Search results for: artificial skin

Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

Procedia PDF Downloads 87

Authors: Liudmila Garzanova, Lidia Ananyeva, Olga Koneva, Olga Ovsyannikova, Oxana Desinova, Mayya Starovoytova, Rushana Shayahmetova, Anna Khelkovskaya-Sergeeva

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Abstract Objectives. Rituximab (RTX) shown a positive effect in the treatment of systemic sclerosis (SSc). But there is still not enough data on comparing the effectiveness of RTX with immunosuppressants (IS). The aim of our study was to compare changes of lung function and skin score in SSc between two groups of patients (pts) - on RXT therapy (prescribed after ineffectiveness of previous therapy with IS) and on therapy with IS only. Methods. This study included 103 pts received RTX as an addition to previous therapy (group 1) and 65 pts received therapy with IS and prednisolone (group 2). The mean follow-up period was 12.6±10.7months. In group 1 the mean age was 47±12.9 years, female – 88 pts (84%), the diffuse cutaneous subset of the disease had 55 pts (53%). The mean disease duration was 6.2±5.5 years. 82% pts had interstitial lung disease (ILD) and 92% were positive for ANA, 67% of them were positive for antitopoisomerase-1. All pts received prednisolone at a dose of 11.3±4.5 mg/day, IS at inclusion received 47% of them. The cumulative mean dose of RTX was 1.7±0.6 g. In group 2 the mean age was 50.8±13.8 years, female-53 pts (82%), the diffuse cutaneous subset of the disease had 44 pts (68%). The mean disease duration was 8.8±7.7 years. 81% pts had ILD and 88% were positive for ANA, 58% of them were positive for antitopoisomerase-1. All pts received prednisolone at a dose of 8.69±4.28 mg/day, IS received 57% of them. Cyclophosphamide (CP) received 45% of pts. The cumulative mean dose of CP was 10.2±15.1g. D-penicillamine received 30% of pts. Other pts was on mycophenolate mofetil or methotrexate therapy in single cases. The pts of the compared groups did not differ in the main demographic and clinical parameters. The results are presented as delta (Δ) - difference between the baseline parameter and follow up point. Results. In group 1 there was an improvement of all outcome parameters: increased of forced vital capacity, % predicted - ΔFVC=4% (p=0.0004); Diffusing capacity for carbon monoxide, % predicted remained stable (ΔDLCO=0.1%); improvement of the Rodnan skin score-ΔmRss=3.4 (p=0.001); decrease of Activity index (EScSG-AI) - ΔActivity index=1.7 (p=0.001). In group 2 the changes was insignificant: ΔFVC=-2.3%, ΔmRss=0.87, ΔActivity index=0.3. But there was a significant decrease of DLCO: ΔDLCO=-5.1% (p=0.001). Conclusion. The results of our study confirm the data on the positive effect of RTX in complex therapy in pts with SSc (decrease of skin induration, increase of FVC, stabilization of DLCO). Meantime, pts on IS and prednisolone therapy shown the worsening of lung function and insignificant changes of other clinical parameters. RTX could be considered as a more effective option in complex treatment of SSc in comparison with IS therapy

Keywords: immunosuppressants, interstitial lung disease, systemic sclerosis, rituximab

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Authors: Saba Atefyekta

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Aim: Control of bacterial bioburden in wounds is an important step for minimizing the risk of wound infection. An antimicrobial hydrogel wound dressing is developed out of soft polymeric hydrogels that contain antimicrobial peptides (AMPs). Such wound dressings can bind and kill all types of bacteria, even the resistance types at the wound site. Methods: AMPs are permanently bonded onto a soft nanostructured polymer via covalent attachment and physical entanglement. This improves stability, rapid antibacterial activity, and, most importantly, prevents the leaching of AMPs. Major Findings: Antimicrobial analysis of antimicrobial hydrogels using in-vitro wound models confirmed >99% killing efficiency against multiple bacterial trains, including MRSA, MDR, E. Coli. Furthermore, the hydrogel retained its antibacterial activity for up to 4 days when exposed to human serum. Tests confirmed no release of AMPs, and it was proven non-toxic to mammalian cells. An in-vivo study on human intact skin showed a significant reduction of bacteria for part of the subject’s skin treated with antibacterial hydrogels. A similar result was detected through a qualitative study in veterinary trials on different types of surgery wounds in cats, dogs, and horses. Conclusions: Antimicrobial hydrogels wound dressings developed by permanent attachment of AMPs can effectively and rapidly kill bacteria in contact. Such antibacterial hydrogel wound dressings are non-toxic and do not release any substances into the wound.

Keywords: antibacterial wound dressing, antimicrobial peptides, post-surgical wounds, infection

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Authors: Fadzil Ahmad, Noor Ashidi Mat Isa

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This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm.

Keywords: genetic algorithm, artificial neural network, pattern clasification, classification accuracy

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Authors: Chen Xi, LIU Xuebin, Kuan Sinman, LI Haofeng, Huang Hongming, Zeng Chengyu, Lao Xuerui

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Stray animals are on the rise in mainland China's cities. There are legal reasons for this, namely the lack of protection for domestic pets in mainland China, where only wildlife protection laws exist. At a social level, the ease with which families adopt pets and the lack of a social view of animal nature have led to the frequent abandonment and loss of stray animals. If left unmanaged, conflicts between humans and stray animals can also increase. This project provides an inexpensive and widely applicable management tool for urban management by collecting videos and pictures of stray animals captured by surveillance or transmitted by humans and using artificial intelligence technology (mainly using Yolov5 recognition technology) and recording and managing them in a database.

Keywords: urban planning, urban governance, artificial intelligence, convolutional neural network, machine vision

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Preventing abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep, long short-term memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviors far from the expected one depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behavior analysis, LSTM

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Authors: Mai Anh Tuan, Wenlong Liu, Meng Li

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Despite warnings and concerns about the use of fast food that has health effects, the fast-food industry is actually a source of profit for the global food industry. Obviously, in the face of such huge economic benefits, investors will not hesitate to continuously add recipes, processing methods, menu diversity, etc., to improve and apply information technology in enhancing the diners' experience; the ultimate goal is still to attract diners to find their brand and give them the fastest, most convenient and enjoyable service. In China, as the achievements of the industrial revolution 4.0, big data and artificial intelligence are reaching new heights day by day, now fast-food diners can instantly pay the bills only by identifying the biometric signature available on the self-ordering kiosk, using their own face without any additional form of confirmation. In this study, the author will evaluate the acceptance level of customers with this new form of payment through a survey of customers who have used and witnessed the use of smart kiosks and biometric payments within the city of Nanjing, China. A total of 200 valid volunteers were collected in order to test the customers' intentions and feelings when choosing and experiencing payment through AI services. 55% think that it bothers them because of the need for personal information, but more than 70% think that smart kiosk brings out many benefits and convenience. According to the data analysis findings, perceived innovativeness has a positive influence on satisfaction which in turn affects behavioral intentions, including reuse and word-of-mouth intentions.

Keywords: artificial intelligence, catering industry, smart kiosks, technology acceptance

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

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Authors: Sandeep Mehmi, Harsh Verma, A. L. Sangal

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Ever since the idea of using computing services as commodity that can be delivered like other utilities e.g. electric and telephone has been floated, the scientific fraternity has diverted their research towards a new area called utility computing. New paradigms like cluster computing and grid computing came into existence while edging closer to utility computing. With the advent of internet the demand of anytime, anywhere access of the resources that could be provisioned dynamically as a service, gave rise to the next generation computing paradigm known as cloud computing. Today, cloud computing has become one of the most aggressively growing computer paradigm, resulting in growing rate of applications in area of IT outsourcing. Besides catering the computational and storage demands, cloud computing has economically benefitted almost all the fields, education, research, entertainment, medical, banking, military operations, weather forecasting, business and finance to name a few. Smart grid is another discipline that direly needs to be benefitted from the cloud computing advantages. Smart grid system is a new technology that has revolutionized the power sector by automating the transmission and distribution system and integration of smart devices. Cloud based smart grid can fulfill the storage requirement of unstructured and uncorrelated data generated by smart sensors as well as computational needs for self-healing, load balancing and demand response features. But, security issues such as confidentiality, integrity, availability, accountability and privacy need to be resolved for the development of smart grid cloud. In recent years, a number of intrusion prevention techniques have been proposed in the cloud, but hackers/intruders still manage to bypass the security of the cloud. Therefore, precise intrusion detection systems need to be developed in order to secure the critical information infrastructure like smart grid cloud. Considering the success of artificial neural networks in building robust intrusion detection, this research proposes an artificial neural network based model for detecting attacks in smart grid cloud.

Keywords: artificial neural networks, cloud computing, intrusion detection systems, security issues, smart grid

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Authors: Akshat Mathur, Sunil Kumar Khatri

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This paper presents brief about how the use of Artificial intelligence in respect to GUI testing can reduce workload by using DL-fueled method. This paper also discusses about how graphical user interface and event driven software testing can derive benefits from the use of AI techniques. The use of AI techniques not only reduces the task and work load but also helps in getting better output than manual testing. Although results are same, but the use of Artifical intelligence techniques for GUI testing has proven to provide ideal results. DL-fueled framework helped us to find imperfections of the entire webpage and provides test failure result in a score format between 0 and 1which signifies that are test meets it quality criteria or not. This paper proposes DL-fueled method which helps us to find the genuine GUI bugs and defects and also helped us to scale the existing labour-intensive and skill-intensive methodologies.

Keywords: graphical user interface, GUI, artificial intelligence, deep learning, ML technology

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Authors: Gurmeet Singh Cheema, Navjotinder Singh, Gurjinder Singh, Amardeep Singh

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Aluminium and its alloys play have excellent corrosion resistant properties, ease of fabrication and high specific strength to weight ratio. In this investigation an attempt has been made to study the effect of different post weld heat treatment methods on the mechanical and metallurgical properties of TIG welded joints of the commercial aluminium alloy. Three different methods of post weld heat treatments are, solution heat treatment, artificial aged and combination of solution heat treatment and artificial aging are given to TIG welded aluminium joints. Mechanical and metallurgical properties of as welded and post weld treated joints of the aluminium alloys was examined.

Keywords: aluminium alloys, TIG welding, post weld heat treatment

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Authors: Ghaleb Bin Huraib

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Atopic dermatitis (AD), also known as atopic eczema, is a chronic inflammatory skin disease characterized by severe itching and recurrent, relapsing eczema-like skin lesions, affecting up to 20% of children and 10% of adults in industrialized countries. AD is a complex multifactorial disease, and its exact etiology and pathogenesis have not been fully elucidated. The aim of this study was to investigate the impact of gene polymorphisms of T helper cell subtype Th1 and Th2 cytokines, interferon-gamma (IFN-γ), interleukin-6 (IL-6) and transforming growth factor (TGF)-β1on AD susceptibility in a Saudi cohort. One hundred four unrelated patients with AD and 195 healthy controls were genotyped for IFN-γ (874A/T), IL-6 (174G/C) and TGF-β1 (509C/T) polymorphisms using ARMS-PCR and PCR-RFLP technique. The frequency of genotypes AA and AT of IFN-γ (874A/T) differed significantly among patients and controls (P 0.001). The genotype AT was increased while genotype AA was decreased in AD patients as compared to controls. AD patients also had a higher frequency of T-containing genotypes (AT+TT) than controls (P = 0.001). The frequencies of alleles T and A were statistically different in patients and controls (P = 0.04). The frequencies of genotype GG and allele G of IL-6 (174G/C) were significantly higher, while genotype GC and allele C were lower in AD patients than in controls. There was no significant difference in the frequencies of alleles and genotypes of TGF-β1 (509C/T) polymorphism between the patient and control groups. These results showed that susceptibility to AD is influenced by the presence or absence of genotypes of IFN-γ (874A/T) and IL-6 (174G/C) polymorphisms. It is concluded T-allele and T-containing genotypes (AT+TT) of IFN-γ (874A/T) and G-allele and GG genotype ofIL-6 (174G/C) polymorphisms are susceptible to AD in Saudis. On the other hand, the TGF-β1 (509C/T) polymorphism may not be associated with AD risk in our population; however, further studies with large sample sizes are required to confirm these results.

Keywords: atopic dermatitis, Polymorphism, Interferon, IL-6

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Authors: Ciro Cursio, Giulia Cursio, Pio Luigi Cursio, Luigi Cursio

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Communication between practitioner and patient is of the utmost importance in aesthetic medicine: as of today, images of previous treatments are the most common tool used by doctors to describe and anticipate future results for their patients. However, using photos of other people often reduces the engagement of the prospective patient and is further limited by the number and quality of pictures available to the practitioner. Pre-existing work solves this issue in two ways: 3D scanning of the area with manual editing of the 3D model by the doctor or automatic prediction of the treatment by warping the image with hand-written parameters. The first approach requires the manual intervention of the doctor, while the second approach always generates results that aren’t always realistic. Thus, in one case, there is significant manual work required by the doctor, and in the other case, the prediction looks artificial. We propose an AI-based algorithm that autonomously generates a realistic prediction of treatment results. For the purpose of this study, we focus on hyaluronic acid treatments in the facial area. Our approach takes into account the individual characteristics of each face, and furthermore, the prediction system allows the patient to decide which area of the face she wants to modify. We show that the predictions generated by our system are realistic: first, the quality of the generated images is on par with real images; second, the prediction matches the actual results obtained after the treatment is completed. In conclusion, the proposed approach provides a valid tool for doctors to show patients what they will look like before deciding on the treatment.

Keywords: prediction, hyaluronic acid, treatment, artificial intelligence

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Authors: Jae Ni Jang, Young Uk Kim

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Background: Chat Generative Pre-training Transformer-3 (ChatGPT; San Francisco, California, Open Artificial Intelligence) is an artificial intelligence chatbot based on a large language model designed to generate human-like text. As the usage of ChatGPT is increasing among less knowledgeable patients, medical students, and anesthesia and pain medicine residents or trainees, we aimed to evaluate the accuracy of ChatGPT-3 responses to questions about the American Society of Anesthesiologists (ASA) classification based on patients’ underlying diseases and assess the quality of the generated responses. Methods: A total of 47 questions were submitted to ChatGPT using textual prompts. The questions were designed for ChatGPT-3 to provide answers regarding ASA classification in response to common underlying diseases frequently observed in adult patients. In addition, we created 18 questions regarding the ASA classification for pediatric patients and pregnant women. The accuracy of ChatGPT’s responses was evaluated by cross-referencing with Miller’s Anesthesia, Morgan & Mikhail’s Clinical Anesthesiology, and the American Society of Anesthesiologists’ ASA Physical Status Classification System (2020). Results: Out of the 47 questions pertaining to adults, ChatGPT -3 provided correct answers for only 23, resulting in an accuracy rate of 48.9%. Furthermore, the responses provided by ChatGPT-3 regarding children and pregnant women were mostly inaccurate, as indicated by a 28% accuracy rate (5 out of 18). Conclusions: ChatGPT provided correct responses to questions relevant to the daily clinical routine of anesthesiologists in approximately half of the cases, while the remaining responses contained errors. Therefore, caution is advised when using ChatGPT to retrieve anesthesia-related information. Although ChatGPT may not yet be suitable for clinical settings, we anticipate significant improvements in ChatGPT and other large language models in the near future. Regular assessments of ChatGPT's ASA classification accuracy are essential due to the evolving nature of ChatGPT as an artificial intelligence entity. This is especially important because ChatGPT has a clinically unacceptable rate of error and hallucination, particularly in pediatric patients and pregnant women. The methodology established in this study may be used to continue evaluating ChatGPT.

Keywords: American Society of Anesthesiologists, artificial intelligence, Chat Generative Pre-training Transformer-3, ChatGPT

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Authors: Seyed Reza Aghili, Tahereh Shokohi, Shirin Sadat Hashemi Fesharaki, Mohammad Ali Boroumand, Bahar Salmanian

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Introduction: Intravenous catheter-associated fungal infection as nosocomial infection continue to be a deep problem among hospitalized patients, decreasing quality of life and adding healthcare costs. The capacity of catheters in the spread of candidemia in heart failure patients is obvious. The aim of this study was to evaluate the prevalence and genetic identification of Candida species in heart disorder patients. Material and Methods: This study was conducted in Tehran Hospital of Cardiology Center (Tehran, Iran, 2014) during 1.5 years on the patients hospitalized for at least 7 days and who had central or peripheral vein catheter. Culture of catheters, blood and skin of the location of catheter insertion were applied for detecting Candida colonies in 223 patients. Identification of Candida species was made on the basis of a combination of various phenotypic methods and confirmed by sequencing the ITS1-5.8S-ITS2 region amplified from the genomic DNA using PCR and the NCBI BLAST. Results: Of the 223 patients samples tested, we identified totally 15 Candida isolates obtained from 9 (4.04%) catheter cultures, 3 (1.35%) blood cultures and 2 (0.90%) skin cultures of the catheter insertion areas. On the base of ITS region sequencing, out of nine Candida isolates from catheter, 5(55.6%) C. albicans, 2(22.2%) C. glabrata, 1(11.1%) C. membranifiaciens and 1 (11.1%) C. tropicalis were identified. Among three Candida isolates from blood culture, C. tropicalis, C. carpophila and C. membranifiaciens were identified. Non-candida yeast isolated from one blood culture was Cryptococcus albidus. One case of C. glabrata and one case of Candida albicans were isolated from skin culture of the catheter insertion areas in patients with positive catheter culture. In these patients, ITS region of rDNA sequence showed a similarity between Candida isolated from the skin and catheter. However, the blood samples of these patients were negative for fungal growth. We report two cases of catheter-related candidemia caused by C. membranifiaciens and C. tropicalis on the base of genetic similarity of species isolated from blood and catheter which were treated successfully with intravenous fluconazole and catheter removal. In phenotypic identification methods, we could only identify C. albicans and C. tropicalis and other yeast isolates were diagnosed as Candida sp. Discussion: Although more than 200 species of Candida have been identified, only a few cause diseases in humans. There is some evidence that non-albicans infections are increasing. Many risk factors, including prior antibiotic therapy, use of a central venous catheter, surgery, and parenteral nutrition are considered to be associated with candidemia in hospitalized heart failure patients. Identifying the route of infection in candidemia is difficult. Non-albicans candida as the cause of candidemia is increasing dramatically. By using conventional method, many non-albicans isolates remain unidentified. So, using more sensitive and specific molecular genetic sequencing to clarify the aspects of epidemiology of the unknown candida species infections is essential. The positive blood and catheter cultures for candida isolates and high percentage of similarity of their ITS region of rDNA sequence in these two patients confirmed the diagnosis of intravenous catheter-associated candidemia.

Keywords: catheter-associated infections, heart failure patient, molecular genetic sequencing, ITS region of rDNA, Candidemia

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Musa H. Arslan, Murat Ceylan, Tayfun Koyuncu

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In this study, an artificial intelligence-based (ANN based) analytical method has been developed for analyzing earthquake performances of the reinforced concrete (RC) buildings. 66 RC buildings with four to ten storeys were subjected to performance analysis according to the parameters which are the existing material, loading and geometrical characteristics of the buildings. The selected parameters have been thought to be effective on the performance of RC buildings. In the performance analyses stage of the study, level of performance possible to be shown by these buildings in case of an earthquake was determined on the basis of the 4-grade performance levels specified in Turkish Earthquake Code- 2007 (TEC-2007). After obtaining the 4-grade performance level, selected 23 parameters of each building have been matched with the performance level. In this stage, ANN-based fast evaluation algorithm mentioned above made an economic and rapid evaluation of four to ten storey RC buildings. According to the study, the prediction accuracy of ANN has been found about 74%.

Keywords: artificial intelligence, earthquake, performance, reinforced concrete

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Authors: Justin Bin

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Artificial intelligence (AI) has been rapidly rising in usage recently, already active in the daily lives of millions, from distinguished AIs like the popular ChatGPT or Siri to more obscure, inconspicuous AIs like those used in social media or internet search engines. As upcoming generations grow immersed in emerging technology, AI will play a vital role in their development. Namely, the education sector, an influential portion of a person’s early life as a student, faces a vast ocean of possibilities concerning the implementation of AI. The main purpose of this study is to analyze the effect that AI will have on the future of the educational field. More particularly, this study delves deeper into the following three categories: school admissions, the productivity of students, and ethical concerns (role of human teachers, purpose of schooling itself, and significance of diplomas). This study synthesizes research and data on the current effects of AI on education from various published literature sources and journals, as well as estimates on further AI potential, in order to determine the main, overarching effects it will have on the future of education. For this study, a systematic organization of data in terms of type (quantitative vs. qualitative), the magnitude of effect implicated, and other similar factors were implemented within each area of significance. The results of the study suggest that AI stands to change all the beforementioned subgroups. However, its specific effects vary in magnitude and favorability (beneficial or harmful) and will be further discussed. The results discussed will reveal to those affiliated with the education field, such as teachers, counselors, or even parents of students, valuable information on not just the projected possibilities of AI in education but the effects of those changes moving forward.

Keywords: artificial intelligence, education, schools, teachers

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Authors: Shen Xiaoyun

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The AI-generated works must intersect with the right holder’s work, thus having a certain impact on the rights and interests of the right holder’s work. The law needs to explore and improve the regulation of the fair use of AI creations and build a compensation system to adapt to the development of the times. The development of AI technology has brought about problems such as the unclear relationship between fair use and infringement of copyright, the unclear general terms and conditions of application, and the incomplete criteria for judging at different stages. Through different theoretical methods, the legitimacy of the rational use of the system can be demonstrated. The compensation standard for fair use of copyright in AI creation can refer to the market pricing of the right holder's work, and the compensation can construct a formula for the amount of damages for AI copyright infringement, and construct the compensation standard based on the main factors affecting the market value of the work, so as to provide a reference for the construction of a compensation system for fair use of works generated by AI.

Keywords: artificial intelligence, creative acts, fair use of copyright, copyright compensation system

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Authors: Azidah Abu Ziden, Adu Ifedayo Emmanuel

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This study investigated the optical board as an artificial technology for peer teaching in a Nigerian university. A design and development research (DDR) design was adopted, which entailed the planning and testing of instructional design models adopted to produce the optical board. This research population involved twenty-five (25) peer-teaching students at a Nigerian university consisting of theatre arts, religion, and language education-related disciplines. Also, using a random sampling technique, this study selected eight (8) students to work on the optical board. Besides, this study introduced a research instrument titled lecturer assessment rubric containing 30-mark metrics for evaluating students’ teaching with the optical board. In this study, it was discovered that the optical board affords students acquisition of self-employment skills through their exposure to the peer teaching course, which is a teacher training module in Nigerian universities. It is evident in this study that students were able to coordinate their design and effectively develop the optical board without lecturer’s interference. This kind of achievement in this research shows that the Nigerian university curriculum had been designed with contents meant to spur students to create jobs after graduation, and effective implementation of the readily available curriculum contents is enough to imbue students with the needed entrepreneurial skills. It was recommended that the Federal Government of Nigeria (FGN) must discourage the poor implementation of Nigerian university curriculum and invest more in the betterment of the readily available curriculum instead of considering a synonymously acclaimed new curriculum for regurgitated teaching and learning process.

Keywords: optical board, artificial technology, peer teaching, educational technology, Nigeria, Malaysia, university, glass, wood, electrical, improvisation

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Authors: Mohammed Eltayeb Salih Elamin

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This study discusses the concept of the artificial heart using engineering concepts, of the fluid mechanics and the characteristics of the non-Newtonian fluid. For the purpose to serve heart patients and improve aspects of their lives and since the Statistics review according to world health organization (WHO) says that heart disease and blood vessels are the first cause of death in the world. Statistics shows that 30% of the death cases in the world by the heart disease, so simply we can consider it as the number one leading cause of death in the entire world is heart failure. And since the heart implantation become a very difficult and not always available, the idea of the artificial heart become very essential. So it’s important that we participate in the developing this idea by searching and finding the weakness point in the earlier designs and hoping for improving it for the best of humanity. In this study a pump was designed in order to pump blood to the human body and taking into account all the factors that allows it to replace the human heart, in order to work at the same characteristics and the efficiency of the human heart. The pump was designed on the idea of the diaphragm pump. Three models of blood obtained from the blood real characteristics and all of these models were simulated in order to study the effect of the pumping work on the fluid. After that, we study the properties of this pump by using Ansys15 software to simulate blood flow inside the pump and the amount of stress that it will go under. The 3D geometries modeling was done using SOLID WORKS and the geometries then imported to Ansys design modeler which is used during the pre-processing procedure. The solver used throughout the study is Ansys FLUENT. This is a tool used to analysis the fluid flow troubles and the general well-known term used for this branch of science is known as Computational Fluid Dynamics (CFD). Basically, Design Modeler used during the pre-processing procedure which is a crucial step before the start of the fluid flow problem. Some of the key operations are the geometry creations which specify the domain of the fluid flow problem. Next is mesh generation which means discretization of the domain to solve governing equations at each cell and later, specify the boundary zones to apply boundary conditions for the problem. Finally, the pre–processed work will be saved at the Ansys workbench for future work continuation.

Keywords: Artificial heart, computational fluid dynamic heart chamber, design, pump

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Authors: Deyu Zhou, Xiao Xue, Lizhen Cui

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With the increasing collaboration among various services and the growing complexity of user demands, there are more and more factors affecting the stable development of the cloud manufacturing service economic system (CMSE). This poses new challenges to the evolution analysis of the CMSE. Many researchers have modeled and analyzed the evolution process of CMSE from the perspectives of individual learning and internal factors influencing the system, but without considering other important characteristics of the system's individuals (such as heterogeneity, bounded rationality, etc.) and the impact of external environmental factors. Therefore, this paper proposes an integrated artificial social model for the cloud manufacturing service economic system, which considers both the characteristics of the system's individuals and the internal and external influencing factors of the system. The model consists of three parts: the Agent model, environment model, and rules model (Agent-Environment-Rules, AER): (1) the Agent model considers important features of the individuals, such as heterogeneity and bounded rationality, based on the adaptive behavior mechanisms of perception, action, and decision-making; (2) the environment model describes the activity space of the individuals (real or virtual environment); (3) the rules model, as the driving force of system evolution, describes the mechanism of the entire system's operation and evolution. Finally, this paper verifies the effectiveness of the AER model through computational and experimental results.

Keywords: cloud manufacturing service economic system (CMSE), AER model, artificial social modeling, integrated framework, computing experiment, agent-based modeling, social networks

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

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Authors: John Joseph S. Martis, Rohanchandra R. Gatty, Aaron Jose Fernandes, Rahul P. Nambiar

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Introduction: Surgery for breast cancer is either mastectomy or breast conservation surgery. The most commonly used incision for modified radical mastectomy is the transverse Stewart incision. But this incision may have the disadvantage of causing disparity between the closure lines of superior and inferior skin flaps in mastectomy and can cause overhanging of soft tissue below and behind the axilla. The curvizigzag incision, on principle, may help in this regard and can prevent scar migration beyond the anterior axillary line. This study aims to compare the two incisions in this regard. Methods: 100 patients with cancer of breast were included in the study after satisfying inclusion and exclusion criteria. They underwent surgery at Father Muller Medical College, Mangalore, India, between November 2019 to September 2021. The patients were divided into two groups. Group A patients were subjected to modified radical mastectomy with curvizigzag incision and group B patients with transverse Stewart incision. Results: Seroma on postoperative day1, day 2 was 0% in both the groups. Seroma on postoperative day 30 was present in 14% of patients in group B. 60% of patients in group B had sag of soft tissue below and behind the axilla, and none of the patients in group A had this problem. In 64% of the patients in group B, the incision crossed the anterior axillary fold, 64% of the patients in group B had tension in the incision site while approximation of the skin flaps. Conclusion: Curvizigzag incision is statistically better with lesser complications when compared to the transverse Stewart incision for modified radical mastectomy for carcinoma breast.

Keywords: breast cancer, curvizigzag incision, transverse Stewart incision, seroma, modified radical mastectomy

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Authors: Ciro Cursio, Pio Luigi Cursio, Giulia Cursio, Isabella Chiardi, Luigi Cursio

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Introduction: Full-face aesthetic treatments often present a difficult task: since different patients possess different anatomical and tissue characteristics, there is no guarantee that the same treatment will have the same effect on multiple patients; additionally, full-face rejuvenation and beautification treatments require not only a high degree of technical skill but also the ability to choose the right product for each area and a keen artistic eye. Method: We present an artificial intelligence-based algorithm that can generate realistic post-treatment 3D models based on the patient’s requests together with the doctor’s input. These 3-dimensional predictions can be used by the practitioner for two purposes: firstly, they help ensure that the patient and the doctor are completely aligned on the expectations of the treatment; secondly, the doctor can use them as a visual guide, obtaining a natural result that would normally stem from the practitioner's artistic skills. To this end, the algorithm is able to predict injection zones, the type and quantity of hyaluronic acid, the injection depth, and the technique to use. Results: Our innovation consists in providing an objective visual representation of the patient that is helpful in the patient-doctor dialogue. The patient, based on this information, can express her desire to undergo a specific treatment or make changes to the therapeutic plan. In short, the patient becomes an active agent in the choices made before the treatment. Conclusion: We believe that this algorithm will reveal itself as a useful tool in the pre-treatment decision-making process to prevent both the patient and the doctor from making a leap into the dark.

Keywords: hyaluronic acid, fillers, full face, artificial intelligence, 3D

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Authors: P. Abtahi, B. Samali

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Façade panels of various shapes, weights, and connections usually act as a barrier between the indoor and outdoor environments. They also play a major role in enhancing the aesthetics of building structures. They are attached by different types of connections to the primary structure or inner panels in double skin façade skins. Structural buildings designed to withstand seismic shocks have been undergoing a critical appraisal in recent years, with the emphasis changing from ‘strength’ to ‘performance’. Performance based design and analysis have found their way into research, development, and practice of earthquake engineering, particularly after the 1994 Northridge and 1995 Kobe earthquakes. The design performance of facades as non-structural elements has now focused mainly on evaluating the damage sustained by façade frames with fixed connections, not movable ones. This paper will review current design standards for structural buildings, including the performance of structural and non-structural components during earthquake excitations in order to overview and evaluate the damage assessment and behaviour of various façade systems in building structures during seismic activities. The proposed solutions for each facade system will be discussed case by case to evaluate their potential for incorporation with newly designed connections. Finally, Double-Skin-Facade systems can potentially be combined with movable facade technology, although other glazing systems would require minor to major changes in their design before being integrated into the system.

Keywords: building performance, earthquake engineering, glazing system, movable façade technology

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Authors: Trung Le Thanh

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Bored piles are always the optimal solution for high-rise building foundations. They have many advantages, such as large diameter, large pile length and construction in all different geological conditions. However, due to construction characteristics, the load-bearing capacity of bored piles is not optimal because wall friction is reduced due to poor contact between the pile and the surrounding soil. Therefore, grouting technology along the pile body helps improve the load-bearing capacity of bored piles significantly through increasing the skin resistance of the pile and surrounding soil. The improvement of pile skin resistance depends on the parameters of grouting technology, especially grouting volume, mortar viscosity, mortar strength,... and different geological conditions. Studies show that the technology of grouting piles on sandy soil is more effective than on clay. This article presents an experimental model to determine the load-bearing capacity of bored piles with a diameter of 400 mm and a length of 3 m on sand with different slurry volume in Tan Uyen city, Binh Duong province. On that basis, analyze the correlation between the increase in load-bearing capacity of bored piles without and with shaft grouting pile. Research results show that the wall resistance of shaft grouted piles increases 2-3 times compared to piles without grouting, and the pile's load-bearing capacity increases significantly. The article's research provides scientific value for consulting work on the design of bored piles when grouted along the pile body.

Keywords: bored pile, shaft grouting, bearing capacity, pile shaft resistance

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Authors: Ying-Chang Yu, Yuan-Lung Lo

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Building ventilation performance is an important indicator of indoor comfort. However, in addition to the geometry of the building or the proportion of the opening, the ventilation performance is also very much related to the actual wind pressure of the building. There are more and more contemporary building designs built with multi-layer exterior envelope. Due to ventilation and view observatory requirement, the porous outer layer of the building is commonly adopted and has a significant wind damping effect, causing the phenomenon of actual wind pressure loss. However, the relationship between the wind damping effect and the actual wind pressure is not linear. This effect can make the indoor ventilation of the building rationalized to reasonable range under the condition of high wind pressure, and also maintain a good amount of ventilation performance under the condition of low wind pressure. In this study, wind tunnel experiments were carried out to simulate the different wind pressures flow through the porous outer layer, and observe the actual wind pressure strength engage with the window layer to find the decreasing relationship between the damping effect of the porous shell and the wind pressure. Experiment specimen scale was designed to be 1:50 for testing real-world building conditions; the study found that the porous enclosure has protective shielding without affecting low-pressure ventilation. Current study observed the porous skin may damp more wind energy to ease the wind pressure under high-speed wind. Differential wind speed may drop the pressure into similar pressure level by using porous skin. The actual mechanism and value of this phenomenon will need further study in the future.

Keywords: multi-layer facade, porous media, wind damping, wind tunnel test, building ventilation

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

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The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of the Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling human behavior. However, the exclusive use of this technology still seems insufficient to control vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: accuracy assessment, AI-driven mobility, artificial intelligence, automated vehicles

Procedia PDF Downloads 113