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The integration of artificial intelligence (AI) into various sectors is crucial for economic development and social progress. Even though AI brings many advantages, it still entails numerous challenges and concerns regarding its use and the consequences of such use. First and foremost, this concerns a fundamental change in the labour market, all labour relations, and the rights of employees and employers. This study aims to examine the implications of AI adoption in labour practices and the adaptability of European countries’ legal systems to it. The study provides insights into effective approaches to regulating AI and protecting labour rights by analysing the experiences of Germany, Finland, and Poland. The findings indicate that Germany and Finland adopt proactive approaches to AI regulation, prioritising stakeholder engagement and balanced frameworks. Conversely, Poland’s emphasis on upskilling workers and Ukraine’s commitment to aligning with EU standards underscore the significance of addressing labour rights and economic competitiveness in the context of AI integration. The study highlights the significance of proactive legislation, stakeholder engagement, and skill development initiatives in addressing the challenges and maximising the benefits of AI in the labour sector.

The digitization of important documents and their segregation can be a beneficial and time-saving activity as individuals will have greater access to important documents and will be able to use them in regular tasks as well as endeavours. In recent years, research into the application of deep networks in robot systems has increased as a direct consequence of the advancements made in classification algorithms over the past few decades. Robotic vision automation for the segregation of sensitive and non-sensitive documents is required for many security concerns. The methodology of this article is initially focused on the identification of a good computer vision-based technique for the classification of sensitive documents from non-sensitive documents. The authors first identified the standard parameters in terms of reliability, loss, precision, and recall by employing deep learning techniques, such as neural networks with convolutions and transfer learning (TL) algorithms. The extraction of features based on pre-trained deep learning models was referenced in numerous publications. Similarly, we applied most of the feature extraction techniques to identify feature extraction from the images. Then, these features were classified by machine and ensemble learning models. However, the pre-trained models-based feature extraction along with machine learning classification resulted better in comparison to the deep learning and TL procedures. Further, the better-identified techniques were applied as the brain behind the vision of a robotic structure to automate the segregation of sensitive documents from non-sensitive documents. This proposed robotic structure could be applied when we have to find some specific and classified document from the haystack.

The use of interactive tools, such as voice assistants and social robots, holds promise as coaching aids during public speaking rehearsals. To create a coach that is both effective and likable, it is important to understand how people perceive these agents when they observe them during actual presentation sessions. Specifically, it is important to assess people’s perceptions of the agents’ physical embodiment and nonverbal social behaviour, taking into account both listening and feedback periods. To this end, we conducted an online study with 168 participants who watched videos of agents acting as public speaking coaches. The study had three conditions: two with a humanoid social robot in either (1) active listening mode, using nonverbal backchannelling, (2) passive listening mode, and (3) a voice assistant agent. The results showed that the social robot in both conditions was perceived more positively in terms of its human-like attributes, and likability than the voice assistant agent. The active listener robot was perceived as more satisfying, more engaging, more natural, and warmer than the voice assistant agent, but this difference was not seen between the passive listener robot and the voice assistant agent. Additionally, the active listener robot was found to be more natural than the passive listening robot. However, there were no significant differences in perceived intelligence, competence, discomfort, and helpfulness between the three agents. Finally, participants’ gender and personality traits were found to affect their evaluations of the agents. The study offered insights into general attitudes towards using social robots and voice assistants as public speaking coaches, which can guide the future design and use of these agents as coaches.

In order to better realize the optimal trajectory planning and trajectory control in industrial robots, a method based on ADAM algorithm is proposed. Taking PUMA 560 industrial robot as the research object, using nonlinear data sets and mathematical ADAM algorithm function planning, an optimal calculation method for time trajectory planning of industrial robot is explored. Finally, the programming, optimization, and simulation of the program code are implemented using MATLAB, and a standardized optimal trajectory planning is established. The experimental results show that the running time difference of the trajectory corresponding to the three joint points is small. In order to synchronize the position of each joint point in time, it is necessary to choose the optimal joint point position according to the time trajectory, so as to ensure the synchronization between each key node. Therefore, the joint node position is adjusted so that the total time and the final simulation results are basically synchronized in time, and both are 10.35 s. It proves that the improved ADAM algorithm realizes the trajectory optimization of industrial robots in terms of time planning, which can make the various joints of industrial robots basically synchronized in the time trajectory.

This study addresses the challenge of low recognition rates in emotion recognition systems, attributed to the vulnerability of sound data to ambient noise. To overcome this limitation, we propose a novel approach that leverages emotional information from diverse modalities. Our method integrates speech and facial expressions through advanced feature layer fusion and decision layer fusion strategies. Unlike traditional fusion algorithms, our proposed multimodal emotion recognition algorithm incorporates a dual fusion process at both the feature layer and the decision layer. This dual fusion not only preserves the distinctive characteristics of emotional information across modalities but also maintains inter-modal correlations. To evaluate the effectiveness of our approach, experiments were conducted using the eNTERFACE’05 multimodal emotion database. The results demonstrate a remarkable recognition accuracy of 89.3%, surpassing the highest recognition rate of 83.92% achieved by the current state-of-the-art kernel space feature fusion method. Our algorithm exhibits a significant improvement of 5.38% in recognition accuracy. By combining emotional data from speech and facial expressions using a data fusion methodology, our study demonstrates a significant improvement of 5.38% in recognition accuracy, contributing to the progress of multimodal emotion recognition systems.

This study investigates automatic sorting and handling robots to enhance intelligence and automation in logistics distribution, improve work efficiency, and reduce logistics costs. The kinematics and dynamics models of the sorting and handling robot are established by the study using single-chip microcomputer control technology. For automated item sorting, radio frequency identification (RFID) card scanning technology is combined and the PID control algorithm is examined. A single-chip microcomputer control technology is used to establish the robot’s kinematics and dynamics models and analyze the PID control algorithm. Employing RFID card reading technology facilitates the transportation and automatic sorting of items. The MATLAB software simulates PID parameters and assesses the stability of PID-controlled motors. The primary controller samples and provides feedback from the motor encoder every 10 ms. Setting the left and right motor encoder’s given values to 40 with a corresponding speed of 40, we observe that Option 1’s P parameter is too small, leading to a slow adjustment speed. In contrast, Option 1’s parameters are extensive. Scheme 3’s P parameter is too large, risking system oscillation. After carefully adjusting and selecting Scheme 2, experimental verification demonstrates the sorting and handling robot’s stable operation, achieving the anticipated effect.

In this work, a method that integrates deep learning and genetic algorithms is proposed to enhance the precision and efficiency of welding robots and achieve optimal robot path planning. The process involves using SolidWorks to create a 3D model, applying the D-H method to obtain data on the connecting rod parameters, performing theoretical calculations for both forward and inverse kinematics solutions, and utilizing the MATLAB robotics toolbox to validate these solutions. Furthermore, joint space trajectory planning is performed using the quintic polynomial curve method. Through analysis, we identified that abrupt acceleration changes at the initial and final positions significantly impact the smoothness of the motion process. The findings reveal that traditional artificial bee colonies tend to stabilize after 190 iterations, whereas genetic algorithms stabilize around 160 iterations, demonstrating superior convergence speed compared to the traditional ABC algorithm. The optimized approach yields an optimal welding obstacle avoidance path with rapid optimization speed and a stable process. The proposed method effectively addresses the obstacle avoidance path planning challenge for welding robots, showcasing improved convergence speed and stability compared to traditional methods.

In recent days, stress is a major phenomenon that adversely affects both individuals and communities. The research in computing the stress factor has wider advantages as it improves personal learning, learning operations, and high productivity that benefits society. Several computational techniques come into concern to avoid and reduce the stress level using the electrocardiogram (ECG) signals. In this study, the stress level was classified using the feature extraction approach in combination with the classifier. The signal is processed using the variational mode decomposition denoising technique to reconstruct the original signal. The decomposed signal was further extracted using the time–frequency domain technique as characteristics of the ECG signal such as R-wave and T-wave constructed. Further, the support vector machine classifier was used to classify the stress level (low, medium, and high) of the extracted signal. Based on stress classification outcomes, the robot offers a range of personalized interventions to users. These interventions include relaxation exercises, deep breathing techniques, or guided mindfulness sessions. The average accuracy obtained using the proposed technique is 98.98% but without using the feature extraction technique, it is 97.71%. The other performance parameters also get improved and the results are finally compared with the existing techniques.