Search results for: artificial microRNA approach

Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Biruhi Tesfaye, Avinash M. Potdar

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The laboratory test process to determine the California bearing ratio (CBR) of black cotton soils is not only overpriced but also time-consuming as well. Hence advanced prediction of CBR plays a significant role as it is applicable In pavement design. The prediction of CBR of treated soil was executed by Artificial Neural Networks (ANNs) which is a Computational tool based on the properties of the biological neural system. To observe CBR values, combined eggshell and waste glass was added to soil as 4, 8, 12, and 16 % of the weights of the soil samples. Accordingly, the laboratory related tests were conducted to get the required best model. The maximum CBR value found at 5.8 at 8 % of eggshell waste glass powder addition. The model was developed using CBR as an output layer variable. CBR was considered as a function of the joint effect of liquid limit, plastic limit, and plastic index, optimum moisture content and maximum dry density. The best model that has been found was ANN with 5, 6 and 1 neurons in the input, hidden and output layer correspondingly. The performance of selected ANN has been 0.99996, 4.44E-05, 0.00353 and 0.0067 which are correlation coefficient (R), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE) respectively. The research presented or summarized above throws light on future scope on stabilization with waste glass combined with different percentages of eggshell that leads to the economical design of CBR acceptable to pavement sub-base or base, as desired.

Keywords: CBR, artificial neural network, liquid limit, plastic limit, maximum dry density, OMC

Procedia PDF Downloads 191

Authors: Ilyes Athimni, Mouna Bouzazi, Mongi Boulehmi, Ahmed Ferchichi

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The principles of good governance, universal values, and digitization are among the tools to fight corruption and improve the quality of service delivery. In recent years, these tools have become one of the most controversial topics in the field of education and a concern of many international organizations and institutions against the problem of corruption. Corruption in the education sector, particularly in higher education, has negative impacts on the quality of education systems and on the quality of administrative or educational services. Currently, the health crisis due to the spread of the COVID-19 pandemic reveals the difficulties encountered by education systems in most countries of the world. Due to the poor governance of these systems, many educational institutions were unable to continue working remotely. To respond to these problems encountered by most education systems in many countries of the world, our initiative is to propose a methodology to reinvent education systems based on global values and digital technologies. This methodology includes a work strategy for educational institutions, whether in the provision of administrative services or in the teaching method, based on information and communication technologies (ICTs), intelligence artificial, and intelligent agents. In addition, we will propose a supervisory law that will be implemented and monitored by intelligent agents to improve accountability, transparency, and accountability in educational institutions. On the other hand, we will implement and evaluate a field experience by applying the proposed methodology in the operation of an educational institution and comparing it to the traditional methodology through the results of teaching an educational program. With these specifications, we can reinvent quality education systems. We also expect the results of our proposal to play an important role at local, regional, and international levels in motivating governments of countries around the world to change their university governance policies.

Keywords: artificial intelligence, corruption in education, distance learning, education systems, ICTs, intelligent agents, good governance

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Authors: Arun Prasad Jaganathan

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In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper introduces an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization

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Authors: Eliska Oberhofnerova, Milos Panek, Stepan Hysek, Martin Lexa

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Nowadays the use of wood, both indoors and outdoors, is constantly increasing. However wood is a natural organic material and in the exterior is subjected to a degradation process caused by abiotic factors (solar radiation, rain, moisture, wind, dust etc.). This process affects only surface layers of wood but neglecting some of the basic rules of wood protection leads to increased possibility of biological agents attack and thereby influences a function of the wood element. The process of wood degradation can be decreased by proper surface treatment, especially in the case of less naturally durable wood species, as spruce. Modern coating systems are subjected to many requirements such as colour stability, hydrophobicity, low volatile organic compound (VOC) content, long service life or easy maintenance. The aim of this study is to evaluate the colour stability of spruce wood (Picea abies), as the basic parameter indicating the coating durability, treated with two layers of transparent natural oil wood stain and exposed to outdoor conditions. The test specimens were exposed for 2 years to natural weathering and 2000 hours to artificial weathering in UV-chamber. The colour parameters were measured before and during exposure to weathering by the spectrophotometer according to CIELab colour space. The comparison between untreated and treated wood and both testing procedures was carried out. The results showed a significant effect of coating on the colour stability of wood, as expected. Nevertheless, increasing colour changes of wood observed during the exposure to weathering differed according to applied testing procedure - natural and artificial.

Keywords: colour stability, natural and artificial weathering, spruce wood, transparent coating

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Authors: Seham Elia Moussa Shenouda

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The relationship between development and human rights has long been the subject of academic debate. To understand the dynamics between these two concepts, various principles are adopted, from the right to development to development-based human rights. Despite the initiatives taken, the relationship between development and human rights remains unclear. However, the overlap between these two views and the idea that efforts should be made in the field of human rights have increased in recent years. It is then evaluated whether the right to sustainable development is acceptable or not. The article concludes that the principles of sustainable development are directly or indirectly recognized in various human rights instruments, which is a good answer to the question posed above. This book therefore cites regional and international human rights agreements such as , as well as the jurisprudence and interpretative guidelines of human rights institutions, to prove this hypothesis.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

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Authors: N. Ploysook, R. Rugsaj, C. Suvanjumrat

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The characteristic requirement for producing rectangular shape bottles was a uniform thickness of the plastic bottle wall. Die shaping was a good technique which controlled the wall thickness of bottles. An advance technology which was the finite element method (FEM) for blowing parison to be a rectangular shape bottle was conducted to reduce waste plastic from a trial and error method of a die shaping and parison control method. The artificial intelligent (AI) comprised of artificial neural network and genetic algorithm was selected to optimize the die gap shape from the FEM results. The application of AI technique could optimize the suitable die gap shape for the parison blow molding which did not depend on the parison control method to produce rectangular bottles with the uniform wall. Particularly, this application can be used with cheap blow molding machines without a parison controller therefore it will reduce cost of production in the bottle blow molding process.

Keywords: AI, bottle, die shaping, FEM

Procedia PDF Downloads 239

Authors: Faezeh Aghazadeh, Mohammad Javad Sharifi

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The application of neural networks to model a full-scale industrial acetylene hydrogenation in olefin plant has been studied. The operating variables studied are the, input-temperature of the reactor, output-temperature of the reactor, hydrogen ratio of the reactor, [C₂H₂]input, and [C₂H₆]input. The studied operating variables were used as the input to the constructed neural network to predict the [C₂H₆]output at any time as the output or the target. The constructed neural network was found to be highly precise in predicting the quantity of [C₂H₆]output for the new input data, which are kept unaware of the trained neural network showing its applicability to determine the [C₂H₆]output for any operating conditions. The enhancement of [C₂H₆]output as compared with [C₂H₆]input was a consequence of low selective acetylene hydrogenation to ethylene.

Keywords: acetylene hydrogenation, Pd-Ag/Al₂O₃, artificial neural network, modeling, optimal design

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Authors: Ivana Kašparová, Emilie Pecharová

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The experience of radiological contamination of land, especially after the Chernobyl and Fukushima disasters have shown the need to explore possibilities to the capture of radionuclides in the area affected and to adapt the landscape management to this purpose ex –ante the considered accident in terms of prevention. The project‚ Minimizing the impact of radiation contamination on land in the emergency zone of Temelin NPP‘ (2012-2015), dealt with the possibility of utilization of wetlands as retention sites for water carrying radionuclides in the case of a radiation accident. A model artificial wetland was designed and adopted as a utility model by the Ministry of Industry and Trade of the Czech Republic. The article shows the conditions of construction of designed wetlands in the landscape with regard to minimizing the negative effect on agricultural production and enhancing the hydrological functionality of the landscape.

Keywords: artificial wetland, land use/ land cover, old maps, surface-to-water transport of radionuclides

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Authors: Sara Segura, Diego Nuñez, Miryam Villamil

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In this work, we studied the microscale interaction of foreign substances with blood inside an artificial transparent artery system that represents medium and small muscular arteries. This artery system had channels ranging from 75 μm to 930 μm and was fabricated using glass and transparent polymer blends like Phenylbis(2,4,6-trimethylbenzoyl) phosphine oxide, Poly(ethylene glycol) and PDMS in order to be monitored in real time. The setup was performed using a computer controlled precision micropump and a high resolution optical microscope capable of tracking fluids at fast capture. Observation and analysis were performed using a real time software that reconstructs the fluid dynamics determining the flux velocity, injection dependency, turbulence and rheology. All experiments were carried out with fully computer controlled equipment. Interactions between substances like water, serum (0.9% sodium chloride and electrolyte with a ratio of 4 ppm) and blood cells were studied at microscale as high as 400nm of resolution and the analysis was performed using a frame-by-frame observation and HD-video capture. These observations lead us to understand the fluid and mixing behavior of the interest substance in the blood stream and to shed a light on the use of implantable devices for drug delivery at arteries with different Endothelial dysfunction. Several substances were tested using the artificial artery system. Initially, Milli-Q water was used as a control substance for the study of the basic fluid dynamics of the artificial artery system. However, serum and other low viscous substances were pumped into the system with the presence of other liquids to study the mixing profiles and behaviors. Finally, mammal blood was used for the final test while serum was injected. Different flow conditions, pumping rates, and time rates were evaluated for the determination of the optimal mixing conditions. Our results suggested the use of a very fine controlled microinjection for better mixing profiles with and approximately rate of 135.000 μm3/s for the administration of drugs inside arteries.

Keywords: artificial artery, drug delivery, microfluidics dynamics, arteriosclerosis

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Authors: Meriem Mossaddek, El Mehdi Laadissi, El Mehdi Loualid, Chouaib Ennawaoui, Sohaib Bouzaid, Abdelowahed Hajjaji

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An electrochemical system is a subset of mechatronic systems that includes a wide variety of batteries and nickel-cadmium, lead-acid batteries, and lithium-ion. Those structures have several non-linear behaviors and uncertainties in their running range. This paper studies an effective technique for modeling Lithium-Ion (Li-Ion) batteries using a Nonlinear Auto-Regressive model with exogenous input (NARX). The Artificial Neural Network (ANN) is trained to employ the data collected from the battery testing process. The proposed model is implemented on a Li-Ion battery cell. Simulation of this model in MATLAB shows good accuracy of the proposed model.

Keywords: lithium-ion battery, neural network, energy storage, battery model, nonlinear models

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Authors: Barkaoui Asma

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In our article, we use the economic approach of measuring advertising effectiveness to show the margin of advertising spread gained through digital communication. For economists, profit maximization depends on determining the optimal advertising budget. For this, they use the theories of the marginalist current to determine when the maximum level of benefits is reached. Using the economic approach we show the significant return on investment for advertisers. We then discuss the risks of perception of advertising pressure by consumers.

Keywords: digital advertising, economic approach, effectiveness, pressure

Procedia PDF Downloads 304

Authors: Weichen Chang

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To create a guided teaching method for AI generative drawing design, this paper develops a set of teaching models for AI generative drawing (DCAI), which combines learning modes such as problem-solving, thematic inquiry, phenomenon-based, task-oriented, and DFC . Through the information security AI picture book learning guided programs and content, the application of participatory action research (PAR) and interview methods to explore the dual knowledge of Context and ChatGPT (DCAI) for AI to guide the development of students' AI learning skills. In the interviews, the students highlighted five main learning outcomes (self-study, critical thinking, knowledge generation, cognitive development, and presentation of work) as well as the challenges of implementing the model. Through the use of DCAI, students will enhance their consensus awareness of generative mapping analysis and group cooperation, and they will have knowledge that can enhance AI capabilities in DCAI inquiry and future life. From this paper, it is found that the conclusions are (1) The good use of DCAI can assist students in exploring the value of their knowledge through the power of stories and finding the meaning of knowledge communication; (2) Analyze the transformation power of the integrity and coherence of the story through the context so as to achieve the tension of ‘starting and ending’; (3) Use ChatGPT to extract inspiration, arrange story compositions, and make prompts that can communicate with people and convey emotions. Therefore, new knowledge construction methods will be one of the effective methods for AI learning in the face of artificial intelligence, providing new thinking and new expressions for interdisciplinary design and design education practice.

Keywords: artificial intelligence, task-oriented, contextualization, design education

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Authors: Dominika Collett

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AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

Procedia PDF Downloads 124

Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

Procedia PDF Downloads 87

Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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Authors: Mohamed Mokhtar, Mostafa F. Shaaban

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Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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A method and system for neuropsychological performance test, comprising a mobile terminal, used to interact with a cloud server which stores user information and is logged into by the user through the terminal device; the user information is directly accessed through the terminal device and is processed by artificial neural network, and the user information comprises user facial emotions information, performance test answers information and user chronometrics. This assessment is used to evaluate the cognitive performance and emotional response of the subject to a series of dichotomous questions describing various situations of daily life and challenging the users' knowledge, values, ethics, and principles. In industrial applications, the timing of this assessment will depend on the users' need to obtain a service from a provider, such as opening a bank account, getting a mortgage or an insurance policy, authenticating clearance at work, or securing online payments.

Keywords: artificial intelligence, neurofinance, neuropsychology, risk management

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Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

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This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

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Authors: Imen Elleuch, Wael Ouarda, Gargouri Bilel

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We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process.

Keywords: deep-learning approach, object-classes, semantic classification, Arabic

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Li Yong

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Using the data of 135 financial institutions in China from 2018 to 2022, this paper deeply analyzes the underlying theoretical mechanism of artificial intelligence (AI) technology promoting financial development and examines the impact of AI technology on the digital transformation performance of financial enterprises. It is found that the level of AI technology has a significant positive impact on the development of finance. Compared with the impact on the expansion of financial scale, AI technology plays a greater role in improving the performance of financial institutions, reflecting the trend characteristics of the current AI technology to promote the evolution of financial structure. By investigating the intermediary transmission effects, we found that AI technology plays a positive role in promoting the performance of financial institutions by reducing operating costs and improving customer satisfaction, but its function in innovating financial products and mitigating financial risks is relatively limited. In addition, the promotion of AI technology in financial development has significant heterogeneity in terms of the type, scale, and attributes of financial institutions.

Keywords: artificial intelligence technology, financial development, China, heterogeneity

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Authors: Florina Dutt, Subhajit Das, Matthew Swartz

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Lighting design and its impact on indoor comfort conditions are an integral part of good interior design. Impact of lighting in an interior space is manifold and it involves many sub components like glare, color, tone, luminance, control, energy efficiency, flexibility etc. While other components have been researched and discussed multiple times, this paper discusses the research done to understand the glare component from an artificial lighting source in an indoor space. Consequently, the paper discusses a parametric model to convey real time glare level in an interior space to the designer/ architect. Our end users are architects and likewise for them it is of utmost importance to know what impression the proposed lighting arrangement and proposed furniture layout will have on indoor comfort quality. This involves specially those furniture elements (or surfaces) which strongly reflect light around the space. Essentially, the designer needs to know the ramification of the ‘discomfortable glare’ at the early stage of design cycle, when he still can afford to make changes to his proposed design and consider different routes of solution for his client. Unfortunately, most of the lighting analysis tools that are present, offer rigorous computation and analysis on the back end eventually making it challenging for the designer to analyze and know the glare from interior light quickly. Moreover, many of them do not focus on glare aspect of the artificial light. That is why, in this paper, we explain a novel approach to approximate interior glare data. Adding to that we visualize this data in a color coded format, expressing the implications of their proposed interior design layout. We focus on making this analysis process very fluid and fast computationally, enabling complete user interaction with the capability to vary different ranges of user inputs adding more degrees of freedom for the user. We test our proposed parametric model on a case study, a Computer Lab space in our college facility.

Keywords: computational geometry, glare impact in interior space, info visualization, parametric lighting analysis

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Authors: Han-Yu Cheng

Abstract:

This study aims to explore the impact of AI-generated illustration works on the aesthetic symbol consumption of consumers in Taiwan. The advancement of artificial intelligence drawing has lowered the barriers to entry, enabling more individuals to easily enter the field of illustration. Using Japanese anime style as an example, with the development of Generative Artificial Intelligence (Generative AI), an increasing number of illustration works are being generated by machines, sparking discussions about aesthetics and art consumption. Through surveys and the analysis of consumer perspectives, this research investigates how this influences consumers' aesthetic experiences and the resulting changes in the traditional art market and among creators. The study reveals that among consumers in Taiwan, particularly those interested in Japanese anime style, there is a pronounced interest and curiosity surrounding the emergence of Generative AI. This curiosity is particularly notable among individuals interested in this style but lacking the technical skills required for creating such artworks. These works, rooted in elements of Japanese anime style, find ready acceptance among enthusiasts of this style due to their stylistic alignment. Consequently, they have garnered a substantial following. Furthermore, with the reduction in entry barriers, more individuals interested in this style but lacking traditional drawing skills have been able to participate in producing such works. Against the backdrop of ongoing debates about artistic value since the advent of artificial intelligence (AI), Generative AI-generated illustration works, while not entirely displacing traditional art, to a certain extent, fulfill the aesthetic demands of this consumer group, providing a similar or analogous aesthetic consumption experience. Additionally, this research underscores the advantages and limitations of Generative AI-generated illustration works within this consumption environment.

Keywords: generative AI, anime aesthetics, Japanese anime illustration, art consumption

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Authors: Ying Zhu

Abstract:

Recent years have witnessed a boom of foreign investments in the field of artificial intelligence (AI). Foreign investments provide critical capital for AI development but also trigger national security concerns of host states. A notable example is an increasing number of cases in which the Committee on Foreign Investment in the United States (CFIUS) has denied Chinese acquisitions of US technology companies on national security grounds. On July 19, 2018, the Congress has reached a deal on the final draft of a new provision to strengthen CFIUS’s authority to review overseas transactions involving sensitive US technology. The question is: how to reconcile the emerging tension between, on the one hand, foreign AI investors’ expectations of a predictable investment environment, and on the other hand, host states’ regulatory power on national security? This paper provides a methodology to reconcile this tension under international investment law. Based on an examination, the national security exception clauses in international investment treaties and the application of national security justification in investor-state arbitration jurisprudence, the paper argues that a traditional interpretation of the national security exception, based on the necessity concept in customary international law, fails to take into account new risks faced by countries, including security concerns over strategic industries such as AI. To overcome this shortage, the paper proposes to incorporate an integrated national security clause in international investment treaties, which includes a two-tier test: a ‘self-judging’ test in the pre-establishment period and a ‘proportionality’ test in the post-establishment period. At the end, the paper drafts a model national security clause for future treaty-drafting practice.

Keywords: foreign investment, artificial intelligence, international investment law, national security exception

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Authors: Georges Bou Ghantous

Abstract:

This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: artificial intelligence, infrastructure as code, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

Abstract:

Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Chivon Choeung, Heng Tang, Panha Soth, Vichet Huy

Abstract:

This paper presents a robust control strategy for a single-phase DC-AC inverter with an output LC-filter. An all-pass filter is utilized to create an artificial β-signal so that the proposed controller can be simply used in dq-synchronous frame. The proposed robust controller utilizes a state feedback control with integral action in the dq-synchronous frame. A linear matrix inequality-based optimization scheme is used to determine stabilizing gains of the controllers to maximize the convergence rate to steady state in the presence of uncertainties. The uncertainties of the system are described as the potential variation range of the inductance and resistance in the LC-filter.

Keywords: single-phase inverter, linear matrix inequality, robust control, all-pass filter

Procedia PDF Downloads 142