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Frequent pattern mining (FIM) identifies the most important patterns in data sets. However, due to the huge and high-dimensional nature of transactional data, classical pattern mining techniques suffer from the limitations of dimensions and data annotations. Recently, data mining while preserving privacy is considered as an important research area. Information privacy is a tradeoff that must be considered when using data. Through many years, privacy-preserving data mining (PPDM) made use of methods that are mostly based on heuristics. The operation of deletion was used to hide the sensitive information in PPDM. In this study, we used deep active learning to protect private and sensitive information. This paper combines entropy-based active learning with an attention-based approach to effectively hide sensitive patterns. The constructed models are then validated using high-dimensional transactional data with attention-based and active learning methods in a reinforcement environment. The results show that the proposed model can support and improve the effectiveness of decision-making by increasing the number of training instances through the use of a pooling technique and an entropy uncertainty measure. The proposed paradigm can achieve data sanitization by the hiding sensitive items and avoiding to hide the non-sensitive items. The model outperforms greedy, genetic, and particle swarm optimization approaches. [ABSTRACT FROM AUTHOR]

Deep learning methods have led to the state-of-the-art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders for further collaboration. However, limited labeled data set limits the deep learning algorithms to be generalized for one domain into another. To handle the problem, meta-learning helps to solve this issue especially it can learn from a small set of data. We proposed a meta-learning-based image segmentation model that combines the learning of the state-of-the-art models and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segment part and remove noise from the new test images. The proposed model can achieve 0.94 precision and 0.92 recall. The ability is to increase 3.3% among the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

BACKGROUND: In recent years, social media have filtered our life both in the professional and personal aspects. Currently, most of us suffer from poor quality of thinking, which is due to the impact of social media towards our lives, particularly in the health care arena. OBJECTIVES: In this article, cultural tension due to social media creates an unwanted risk to the youngsters and others with sleep deprivation. They become dependent on staying dynamic via social networking sites media all the time. As indicated by an ongoing report, there is a reliable connection between the measure of time spent via web-based networking media and depression among youthful grown-ups, which creates unprofessional problems and potential healthcare risk in individuals due to the usage of social media. RESULTS: This article speaks about the research gap and possible risks reforming strategies on healthcare communication in social media through statistical analysis. CONCLUSION: The experimental validation of case studies shows prominent solutions that have not been addressed in traditional methods. [ABSTRACT FROM AUTHOR]

BACKGROUND: The manufacturing industry undergoes a new age, with significant changes taking place on several fronts. Companies devoted to digital transformation take their future plants inspired by the Internet of Things (IoT). The IoT is a worldwide network of interrelated physical devices, which is an essential component of the internet, including sensors, actuators, smart apps, computers, mechanical machines, and people. The effective allocation of the computing resources and the carrier is critical in the Industrial Internet of Things (IIoT) for smart production systems. Indeed, the existing assignment method in the smart production system cannot guarantee that resources meet the inherently complex and volatile requirements of the user are timely. Many research results on resource allocations in auction formats which have been implemented to consider the demand and real-time supply for smart development resources, but safety privacy and trust estimation issues related to these outcomes are not actively discussed. OBJECTIVES: The paper proposes a Hierarchical Trustful Resource Assignment (HTRA) and Trust Computing Algorithm (TCA) based on Vickrey Clarke-Groves (VGCs) in the computer carriers necessary resources to communicate wirelessly among IIoT devices and gateways, and the allocation of CPU resources for processing information at the CPC. RESULTS: Finally, experimental findings demonstrate that when the IIoT equipment and gateways are valid, the utilities of each participant are improved. CONCLUSION: This is an easy and powerful method to guarantee that intelligent manufacturing components genuinely work for their purposes, which want to integrate each element into a system without interactions with each other. [ABSTRACT FROM AUTHOR]

BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease. OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process. RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset. CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall. [ABSTRACT FROM AUTHOR]

BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system. OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements. RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time. CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate. [ABSTRACT FROM AUTHOR]

BACKGROUND: Robots communicate with the physical world program with the mechanic's simulations. They recommend that people-to-people robotics will prepare for cognitive models. Presently, there is a considerable concern for greater flexibility and efficiency in the scope of human-robot interfacing collaboration across hospitals. Nevertheless, interfacing is still in its infancy in manufacturing; industrial practitioners have many questions and doubts about the efficiency of the device and the health of human operators. OBJECTIVES: Therefore, research on processes and methods of design is required to ensure that the intended human-computer interaction-based workstations effectively meet system performance, human safety, and ergonomics standards for realistic applications. This study provides a design process for a workstation appropriate for occupational health and safety. This article outlines the perspectives learned from incorporation into the preparation and operation of robotics of digital cognitive models. RESULTS: This ends with an overarching game-theoretical model of contact and analyses how different approaches contribute to effective communicating activities for the robot in its interaction with people. CONCLUSION: The new feature of this design process is the approach for testing alternative workstation designs, taking into account efficiency and safety features with computer simulations. [ABSTRACT FROM AUTHOR]

BACKGROUND: In recent years, several tracker systems have been developed to monitor a 3-dimensional skull position for facial action whereas, various tracker systems simultaneously analyze the single sequence of video, which can be provided with low-quality cameras and less security. Initially, implementing a 2-D face detector and an unrepentance system has been suggested; furthermore, it has been improved using an integrated 3-D face initialized scheme for the real-time tracker in the present face recognition systems. OBJECTIVES: To overcome the present setbacks of the conventional systems, Face Pose Detection assisted controlled FACE++ tool position of Three-Dimensional Robot (FPLF3D) has been proposed in this article. Furthermore, the suggested proposed configuration has a high-end monitoring approach, which is used to improve the reliability of the robot's human-machine contact in the workplace environment for security assistance. Additionally, the robot's direction can be controlled by the operator's head position assessment of the camera (or any active viewing system) using a three-dimensional robot. RESULTS: Besides, the applications that are imitated by headers like telepresence, computer-generated reality, and video competitions will directly take advantage of the strategies introduced in this paper. CONCLUSION: Finally, real video tests at the lab-scale level show the accuracy and usefulness of the approaches proposed in this research outperform the existing methods used for tracking. [ABSTRACT FROM AUTHOR]

BACKGROUND: For campus workplace secure text mining, robotic assistance with feature optimization is essential. The space model of the vector is usually used to represent texts. Besides, there are still two drawbacks to this basic approach: the curse and lack of semantic knowledge. OBJECTIVES: This paper proposes a new Meta-Heuristic Feature Optimization (MHFO) method for data security in the campus workplace with robotic assistance. Firstly, the terms of the space vector model have been mapped to the concepts of data protection ontology, which statistically calculate conceptual frequency weights by term various weights. Furthermore, according to the designs of data protection ontology, the weight of theoretical identification is allocated. The dimensionality of functional areas is reduced significantly by combining standard frequency weights and weights based on data protection ontology. In addition, semantic knowledge is integrated into this process. RESULTS: The results show that the development of the characteristics of this process significantly improves campus workplace secure text mining. CONCLUSION: The experimental results show that the development of the features of the concept hierarchy structure process significantly enhances data security of campus workplace text mining with robotic assistance. [ABSTRACT FROM AUTHOR]

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities' security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains. [ABSTRACT FROM AUTHOR]

BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users. OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection. RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs. CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses. [ABSTRACT FROM AUTHOR]

BACKGROUND: The selection of orders is the method of gathering the parts needed to assemble the final products from storage sites. Kitting is the name of a ready-to-use package or a parts kit, flexible robotic systems will significantly help the industry to improve the performance of this activity. In reality, despite some other limitations on the complexity of components and component characteristics, the technological advances in recent years in robotics and artificial intelligence allows the treatment of a wide range of items. OBJECTIVE: In this article, we study the robotic kitting system with a Robotic Mounted Rail Arm System (RMRAS), which travels narrowly to choose the elements. RESULTS: The objective is to evaluate the efficiency of a robotic kitting system in cycle times through modeling of the elementary kitting operations that the robot performs (pick and room, move, change tools, etc.). The experimental results show that the proposed method enhances the performance and efficiency ratio when compared to other existing methods. CONCLUSION: This study with the manufacturer can help him assess the robotic area performance in a given design (layout and picking a policy, etc.) as part of an ongoing project on automation of kitting operations. [ABSTRACT FROM AUTHOR]

BACKGROUND: An isolated robot must take account of uncertainty in its world model and adapt its activities to take into account such as uncertainty. In the same way, a robot interaction with security and privacy issues (RISAPI) with people has to account for its confusion about the human internal state, as well as how this state will shift as humans respond to the robot. OBJECTIVES: This paper discusses RISAPI of our original work in the field, which shows how probabilistic planning and system theory algorithms in workplace robotic systems that work with people can allow for that reasoning using a security robot system. The problem is a general way as an incomplete knowledge 2-player game. RESULTS: In this general framework, the various hypotheses and these contribute to thrilling and complex robot behavior through real-time interaction, which transforms actual human subjects into a spectrum of production systems, robots, and care facilities. CONCLUSION: The models of the internal human situation, in which robots can be designed efficiently, are limited, and achieve optimal computational intractability in large, high-dimensional spaces. To achieve this, versatile, lightweight portrayals of the human inner state and modern algorithms offer great hope for reasoning. [ABSTRACT FROM AUTHOR]

BACKGROUND: Nowadays, workplace violence is found to be a mental health hazard and considered a crucial topic. The collaboration between robots and humans is increasing with the growth of Industry 4.0. Therefore, the first problem that must be solved is human-machine security. Ensuring the safety of human beings is one of the main aspects of human-robotic interaction. This is not just about preventing collisions within a shared space among human beings and robots; it includes all possible means of harm for an individual, from physical contact to unpleasant or dangerous psychological effects. OBJECTIVE: In this paper, Non-linear Adaptive Heuristic Mathematical Model (NAHMM) has been proposed for the prevention of workplace violence using security Human-Robot Collaboration (HRC). Human-Robot Collaboration (HRC) is an area of research with a wide range of up-demands, future scenarios, and potential economic influence. HRC is an interdisciplinary field of research that encompasses cognitive sciences, classical robotics, and psychology. RESULTS: The robot can thus make the optimal decision between actions that expose its capabilities to the human being and take the best steps given the knowledge that is currently available to the human being. Further, the ideal policy can be measured carefully under certain observability assumptions. CONCLUSION: The system is shown on a collaborative robot and is compared to a state of the art security system. The device is experimentally demonstrated. The new system is being evaluated qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

BACKGROUND: Employers must provide their workers with a safe working environment. Violence at the workplace is considered to pose risks for mental health. However, it is rarely investigated whether or not violence at the workplace in a setting can further increase the risk of mental disorders among employees. Risk assessment of workplace violence is still a major challenge for law enforcement, mental health, and other professionals. These critical and specific evaluations need an innovative approach. OBJECTIVES: In this paper, the Optimized Grey Dynamic System Theory (OGDST) is used to analyze work-related incidents and hazard assessment. The forecasting model is built using annual data sets of work-related incidents. RESULTS: Research shows that aggressive psychological behavior often precedes the physical abuse of the workplace, whereas employers often ignore signs of warning even when identified by employees. Effectiveness tests demonstrate the efficiency of these suggested models. CONCLUSION: The results convey information supporting the conceptualization and assessment of models of workplace violence as a phenomenon arising from negative physical and psychological experiences of individuals at the workplace. [ABSTRACT FROM AUTHOR]

BACKGROUND: The increasing use of robotics in the work of co-workers poses some new problems in terms of occupational safety and health. In the workplace, industrial robots are being used increasingly. During operations such as repairs, unmanageable, adjustment, and set-up, robots can cause serious and fatal injuries to workers. Collaborative robotics recently plays a rising role in the manufacturing filed, warehouses, mining agriculture, and much more in modern industrial environments. This development advances with many benefits, like higher efficiency, increased productivity, and new challenges like new hazards and risks from the elimination of human and robotic barriers. OBJECTIVES: In this paper, the Advanced Human-Robot Collaboration Model (AHRCM) approach is to enhance the risk assessment and to make the workplace involving security robots. The robots use perception cameras and generate scene diagrams for semantic depictions of their environment. Furthermore, Artificial Intelligence (AI) and Information and Communication Technology (ICT) have utilized to develop a highly protected security robot based risk management system in the workplace. RESULTS: The experimental results show that the proposed AHRCM method achieves high performance in human-robot mutual adaption and reduce the risk. CONCLUSION: Through an experiment in the field of human subjects, demonstrated that policies based on the proposed model improved the efficiency of the human-robot team significantly compared with policies assuming complete human-robot adaptation. [ABSTRACT FROM AUTHOR]