This article presents a method for the spatial identification of objects for bionic upper limb prostheses, utilizing the analysis of digital images captured by an optoelectronic module based on the ESP32-CAM and classified using neural network algorithms, specifically FOMO (MobileNetV2). Modern bionic prostheses that imitate natural limb functions, as well as their advantages and significance for restoring the functionality of the human body, are analysed. An algorithm for a grip-type recognition system is proposed, integrating spatial identification of object shapes with the analysis of myographic signals to enable accurate selection and execution of appropriate manipulations. The neural network was trained on a set of images of basic shapes (spherical, rectangular, cylindrical), which achieved an average identification accuracy of over 89% with a processing time of one image of 2 ms. Due to its compactness and low cost, the developed system is suitable for integration into low-cost prostheses, ensuring adaptation of the movements of the artificial limb to the shape of the objects of manipulation and minimizing the risk of slipping objects. The proposed approach helps to increase the accuracy of movement execution and reduce dependence on expensive and complex technologies. The system has potential for further improvement, as it can operate with objects of complex shapes and handle scenarios involving multiple objects within the camera's field of view simultaneously.

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This study investigates the feasibility of using the extended finite element method (XFEM) in the ABAQUS commercial software, employing the maximum principal stress as the damage parameter. The primary objective of this work is to calculate the mode I stress intensity factor, a key parameter for understanding the crack initiation mechanisms in pressurized pipelines. Initially, an analysis of Von Mises stresses was conducted, followed by a theoretical calculation of stress intensity factors based on analytical methods from the literature. The results were compared with those obtained from numerical simulations using XFEM. Validation of the findings was also carried out by benchmarking them against previous studies employing the classical finite element method (FEM). Additionally, various parameters, such as internal pressure and initial crack length, were examined to assess their impact on the fatigue behavior of the structure. The numerical and analytical results demonstrated strong agreement, highlighting the robustness of the XFEM approach for the analysis of cracked structures. This study aims to enhance the understanding of longitudinal crack initiation mechanisms in pipelines to facilitate the development of a proactive maintenance strategy that ensures their durability and reliability.

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Recent advancements have shown that shallow and deep learning models achieve impressive performance accuracies of over 97% and 98%, respectively, in providing precise evidence for malaria control and diagnosis. This effectiveness highlights the importance of these models in enhancing our understanding of malaria management, which includes critical areas such as malaria control, diagnosis and the economic evaluation of the malaria burden. By leveraging predictive systems and models, significant opportunities for eradicating malaria, empowering informed decision-making and facilitating the development of effective policies could be established. However, as the global malaria burden is approximated at 95%, there is a pressing need for its eradication to facilitate the achievement of SDG targets related to good health and well-being. This paper presents a scoping review covering the years 2018 to 2024, utilizing the PRISMA-ScR protocol, with articles retrieved from three scholarly databases: Science Direct (9%), PubMed (41%), and Google Scholar (50%). After applying the exclusion and inclusion criteria, a final list of 61 articles was extracted for review. The results reveal a decline in research on shallow machine learning techniques for malaria control, while a steady increase in deep learning approaches has been noted, particularly as the volume and dimensionality of data continue to grow. In conclusion, there is a clear need to utilize machine learning algorithms through real-time data collection, model development, and deployment for evidence-based recommendations in effective malaria control and diagnosis. Future research directions should focus on standardized methodologies to effectively investigate both shallow and deep learning models.

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This study aims to provide an overall view of the current status of AI publications in the entire field of oncology, encompassing productivity, emerging trends, and researchers’ sentiments. A total of 1,296 papers published between January 2019 and January 2024, were selected using the PRISMA framework. Citespace software and the R package “Biblioshiny” were utilized for bibliographic analysis. China has been the leading contributor to global production with over 2,596 publications, followed by Europe. Among 8339 authors, Kather JN was the third most prolific author and held a central position in the co-authorship network. The most prominent article emphasized the Explainability of AI methods (XAI) with a profound discussion of their potential implications and privacy in data fusion contexts. Current trends involve the utilization of supervised learning methods such as CNN, Bayesian networks, and extreme learning machines for various cancers, particularly breast, lung, brain, and skin cancer. Late image-omics fusion was the focus of various studies during 2023. Recent advancements include the use of "conductive hydrogels" and "carbon nanotubes" for flexible electronic sensors. Ninety and a half percent of the researchers viewed these advancements positively. To our knowledge, this study is the first in the field to utilize merged databases from WoS, Scopus, and PubMed. Supervised ML methods, Multimodal DL, chatbots, and intelligent wearable devices have garnered significant interest from the scientific community. However, issues related to data-sharing and the generalizability of AI algorithms are still prevalent.

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Artificial Intelligence (AI) is now involved in almost every field of activity, with its expansion driven by the significant benefits it brings to our daily activities. This research paper examines the evolution and impact of AI applications in market analysis through a comprehensive bibliometric study. To the best of our knowledge, this paper is unique by considering various papers related to market analysis, including market trend analysis, market segmentation, consumer behavior, and competitive analysis in this bibliometric study. It also identifies global regions where AI techniques are most extensively developed for these purposes. This research is based on 4,051 relevant documents related to AI and market analysis, published in the Scopus database over the last thirty-five years. The findings indicate a significant exponential increase in scientific output related to AI applications in market analysis, particularly started from 2010. The countries leading in AI-driven market analysis research include India, China, and the USA.

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The aim of this study is to demonstrate the capabilities of the Jamovi software in analyzing complex economic models, using the case of examining the impact of various factors on enterprise innovation activity. Structural Equation Modeling (SEM) was employed to identify both direct and mediated relationships among economic and digital infrastructure, factors related to research and development (R&D), and innovation activity. The analysis results confirmed the positive impact of economic infrastructure on the level of R&D and the innovation activity of enterprises, as well as the mediating role of R&D factors in transmitting the effects of infrastructure. Meanwhile, the influence of digital infrastructure was found to be weak, highlighting the need for further development of digital technologies and their integration into economic activities. The findings and methodology of the study can be utilized to enhance the competitiveness of enterprises and to develop effective measures for state support of innovation activities in various regions.

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The increasing sophistication of cyber threats poses significant challenges to network security. This makes effective intrusion detection system (IDS) more important than ever before. Conventional IDS methods, which often rely on signatures or rules it will struggle to keep up with its complex attacks and evolution. This thesis evaluates and analyze the performance of DL algorithms. They include convolutional neural networks (CNN), recurrent neural networks (RNN), deep belief networks (DBN), and Auto-encoder. Using the models, these models are trained and tested only on the NSL-set. KDD data, which is a widely accepted benchmark for evaluating IDS performance. Results show that the proposed deep learning approach significantly outperforms traditional methods, has a higher detection rate, reduce the false positive rate and the ability to identify both known and unknown intrusions. They leverage the strengths of CNN, RNN, DBN, and autoencoders. Doing this research Advances IDS capabilities by providing a robust and adaptable solution to enhance network security.

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Diabetes is a disruption in metabolism that leads to elevated levels of glucose in the bloodstream and causes many other problems, such as stroke, kidney failure, heart, and nerve issues that are of serious concern globally. Because many researchers have attempted to build accurate Diabetes prediction models, this field has seen significant advancements. Nevertheless, performance issues are still a substantial challenge in model building. Machine Learning techniques have shown strong performance in prediction and classification tasks. Unfortunately, they often encounter challenges due to noisy features and high feature space dimensionality, significantly affecting Diabetes prediction performance. To address the problems, we can employ metaheuristic algorithm-based feature selection. However, there has been limited research on metaheuristic algorithm-based feature selections for Diabetes prediction. Therefore, this paper presents a systematic literature review of Diabetes prediction using metaheuristic algorithm-based feature selections. The data used in this study is the last ten years of published articles from 2014 to 2024. For this extensive investigation, 50 scholarly papers were gathered and analyzed to extract meaningful information about metaheuristic algorithm-based feature selections. This paper reviews metaheuristic algorithm-based feature selection, focusing on the algorithms used and the challenges faced in diabetes prediction.

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Requirements for product traceability in certain industrial sectors make Production Flow Control Systems (PFC) a desirable component in the operation of production enterprises. Such a system serves as a valuable tool for companies by preventing defective products from being sent to customers, enabling the automatic blocking of defective parts once the cause of the defect is identified. This article discusses a proposed PFC system toolset that meets fundamental industrial requirements in the field of discrete manufacturing of mechanical products. The system integrates key elements such as rework, disassembly, single non-controlled stations, as well as various essential and optional software applications and modules.

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The current technological evolution has resulted in changes in the global business landscape. The technological changes have resulted in an organization's business processes changing due to increased customer expectations that have also changed service standards with customer management tools. The adoption of new technologies, namely artificial intelligence, has become an innovative approach strategy to Customer Relationship Management for organizational sustainability. This article aims to provide literature about integrating AI into CRM using a systematic literature review with bibliometric analysis to explore the latest trends in CRM development influenced by AI technology and the benefits and challenges faced by organizations from AI technology. This study is essential for any industry that adopts AI technology in CRM in organizations. This research was conducted by reading and analyzing 25 articles and papers related to AI and CRM. The 25 studies will be processed using the Vos Viewer tool to visualize the development of CRM trends influenced by AI technology. The study found the benefits of integrating AI-CRM into organizations by increasing operational efficiency and effectiveness, increasing customer interaction, and personalizing services. However, organizations face challenges that require aligning AI-CRM models with their specific needs, cultural transformation, and balancing the roles and responsibilities of humans and AI within CRM operations. In conclusion, the study found that a thorough understanding of the impact and purpose of the role of AI-CRM is needed, conditioned by the organizational situation, to maximize the benefits and minimize the risks of integrating AI with CRM in an organization.

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Poor pavement condition leads to increased risk of accidents, vehicle damage, and reduced transportation efficiency. The author points out that traditional methods of monitoring road conditions are time-consuming and costly, so a modern approach based on the use of developed neural network model is presented. The main aim of this paper is to create a model that can infer in real time, with less computing power and maintaining or improving the metrics of the base model, YOLOv8. Based on this assumption, the architecture of the LANA-YOLOv8 (Large Kernel Attention Involution Asymptotic Feature Pyramid) is proposed. The model's architecture is tailored to operate in environments with limited resources, including single-board minicomputers. In addition, the article presents Basic Involution Block (BIB) that uses the involution layer to provide better performance at a lower cost than convolution layers. The model was compared with other architectures on a public dataset as well as on a dataset specially created for these purposes. The developed solution has lower computing power requirements, which translates into faster inference times. At the same time, the developed model achieved better results in validation tests against the base model.

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