Merging Artificial Intelligence Research Articles

Engaging Curiosity:Roundtable Strategies for Merging Artificial Intelligence with Appreciative Inquiry

In a world saturated with technological advancements, the ‘AI’ defined as Artificial Intelligence typically conjures images of algorithms and automation. Yet, for those of us in the field of Appreciative Inquiry, Artificial Intelligence assumes a different guise – it stands as a catalyst for generative conversations and transformative possibilities. This article aims to bridge these two distinct interpretations of AI – Artificial Intelligence and Appreciative Inquiry – exploring how they can synergistically drive meaningful change.

Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasons

Seasonal Respiratory Diseases (SRDs) usually produce a heightened number of Emergency Department (ED) attendances due to their rapid dissemination within the community and the ineffective prevention measures. Such a context requires effective management of the emergency care processes to provide in-time diagnosis and treatment to infected patients. Nonetheless, EDs have evidenced severe operational deficiencies during these periods, thereby provoking extended bed waiting times in Hospitalization Departments (HDs). Therefore, this paper presents a hybrid approach merging Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to shorten the bed waiting times in HDs considering patient records collated in the first emergency care stages. First, we implemented Random Forest (RF) to estimate the probability of respiratory worsening based on sociodemographic and clinical patient data. Second, we inserted these probabilities into a DES model mimicking the emergency care from the admission to the HD. We then pretested different HD configurations and strategies seeking to reduce the HD bed waiting time. A case study of a European hospital group was used to validate the suggested framework. The AI-DES model enabled decision-makers to identify an improvement proposal with hospitalization bed waiting time lessening, oscillating between 7.93 and 7.98 h.

Enhanced Semantic Visual Cryptography with AI-driven error reduction for improved two-dimensional image quality and security

Abstract Visual Cryptography (VC) has emerged as a vital technique in the information security domain, with the fundamental purpose of securing 2-Dimensional (2D) image content through encryption and facilitating secure communication. While traditional VC has been instrumental in safeguarding data, it often falls short in maintaining image quality and semantic accuracy upon reconstruction. To address these limitations, this research encompasses the development of an Enhanced Semantic VC (ESVC) model, which aims to refine the encryption process while ensuring the semantic integrity of the images. The ESVC model introduces a new approach that merges VC with artificial intelligence (AI) to enhance 2D image encryption and decryption. The novel aspect of this research lies in the integration of AI-driven reinforcement learning (RL) to increase the quality of the 2D image by measuring the errors between the original secret image and the reconstructed image. This innovative framework is tailored for the secure transmission of 2D grayscale images, ensuring the preservation of semantic integrity while measuring and minimizing image quality loss. By integrating RL algorithms with a measurement of error reduction protocol, the model promises robust encryption capabilities with enhanced resilience against a plethora of cyber threats, thereby elevating the standard for secure image communication. Empirical evaluation of the ESVC model yields promising results, with the Peak Signal-to-Noise Ratio (PSNR) of reconstructed images achieving impressive values between +39 and +42 decibels (dB). These findings underscore the ESVC model’s capability to produce high-fidelity decrypted images, significantly surpassing traditional VC methods in both security and image quality. The research findings illuminate the potential of merging AI with VC to achieve a harmonious balance between computational efficiency and encryption strength, marking a significant advancement in the domain of visual data protection.

Natural language processing for automated breast cancer recurrence detection and classification in computed tomography reports.

e13591 Background: Relapse is a major concern for oncologists and breast cancer survivors that necessitates additional treatment and often leads to mortality. Cancer registries routinely track cancer mortality, but few monitor for relapse because of logistical challenges and prohibitive costs. In this context, Natural Language Processing (NLP) is a promising tool. Merging artificial intelligence with linguistics, NLP can rapidly analyze vast volumes of text in electronic health records. This capability of NLP is particularly valuable for Computed Tomography (CT) scans used in breast cancer care. CT scans are routinely used to characterize breast cancer progression and are described in transcribed dictations by radiologists. We aimed to apply NLP to these text reports to identify breast cancer relapses. Objective: To automate breast cancer relapse detection and classification in CT text reports using NLP. Methods: We analyzed 1,445 CT text reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014. These reports underwent manual review by trained human annotators. Text was annotated to identify terminology defining local, regional, and distant breast cancer relapses. Annotated reports were partitioned into a training-validation set (90% cohort) and a test set (10% cohort) for NLP model development. Results: In our dataset of 1,445 CT text reports, 72 (5.0%) were classified as local relapse, 97 (6.7%) as regional relapse, and 743 (51.4%) as distant relapse. The performance of our NLP model using the training-validation dataset can be summarized by the following metrics and 95% confidence intervals: 94% (±3.2) accuracy for detection and 96% (±2.9) accuracy for classification. The performance of our NLP model was confirmed using the test dataset, with 90% (±4.5) accuracy for detection and 91% (±6.3) accuracy for classification. For reference, all metrics are outlined (Table). Conclusions: Our model for identifying regional and distant relapses in CT reports had excellent performance, but had lower sensitivity for local relapses, posing a risk of false negatives. Automating the identification and classification of breast cancer relapses, if used retrospectively, can enhance cancer registry data about patient outcomes and, if used prospectively, holds the potential for enhancing patient care. [Table: see text]

The use of META (virtual simulations) in Canadian junior military leadership development

The use of META (virtual simulations) in Canadian junior military leadership development

Artificial intelligence's role in the realm of endangered languages: Documentation and teaching

With numerous languages nearing extinction, the urgency to preserve endangered languages has become a prominent focus in the linguistic field. This paper delves into the transformative role of Artificial Intelligence (AI) in the domains of documentation and pedagogy for endangered languages, particularly highlighting its innovative applications and the associated challenges. It delves into how AI-powered tools reshape linguistic fieldwork, offering accelerated annotation, consistent data collection, and deeper analytical endeavors. Furthermore, this exploration highlights the potential of AI in revolutionizing the teaching of these languages, ushering in a new era marked by dynamic, scalable, and engaging learning experiences. While AI presents unparalleled efficiencies, its challenges, ranging from data scarcity to the looming digital divide, are addressed critically. As the digital age continues to evolve, merging AIs capabilities with traditional linguistic approaches holds the promise of a more inclusive and comprehensive strategy to rejuvenate and preserve the worlds rich linguistic tapestry. This paper has summarized and provided an outlook on the research topic at hand.

Necessity of artificial intelligence techniques for power quality issues in electric vehicles

<p>World transportation is in an electrified mass transitional phase due to the predominant features such as a greener environment, fewer maintenance requirements, improved reliability and energy management efficiency compared to conventional transportation. Numerous difficulties have emerged since electric vehicle sales hit record highs, including how electrified transportation works with the grid. Emerging innovations and technological advances in the field of artificial intelligence have led to the development of enhanced controllers for electric vehicle penetration with the grid. This work includes information on the integration of artificial intelligence strategies used for the mitigation of power quality issues. By merging artificial intelligence techniques into the grid, users can achieve efficient energy use and beneficial power interactions between the grid and electric vehicles. To assess the network's practicality, this research develops a charging model that uses a Tasmanian Devil optimization (TDO) algorithm based on MATLAB software. According to research, TDO improves the waveform quality by reducing the THD with better performance and achieves efficient power transfer between the grid and EVs.</p>

AI-Driven Urban Traffic Optimization to Assess Complex Traffic Patterns for Public Traffic Control and Mobility

Abstract: The project "AI for Urban Traffic Optimization" is a major and novel method for addressing the issues of modern urban transportation networks. This research study highlights the potential of artificial intelligence (AI) in transforming traffic management by merging AI with the Internet of Things (IoT). It has several advantages, such as efficient traffic flow optimization, predictive congestion analysis, adaptive traffic signal management, speedy accident detection and reaction, public transit optimization, and enhanced traffic enforcement. These developments have the potential to reduce traffic congestion, fuel consumption, and pollution, eventually fostering a cleaner and more sustainable urban environment.The study acknowledges the difficulties, such as privacy issues and potential biases, but underlines the importance of rigorous preparation, openness, and public input in order to achieve responsible AI adoption. AI traffic management has a bright future as it continues to improve and transform modern cities, providing efficient, safe, and environmentally friendly transportation solutions. Collaboration among researchers, politicians, and industry stakeholders is critical to advancing this breakthrough technology and creating more livable urban settings

AI-Powered Intelligent Seaport Mobility: Enhancing Container Drayage Efficiency through Computer Vision and Deep Learning

The rapid urbanization phenomenon has introduced multifaceted challenges across various domains, including housing, transportation, education, health, and the economy. This necessitates a significant transformation of seaport operations in order to optimize smart mobility and facilitate the evolution of intelligent cities. This conceptual paper presents a novel mathematical framework rooted in deep learning techniques. Our innovative model accurately identifies parking spaces and lanes in seaport environments based on crane positions, utilizing live Closed-Circuit Television (CCTV) camera data for real-time monitoring and efficient parking space allocation. Through a comprehensive literature review, we explore the advantages of merging artificial intelligence (AI) and computer vision (CV) technologies in parking facility management. Our framework focuses on enhancing container drayage efficiency within seaports, emphasizing improved traffic management, optimizing parking space allocation, and streamlining container movement. The insights from our study provide a foundation that could have potential implications for real-world applications. By integrating cutting-edge technologies, our proposed framework not only enhances the efficiency of seaport operations, but also lays the foundation for sustainable and intelligent seaport systems. It signifies a significant leap toward the realization of intelligent seaport operations, contributing profoundly to the advancement of urban logistics and transportation networks. Future research endeavors will concentrate on the practical implementation and validation of this pioneering mathematical framework in real-world seaport environments. Additionally, our work emphasizes the crucial need to explore further applications of AI and CV technologies in seaport logistics, adapting the framework to address the evolving urbanization and transportation challenges. These efforts will foster continuous advancements in the field, shaping the future of intelligent seaport operations.

Analysis of the Impact of Artificial Intelligence on Digital Marketing

In today's fast-paced technological landscape, the synergy between Artificial Intelligence (AI) and digital marketing stands out, transforming business operations and influencing customer relationships. Digital marketing, having evolved from the Internet era, is on the brink of another significant evolution, propelled by AI's unmatched data analysis, prediction, and personalization capabilities. The study focuses on exploring the less studied connection between AI's technical aspects and individual digital marketing strategies. By investigating this area, the research discovers that AI can significantly enhance predictive analytics, improve customer segmentation, and help create more personalized content in digital marketing. However, merging AI with digital marketing also presents ethical and practical challenges that must be addressed. Venturing into this intersection reveals AI's vast potential in redefining critical elements of digital marketing. Notable impact areas include predictive analytics, accurate customer segmentation, and crafting tailored content. Beyond the promising opportunities, the integration of AI in digital marketing highlights the transformative role AI could play in honing and amplifying strategic outreach. Yet, this potential comes with its set of challenges. Careful consideration of the ethical and practical ramifications arising from the fusion of AI and digital marketing is imperative.

Computational Engineering based approach on Artificial Intelligence and Machine learning-Driven Robust Data Centre for Safe Management

This research explores the integration of Artificial Intelligence (AI), specifically the Recurrent Neural Network (RNN) model, into the optimization of data center cooling systems through Computational Engineering. Utilizing Computational Fluid Dynamics (CFD) simulations as a foundational data source, the study aimed to enhance operational efficiency and sustainability in data centers through predictive modeling. The findings revealed that the RNN model, trained on CFD datasets, proficiently forecasted key data center conditions, including temperature variations and airflow dynamics. This AI-driven approach demonstrated marked advantages over traditional methods, significantly minimizing energy wastage commonly incurred through overcooling. Additionally, the proactive nature of the model allowed for the timely identification and mitigation of potential equipment challenges or heat hotspots, ensuring uninterrupted operations and equipment longevity. While the research showcased the transformative potential of merging AI with data center operations, it also indicated areas for further refinement, including the model's adaptability to diverse real-world scenarios and its management of long-term dependencies. In conclusion, the study illuminates a promising avenue for enhancing data center operations, highlighting the significant benefits of an AI-driven approach in achieving efficiency, cost reduction, and environmental sustainability.

Analysis and Performance Evaluation of Transfer Learning Algorithms for 6G Wireless Networks

The development of the 5G network and the transition to 6G has given rise to multiple challenges for ensuring high-quality and reliable network services. One of these main challenges is the emergent intelligent defined networks (IDN), designed to provide highly efficient connectivity, by merging artificial intelligence (AI) and networking concepts, to ensure distributed intelligence over the entire network. To this end, it will be necessary to develop and implement proper machine learning (ML) algorithms that take into account this new distributed nature of the network to represent increasingly dynamic, adaptable, scalable, and efficient systems. To be able to cope with more stringent service requirements, it is necessary to renew the ML approaches to make them more efficient and faster. Distributed learning (DL) approaches are shown to be effective in enabling the possibility of deploying intelligent nodes in a distributed network. Among several DL approaches, transfer learning (TL) is a valid technique to achieve the new objectives required by emerging networks. Through TL, it is possible to reuse ML models to solve new problems without having to recreate a learning model from scratch. TL, combined with distributed network scenarios, turns out to be one of the key technologies for the advent of this new era of distributed intelligence. The goal of this paper is to analyze TL performance in different networking scenarios through a proper MATLAB implementation.

Revisiting Electrocatalyst Design by a Knowledge Graph of Cu-Based Catalysts for CO2 Reduction

Electrocatalysis takes a significant role in the production of sustainable fuels and chemicals. The combination of artificial intelligence and catalytic science is exhibiting great potential to extract, analyze, and predict electrocatalysts. However, the currently developed machine learning approach usually requires a mass of data from density functional theory calculations to train and optimize models. In contrast, a knowledge graph has the potential to extract useful information from a large amount of the literature without referring to density functional theory. Herein, a knowledge graph of Cu-based electrocatalysts for electrocatalytic CO2 reduction is constructed based on a linguistically enriched SciBERT-based framework. This framework retrieves multiple types of entities including material, regulation method, product, Faradaic efficiency, etc. from 757 scientific literature, generates representations with abundant domain-specific semantic information, and exhibits the capability to deal with electrocatalysts for CO2 reduction. The obtained graph shows the development history of related catalysts, builds relationships between the factors associated with catalysis, and provides intuitive charts for researchers to gain useful information. Furthermore, we propose a deep learning-based prediction model, which integrates the semantic information from the scientific literature (word embedding) with the correlation of knowledge triples (graph embedding) and realizes the prediction of the Faradaic efficiency for a targeted case. This work paves the way for catalyst design in the manner of merging artificial intelligence with catalytic science.

Hypergraph models of the mental lexicon capture greater information than pairwise networks for predicting language learning

Human memory is a complex system that works in associative ways: Reading a cue word can lead to the recollection of associated concepts. The network structure of memory recall patterns has been shown to contain insights about a wide variety of cognitive phenomena, including language acquisition. However, most current network approaches use pairwise connections, i.e. links between only two words at a time. This ignores the possibility that more than two concept representations might be simultaneously associated in memory. We overcome this modelling limitation by introducing cognitive hypergraphs as models of human memory. We model memory recall patterns through word associations from the Small World of Words project for N=6003 concepts (Study 1) and for N=497 concepts (Study 2). In each study we represent word associations as either a pairwise network or a hypergraph. By combining psycholinguistic norms and network centrality measures with machine learning, we quantitatively investigate whether there is any benefit to using the hypergraph model over a pairwise network in predicting test-based age of acquisition norms in children up to age 9 years (Study 1) or normative learning in toddlers up to age 30 months (Study 2, based on CHILDES data). We show that cognitive hypergraphs capture more information than pairwise networks from the same data: Cognitive hypergraphs are considerably more powerful than pairwise networks at predicting age of acquisition trends in toddlers, children and teenagers. Our studies showcase how novel approaches merging artificial intelligence and higher-order interactions can help us understand cognitive development.

Parameter estimation of the Lomax distribution using genetic algorithm based on the ranked set samples

ABSTRACT Due to the importance of business process automation in industry, hyperautomation and Robotic Process Automation (RPA) attract increasing attention in the scientific field as well. Hyperautomation allows automation to do important tasks performed by business people by merging Artificial Intelligence (AI) technologies with RPA. Lomax distribution has been widely used in engineering science, service times, use and supply times of products in enterprise information systems, income and wealth data, business failure data, medical and biological science, life test and other fields. In this paper, the maximum likelihood estimation of unknown parameters of the Lomax distribution is considered using simple random sample, ranked set sample, median ranked set sample and extreme ranked set sample. Model parameters are estimated by the maximum likelihood estimation method based on Genetic Algorithm. An extensive Monte Carlo simulation study, considering perfect and imperfect ranking, is carried out in order to compare the performance of the genetic algorithm and classical optimisation method from ranked set sample-based designs with corresponding simple random sample estimators. Real-life data has been used to illustrate the application of the results obtained.

Shaping the Future of Smart Dentistry: From Artificial Intelligence (AI) to Intelligence Augmentation (IA)

Digitization is the emerging process in the current transformation of industry. Understanding the role and socio-economic consequences of digitalization is crucial for the way technology is being deployed in each sector. One of the affected sectors is dentistry. This study highlights the current advances and challenges in integrating and merging artificial intelligence (AI), intelligence augmentation (IA), and machine learning (ML) in dentistry. We conduct a comparative analysis to give an overview of which technology is being currently deployed and what role IA and AI will play in dentistry, as AI plays an assistive role in advancing human capabilities. We find that challenges range from AI finding its way into routine medical practice to qualitative challenges of retrieving adequate data input. Other challenges lie in the yet unanswered questions of liability in how to reduce deployment costs of new technology. Given these challenges, we provide an outlook of how future technology can be deployed in daily-life dentistry and how robots and humans will interact, given the current technology developments. The aim of this paper is to discuss the future of dentistry and whether it is AI or IA conquering the modern dentistry era.

Blockchain and artificial intelligence for 5G‐enabled Internet of Things: Challenges, opportunities, and solutions

AbstractInternet of Things (IoT) has revolutionized the digital world by connecting billions of electronic devices over the internet. IoT devices play an essential role in the modern era when conventional devices become more autonomous and smart. On the one hand, high‐speed data transfer is a major issue where the 5G‐enabled environment plays an important role. On the other hand, these IoT devices transfer the data by using protocols based on centralized architecture and may cause several security issues for the data. Merging artificial intelligence to 5G wireless systems solves several issues such as autonomous robots, self‐driving vehicles, virtual reality, and engender security problems. Building trust among the network users without trusting third party authorities is the system's primary concern. Blockchain emerged as a key technology based on a distributed ledger to maintain the network's event logs. Blockchain provides a secure, decentralized, and trustless environment for IoT devices. However, integrating IoT and blockchain also has several challenges; for example, major challenge is low throughput. Currently, the ethereum blockchain network can process approximately 12 to 15 transactions per second, while IoT devices require relatively higher throughput. Therefore, blockchains are incapable of providing functionality for a 5G‐enabled IoT based network. The limiting factor of throughput in the blockchain is their network. The slow propagation of transactions and blocks in the P2P network does not allow miners and verifiers to fastly mine and verify new blocks, respectively. Therefore, network scalability is the major issue of IoT based blockchains. In this work, we solved the network scalability issue using blockchain distributed network while to increase the throughput of blockchain, this article uses the Raft consensus algorithm. Another most important issue with IoT networks is privacy. Unfortunately, the blockchain distributed ledgers are public and sensitive information is available on the network for everyone are private, but in such cases, third party editing is not possible without revealing the original contents. To solve privacy issues, we used zkLedger as a solution that is based on zero knowledge‐based cryptography.

Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective

Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.

Artificial Intelligence for Software Engineering: An Initial Review on Software Bug Detection and Prediction

The need for speed and quality in delivering all software engineering artifacts has inevitably remained the biggest challenge in today’s software development environment. While everyone caters to complex software engineering processes, new releases are expected by the market on almost a daily basis. Thus, several Artificial Intelligence (AI) techniques have been introduced that are intensively used in the modern software engineering industry to fulfill market needs. This paper presents the initial results of our review work on software bug detection and prediction studies using AI techniques. Our focus is to (i) identify factors affecting the effectiveness of current software bug detection and prediction techniques and (ii) identify the effectiveness of AI techniques in improving current software bug detection and prediction techniques. The evidence showed that the software engineering domain has utilized artificial intelligence approaches and techniques to facilitate the complex tasks of software bug detection and bug prediction. It mainly demonstrates the significance of merging artificial intelligence with the software engineering domain in terms of reduced overhead and efficient results to enhance the quality of software products.

Merging Artificial Intelligence & Blockchain Technologies to Solve Academic Qualification Forgery Issues

This paper emphasizes on the possibility of merging Artificial Intelligence and Blockchain technologies to solve academic qualifications forgery issues in the educational sectors. Empirical data is collected through interviews with many specialist and technical people who is interested in the emerging technologies of the Fourth Industrial Revolution and focused group discussion in the field, as well as from reports in the reviewed literary articles. Scientific journals have also been accessed to analyze the paper goals and objectives. The findings are based on the conclusion suggest that emerging technologies can be integrated to become more efficient and effective in detecting fraud and forgery before it occurs. Considerable attention should be given to reducing and combating these issues because they have significant negative impacts on the economy and education. Accordingly, the study makes recommendations based on the results and areas of future research, considering the establishment of a unified and integrated system. Initially will be applied as a pilot in Sultanate of Oman, then gradually will be extended to the Gulf Cooperation Council States (GCC) and internationally particularly the affiliated and the recognized educational institutions to avoid the phenomena that affect the reputation and quality of education institutions and academic qualifications. In the conclusion considering the impacts of the proposed system in the education and economy as well in general. This research aims to investigate academic forgery cases in the world and then focuses in Oman. Furthermore, it explores the possibilities of merging technologies (Artificial Intelligence and Blockchain) to contribute in eliminating fraud before it occurs. In addition, to propose a framework that will help to find solutions based on the suggested integrated technologies. Finally, it assess the probabilities of the proposed solutions performance and its impacts in the academic sector.