristic of the encoded data can be obtained by neuron grouping convolution, and then classified for diagnosis. Experimental results on Tennessee Eastman process benchmark show that TVAE is superior to the state-of-the-art methods and provides a new paradigm for industrial process fault detection and diagnosis. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (I-IoT) has become an emerging driver to operate industrial systems and a primary empowerer to future industries. With the advanced technologies such as artificial intelligence (AI) and machine learning widely used in IoT, the Industrial IoT is also witnessing changes driven by new technologies. Generally, AI technologies require centralized data collection and processing to learn from the data to obtain viable models for application. In industrial IoT, data security and privacy problems associated with reliable and interconnected end devices are being faced and reliable solutions are urgently needed. A trusted execution environment in IoT devices is gradually becoming a feasible approach, and a distributed solution is a natural choice for artificial intelligence technologies in I-IoT. Moreover, Federated Learning as a distributed machine learning paradigm with privacy-preserving properties can be used in I-IoT. This paper introduces a feasible secure data circulation and sharing scheme for I-IoT devices in a trusted implementation platform by employing federated learning. The suggested framework has proved to be efficient, reliable, and accurate. [ABSTRACT FROM AUTHOR]