Artificial Intelligence Features Research Articles

Semantic analysis of blockchain intelligence with proposed agenda for future issues

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

Visual Positioning of Nasal Swab Robot Based on Hierarchical Decision.

This study focuses on a robot vision localization method for coping with the operational task of automatic nasal swab sampling. The application is important in the detection and epidemic prevention of Corona Virus Disease 2019 (COVID-19) to alleviate the large-scale negative impact of individuals suffering from pneumonia owing to COVID-19. In this method, the idea of a hierarchical decision network is used to consider the strong infectious characteristics of the COVID-19, which is followed by processing the robot behavior constraint condition. The visual navigation and positioning method using a single-arm robot for sampling is also planned, which considers the operation characteristics of medical staff. In the decision network, the risk factor for potential contact infection caused by swab sampling operations is established to avoid the spread among personnel. A robot visual servo control with artificial intelligence characteristics is developed to achieve a stable and safe nasal swab sampling operation. Experiments demonstrate that the proposed method can achieve good vision positioning for the robots and provide technical support for managing new major public health situations.

Quantum field lens coding and classification algorithm to predict measurement outcomes

This study develops a method to implement a quantum field lens coding and classification algorithm for two quantum double-field (QDF) system models: 1- a QDF model, and 2- a QDF lens coding model by a DF computation (DFC). This method determines entanglement entropy (EE) by implementing QDF operators in a quantum circuit. The physical link between the two system models is a quantum field lens coding algorithm (QF-LCA), which is a QF lens distance-based, implemented on real N-qubit machines. This is with the possibility to train the algorithm for making strong predictions on phase transitions as the shared objective of both models. In both system models, QDF transformations are simulated by a DFC algorithm where QDF data are collected and analyzed to represent energy states and transitions, and determine entanglement based on EE. The method gives a list of steps to simulate and optimize any thermodynamic system on macro and micro-scale observations, as presented in this article:•The implementation of QF-LCA on quantum computers with EE measurement under a QDF transformation.•Validation of QF-LCA as implemented compared to quantum Fourier transform (QFT) and its inverse, QFT−1.•Quantum artificial intelligence (QAI) features by classifying QDF with strong measurement outcome predictions.

Defining the Future: The AI Act's Potential in equitably Safeguarding Fundamental Rights and Promoting AI Innovation

The recent advancements in the realm of Artificial Intelligence (AI) feature a landscape filled with exciting prospects and lurking dangers in equal measure, necessitating a nuanced, human-centred regulatory approach. The EU is the first organisation to have undertaken this challenge with binding law through its AI Act. This paper delves into the complex task of this founding stone of EU AI law to balance promoting innovation and safeguarding fundamental rights. It addresses both the Act’s pre-history – encompassing the relevant policy documents before the European Commission's proposal – and its history, which includes the negotiations and amendments leading up to the final trilogue session on the 6th of December 2023 and the consequential provisional agreement, followed by its approval by COREPER and the IMCO and LIBE committees and its final upvoting in the European Parliament on the 13th of March 2024. Keeping track of the Act’s legislative journey, including the context in which said journey evolved, shall be crucial in order to comprehensively understand, interpret, and apply what is expected to be one of Europe’s most impactful pieces of legislation to date. The paper aims to bridge a gap in scholarly discussion around the AI Act, which has so far focused solely on evaluating fundamental rights protection or assessing cost-efficiency, providing a practical approach that consolidates the legislative process and appraises the AI Act’s overall potential in protecting fundamental rights, while equitably maintaining AI innovation. The research question it seeks to answer is: How can the AI Act promise sufficient fundamental rights protection without compromising AI innovation and overburdening enterprises? The paper is structured to provide a comprehensive analysis of the Act in view of answering the research question. The Overview section (II.) will give the historical background of AI and track the main policy documents that led to the AI Act’s proposal offering a source also for retrospective research in future. It will also identify specific key points of the Commission’s original proposal that call for examination and establish its connection to fundamental rights protection, in view of being presented as a product safety regulation. The section on the Legislative State of the Art and Remaining Pitfalls (III.) will present the fruits the legislative process has yielded up until the confirmation of the agreed final draft by the IMCO and LIBE committees on the 13th of February 2024 and its subsequent approval in the Parliament a month later. It will enumerate the main points introduced with the negotiated amendments to then identify still existing potential pitfalls with regards to the Act’s scope, enforcement mechanism and future proofness, and how this creates weaknesses for innovation and fundamental rights. The Balancing section (IV.) offers suggestions to counter the weaknesses that were identified in the previous part, uncovering the balancing point between fundamental rights protection and innovation. Finally, the paper will conclude (V.) by summarising the findings and the answer to the research question while leaving room for future discussion.

Problems of Artificial Intelligence: Philosophical Analysis

Artificial intelligence can be understood as the pinnacle of development of the science of cybernetics. One of the key features of artificial intelligence is that, unlike simple computers, artificial intelligence has the ability to independently process information coming from outside and perform complex, extraordinary operations. That is, if a simple machine is loaded with specific algorithms and execute them, artificial intelligence can itself participate in the design of new tasks. Already today, such artificial intelligence capabilities are used in various spheres of society, for example, in such areas as economics and finance. In the light of such trends, classical philosophical questions acquire new guidelines. What kind of future is possible for humanity? Is artificial intelligence a force for advancing society or an initiative that poses a serious threat to humanity? The authors are close to the point of view that a complete rejection of the capabilities of artificial intelligence is impossible and there is no need for this. But deeper ethical reflection would help prevent threats to the future of humanity as a result of the widespread use of artificial intelligence. The article examines philosophical discourses regarding achievements in the field of artificial intelligence. Issues of a methodological, ethical epistemological nature are considered. The judgments and assumptions of researchers were analyzed within the framework of such issues as the improvement of artificial intelligence, its role and place in modern life, and its significance in the life of society. Of course, the risks associated with the development of artificial intelligence require serious philosophical reflection. Therefore, the main emphasis of the article is focused on the analyses of existing current discourses. The authors analyzed a number of scenarios related to the topic of artificial intelligence, its development, as well as the categorical apparatus in this area.

ARTIFICIAL INTELLIGENCE AS PRIMARY SCHOOL TEACHER ASSISTANT

Artificial intelligence is one of the leading technologies today, which is rapidly spreading into all spheres of human activities including education. In 2023, GPT chat was ranked fourth in the Top 100 Tools for Learning rating, which demonstrates the powerful potential of artificial intelligence tools for the development of innovative processes in education. Accordingly, educators need to understand what artificial intelligence is, its features, and how it can be used to replace the routine activities of both teachers and students in teaching and learning. The authors conducted a survey of primary school teachers about their understanding of this notion, ways to use it as their assistant, advantages and disadvantages of artificial intelligence, and prospects for its development and application in all areas of human activity. The level of respondents' knowledge and skills in artificial intelligence, its features and functions was determined. The professional standard of a primary school teacher and the types of activities, through which the teacher's labor functions are realized and for which it is advisable to use artificial intelligence tools, are analyzed. The development of primary school teachers' competencies can be positively influenced by the use of artificial intelligence in their professional activities. The authors describe digital artificial intelligence tools that will be useful for primary school teachers and classify them according to the types of their pedagogical activities. The areas of activity in which artificial intelligence can act as a teacher's assistant, as well as areas that are currently beyond the capabilities of artificial intelligence, are identified. It is concluded that primary school teachers need to be trained to use artificial intelligence in their professional activities and to transfer knowledge about artificial intelligence to pupils, as well as ways to teach pupils the features of artificial intelligence, methods of its application for teaching pupils and for their own professional development.

Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning

Introduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and Methods: We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People’s Hospital from the renal disease treatment information system. IDH was defined as a systolic blood pressure drop ≥20 mm Hg, a mean arterial pressure drop ≥10 mm Hg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation was used to explain the contribution of each variable to the best predictive model. Results: A total of 3,906 patients and 314,534 dialysis sessions were included, of which 142,237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval [CI], 0.811–0.813), 0.748 (95% CI, 0.747–0.749), and 0.743 (95% CI, 0.742–0.744), respectively. Conclusion: Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions.

Generating post-hoc explanation from deep neural networks for multi-modal medical image analysis tasks

Explaining model decisions from medical image inputs is necessary for deploying deep neural network (DNN) based models as clinical decision assistants. The acquisition of multi-modal medical images is pervasive in practice for supporting the clinical decision-making process. Multi-modal images capture different aspects of the same underlying regions of interest. Explaining DNN decisions on multi-modal medical images is thus a clinically important problem. Our methods adopt commonly-used post-hoc artificial intelligence feature attribution methods to explain DNN decisions on multi-modal medical images, including two categories of gradient- and perturbation-based methods.• Gradient-based explanation methods – such as Guided BackProp, DeepLift – utilize the gradient signal to estimate the feature importance for model prediction.• Perturbation-based methods – such as occlusion, LIME, kernel SHAP – utilize the input-output sampling pairs to estimate the feature importance.• We describe the implementation details on how to make the methods work for multi-modal image input, and make the implementation code available.

Determination of Complex Aircraft - Operator Control System Properties

The automatization of particularly fast maneuvering aircraft has greatly reduced the sphere of man's scope by freeing him from routine operator work and moving it to the highest hierarchical level of the system. This forces aerospace manufacturers and users to address questions of a kind in a new way: how to design an ergatic system (ergatic system), how to optimize the coordination or human operator's entry into the "machine part", that has a distinctive feature of artificial intelligence. In this article, the properties of the ergatic system are investigated in order to determine the stability of the system in the longitudinal steady-state equilibrium flight mode at 22 965 ft, Mach 0.8, VTAS = 250 m/s. The Control System Toolbox, which is focused on solving tasks related to the analysis and synthesis of linear time - invariant dynamic systems, was used to solve the example. The basic prerequisite for the use of individual toolbox modules is knowledge mathematical models of controlled processes described in the state space, by means of transfer functions in s - area, z - area, time area, in the form of poles, zeros and amplification.

Determination of Complex Aircraft - Operator Control System Properties

The automatization of particularly fast maneuvering aircraft has greatly reduced the sphere of man's scope by freeing him from routine operator work and moving it to the highest hierarchical level of the system. This forces aerospace manufacturers and users to address questions of a kind in a new way: how to design an ergatic system (ergatic system), how to optimize the coordination or human operator's entry into the "machine part", that has a distinctive feature of artificial intelligence. In this article, the properties of the ergatic system are investigated in order to determine the stability of the system in the longitudinal steady-state equilibrium flight mode at 22 965 ft, Mach 0.8, VTAS = 250 m/s. The Control System Toolbox, which is focused on solving tasks related to the analysis and synthesis of linear time - invariant dynamic systems, was used to solve the example. The basic prerequisite for the use of individual toolbox modules is knowledge mathematical models of controlled processes described in the state space, by means of transfer functions in s - area, z - area, time area, in the form of poles, zeros and amplification.

An Integrated Social and Behavioral System Approach to Evaluation of Healthcare Information Technology for Polychronic Conditions

Healthcare delivery systems are evolving with the advances in health information technology (HIT) development and its applications to coordinated or guided care for polychronic conditions. The design features of artificial intelligence in healthcare reflect the public interest in optimizing care coordination and communication between providers and patients. This article offers a practical evaluation and assessment of the relevance of theoretical frameworks and appropriate methodologies to formalize a multi-criteria optimization of a logic model applicable for achieving the system’s efficiency and effectiveness. In specifying theoretical constructs and evaluation methods for HIT evaluation, a three-fold purpose is to show the relevance of personal and behavioral determinants of HIT use, articulate the need for developing a transdisciplinary framework, and formulate appropriate multilevel modeling and causal analysis of the determinants of HIT use and its impacts on chronic care.

A Deep Neural Network Approach on Spleen Cancer Detection using DenseNet-201

Abstract: Spleen is the largest secondary lymphoid organ and play a crucial role in the regulation of innate and adaptive immune. Among all cancers, spleen cancer is one of the serious diseases which effects minor population across the globe but is potentially fatal, especially if diagnosed in a later stage of development with a 20% survival rate at 6 months. Given the intricacy of the problem, several computer-aided diagnostic methods have been proposed and developed to increase the survival rate Spleen Cancer is a malignancy of white blood cells involving tumour deposits in the spleen. Most splenic cancer do not start in the spleen, and those that do, are almost always lymphomas. Lymphoma is a type of blood cancer that develops in the lymphatic system. Although there are many systems available with the medical industry, but this research is proposed to improve the performance of existing system by employing deep learning feature of Artificial Intelligence (AI) using Convolution Neural Networks (CNN), where Convolution Neural Networks (CNN) has been implemented using DenseNet-201. This research accomplished the desired parameters with values to achieve 99.60% accuracy and 0.1240 % loss while training and testing the model. The research has been supported with the datasets from Kaggle, IEEE transactions on information technology in biomedicine and IEEE International Symposium on Biomedical Imaging. The data set contains three clinical types spleen cancer, such as CLL (chronic lymphocytic leukemia); FL (follicular lymphoma); MCL (mantle cell lymphoma).

Augmented Analytics Driven by AI: A Digital Transformation beyond Business Intelligence.

Lately, Augmented Analytics (AA) has increasingly been introduced as a tool for transforming data into valuable insights for decision-making, and it has gained attention as one of the most advanced methods to facilitate modern analytics for different types of users. AA can be defined as a combination of Business Intelligence (BI) and the advanced features of Artificial Intelligence (AI). With the massive growth in data diversity, the traditional approach to BI has become less useful and requires additional work to obtain timely results. However, the power of AA that uses AI can be leveraged in BI platforms with the use of Machine Learning (ML) and natural language comprehension to automate the cycle of business analytics. Despite the various benefits for businesses and end users in converting from BI to AA, research on this trend has been limited. This study presents a comparison of the capabilities of the traditional BI and its augmented version in the business analytics cycle. Our findings show that AA enhances analysis, reduces time, and supports data preparation, visualization, modelling, and generation of insights. However, AI-driven analytics cannot fully replace human decision-making, as most business problems cannot be solved purely by machines. Human interaction and perspectives are essential, and decision-makers still play an important role in sharing and operationalizing findings.

Artificial Intelligence and Human Psychology in Online Transaction Fraud.

Banking operations have changed due to technological advancement. On one hand, modernization in technology has facilitated the daily operation of banks; on the other hand, this has also resulted in an increase in the number of cyber-attacks. Artificial Intelligence has introduced new models to detect and prevent cybercrimes. Some fraud has also occurred due to the involvement of employees inside particular organizations. So, this study has focused on both sides: the machine as well as the human. Firstly, the research has focused on fraud diamond theory and has analyzed factors such as rationalization, capabilities, perceived pressure, and perceived opportunities to understand the psychology of the fraudster. Secondly, Artificial Intelligence characteristics, threat exposure, big data management, explainability, cost effectiveness, and risk prediction are evaluated to explore their use in fraud reduction in banks. The research data have been collected from 15 Banks in Pakistan with the help of a questionnaire using five-item Likert scales. The data have been analyzed using IBM SPSS Software. The results gained after correlation and regression analysis proved that Fraud diamond theory and AI characteristics have positive and significant effects on cybercrimes. This study is a great contribution to the banking industry of Pakistan as it provides a complete analysis to control fraud inside organizations by understanding the mindset of fraudsters with the help of fraud diamond theory. At the same time, outside fraud will be handled with the help of Artificial Intelligence. This will result in banks growth, which ultimately boosts the economy of a country.

A Detection and Prediction Model Based on Deep Learning Assisted by Explainable Artificial Intelligence for Kidney Diseases

Kidney diseases are one of the most common diseases worldwide and cause unbearable pain in most people. In this study aims to detecting the cyst and stone in the kidney. For the this purpose, YOLO architecture designs were used for detection of kidney, kidney cyst and kidney stone. The YOLO architecture designs were supported by the explainable artificial intelligence (xAI) feature. CT images in three classes, namely 72 kidney cysts, 394 kidney stones and 192 healthy kidneys were used in the performance analysis part of the YOLO architecture designs. As a result, YOLOv7 architecture design outperformed the YOLOv7 Tiny architecture design. YOLOv7 architecture design achieved the mAP50 of 0.85, precision of 0.882, sensitivity of 0.829 and F1 score of 0.854. Consequently, deep learning based xAI assisted computer aided diagnosis (CAD) system was developed for diagnosis of kidney diseases.

Analysis of Impact of Methodologies of Intelligent Automation on Service and Knowledge Work

Intelligent automation process is the software which uses the features of artificial intelligence and robotic engineering to perform steps or tasks in the business or organization which are repetitive. It is the concept where the tasks which repeats in each process mostly done by humans are replaced by intelligent automation process. Its aim is to work on the unstructured data or content which involves repetitive tasks. It also helps to automate data which includes images and pictures. Many organizations have challenges gathering large number of data sets for training purpose, but with the use of concept of transfer learning and intelligent automation, this challenge has been resolved to large extent. Intelligent automation also provides solutions for unstructured as well as structured or semi-structured data which contributes largely in analysis of financial documents, analysis of contracts made, insurance claims etc. The paper will target description of process to implement intelligent automation in organization, applications in which intelligent automation is used, various methodologies, advantages and disadvantages.

Microbiology 2.0-A "behind the scenes" consideration for artificial intelligence applications for interpretive culture plate reading in routine diagnostic laboratories.

Laboratory automation with Artificial Intelligence (AI) features have now emerged into routine diagnostic clinical use to interpret growth on agar plates. Applications are currently limited to urine samples and infection control screens, yet some of the details around the development of algorithms remain entrenched with AI development specialists and are not well understood by laboratorians. The generation of algorithms is not a trivial task and is a highly structured process, with several considerations needed to develop the appropriate data for specific intended uses. Understanding these considerations highlights the limitations of any algorithm created and informs better design practices so that algorithm objectives can be thoroughly tested prior to routine use.

Using Open-Source Software for Business, Urban, and Other Applications of Deep Neural Networks, Machine Learning, and Data Analytics Tools

This article provides an overview with examples of what Neural Networks (NN), Machine Learning (ML), and Artificial Intelligence (AI) and Data Analytics are, and with their applications in business, urban and biomedical situations. The characteristics of 29 types of neural networks are provided including their distinctive graphical illustrations. A survey of current open-source software (OSS) for neural networks, neural network software available for free trial download for limited time use, open-source software (OSS) for Machine Learning (ML), and open-source software (OSS) for Data Analytics tools are provided. Characteristics of Artificial Intelligence (AI) technologies for Machine Learning available as open-source are discussed. Illustrations of applications of Neural Networks, Machine Learning, and Artificial Intelligence are presented as used in the daily operations of a large international-based software company for optimal configuration of their Helix Data Capacity system and other.

Smart quantum statistical imaging beyond the Abbe-Rayleigh criterion

The wave nature of light imposes limits on the resolution of optical imaging systems. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of imaging instruments. Recently, there has been interest in using spatial projective measurements to enhance the resolution of imaging systems. Unfortunately, these schemes require a priori information regarding the coherence properties of “unknown” light beams and impose stringent alignment conditions. Here, we introduce a smart quantum camera for superresolving imaging that exploits the self-learning features of artificial intelligence to identify the statistical fluctuations of unknown mixtures of light sources at each pixel. This is achieved through a universal quantum model that enables the design of artificial neural networks for the identification of photon fluctuations. Our protocol overcomes limitations of existing superresolution schemes based on spatial mode projections, and consequently provides alternative methods for microscopy, remote sensing, and astronomy.

Design and Simulation of DFIG based Wind Energy System using AI EMS

Abstract: Renewable energy sources are considered to be an unpredictable resource especially in wind turbines; rapid variation in wind speeds cause power fluctuations leads to interrupt the power system stability during the high-level power operation. To overcome these issue energy storage systems such as Battery storage is added to balance the grid and helps to balance the generated power and the power demand. The battery system is also used to mitigate the power fluctuations and helps to give more power to balance the load requirement. The proposed (EMS) Energy Management System integrates with the Artificial Neural Network for operation of both battery and wind system depending upon the demand need. In the case of the battery management system, the intelligent controllers help to control the State of Charge (SoC) limits of the battery to make sure the required time of charging and discharging. The proposed Artificial Intelligent Controller acts as an Energy Management System and Battery Management System as well as it is used for power balance generation and load also efficiently mitigating the voltage fluctuation during variation in the load. MATLAB is used for the simulation process since it contains the features of Artificial Intelligence which can in turn function as switching device. Keywords: Renewable energy sources, Battery Storage, Energy management system, State of charge, Artificial neural network etc.