Artificial Intelligence Frameworks Research Articles

Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism

Artificial neural network framework for the selection of deep eutectic solvents promoted enhanced oil recovery by interfacial tension reduction mechanism

Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence

Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence

AI optimization framework using digital layouts of array structures: A case study for fuel cells

AI optimization framework using digital layouts of array structures: A case study for fuel cells

Artificial Intelligence for Sustainable Energy Transition: Optimising Renewable Energy Integration and Management

As climate change and long-term energy security drive the global energy sector towards renewable resources, powerful tools are required to optimise integration and management. A novel framework is proposed for effectively utilising Artificial Intelligence (AI) in the renewable energy landscape. AI algorithms can analyse weather patterns, historical generation data, and environmental factors to predict renewable energy output. Energy dispatch is optimised, grid integration is improved, and energy storage requirements are reduced. A system powered by artificial intelligence also significantly reduces downtime, optimises maintenance schedules, and minimises operational costs in wind turbines, solar panels, and other renewable infrastructure. AI can also optimise energy flows, reduce grid instability, and ensure efficient resource utilisation within the smart grid by dynamically managing renewable sources, energy storage systems, and demand profiles. Furthermore, AI-driven spatial analysis and resource mapping can identify optimal locations for renewable installations, considering factors like wind speed, solar irradiance, and environmental constraints. This paper presents two AI frameworks, one for solar energy and one for wind energy, to demonstrate possible applications. They both utilise comprehensive data acquisition, including real-time sensor data and external factors like weather forecasts and historical generation patterns. AI algorithms use these combined data to perform critical tasks such as predictive maintenance, minimising downtime, and maximising efficiency. Power output forecasting enables real-time adjustments based on weather, and optimal site selection maximises energy production. AI is used for proactive issue identification, accurate power output forecasting based on wind conditions, grid demand, storage capacity, dynamic load optimisation for maximum energy efficiency, and wind farm site selection. Integrating these tailored AI frameworks with solar and wind energy can achieve significant benefits such as increased efficiency, reduced operational costs, and seamless grid integration. In addition to analysing the challenges and opportunities associated with this AI integration, the paper explores infrastructure development, ethical considerations, and data acquisition. A second benefit of the research methodology is that it highlights how these tailored AI frameworks can optimise the integration of solar and wind renewable energy sources, providing valuable insights for researchers, practitioners, and policymakers who wish to use AI to create a more sustainable and efficient energy system. Keyword: Artificial Intelligence, renewable energy, climate change.

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non–Small Cell Lung Cancer From Hematoxylin and Eosin–Stained Slides Using Deep Learning

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non–Small Cell Lung Cancer From Hematoxylin and Eosin–Stained Slides Using Deep Learning

Initial orbit determination via artificial intelligence for too-short arcs

Initial orbit determination via artificial intelligence for too-short arcs

AIfES: A Next-Generation Edge AI Framework.

Edge Artificial Intelligence (AI) relies on the integration of Machine Learning (ML) into even the smallest embedded devices, thus enabling local intelligence in real-world applications, e.g. for image or speech processing. Traditional Edge AI frameworks lack important aspects required to keep up with recent and upcoming ML innovations. These aspects include low flexibility concerning the target hardware and limited support for custom hardware accelerator integration. Artificial Intelligence for Embedded Systems Framework (AIfES) has the goal to overcome these challenges faced by traditional edge AI frameworks. In this paper, we give a detailed overview of the architecture of AIfES and the applied design principles. Finally, we compare AIfES with TensorFlow Lite for Microcontrollers (TFLM) on an ARM Cortex-M4-based System-on-Chip (SoC) using fully connected neural networks (FCNNs) and convolutional neural networks (CNNs). AIfES outperforms TFLM in both execution time and memory consumption for the FCNNs. Additionally, using AIfES reduces memory consumption by up to 54% when using CNNs. Furthermore, we show the performance of AIfES during the training of FCNN as well as CNN and demonstrate the feasibility of training a CNN on a resource-constrained device with a memory usage of slightly more than 100 kB of RAM.

A guide to evaluating AI vendors: Key questions to mitigate security risks

The proliferation of Generative AI (GenAI) marketing claims has thrust businesses into high-stakes decisions amid evolving societal attitudes towards emerging technologies. Exercising caution is paramount, however, given the prevalence of forward-looking assertions by nascent artificial intelligence (AI) vendors regarding future capabilities. This paper advocates for a pragmatic approach, underscoring the significance of tackling immediate safety concerns while manoeuvring through the realm of AI solutions. With insights gleaned from over five decades of AI evolution, the paper highlights the cyclical nature of AI’s allure and underscores the importance of drawing lessons from past cycles. It champions a methodical assessment of risks associated with adopting generative AI solutions, stressing the imperatives of transparency and accountability. A pivotal suggestion is to demand tailored AI solutions from vendors, ensuring that AI frameworks prioritise the welfare of data proprietors. The paper cautions against vendors putting their own interests before those of their clientele, urging readers to pose probing inquiries before committing to AI deployments. Ultimately, the paper endeavours to arm readers with the acumen and tools necessary to navigate the intricacies of AI vendor relationships. By furnishing actionable guidance on recognising and mitigating AI vendor risks, it endeavours to embolden businesses to innovate judiciously in the ever-evolving AI landscape.

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases. This resource equips users to navigate the diverse XAI landscape and select the most suitable framework for their specific needs. Furthermore, the study proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption. This framework empowers users to assess different XAI options based on their application context objectively. This will lead to more responsible AI development by fostering transparency and trust. Finally, the research identifies the limitations and challenges associated with the existing XAI frameworks, paving the way for future advancements. By highlighting these areas, the study guides researchers and developers in enhancing the capabilities of Explainable AI.

Diversity, Equity, and Inclusion, and the Deployment of Artificial Intelligence Within the Department of Defense

Artificial Intelligence (AI) adoption has seen substantial growth across industries. This paper explores the escalating use of AI within the United States Department of Defense (DoD) and the implications that diversity, equity, and inclusion (DEI) have on Service members and Civilians across the Department. More specifically, this paper explores the DEI considerations within AI technologies on individual, team, and Department readiness. The DoD's AI usage spans various strategic and operational capabilities, however this paper explores two critical domains: healthcare and recruitment. In healthcare, AI offers the promise of early disease detection, enhanced diagnostic capabilities, and streamlined administrative processes. However, potential biases stemming from homogenous training data threaten the accuracy and reliability of these systems, jeopardizing Service member health and eroding trust in AI-assisted medical decision-making and potentially the DoD at large. In recruitment, while AI promises efficiency in identifying ideal candidates, its deployment can perpetuate biases, especially when the training data used is not representative of all demographics. Despite efforts to design "unbiased" systems by excluding demographic data, such strategies may inadvertently overlook the unique challenges faced by marginalized communities, further entrenching existing disparities. Both case studies underscore the importance of considering DEI in the development and deployment of AI systems. As the DoD continues to integrate AI into its operations, this paper’s recommendations stress the necessity of continuous DEI assessment to ensure that AI serves as an asset rather than a liability. The authors recommend the following: 1. Data diversity & review 2. Continuous monitoring and calibration 3. Stakeholder engagement 4. Adoption of DEI requirements within Ethical AI Frameworks 5. Further research

Gravitating towards Artificial Intelligence on Anti-Money Laundering A PRISMA Based Systematic Review

In Bangladesh, like many other countries, the implementation of Anti-Money Laundering (AML) measures plays a crucial role in the fight against financial offences and the maintenance of the financial system's integrity. The integration of Artificial Intelligence (AI) is one of the technologies for AML that has emerged as a promising approach to enhance the detection and prevention of financial crimes. The researcher aims to investigate the current state of AML, the impact of AI, the roles involved, and the legislation to achieve economic stability for the transformation of Bangladesh into a smart nation by 2041. In this systematic review study, the PRISMA framework is utilized to provide a comprehensive analysis of existing literature on the application of AI in AML, with a focus on risks, laws, and implications, particularly within the context of Bangladesh. The review synthesizes findings from diverse sources (Web of Sciences, Scopus, PubMed, and DOAJ indexing articles and reports) including academic research, industry reports, and regulatory documents, to assess the effectiveness, challenges, and ethical considerations associated with AI-driven AML solutions. Through a structured search strategy and rigorous selection criteria, the researchers discovered a total of 299 research papers, consisting of 181 latest studies and 117 previous relevant studies identified and analyzed to elucidate key trends, methodologies, and gaps in the current landscape of AI-enabled AML practices. The review also offers insights and recommendations for policymakers, practitioners, and researchers interested in leveraging AI technologies to combat money laundering effectively while navigating regulatory frameworks and safeguarding privacy rights. By critically examining the intersection of AI, AML, and legal frameworks, this study contributes to the ongoing discourse on the role of technology in addressing financial crime challenges in Bangladesh and beyond.

Discussing the Role of Explainable AI and Evaluation Frameworks for Safe and Effective Integration of Large Language Models in Healthcare

Discussing the Role of Explainable AI and Evaluation Frameworks for Safe and Effective Integration of Large Language Models in Healthcare

Exploring Cryptocurrency Market Trends Using Artificial Intelligence

Cryptocurrency has emerged as a transformative force in the financial realm, garnering widespread attention and acceptance. However, its dynamic nature and inherent uncertainties pose significant challenges for investors. This study delves into the factors shaping cryptocurrency value formation by harnessing the power of advanced artificial intelligence frameworks. Specifically, we employ fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network to analyze the price dynamics of prominent cryptocurrencies such as Bitcoin, Ethereum, and Ripple. Our research reveals that ANN tends to rely more heavily on long-term historical data, whereas LSTM exhibits a penchant for short-term dynamics. Interestingly, LSTM demonstrates superior efficiency in leveraging historical information, yet with adequate data, ANN can achieve comparable accuracy. Our findings shed light on the predictability of cryptocurrency market prices, albeit the interpretation may vary depending on the machine-learning model utilized. This study underscores the significance of leveraging artificial intelligence in comprehending and forecasting cryptocurrency market trends, thereby mitigating investment risks in this dynamic landscape. Keyword: Cryptocurrency, Artificial Intelligence, Market Trends, Price Dynamics, Bitcoin.

223 An Artificial Intelligence Framework for Brain Tumor Delineation in Intraoperative Ultrasound

INTRODUCTION: Automated delineation of gliomas in intraoperative ultrasound (iUS) could provide timely information to surgeons. While artificial intelligence (AI) frameworks have shown promising results for delineating gliomas in preoperative MRI, automated delineation in iUS remains a challenging and open problem. METHODS: A deep learning algorithm was developed to automatically delineate gliomas in intraoperative ultrasound. Instead of using time-consuming manual delineations from experts, the algorithm was developed using MR-based automated delineations. First, a MR nnUnet was trained for delineating gliomas in 3D preoperative MRI using an annotated public dataset (N=1000; RSNA-ASNR-MICCAI-BraTS 2021). Then, this algorithm delineated gliomas in preoperative MRI of patients (N = 84) in whom pre-dural opening 3D iUS reconstructed from a tracked handheld 2D probe was also acquired. To obtain the MR-based delineations in iUS, preoperative MRI and iUS were aligned using NiftyReg. Finally, a 3D iUS nnUnet was trained using a random subset (N = 78) of this pseudo-annotated iUS dataset. The method was assessed on the remaining test set (N = 943 slices from 6 volumes), with a treating neurosurgeon delineating gliomas in 2D iUS only and then in 2D iUS and aligned MR slices to obtain gold-standard delineations. RESULTS: The median Dice scores of the nnUnet and the neurosurgeon were respectively 78.6% (IQ:15.5%) and 84.3% (IQ: 21.1%) against the gold-standard delineations. In this preliminary study, the network's performance was comparable to the neurosurgeon's (two paired one-sided tests, equivalence margin:5%, p<1e-5, N = 943), with rapid delineation (<10 second) of 3D iUS (95-216 slices) compared to neurogeons (32-93 minutes), making automated delineation practical during surgery. CONCLUSIONS: The results demonstrate the feasibility of using AI-based methods for automated delineation of brain tumors in iUS, which could improve surgical outcomes by providing real-time information to surgeons.

Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability

As artificial intelligence (AI) continues to permeate various aspects of our lives, the ethical challenges associated with its development become increasingly apparent. This paper navigates and reviews the ethical dilemmas in AI development, focusing on strategies to promote transparency, fairness, and accountability. The rapid growth of AI technology has given rise to concerns related to bias, lack of transparency, and the need for clear accountability mechanisms. In this exploration, we delve into the intricate ethical landscape of AI, examining issues such as bias and fairness, lack of transparency, and the challenges associated with accountability. To address these concerns, we propose strategies for transparency, including the implementation of Explainable AI (XAI), advocating for open data sharing, and embracing ethical AI frameworks. Furthermore, we explore strategies to promote fairness in AI algorithms, emphasizing the importance of fairness metrics, diverse training data, and continuous monitoring for iterative improvement. Additionally, the paper delves into strategies to ensure accountability in AI development, considering regulatory measures, ethical AI governance, and the incorporation of human-in-the-loop approaches. To provide practical insights, case studies and real-world examples are analyzed to distill lessons learned and best practices. The paper concludes with a comprehensive overview of the proposed strategies, emphasizing the importance of balancing innovation with ethical responsibility in the evolving landscape of AI development. This work contributes to the ongoing discourse on AI ethics, offering a roadmap for navigating the challenges and fostering responsible AI development practices.

Theoretical frameworks in AI for credit risk assessment: Towards banking efficiency and accuracy

This paper delves into theoretical frameworks in AI for credit risk assessment, exploring how these frameworks enhance banking efficiency and accuracy. It discusses various AI techniques such as machine learning algorithms, neural networks, and natural language processing, and their application in credit risk assessment. Furthermore, it examines the challenges and opportunities presented by these frameworks, highlighting their potential to revolutionize the banking sector. Revolutionizing Credit Risk Assessment in Banking, The Role of Artificial Intelligence In the dynamic realm of finance, the assessment of credit risk stands as a fundamental pillar for banking institutions. Traditionally, this process has heavily relied on statistical models and historical data. However, the emergence of Artificial Intelligence (AI) has catalyzed a transformative shift in this domain. This paper elucidates the theoretical underpinnings of AI frameworks employed in credit risk assessment and investigates their profound implications for enhancing the efficiency and accuracy of banking operations. The exploration begins by delineating various theoretical frameworks in AI pertinent to credit risk assessment. Leveraging machine learning algorithms, neural networks, and natural language processing techniques, these frameworks offer innovative approaches to evaluate creditworthiness. Unlike conventional methods, AI-driven models possess the capacity to ingest vast datasets, identify intricate patterns, and adapt dynamically to evolving market dynamics. Such capabilities empower banks to make more informed and timely decisions regarding lending activities. Moreover, this paper delves into the practical application of AI techniques in credit risk assessment. Through case studies and empirical evidence, it elucidates how these advanced methodologies enable banks to mitigate risks while maximizing profitability. By harnessing AI, financial institutions can optimize credit scoring processes, identify potential defaulters with greater accuracy, and customize lending terms based on individual risk profiles. Additionally, AI facilitates real-time monitoring of credit portfolios, allowing proactive risk management and timely interventions to prevent adverse outcomes.

Analyzing Cryptocurrency Prices through Artificial Intelligence

Cryptocurrency is reshaping the financial landscape, gaining popularity and acceptance among merchants. Despite the increasing investments in cryptocurrency, its dynamic features, uncertainties, and predictability remain largely unknown, posing significant investment risks. This study aims to understand the factors influencing cryptocurrency value formation. Leveraging advanced artificial intelligence frameworks like fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network, we analyze the price dynamics of Bitcoin, Ethereum, and Ripple. Our findings indicate that ANN relies more on long-term history, while LSTM focuses on short-term dynamics, suggesting LSTM's superior efficiency in utilizing historical information. However, with sufficient historical data, ANN can achieve comparable accuracy to LSTM. This study underscores the predictability of cryptocurrency market prices, though the explanation may vary depending on the machine-learning model employed.

AI for smart cities opportunities and promising directions

The transformative potential of Artificial Intelligence (AI) in the realm of smart cities is an evolving landscape of innovation and challenges. This research undertook a comprehensive exploration of AI's impact, harnessing both quantitative and qualitative methodologies. Detailed analyses were performed on data from select smart cities globally, focusing on sectors such as energy, traffic, health services, and waste management. Additionally, perceptions and experiences of urban stakeholders were captured through interviews. The results solidified AI's tangible benefits in enhancing urban life quality, while also bringing forth concerns about data privacy, algorithmic biases, and socio-economic implications. The study concludes with a call for holistic AI frameworks, heightened public engagement, and interdisciplinary collaborations to ensure the ethical and sustainable evolution of AI-integrated smart cities.

Advanced Applications of Artificial Intelligence in Pharmacovigilance: Current Trends and Future Perspectives

The primary goal of pharmacovigilance, the cornerstone of public health, is to track and evaluate adverse drug reactions in order to guarantee patient safety. Conventional approaches suffer from biases in human error, inefficiency, and scalability problems. A new era in pharmacovigilance is being ushered in by the introduction of artificial intelligence (AI), which holds the promise of vast data analysis, automated procedures, and enhanced safety signal detection. Artificial intelligence technologies improve adverse event detection and signal identification by providing unparalleled speed, accuracy, and scalability. They are adept at applying sophisticated algorithms, machine learning models, and natural language processing to glean insights from unstructured data sources like clinical notes, patient narratives, and regulatory reports. This capacity makes it possible to implement risk management plans that are more thorough and proactive. However, using artificial intelligence in pharmacovigilance necessitates large expenditures for processing power, infrastructure, and regulatory compliance. For artificial intelligence-driven systems to be accurate, dependable, and applicable, ongoing validation, monitoring, and improvement efforts are essential. The ethical and legal implications of patient privacy, data security, and regulatory compliance underscore the necessity of cautious artificial intelligence deployment in order to maintain public trust and protect patient rights. Regulatory agencies, healthcare professionals, and artificial intelligence developers must work together efficiently to implement explainable artificial intelligence frameworks, adaptive surveillance techniques, and improved signal validation processes in order to fully realize the potential of artificial intelligence in pharmacovigilance. This will usher in a new era of proactive risk assessment and improved public health outcomes. Keywords Pharmacovigilance, Artificial Intelligence, Signal Detection, Regulatory Affairs, Patient Safety, Public Health

An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates.

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced-under pertinent simplifications-via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations.