Search results for: artificial microRNA approach

Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 181

Authors: Nadia Ben Youssef, Aicha Bouzid

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Gradient estimation is one of the most fundamental tasks in the field of image processing in general, and more particularly for color images since that the research in color image gradient remains limited. The widely used gradient method is Di Zenzo’s gradient operator, which is based on the measure of squared local contrast of color images. The proposed gradient mechanism, presented in this paper, is based on the principle of the Di Zenzo’s approach using quaternion representation. This edge detector is compared to a marginal approach based on multiscale product of wavelet transform and another vector approach based on quaternion convolution and vector gradient approach. The experimental results indicate that the proposed color gradient operator outperforms marginal approach, however, it is less efficient then the second vector approach.

Keywords: gradient, edge detection, color image, quaternion

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: Gehan Labib Abuelenain, Azza Fahmi, Salma Awad Mahmoud

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Toxoplasmosis is a global parasitic disease caused by the protozoan Toxoplasma gondii (T. gondii), with a high infection rate that affects one third of the human population and results in severe implications in pregnant women, neonates, and immunocompromised patients. Anti-parasitic treatments and schemes available against toxoplasmosis have barely evolved over the last two decades. The available T. gondii therapeutics cannot completely eradicate tissue cysts produced by the parasite and are not well-tolerated by immunocompromised patients. This work aims to highlight new trends in Toxoplasma gondii diagnosis by providing a comprehensive overview of the field, summarizing recent findings, and discussing the new technological advancements in toxoplasma diagnosis and treatment. Advancements in therapeutics utilizing trends in molecular biophysics, such as biosensors, epigenetics, and artificial intelligence (AI), might provide solutions for disease management and prevention. These insights will provide tools to identify research gaps and proffer planning options for disease control.

Keywords: toxoplamosis, diagnosis, therapeutics, biosensors, AI

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Authors: Tugba Bayoglu

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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

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Authors: Erol Kam, Z. U. Yümün

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Natural and artificial radionuclides cause radioactive contamination in environments, just as the other non-biodegradable pollutants (heavy metals, etc.) sink to the sea floor and accumulate in sediments. Especially the habitat of benthic foraminifera living on the surface of sediments or in sediments at the seafloor are affected by radioactive pollution in the marine environment. Thus, it is important for pollution analysis to determine the radionuclides. Radioactive pollution accumulates in the lowest level of the food chain and reaches humans at the highest level. The more the accumulation, the more the environment is endangered. This study used gamma spectrometry to investigate the natural and artificial radionuclide distribution of sediment samples taken from living benthic foraminifera habitats in the Western Marmara Sea. The radionuclides, K-40, Cs-137, Ra-226, Mn 54, Zr-95+ and Th-232, were identified in the sediment samples. For this purpose, 18 core samples were taken from depths of about 25-30 meters in the Marmara Sea in 2016. The locations of the core samples were specifically selected exclusively from discharge points for domestic and industrial areas, port locations, and so forth to represent pollution in the study area. Gamma spectrometric analysis was used to determine the radioactive properties of sediments. The radionuclide concentration activity values in the sediment samples obtained were Cs-137=0.9-9.4 Bq/kg, Th-232=18.9-86 Bq/kg, Ra-226=10-50 Bq/kg, K-40=24.4–670 Bq/kg, Mn 54=0.71–0.9 Bq/kg and Zr-95+=0.18–0.19 Bq/kg. These values were compared with the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) data, and an environmental analysis was carried out. The Ra-226 series, the Th-232 series, and the K-40 radionuclides accumulate naturally and are increasing every day due to anthropogenic pollution. Although the Ra-226 values obtained in the study areas remained within normal limits according to the UNSCEAR values, the K-40, and Th-232 series values were found to be high in almost all the locations.

Keywords: Ra-226, Th-232, K-40, Cs-137, Mn 54, Zr-95+, radionuclides, Western Marmara Sea

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

Procedia PDF Downloads 419

Authors: Palingba Aimé Marie Doilkom, Mahamadou Koïta, Jean-michel Vouillamoz, Angelbert Biaou

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Most African populations rely on groundwater in rural areas for their consumption. Indeed, in the face of climate change and strong demographic growth, groundwater, particularly in the basement, is increasingly in demand. The question of the sustainability of water resources in this type of environment is therefore becoming a major issue. Groundwater recharge can be natural or artificial. Unlike natural recharge, which often results from the natural infiltration of surface water (e.g. a share of rainfall), artificial recharge consists of causing water infiltration through appropriate developments to artificially replenish the water stock of an aquifer. Artificial recharge is, therefore, one of the measures that can be implemented to secure water supply, combat the effects of climate change, and, more generally, contribute to improving the quantitative status of groundwater bodies. It is in this context that the present research is conducted with the aim of developing artificial recharge in order to contribute to the sustainability of basement aquifers in a context of climatic variability and constantly increasing water needs of populations. In order to achieve the expected results, it is therefore important to determine the characteristics of the infiltration basins and to identify the areas suitable for their implementation. The geometry of the aquifer was reproduced, and the hydraulic properties of the aquifer were collected and characterized, including boundary conditions, hydraulic conductivity, effective porosity, recharge, Van Genuchten parameters, and saturation indices. The aquifer of the Sanon experimental site is made up of three layers, namely the saprolite, the fissured horizon, and the healthy basement. Indeed, the saprolite and the fissured medium were considered for the simulations. The first results with FEFLOW model show that the water table reacts continuously for the first 100 days before stabilizing. The hydraulic charge increases by an average of 1 m. The further away from the basin, the less the water table reacts. However, if a variable hydraulic head is imposed on the basins, it can be seen that the response of the water table is not uniform over time. The lower the basin hydraulic head, the less it affects the water table. These simulations must be continued by improving the characteristics of the basins in order to obtain the appropriate characteristics for a good recharge.

Keywords: basement area, FEFLOW, infiltration basin, MAR

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Authors: Dariusz M. Trzmielak, William Bradley Zehner, Elin Oftedal, Ilona Lipka-Matusiak

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The use of new technologies should create new value for all stakeholders in the healthcare system. The article focuses on demonstrating that technologies or products typically enable new functionality, a higher standard of service, or a higher level of knowledge and competence for clinicians. It also highlights the key benefits that can be achieved through the use of artificial intelligence, such as relieving clinicians of many tasks and enabling the expansion and greater specialisation of healthcare services. The comparative analysis allowed the authors to create a classification of new technologies in e-health according to health needs and benefits for patients, doctors, and healthcare systems, i.e., the main stakeholders in the implementation of new technologies and products in healthcare. The added value of the development of new technologies in healthcare is diagnosed. The work is both theoretical and practical in nature. The primary research methods are bibliographic analysis and analysis of research data and market potential of new solutions for healthcare organisations. The bibliographic analysis is complemented by the author's case studies of implemented technologies, mostly based on artificial intelligence or telemedicine. In the past, patients were often passive recipients, the end point of the service delivery system, rather than stakeholders in the system. One of the dangers of powerful new technologies is that patients may become even more marginalised. Healthcare will be provided and delivered in an increasingly administrative, programmed way. The doctor may also become a robot, carrying out programmed activities - using 'non-human services'. An alternative approach is to put the patient at the centre, using technologies, products, and services that allow them to design and control technologies based on their own needs. An important contribution to the discussion is to open up the different dimensions of the user (carer and patient) and to make them aware of healthcare units implementing new technologies. The authors of this article outline the importance of three types of patients in the successful implementation of new medical solutions. The impact of implemented technologies is analysed based on: 1) "Informed users", who are able to use the technology based on a better understanding of it; 2) "Engaged users" who play an active role in the broader healthcare system as a result of the technology; 3) "Innovative users" who bring their own ideas to the table based on a deeper understanding of healthcare issues. The authors' research hypothesis is that the distinction between informed, engaged, and innovative users has an impact on the perceived and actual quality of healthcare services. The analysis is based on case studies of new solutions implemented in different medical centres. In addition, based on the observations of the Polish author, who is a manager at the largest medical research institute in Poland, with analytical input from American and Norwegian partners, the added value of the implementations for patients, clinicians, and the healthcare system will be demonstrated.

Keywords: innovation, management, medicine, e-health, artificial intelligence

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Authors: Thomas Schalow

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It is commonly recognized that our present curriculum is not preparing students to function in the 21st century. This is particularly true in regard to communication needs across cultures - both human and non-human. In this paper, a comprehensive theory of communication-based on communication with non-human cultures and intelligences is presented to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with the argument that we need to become much more serious about communicating with the non-human, intelligent life forms that already exists around us here on Earth. We need to broaden our definition of communication and reach out to other sentient life forms in order to provide humanity with a better perspective of its place within our ecosystem. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it could prove useful even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised in accordance with the communication theory being proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences. Humanity has never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other cultures can provide us with a framework for this communication. The basic concepts behind intercultural communication can be applied to the three types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will yield substantial gains. A curriculum that is truly ready for the 21st century needs to be aligned with this new theory of communication.

Keywords: artificial intelligence, CETI, communication, language

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Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

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Authors: Raja Omman Zafar, Yves Rybarczyk, Johan Borg

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This article proposes a conceptual framework for the application of digital twin technology in home care. The main goal is to bridge the gap between advanced digital twin concepts and their practical implementation in home care. This study uses a literature review and thematic analysis approach to synthesize existing knowledge and proposes a structured framework suitable for homecare applications. The proposed framework integrates key components such as IoT sensors, data-driven models, cloud computing, and user interface design, highlighting the importance of personalized and predictive homecare solutions. This framework can significantly improve the efficiency, accuracy, and reliability of homecare services. It paves the way for the implementation of digital twins in home care, promoting real-time monitoring, early intervention, and better outcomes.

Keywords: digital twin, homecare, older adults, healthcare, IoT, artificial intelligence

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Authors: Vijay Pal Singh, Yogesh Chandra Kotiyal

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Structures are a combination of various load carrying members which transfer the loads to the foundation from the superstructure safely. At the design stage, the loading of the structure is defined and appropriate material choices are made based upon their properties, mainly related to strength. The strength of materials kept on reducing with time because of many factors like environmental exposure and deformation caused by unpredictable external loads. Hence, to predict the strength of materials used in structures, various techniques are used. Among these techniques, Non-Destructive Techniques (NDT) are the one that can be used to predict the strength without damaging the structure. In the present study, the compressive strength of concrete has been predicted using Artificial Neural Network (ANN). The predicted strength was compared with the experimentally obtained actual compressive strength of concrete and equations were developed for different models. A good co-relation has been obtained between the predicted strength by these models and experimental values. Further, the co-relation has been developed using two NDT techniques for prediction of strength by regression analysis. It was found that the percentage error has been reduced between the predicted strength by using combined techniques in place of single techniques.

Keywords: rebound, ultra-sonic pulse, penetration, ANN, NDT, regression

Procedia PDF Downloads 428

Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

Procedia PDF Downloads 291

Authors: Laura Brennan, Ram Balakumar

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ChatGPT has been developed by Open AI (Nov 2022) as a powerful artificial intelligence (AI) language model which has been designed to produce human-like text from user written prompts. To gain the most from the system, user written prompts must give context specific information. This article aims to give guidance on how to optimise the ChatGPT system in the context of education for otolaryngology. Otolaryngology is a specialist field which sees little time dedicated to providing education to both medical students and doctors. Additionally, otolaryngology trainees have seen a reduction in learning opportunities since the COVID-19 pandemic. In this article we look at these various barriers to medical education in Otolaryngology training and suggest ways that ChatGPT can overcome them and assist in simulation-based training. Examples provide how this can be achieved using the Authors’ experience to further highlight the practicalities. What this article has found is that while ChatGPT cannot replace traditional mentorship and practical surgical experience, it can serve as an invaluable supplementary resource to simulation based medical education in Otolaryngology.

Keywords: artificial intelligence, otolaryngology, surgical training, medical education

Procedia PDF Downloads 159

Authors: Sepinoud Hamedi

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Over the past few decades, companies have appeared developing concerns in connection to the natural affect of their fabricating exercises. Green supply chain administration has been considered by the producers as a attainable choice to decrease the natural affect of operations whereas at the same time moving forward their operational execution. Contemporaneously the coming of digitalization and globalization within the supply chain space has driven to a developing acknowledgment of the importance of data preparing methodologies, such as enormous information analytics and fake insights innovations, in improving and optimizing supply chain execution. Also, supply chain collaboration in part intervenes the relationship between manufactured innovation and supply chain execution Ponders appear that the use of BDA-AI advances includes a significant impact on natural handle integration and green supply chain collaboration conjointly underlines that both natural handle integration and green supply chain collaboration have a critical affect on natural execution. Correspondingly savvy supply chain contributes to green execution through overseeing green connections and setting up green operations.

Keywords: green supply chain, artificial intelligence, manufacturers, technology, environmental

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Authors: Y. Kourd, D. Lefebvre, N. Guersi

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This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty behaviors Models (NNFM's). NNFM's are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it becomes possible to take the appropriate decision regarding the actual process behavior by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.

Keywords: neural networks, decision trees, diagnosis, behaviors

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Authors: Eunice Tan

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The purpose of this presentation is to look at an alternative to the approach and methodologies of working with a child with special needs. The strength-based approach to education embodies a paradigm shift. It is a strategy to move away from a deficit-based methodology which inadvertently may lead to an extensive list of things that the child cannot do or is unable to do. Today, many parents of individuals with special needs are focused on the individual’s deficits rather than on his or her strengths. Even when parents recognise and identify their child’s savant strengths to be valuable and wish to develop their abilities, they face the challenge that there are insufficient programs committed to supporting the development and improvement of such abilities. What is a strength-based approach in education? A strength-based approach in education focuses on students' positive qualities and contributions to class instead of the skills and abilities they may not have. Many schools are focused on the child’s special educational needs rather than the whole child. Parents interviewed have said that they have to engage external tutors to help hone in on their child’s interests and strengths. The strength-based approach to writing statements encourages educators to find out: • What a child can do • What a child can do when he or she is given educational support • Learning more about children with special needs and their strengths and talents will broaden our understanding of how we can help them with language acquisition, social skills, as well as self-help and independence skills.

Keywords: special needs, strengths, and talents, alternative educational approach, strength based approach

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

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Authors: Marianna Iaroslavska

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ChatGPT is becoming commonplace. However, only a few people think about the legal risks of using Large Language Model in their daily work. The main dilemmas concern the following areas: who owns the copyright to what somebody creates through ChatGPT; what can OpenAI do with the prompt you enter; can you accidentally infringe on another creator's rights through ChatGPT; what about the protection of the data somebody enters into the chat. This paper will present these and other legal risks of using large language models at work using dogmatic methods and case studies. The paper will present a legal analysis of AI risks against the background of European Union law and Polish law. This analysis will answer questions about how to protect data, how to make sure you do not violate copyright, and what is at stake with the AI Act, which recently came into force in the EU. If your work is related to the EU area, and you use AI in your work, this paper will be a real goldmine for you. The copyright law in force in Poland does not protect your rights to a work that is created with the help of AI. So if you start selling such a work, you may face two main problems. First, someone may steal your work, and you will not be entitled to any protection because work created with AI does not have any legal protection. Second, the AI may have created the work by infringing on another person's copyright, so they will be able to claim damages from you. In addition, the EU's current AI Act imposes a number of additional obligations related to the use of large language models. The AI Act divides artificial intelligence into four risk levels and imposes different requirements depending on the level of risk. The EU regulation is aimed primarily at those developing and marketing artificial intelligence systems in the EU market. In addition to the above obstacles, personal data protection comes into play, which is very strictly regulated in the EU. If you violate personal data by entering information into ChatGPT, you will be liable for violations. When using AI within the EU or in cooperation with entities located in the EU, you have to take into account a lot of risks. This paper will highlight such risks and explain how they can be avoided.

Keywords: EU, AI act, copyright, polish law, LLM

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Authors: Gamze Dogan Birer

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Women’s shelters provide service to women who had been subjected to physical, psychological, economical, and sexual violence. It is proposed that adopting a trauma-informed approach in these shelters would contribute to the ‘woman-defined’ success of the service. This includes reshaping the physical qualities of the shelter, contacts, and interventions that women face during their stay in a way that accepts and addresses their traumatic experiences. It is stated in this paper that the trauma-informed approach has commonalities with the basic psychological needs that are proposed by self-determination theory. Therefore, it is proposed that self-determination theory can be used as a theoretical background for trauma-informed approach

Keywords: self determination theory, trauma informed approach, violence against women, women's shelters

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Umamaheswari Shanmugam, Silvia Ronchi, Radu Vornicu

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that are able to use the large amount and variety of data generated during healthcare services every day. As we read the news, over 500 machine learning or other artificial intelligence medical devices have now received FDA clearance or approval, the first ones even preceding the year 2000. One of the big advantages of these new technologies is the ability to get experience and knowledge from real-world use and to continuously improve their performance. Healthcare systems and institutions can have a great benefit because the use of advanced technologies improves the same time efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and also to protect patients’ safety. The evolution and the continuous improvement of software used in healthcare must take into consideration the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device approval, but they are necessary to ensure performance, quality, and safety, and at the same time, they can be a business opportunity if the manufacturer is able to define in advance the appropriate regulatory strategy. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems.

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

Procedia PDF Downloads 208

Authors: M. Khajezadeh, M. Saied Fallah Niasar, S. Ali Asli, D. Davani Davari, M. Godarzi, Y. Asgari

Abstract:

This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix.

Keywords: artificial neural network, portfolio analysis, BCG matrix, Ansoff matrix

Procedia PDF Downloads 143

Authors: Jamison Duckworth, Shankarachary Ragi

Abstract:

Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

Procedia PDF Downloads 127

Authors: Hanbey Hazar, Hakan Gul

Abstract:

In this study; piston, exhaust, and suction valves of a diesel engine were coated in 300 mm thickness with Tungsten Carbide (WC) by using the HVOF coating method. Mathematical modeling of a coated and uncoated (standardized) engine was performed by using ANN (Artificial Neural Networks). The purpose was to decrease the number of repetitions of tests and reduce the test cost through mathematical modeling of engines by using ANN. The results obtained from the tests were entered in ANN and therefore engines' values at all speeds were estimated. Results obtained from the tests were compared with those obtained from ANN and they were observed to be compatible. It was also observed that, with thermal barrier coating, hydrocarbon (HC), carbon monoxide (CO), and smoke density values of the diesel engine decreased; but nitrogen oxides (NOx) increased. Furthermore, it was determined that results obtained through mathematical modeling by means of ANN reduced the number of test repetitions. Therefore, it was understood that time, fuel and labor could be saved in this way.

Keywords: Artificial Neural Network, Diesel Engine, Mathematical Modelling, Thermal Barrier Coating

Procedia PDF Downloads 528

Authors: Yu-Lin Shen, Ming-Kuen Chang

Abstract:

In addition to a considerable amount of machinery and equipment, intricacies of the transmission pipeline exist in Petrochemical plants. Long term corrosion may lead to pipeline thinning and rupture, causing serious safety concerns. With the advances in non-destructive testing technology, more rapid and long-range ultrasonic detection techniques are often used for pipeline inspection, EMAT without coupling to detect, it is a non-contact ultrasonic, suitable for detecting elevated temperature or roughened e surface of line. In this study, we prepared artificial defects in pipeline for Electromagnetic Acoustic Transducer Testing (EMAT) to survey the relationship between the defect location, sizing and the EMAT signal. It was found that the signal amplitude of EMAT exhibited greater signal attenuation with larger defect depth and length.. In addition, with bigger flat hole diameter, greater amplitude attenuation was obtained. In summary, signal amplitude attenuation of EMAT was affected by the defect depth, defect length and the hole diameter and size.

Keywords: EMAT, artificial defect, NDT, ultrasonic testing

Procedia PDF Downloads 333

Authors: Javad Abdi, Azam Famil Khalili

Abstract:

Modeling emotions have attracted much attention in recent years, both in cognitive psychology and design of artificial systems. However, it is a negative factor in decision-making; emotions have shown to be a strong faculty for making fast satisfying decisions. In this paper, we have adapted a computational model based on the limbic system in the mammalian brain for control engineering applications. Learning in this model based on Temporal Difference (TD) Learning, we applied the proposed controller (termed BELBIC) for a simple model of a submarine. The model was supposed to reach the desired depth underwater. Our results demonstrate excellent control action, disturbance handling, and system parameter robustness for TDBELBIC. The proposal method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attained in the least amount of time and the best way.

Keywords: artificial neural networks, temporal difference, brain emotional learning based intelligent controller, heating- ventilating and air conditioning

Procedia PDF Downloads 433

Authors: Saeed Delara, Kendra MacKay

Abstract:

Varying methodologies are considered for pile design for both Russian and Western approaches. Although both approaches rely on toe and side frictional resistances, different calculation methods are proposed to estimate pile capacity. The Western approach relies on compactness (internal friction angle) of soil for cohesionless soils and undrained shear strength for cohesive soils. The Russian approach relies on grain size for cohesionless soils and liquidity index for cohesive soils. Though most recommended methods in the Western approaches are relatively simple methods to predict pile settlement, the Russian approach provides a detailed method to estimate single pile and pile group settlement. Details to calculate pile axial capacity and settlement using the Russian and Western approaches are discussed and compared against field test results.

Keywords: pile capacity, pile settlement, Russian approach, western approach

Procedia PDF Downloads 167