Automation Of Tasks Research Articles

Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

The advancement of cutting-edge technologies significantly transforms urban lifestyles and is indispensable in sustainable urban design and planning. This systematic review focuses on the critical role of innovative technologies and digitalization, particularly artificial intelligence (AI), in urban planning through geo-design, aiming to enhance urban life. It begins with exploring the importance of AI and digital tools in revolutionizing contemporary urban planning practices. Through the methodology based on the Systematic Reviews and Meta-Analyses (PRISMA) protocol, this review sifts through relevant literature over the past two decades by categorizing artificial intelligence technologies based on their functionalities. These technologies are examined for their utility in urban planning, environmental modeling, and infrastructure development, highlighting how they contribute to creating smarter and more livable cities. For instance, machine learning techniques like supervised learning excel in forecasting urban trends, whereas artificial neural networks and deep learning are superior in pattern recognition and vital for environmental modeling. This analysis, which refers to the comprehensive evaluation conducted in this Systematic Review, encompasses studies based on diverse data inputs and domains of application, revealing a trend toward leveraging AI for predictive analytics, decision-making improvements, and the automation of complex geospatial tasks in urban areas. The paper also addresses the challenges encountered, including data privacy, ethical issues, and the demand for cross-disciplinary knowledge. The concluding remarks emphasize the transformative potential of innovative technologies and digitalization in urban planning, advocating for their role in fostering better urban life. It also identifies future research avenues and development opportunities. In light of our review findings, this study concludes that AI technologies indeed hold transformative promise for the field of geo-design and urban planning. They have proven instrumental in advancing predictive analytics, refining decision-making, and streamlining complex geospatial tasks. The AI’s capacity to process expansive datasets and improve urban planning accuracy has facilitated more sustainable urban development and enhanced the resilience of urban environments.

Historical Documents and Automatic Text Recognition: Introduction

With this special issue of the Journal of Data Mining and Digital Humanities (JDMDH), we bringtogether in one single volume several experiments, projects and reflections related to automatic textrecognition applied to historical documents.More and more research projects1 now include automatic text acquisition in their data processing chain,and this is true not only for projects focussed on Digital or Computational Humanities but increasinglyalso for those that are simply using existing digital tools as the means to an end. The increasing useof this technology has led to an automation of tasks that affects the role of the researcher in the textualproduction process. This new data-intensive practice makes it urgent to collect and harmonise the corporanecessary for the constitution of training sets, but also to make them available for exploitation. Thisspecial issue is therefore an opportunity to present articles combining philological and technical questionsto make a scientific assessment of the use of automatic text recognition for ancient documents, itsresults, its contributions and the new practices induced by its use in the process of editing and exploringtexts. We hope that practical aspects will be questioned on this occasion, while raising methodologicalchallenges and its impact on research data.The special issue on Automatic Text Recognition (ATR) is therefore dedicated to providing a comprehensiveoverview of the use of ATR in the humanities field, particularly concerning historical documentsin the early 2020s. This issue presents a fusion of engineering and philological aspects, catering to bothbeginners and experienced users interested in launching projects with ATR. The collection encompassesa diverse array of approaches, covering topics such as data creation or collection for training genericmodels, reaching specific objectives, technical and HTR machine architecture, segmentation methods,and image processing.

Designing Automation for Pickup and Delivery Tasks in Modern Warehouses Using Multi Agent Path Finding (MAPF) and Multi Agent Reinforcement Learning (MARL) Based Approaches

A warehouse pickup and delivery problem finds its solution using multi agent path finding (MAPF) approach. Also, the problem has been used to showcase the capabilities of the multi agent reinforcement learning (MARL). The warehouse pickup and delivery work needs the agent to pick up a requested item and successfully deliver it to the intended location within the warehouse. The problem has been solved based on two approaches that include single shot and lifelong problem solution. The single shot solution has the delivery as the final goal and thus once it reaches the delivery address, it stops whereas in case of lifelong, the agent needs to deliver the item which it had picked, deliver it to the required place and then again pick up new item until requests are satisfied. The strategy used by multi agent path finding (MAPF) approach aims at constructing collision free paths to reach the delivery location but in case of multi agent reinforcement learning (MARL), the agents’ decision making tactics (or policies) are learned which are then used to help agents decide path to be followed based on environment state and agent’s position. The results show that the lifelong conflict based search (CBS) is a better option when the agents are less in number as in that case, the re-planning will take overall less time but when the agents are large in number then this re-planning can take very long to produce conflict free paths from source to goal nodes. In this case, shared experience action critic (SEAC) which is based on multi agent reinforcement learning (MARL) approach can be more efficient choice as it takes the current environment state to give the most suitable action for that time t. For this study the agents taken for learning are homogeneous in nature that can pickup and deliver any type of requested item. We can address the same pickup and delivery problem when the agents are not all same and differ in their capabilities and the type of item they can handle.

Early Validation and Verification of System Behaviour in Model-based Systems Engineering: A Systematic Literature Review

In the Systems Engineering (SE) domain there has been a paradigm shift from document-based to model-based system development artefacts; in fact, new methodologies are emerging to meet the increasing complexity of current systems and the corresponding growing need of digital workflows. In this regard, Model-Based Systems Engineering (MBSE) is considered as a key enabler by many central players of the SE community. MBSE has reached an adequate level of maturity, and there exist documented success stories in its adoption in industry. In particular, one significant benefit of utilising MBSE when compared to the traditional manual and document-centric workflows is that models are available from early phases of systems development; these enable a multitude of analyses prior any implementation effort together with other relevant capabilities, like the automation of development tasks. Nonetheless, it is noticeable there is a lack of a common understanding for how formal analyses for the verification and validation (V&V) of systems behaviour, specifically in the early phases of development, could be placed in an MBSE setting. In this article, we report on the planning, execution, and results of a systematic literature review regarding the early V&V of systems behaviour in the context of model-based systems engineering. The review aims to provide a structured representation of the state of the art with respect to motivations, proposed solutions, and limitations. From an initial set of potentially relevant 701 peer-reviewed publications we selected 149 primary studies, which we analysed according to a rigorous data extraction, analysis, and synthesis process. Based on our results, early V&V has usually the goal of checking the quality of a system design to avoid discovering flaws when parts are being concretely realised; SysML is a de facto standard for describing the system under study, while the solutions for the analyses tend to be varied; also V&V analyses tend to target varied properties with a slight predominance of functional concerns, and following the variation mentioned so far the proposed solutions are largely context specific; the proposed approaches are usually presented without explicit limitations, while when limitations are discussed, readiness of the solutions, handling of analyses simplifications/assumptions, and languages/tools integration are among the most frequently mentioned issues. Based on the survey results and the standard SE practices, we discuss how the current state-of-the-art MBSE supports early V&V of systems behaviour with a special focus on industrial adoption and identify relevant challenges to be researched further.

Creating A Security Baseline and Cybersecurity Framework for the Internet of Things Via Security Controls

The Internet of Things (IoT) has revolutionized how we interact with the world and has become an essential ingredient for various industries for service efficiency and effectiveness. IoT is a common building block for automation tasks to help businesses enhance productivity and performance. IoT is observed anywhere and everywhere and in almost every industry. The technology that was supposed to make our lives easier brought forth a cybersecurity storm for which the world is unprepared. To mitigate this issue, these devices need a proper security baseline and cybersecurity framework to support them. This design science study proposed a framework to create a security baseline based on the foundation of security controls. The proposed framework uses the NIST SP800-53 controls as requirements for management, operational, and technical implementations. The study investigated how and which controls are selected for requirements. This approach can be used as a guideline for organizations to develop their security baseline to support and secure the IoT systems.

Quick review of pedagogical experiences using GPT-3 in education

GPT-3 is a neuronal language model that performs tasks such as classification, question-answering and text summarization. Although chatbots like BlenderBot-3 work well in a conversational sense, and GPT-3 can assist experts in evaluating questions, they are quantifiably worse than real teachers in several pedagogical dimensions. We present the first systematic literature review that analyzes the main contributions and uses of GPT-3 in the field of education. The protocols suggested in the PRISMA 2020 statement were followed for the drafting of the review. According to the results, 34 significant productions were identified through a systematic search in ISI Web of Science, SCOPUS and Google Scholar. GPT-3 has been considered in the academic, ethical and medical fields, in humanities and in computer science, in the formulation of questions and answers, and through cooperative educational dialogs. GPT-3 has been proven to have valuable applications in education, such as the automation of routine tasks, in making quick diagnoses of the students’ weaknesses and in the automatic generation of questions, but it still faces challenges and limitations that require additional investigation. We discuss the educational possibilities and the limitations to the use of GPT-3.

Enhancing User Experience: A Study on Dialogflow Chatbot Implementation

In recent years, chatbots have become essential for customer service, task automation, and information dissemination. Google's Dialogflow platform offers intuitive tools for building conversational agents. This paper provides an overview of developing smart chatbots with Dialogflow, emphasizing natural language understanding. It covers Dialogflow's architecture, including intents, entities, and fulfillment, and discusses training techniques for chatbot optimization. Additionally, it explores advanced features like external service integration and multilingual support. Overall, it serves as a practical guide for developers and organizations interested in leveraging Dialogflow to create personalized chatbot experiences. Keywords : User input, Conversation flow, Dialog management, User experience (UX)

The Impact of Artificial Intelligence on Employment and Income Distribution

This study delves into the extensive ramifications of AI's proliferation on employment and income distribution, especially in light of the COVID-19 pandemic, by examining job displacement, the emergence of new roles, and the widening income gap between high and low-income groups. The integration of AI into industries has had a significant impact on employment. It has led to the automation of routine and repetitive tasks, resulting in job displacement, especially in the manufacturing and administrative sectors. However, this phenomenon is not solely marked by job loss. AI has also brought about job transformation, necessitating individuals to acquire new skill sets and collaborate effectively with AI systems. This transformation has paved the way for new employment opportunities, particularly in AI engineering, data science, AI regulation, and the development of AI-driven technologies like self-driving vehicles. These changes underscore the need for adaptability in the workforce and have potential implications for income distribution, as high-skilled AI-related positions offer higher salaries, contributing to income inequality. The proliferation of AI has brought about both challenges and opportunities in employment and income distribution, with job displacement coexisting alongside the emergence of new roles. The COVID-19 pandemic has underscored the need for workforce adaptability, emphasizing the importance of addressing income inequality as we move into an AI-dominated era to foster a more equitable future.

Exploring IoT Integration for Innovative Advancements in Civil Engineering

Civil engineering developments have allocation with the recent trends of integration with IoT technologies and devices. This technology significantly improves automation and remote monitoring for various tasks in the construction process, building maintenance and safety, allowing operators to remain remote free ever than before. IoT is simply the connection of several devices to the internet as per the need. The real implication of IoT is beyond and complex than this. The IoT is meant to signify the establishment of entire networks of devices which are connected to the internet and with each other. Various sensors and monitors recognize the conditions, communicate them to online network or app, and transmit relevant information to your phone automatically. Internet of Things (IoT) is the technology that transforming construction industries, automate processes, and improves construction area and other relevant fields in civil like Global Positioning System (GPS), Water Resources Management and Water Supply, Smart Construction, Water & Waste Water Treatment, Traffic Management and Control, Waste Management, Concrete Technology, Environmental Impact Assessment & Monitoring and Smart Lighting System. This research paper covers implementation of IoT in all these fields of civil engineering and updating the current scenario. IoT is potentially used to increase the productivity, on-site safety, and operational efficiency. Through the placement of low-power sensors, managers can improve worksite visibility at every stage of a project in realtime, from planning to construction, and even task post-construction. The construction industry is bringing real-time information into processes that are long-standing. Internet of Things (IoT) devices and sensors are collecting job site data in a more affordable, efficient and effective way than can ever previously imagined. This latest technology represents exciting hikes forward in the civil engineering world. Foremost principles among these are integrated in this study for Internet of Things with the world of civil engineering.

BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models.

As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

AMU patient list generation: from junior scribe to junior doctor

This quality improvement project (QIP) aimed to assess the impact of automating patient list generation on the acute medical unit (AMU) at Sandwell and West Birmingham Hospitals NHS Trust. The...

A Comprehensive Review of the Significance of Low-code Automation in Risk Management for Banks

This study provides a comprehensive examination of the influence of low-code development platforms in the risk management area within the banking sector. This research delves into the tactical utilization of low-code automation in the realm of risk management, with a particular emphasis on compliance monitoring, fraud detection, credit risk evaluation, and operational risk management. Additionally, the study centers on the metamorphosis of software design, development, and deployment processes, made possible by the easy accessibility of low-code platforms. The objective of the study is to illustrate how the digitization and automation of manual tasks in these domains augment service. provision, expedite the identification of compliance violations, and simplify credit risk evaluation, thereby conferring advantages to both banks and their clientele. Through meticulous analysis, this investigation seeks to furnish important perspectives on the uptake and influence of low-code development platforms in the banking sector, illuminating the potential for improved operational efficiency, regulatory adherence, and superior risk management.

Assessing the Effect of Task Automation in Labor Markets: Case of IT Services Industry

Assessing the Effect of Task Automation in Labor Markets: Case of IT Services Industry

The impact of artificial intelligence on the labor market in the world and particularly in Ukraine

Introduction. In the contemporary context of global environmental, economic, and epidemiological challenges, the study of artificial intelligence (AI) and its impact on the labor market is crucial. The purpose of this paper is to study the impact of artificial intelligence on the labor market in the world and particularly in Ukraine. The results of the research highlight a global trend of job displacement due to technological advancements, particularly in robotics and AI. The study defines AI and highlights its potential in tasks requiring human intelligence. A McKinsey Global Institute study suggests up to 300 million jobs could be lost globally by 2030, emphasizing concerns about employment stability. The paper explores industry-specific data, identifying high-risk areas like routine tasks for automation and less susceptible roles requiring creativity. The impact on the labor market is nuanced, with most jobs only partially automated, offering opportunities for complementation. Positive aspects of AI, such as creating new employment opportunities and enhancing worker safety, are underscored. Governments and businesses are urged to proactively retrain workers at risk of automation. The potential for job reduction in Ukraine is discussed, emphasizing the need for education reform. Conclusion. The multifaceted impact of AI on employment necessitates careful planning and consideration. For Ukraine, the focus should be on creating competitive AI specialists through specialized education, international collaboration, and skills development, enabling the country to navigate the evolving global environment.

Improvement of Machine Vision Video Signal Processing Algorithm for Higher Accuracy in Extended Object Speed Measurements

Machine vision systems are widely used for monitoring of the railway infrastructure condition. High reliability of machine vision systems allows using them not only for video recording but also for automation of technological processes. One of the important tasks in the process automation is measurement of an extended object speed (rail-road cars, electric locomotives and rolling stock). Some systems have a requirement to estimate speed in real time (braking of cars at a shunting yard). In this case the speed is calculated by two adjacent frames to ensure the minimum delay for measurement. However, the image of an extended object can be uninformative (image fragments are uniform in brightness), the weather can cause interference. It leads to high speed estimation error. This article describes the improvement for the existing real-time algorithm which allows for higher accuracy in speed measurements. The improvement implies selection of an informative image area corresponding to an extended object. A comparative analysis of the existing and improved algorithm showed a significant reduction in the estimation error in speed measurements.

Streamlining marketing activities: essential tools for organizational success

The object of this research is the role of organizational tools in enhancing the efficiency, productivity, and overall performance of marketing teams. Despite the immense potential of organizational tools to revolutionize marketing operations, many teams fail to take full advantage of their benefits. This is due to a combination of factors, including lack of awareness, where a significant proportion of marketing teams are unaware of the vast array of organizational tools available and their potential to improve productivity. And a lack of training, where even when teams are aware of these tools, they often lack adequate training or guidance on how to integrate and use them effectively. And cost concerns, where the perceived high cost of organizational tools often deters teams from investing in these technologies. The research indicates that organizational tools provide significant benefits to marketing teams, including improved efficiency through task automation, streamlined workflows, and centralized information access, as well as enhanced productivity through improved collaboration, data-driven decision-making, and project management capabilities. Effective marketing efforts supported by organizational tools can boost performance, resulting in improved customer engagement, increased ROI, and overall business growth. This paper aimed to provide marketing teams with a comprehensive insight into the benefits and applications of organizational tools. It is examined the wide range of organizational tools available and provides guidance on selecting the most appropriate tools to meet specific marketing needs. In addition, the paper addressed common challenges that marketing teams face in adopting and maximizing the effectiveness of organizational tools.

GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots.

Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While mAP@0.5 increased by 0.514% to 95.364, the model simultaneously reduced the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system localization accuracy and robustness. It shows a remarkable 92.59% improvement in RMSE for Absolute Trajectory Error (ATE), along with a 93.11% improvement in RMSE for the translational drift of Relative Pose Error (RPE) and a 92.89% improvement in RMSE for the rotational drift of RPE. Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.

The Educational Affordances and Challenges of ChatGPT: State of the Field

ChatGPT was released to the public in November 30, 2022. This study examines how ChatGPT can be used by educators and students to promote learning and what are the challenges and limitations. This study is unique in providing one of the first systematic reviews using peer review studies to provide an early examination of the field. Using PRISMA principles, 44 articles were selected for review. Grounded coding was then used to reveal trends in the data. The findings show that educators can use ChatGPT for teaching support, task automation, and professional development. These were further delineated further by axial sub codes. Eight student uses were 24/7 support, explain difficult concepts, conversational partner, personalized feedback and materials, provide writing support, offer self-assessment, facilitate engagement, and self-determination. In addition to be affordances of the AI, the data from the articles also showed limitations to ChatGPT and misuses, specifically, inaccuracies and hallucinations, potential bias, and tool limitations. Misuses are plagiarism and cheating, privacy issues and spread of false information. This study is a springboard for researchers, practitioners, policy makers and funders in understanding the emerging state of the field of ChatGPT.

A Study to Know Impact of AI on CRM

This research paper delves into the dynamic intersection of Artificial Intelligence (AI) and Customer Relationship Management (CRM), exploring the profound effects of AI technologies on modern business practices. As AI continues to evolve, it reshapes how organizations manage and nurture their relationships with customers, presenting new opportunities and challenges. The study investigates the role of AI in personalizing customer experiences, emphasizing the utilization of advanced algorithms to analyze vast datasets and derive actionable insights. AI's influence on data management, analysis, and the subsequent enhancement of customer insights are scrutinized, providing a comprehensive understanding of its impact on informed decision-making. Furthermore, the paper examines the integration of AI-powered chatbots and virtual assistants in CRM systems, evaluating their effectiveness in providing real-time support, streamlining interactions, and improving overall customer satisfaction. The automation of repetitive CRM tasks through AI technologies is also explored, highlighting the resulting efficiencies and the potential for human resources to engage in more strategic aspects of customer relationship management. Sales forecasting, customer segmentation, and sentiment analysis emerge as key focal points, illustrating how AI contributes to more accurate predictions, targeted marketing strategies, and proactive reputation management. The impact of AI on cross-selling, upselling, and customer retention strategies is scrutinized, offering insights into how businesses can leverage AI to optimize revenue and foster enduring customer loyalty. As businesses navigate the rapidly evolving landscape of AI in CRM, this research paper aims to provide a comprehensive overview of the transformative dynamics at play. By understanding the nuanced influence of AI on customer relationships, organizations can adapt their strategies to align with the evolving expectations and demands of the contemporary market.

Implementation of FAIR Practices in Computational Metabolomics Workflows-A Case Study.

Scientific workflows facilitate the automation of data analysis tasks by integrating various software and tools executed in a particular order. To enable transparency and reusability in workflows, it is essential to implement the FAIR principles. Here, we describe our experiences implementing the FAIR principles for metabolomics workflows using the Metabolome Annotation Workflow (MAW) as a case study. MAW is specified using the Common Workflow Language (CWL), allowing for the subsequent execution of the workflow on different workflow engines. MAW is registered using a CWL description on WorkflowHub. During the submission process on WorkflowHub, a CWL description is used for packaging MAW using the Workflow RO-Crate profile, which includes metadata in Bioschemas. Researchers can use this narrative discussion as a guideline to commence using FAIR practices for their bioinformatics or cheminformatics workflows while incorporating necessary amendments specific to their research area.