Search results for: medical image processing

Authors: Ferdi Afian, Inne Yuliawati

Abstract:

Background: The change in causes of death from infectious diseases to non-communicable diseases also occurs in the aviation community in Indonesia. Non-communicable diseases are influenced by several internal risk factors, such as age, lifestyle changes and the presence of other diseases. These risk factors will increase the incidence of heart diseases resulting in the incapacity of Indonesian aviators which will disrupt flight safety. Method: The study was conducted by collecting secondary data. The retrieval of primary data was obtained from medical records at the Indonesian Aviation Health Center in 2016-2017. The subjects in this study were all cases of incapacity in Indonesian aviators medical conditions. Results: In this study, there were 15 cases of aviators in Indonesia who experienced incapacity of medical conditions related to heart and lung diseases in 2016-2017. Based on the secondary data contained in the flight medical records at the Aviation Health Center Aviation, it was found that several factors related to aviators incapacity causing its inability to carried out flight duties. Conclusion: Incapacity of Indonesian aviators medical conditions are most affected by the high value of Body Mass Index (86%) and less affected by high of Uric Acid in the blood (26%) and Hyperglycemia (26%).

Keywords: incapacity, aviators, flight, Indonesia

Procedia PDF Downloads 119

Authors: Yunyong Guo, Sudhakar Ganti, Bryan Guo

Abstract:

The rapid growth of telehealth Internet of Things (IoT) devices has raised concerns about energy consumption and efficient data processing. This paper introduces an energy-efficient model that integrates telehealth IoT devices with a fog and cloud computing-based platform, offering a sustainable and robust solution to overcome these challenges. Our model employs fog computing as a localized data processing layer while leveraging cloud computing for resource-intensive tasks, significantly reducing energy consumption. We incorporate adaptive energy-saving strategies. Simulation analysis validates our approach's effectiveness in enhancing energy efficiency for telehealth IoT systems integrated with localized fog nodes and both private and public cloud infrastructures. Future research will focus on further optimization of the energy-saving model, exploring additional functional enhancements, and assessing its broader applicability in other healthcare and industry sectors.

Keywords: energy-efficient, fog computing, IoT, telehealth

Procedia PDF Downloads 67

Authors: Zahra Mehrabbeygi

Abstract:

This research is opted to analyze and survey discourse variety and news image-making in western media regarding the news coverage of the Russian army intrusion into Ukraine. This research will be done on the news coverage of Ukraine in a period from February 2022 to May 2022 in five western media, "BBC, CBS, NBC, Al Jazeera, and Telegraph." This research attempts to discover some facts about the news policies of the five western news agencies during the circumstances of the Ukraine-Russia war. Critical theories in the news, such as Framing, Media Imperialism of News, Image Making, Discourse, and Ideology, were applied to achieve this goal. The research methodology uses Van Dijk's discourse exploration method based on discourse analysis. The research's statistical population is related to all the news about racial discrimination during the mentioned period. After a statistical population survey with Targeted Sampling, the researcher randomly selected ten news cases for exploration. The research findings show that the western media have similarities in their texts via lexical items, polarization, citations, persons, and institutions. The research findings also imply pre-suppositions, connotations, and components of consensus agreement and underlying predicates in the outset, middle, and end events. The reaction of some western media not only shows their bewilderment but also exposes their prejudices rooted in racism.

Keywords: news discourse analysis, western media, racial discrimination, Ukraine-Russia war

Procedia PDF Downloads 85

Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

Abstract:

Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

Procedia PDF Downloads 338

Authors: Yi-Hsiu Lai

Abstract:

The present study aimed to elucidate the lexical-semantic processing for Chinese as second language (CSL) learners. Twenty L1 speakers of Chinese and twenty CSL learners in Taiwan participated in a picture naming task and a category fluency task. Based on their Chinese proficiency levels, these CSL learners were further divided into two sub-groups: ten CSL learners of elementary Chinese proficiency level and ten CSL learners of intermediate Chinese proficiency level. Instruments for the naming task were sixty black-and-white pictures: thirty-five object pictures and twenty-five action pictures. Object pictures were divided into two categories: living objects and non-living objects. Action pictures were composed of two categories: action verbs and process verbs. As in the naming task, the category fluency task consisted of two semantic categories – objects (i.e., living and non-living objects) and actions (i.e., action and process verbs). Participants were asked to report as many items within a category as possible in one minute. Oral productions were tape-recorded and transcribed for further analysis. Both error types and error frequency were calculated. Statistical analysis was further conducted to examine these error types and frequency made by CSL learners. Additionally, category effects, pictorial effects and L2 proficiency were discussed. Findings in the present study helped characterize the lexical-semantic process of Chinese naming in CSL learners of different Chinese proficiency levels and made contributions to Chinese vocabulary teaching and learning in the future.

Keywords: lexical-semantic processing, Mandarin Chinese, naming, category effects

Procedia PDF Downloads 447

Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

Abstract:

Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

Procedia PDF Downloads 94

Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

Abstract:

The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining

Procedia PDF Downloads 204

Authors: R. Govender, T. C. Katsande, E. Madoroba, N. M. Thiebaut, D. Naidoo

Abstract:

South African control over meat safety is managed by the Department of Agriculture, Forestry and Fisheries (DAFF). Veterinary services department in each of the nine provinces in the country is tasked with overseeing the farm and abattoir segments of the meat supply chain. Abattoirs are privately owned. The number of abattoirs over the years has increased. This increase has placed constraints on government resources required to monitor these abattoirs. This paper presents empirical research results on the hygienic processing of meat in high and low throughout abattoirs. This paper presents a case for the adoption of risk-based regulation as a method of government control over hygiene and safe meat processing at abattoirs in South Africa. Recommendations are made to the DAFF regarding policy considerations on risk-based regulation as a model of control in South Africa.

Keywords: risk-based regulation, abattoir, food control, meat safety

Procedia PDF Downloads 297

Authors: Ling-Lang Huang, Yih-Jer Wu

Abstract:

Empathy is crucial for a patient-physician relationship and medical professionalism. Internet activity, gaming, or even addiction, have been more and more common among medical students. However, there’s been no report showing whether internet behavior has a substantial impact on empathy in medical students to our best knowledge. All year-2 medical students taking the optional course 'Narrative, Comprehension, and Communication' were enrolled. Internet behaviors are divided into two groups, 'internet users without online gaming (IU)' and 'internet users with online gaming (IG)', each group was further divided into 3 groups according to their average online retention time each day (< 2, 2 - 6, > 6 hours). Empathy was evaluated by the scores of the reports and humanities reflection after watching indicated movies, and by self-measured empathy questionnaire. All students taking the year-2 optional course 'Narrative, Comprehension, and Communication' were enrolled. As compared with students in the IU group, those in the IG group had significantly lower scores for the reports (81.3 ± 3.7 vs. 86.4 ± 5.1, P = 0.014). If further dividing the students into 5 groups (IU < 2, IU 2-6, IG < 2, IG 2 - 6, and IG > 6 hours), the scores were significantly and negatively correlated to online gaming with longer hours (r = -0.556, P = 0.006). However, there was no significant difference between IU and IG groups (33.0 ± 5.4 vs. 34.8 ± 3.2, P = n.s.), in terms of scores in the self-measured empathy questionnaire, neither was there any significant trend of scores along with longer online hours across the 5 groups (r = -0.164, P = n.s.). To date, there has been no evidence showing whether different internet behaviors (with or without online gaming) have distinct impacts on empathy. Although all of the medical students had a similarly good self-perception for empathy, our data suggested that online gaming did have a negative impact on their actual expression of empathy. Our observation has brought up an important issue for pondering: May IT- or gaming-assisted medical learning actually harm students’ empathy? In conclusion, this data suggests that long hours of online gaming harms expression of empathy, though all medics think themselves a person of high empathy.

Keywords: empathy. Internet, medical students, online gaming

Procedia PDF Downloads 121

Authors: A. Chenafa, S. Boulenouar, M. Zitouni, M. Boukouria

Abstract:

The sterilization of medical devices constitutes for all the medical professions, an inescapable obligation. It has for objective to prevent the infectious risk, both for the patient and for the medical team. The Dental surgeon as every healthcare professional has to master perfectly this subject and to train his staff to the various techniques of sterilization. It is the only way to assure the patients all the security for which they are entitled to wait when they undergo a dental care. It’s for it, that we undertook to lead an investigation aiming at estimating the sterilization practice at the dental surgeon of Sidi bel Abbes. The survey result showed a youth marked with the profession with a majority use of autoclave with cycle B and an almost total absence of the sterilization controls (test of Bowie and Dick). However, the majority of the dentists control and validate their sterilizers. Finally, our survey allowed us to describe some practices which must be improved regarding control, regarding qualification and regarding staff training. And suggestions were made in this sense.

Keywords: dental surgeon, medical devices, sterilization, survey

Procedia PDF Downloads 387

Authors: Nitin Joseph

Abstract:

Objectives: This study was done to assess emotional intelligence levels and to find out its association with socio demographic variables and perceived stress among medical students. Material and Methods: This study was done among first and second year medical students. Data was collected using a self-administered questionnaire. Results: Emotional intelligence scores was found to significantly increase with age of the participants (F=2.377, P < 0.05). Perceived stress was found to be significantly more among first year (t=1.997, P=0.05). Perceived stress was found to significantly decrease with increasing emotional intelligence scores (r = – 0.226, P < 0.001). Conclusion: First year students were found to be more vulnerable to stress than their seniors probably due to lesser emotional intelligence. As both these parameters are related, ample measures to improve emotional intelligence needs to be supported in the training curriculum of beginners so as to make them more stress free during early student life.

Keywords: emotional intelligence, medical students, perceived stress, socio demographic variables

Procedia PDF Downloads 439

Authors: Tahsien Mohamed Okasha

Abstract:

Simulation is one of the emerging educational strategies that depend on the creation of scenarios to imitate what could happen in real life. At the time of COVID, we faced big obstacles in medical education, specially the clinical part and how we could apply it, the simulation was the golden key. Simulation is a very important tool of education for medical sector students, through creating a safe, changeable, quiet environment with less anxiety level for students to practice and to have repeated trials on their competencies. That impacts the level of practice, achievement, and the way of acting in real situations and experiences. A blind Random sample of students from different specialties and colleges who came and finished their training in an integrated environment was collected and tested, and the responses were graded from (1-5). The results revealed that 77% of the studied subjects agreed that dealing and interacting with different medical sector candidates in the same place was beneficial. 77% of the studied subjects agreed that simulations were challenging in thinking and decision-making skills .75% agreed that using high-fidelity manikins was helpful. 75% agree .76% agreed that working in a safe, prepared environment is helpful for realistic situations.

Keywords: simulation, clinical training, education, medical sector students

Procedia PDF Downloads 13

Authors: Pranee Pathomchaiwat

Abstract:

This research aims to study tourism data and behavior of foreign tourists visited Wat Phrachetuponwimolmangkalaram (Wat Po) Sample groups are tourists who visited inside the temple, during February, March, April and May 2013. Tools used in the research are questionnaires constructed by the researcher, and samples are dawn by Convenience sampling. There are 207 foreign tourists who are willing to be respondents. Statistics used are percentage, average mean and standard deviation. The results of the research reveal that: A. General Data of Respondents: The foreign tourists who visited the temple are mostly female (57.5 %), most respondents are aged between 20-29 years (37.2%). Most respondents live in Europe (62.3%), most of them got the Bachelor’s degree (40.1%), British are mostly found (16.4%), respondents who are students are also found (23.2%), and Christian are mostly found (60.9%). B. Tourists’ Behavior While Visiting the Temple Compound: The result shows that the respondents came with family (46.4%), have never visited the temples (40.6%), and visited once (42 %). It is found that the foreign tourists’ inappropriate behavior are wearing revealing attires (58.9%), touching or getting closed to the monks (55.1%), and speaking loudly (46.9%) respectively. The respondents’ outstanding objectives are to visit inside the temple (57.5%), to pay respect to the Reclining Buddha Image in the Viharn (44.4%) and to worship the Buddha image in the Phra Ubosod (37.7%) respectively. C. The Respondents’ Self-evaluation of Performance: It is found that over all tourists evaluated themselves in the highest level averaged 4.40. When focusing on each item, it is shown that they evaluated themselves in the highest level on obeying the temple staff averaged 4.57, and cleanness concern of the temple averaged 4.52, well-behaved performance during the temple visit averaged 4.47 respectively.

Keywords: deportment, traveler, foreign tourists, temple

Procedia PDF Downloads 291

Authors: Syed Mahbubuz Zaman, A. B. M. Abrar Haque, Mehedi Hassan Nayeem, Misbah Uddin Sagor

Abstract:

Everyday e-mails are being used by millions of people as an effective form of communication over the Internet. Although e-mails allow high-speed communication, there is a constant threat known as spam. Spam e-mail is often called junk e-mails which are unsolicited and sent in bulk. These unsolicited emails cause security concerns among internet users because they are being exposed to inappropriate content. There is no guaranteed way to stop spammers who use static filters as they are bypassed very easily. In this paper, a smart system is proposed that will be using neural networks to approach spam in a different way, and meanwhile, this will also detect the most relevant features that will help to design the spam filter. Also, a comparison of different parameters for different neural network models has been shown to determine which model works best within suitable parameters.

Keywords: long short-term memory, bidirectional long short-term memory, gated recurrent unit, natural language processing, natural language processing

Procedia PDF Downloads 189

Authors: Liu Jia’nan

Abstract:

The default accounts hold the view that there exists a kind of scalar implicature which can be processed without context and own a psychological privilege over other scalar implicatures which depend on context. In contrast, the Relevance Theorist regards context as a must because all the scalar implicatures have to meet the need of relevance in discourse. However, in Katsos, the experimental results showed: Although quantitatively the adults rejected under-informative utterance with lexical scales (context-independent) and the ad hoc scales (context-dependent) at almost the same rate, adults still regarded the violation of utterance with lexical scales much more severe than with ad hoc scales. Neither default account nor Relevance Theory can fully explain this result. Thus, there are two questionable points to this result: (1) Is it possible that the strange discrepancy is due to other factors instead of the generation of scalar implicature? (2) Are the ad hoc scales truly formed under the possible influence from mental context? Do the participants generate scalar implicatures with ad hoc scales instead of just comparing semantic difference among target objects in the under- informative utterance? In my Experiment 1, the question (1) will be answered by repetition of Experiment 1 by Katsos. Test materials will be showed by PowerPoint in the form of pictures, and each procedure will be done under the guidance of a tester in a quiet room. Our Experiment 2 is intended to answer question (2). The test material of picture will be transformed into the literal words in DMDX and the target sentence will be showed word-by-word to participants in the soundproof room in our lab. Reading time of target parts, i.e. words containing scalar implicatures, will be recorded. We presume that in the group with lexical scale, standardized pragmatically mental context would help generate scalar implicature once the scalar word occurs, which will make the participants hope the upcoming words to be informative. Thus if the new input after scalar word is under-informative, more time will be cost for the extra semantic processing. However, in the group with ad hoc scale, scalar implicature may hardly be generated without the support from fixed mental context of scale. Thus, whether the new input is informative or not does not matter at all, and the reading time of target parts will be the same in informative and under-informative utterances. People’s mind may be a dynamic system, in which lots of factors would co-occur. If Katsos’ experimental result is reliable, will it shed light on the interplay of default accounts and context factors in scalar implicature processing? We might be able to assume, based on our experiments, that one single dominant processing paradigm may not be plausible. Furthermore, in the processing of scalar implicature, the semantic interpretation and the pragmatic interpretation may be made in a dynamic interplay in the mind. As to the lexical scale, the pragmatic reading may prevail over the semantic reading because of its greater exposure in daily language use, which may also lead the possible default or standardized paradigm override the role of context. However, those objects in ad hoc scale are not usually treated as scalar membership in mental context, and thus lexical-semantic association of the objects may prevent their pragmatic reading from generating scalar implicature. Only when the sufficient contextual factors are highlighted, can the pragmatic reading get privilege and generate scalar implicature.

Keywords: scalar implicature, ad hoc scale, dynamic interplay, default account, Mandarin Chinese processing

Procedia PDF Downloads 302

Authors: K. A. Ghazaryan, T. H. Derdzyan

Abstract:

For assessment of the soils pollution level of the open mine and tailing dump of surrounding territories of Akhtala ore processing combine by heavy metals in 2013 collected soil samples and analyzed for different heavy metals, such as Cu, Zn, Pb, Ni and Cd. The main soil type in the study sites was the mountain cambisol. To classify soil pollution level contamination indices like Contamination factors (Cf), Degree of contamination (Cd), Pollution load index (PLI) and Geoaccumulation index (I-geo) are calculated. The distribution pattern of trace metals in the soil profile according to I geo, Cf and Cd values shows that the soil is very polluted. And also the PLI values for the 19 sites were >1, which indicates deterioration of site quality.

Keywords: soils pollution, heavy metal, geoaccumulation index, pollution load index, contamination factor

Procedia PDF Downloads 419

Authors: O. Hinrichs, H. Franz, G. Reiter

Abstract:

Electron guns have a key function in a series of thermal processes, like EB (electron beam) melting, evaporation or welding. These techniques need a high-intensity continuous electron beam that defocuses itself due to high space charge forces. A proper beam transport throughout the magnetic focusing system can be ensured by a space charge compensation via residual gas ions. The different pressure stages in the EB gun cause various degrees of compensation. A numerical model was installed to simulate realistic charge distributions within the beam by using CST-Particle Studio code. We will present current status of beam dynamic simulations. This contribution will focus on the creation of space charge ions and their influence on beam and gun components. Furthermore, the beam transport in the gun will be shown for different beam parameters. The electron source allows to produce beams with currents of 3 A to 15 A and energies of 40 keV to 45 keV.

Keywords: beam dynamic simulation, space charge compensation, thermionic electron source, EB melting, EB thermal processing

Procedia PDF Downloads 323

Authors: Chan Kwong Tung

Abstract:

Since Baddeley and Hitch’s seminal research in 1974 on working memory (WM), this topic has been of great interest to language educators. Although there are some variations in the definitions of WM, recent findings in WM have contributed vastly to our understanding of language learning, especially its effects on second language acquisition (SLA). For example, the phonological component of WM (PWM) and the executive component of WM (EWM) have been found to be positively correlated with language learning. This paper discusses two general, yet highly relevant WM findings that could directly affect the effectiveness of Chinese Language (CL) vocabulary teaching and learning, as well as the quality of its assessment. First, PWM is found to be critical for the long-term learning of phonological forms of new words. Second, EWM is heavily involved in interpreting the semantic characteristics of new words, which consequently affects the quality of learners’ reading comprehension. These two ideas are hardly discussed in the Chinese literature, both conceptual and empirical. While past vocabulary acquisition studies have mainly focused on the cognitive-processing approach, active processing, ‘elaborate processing’ (or lexical elaboration) and other effective learning tasks and strategies, it is high time to balance the spotlight to the WM (particularly PWM and EWM) to ensure an optimum control on the teaching and learning effectiveness of such approaches, as well as the validity of this language assessment. Given the unique phonological, orthographical and morphological properties of the CL, this discussion will shed some light on the vocabulary acquisition of this Sino-Tibetan language family member. Together, these two WM concepts could have crucial implications for the design, development, and planning of vocabularies and ultimately reading comprehension teaching and assessment in language education. Hopefully, this will raise an awareness and trigger a dialogue about the meaning of these findings for future language teaching, learning, and assessment.

Keywords: Chinese Language, working memory, vocabulary assessment, vocabulary teaching

Procedia PDF Downloads 325

Authors: Ahmet Karagoz, Irfan Karagoz

Abstract:

Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

Procedia PDF Downloads 348

Authors: William Michael Short

Abstract:

Semantic Web’ technologies such as the Unified Medical Language System Metathesaurus, SNOMED-CT, and MeSH have been touted as transformational for the way users access online medical and health information, enabling both the automated analysis of natural-language data and the integration of heterogeneous healthrelated resources distributed across the Internet through the use of standardized terminologies that capture concepts and relationships between concepts that are expressed differently across datasets. However, the approaches that have so far characterized ‘semantic bioinformatics’ have not yet fulfilled the promise of the Semantic Web for medical and health information retrieval applications. This paper argues within the perspective of cognitive linguistics and cognitive anthropology that four features of human meaning-making must be taken into account before the potential of semantic technologies can be realized for this domain. First, many semantic technologies operate exclusively at the level of the word. However, texts convey meanings in ways beyond lexical semantics. For example, transitivity patterns (distributions of active or passive voice) and modality patterns (configurations of modal constituents like may, might, could, would, should) convey experiential and epistemic meanings that are not captured by single words. Language users also naturally associate stretches of text with discrete meanings, so that whole sentences can be ascribed senses similar to the senses of words (so-called ‘discourse topics’). Second, natural language processing systems tend to operate according to the principle of ‘one token, one tag’. For instance, occurrences of the word sound must be disambiguated for part of speech: in context, is sound a noun or a verb or an adjective? In syntactic analysis, deterministic annotation methods may be acceptable. But because natural language utterances are typically characterized by polyvalency and ambiguities of all kinds (including intentional ambiguities), such methods leave the meanings of texts highly impoverished. Third, ontologies tend to be disconnected from everyday language use and so struggle in cases where single concepts are captured through complex lexicalizations that involve profile shifts or other embodied representations. More problematically, concept graphs tend to capture ‘expert’ technical models rather than ‘folk’ models of knowledge and so may not match users’ common-sense intuitions about the organization of concepts in prototypical structures rather than Aristotelian categories. Fourth, and finally, most ontologies do not recognize the pervasively figurative character of human language. However, since the time of Galen the widespread use of metaphor in the linguistic usage of both medical professionals and lay persons has been recognized. In particular, metaphor is a well-documented linguistic tool for communicating experiences of pain. Because semantic medical knowledge-bases are designed to help capture variations within technical vocabularies – rather than the kinds of conventionalized figurative semantics that practitioners as well as patients actually utilize in clinical description and diagnosis – they fail to capture this dimension of linguistic usage. The failure of semantic technologies in these respects degrades the efficiency and efficacy not only of medical research, where information retrieval inefficiencies can lead to direct financial costs to organizations, but also of care provision, especially in contexts of patients’ self-management of complex medical conditions.

Keywords: ambiguity, bioinformatics, language, meaning, metaphor, ontology, semantic web, semantics

Procedia PDF Downloads 116

Authors: Mahnaz Khalafehnilsaz, Rozina Rahnama

Abstract:

Introduction: The elderly population is growing rapidly, and with this growth comes an increased demand for healthcare services. Artificial intelligence (AI) has the potential to revolutionize the delivery of healthcare services to the elderly population. In this study, the various ways in which AI is used to create health content for elderly people and its transformative impact on the healthcare industry will be explored. Method: A systematic review of the literature was conducted to identify studies that have investigated the role of AI in creating health content specifically for elderly people. Several databases, including PubMed, Scopus, and Web of Science, were searched for relevant articles published between 2000 and 2022. The search strategy employed a combination of keywords related to AI, personalized health content, and the elderly. Studies that utilized AI to create health content for elderly individuals were included, while those that did not meet the inclusion criteria were excluded. A total of 20 articles that met the inclusion criteria were identified. Finding: The findings of this review highlight the diverse applications of AI in creating health content for elderly people. One significant application is the use of natural language processing (NLP), which involves the creation of chatbots and virtual assistants capable of providing personalized health information and advice to elderly patients. AI is also utilized in the field of medical imaging, where algorithms analyze medical images such as X-rays, CT scans, and MRIs to detect diseases and abnormalities. Additionally, AI enables the development of personalized health content for elderly patients by analyzing large amounts of patient data to identify patterns and trends that can inform healthcare providers in developing tailored treatment plans. Conclusion: AI is transforming the healthcare industry by providing a wide range of applications that can improve patient outcomes and reduce healthcare costs. From creating chatbots and virtual assistants to analyzing medical images and developing personalized treatment plans, AI is revolutionizing the way healthcare is delivered to elderly patients. Continued investment in this field is essential to ensure that elderly patients receive the best possible care.

Keywords: artificial intelligence, health content, older adult, healthcare

Procedia PDF Downloads 49

Authors: Hyojin Lim, Cuong Nguyen Khac, Ho-Youl Jung

Abstract:

In this paper, we propose moving object detection method which is helpful for driver to safely take his/her car out of parking lot. When moving objects such as motorbikes, pedestrians, the other cars and some obstacles are detected at the rear-side of host vehicle, the proposed algorithm can provide to driver warning. We assume that the host vehicle is just before departure. Gaussian Mixture Model (GMM) based background subtraction is basically applied. Pre-processing such as smoothing and post-processing as morphological filtering are added.We examine “which color space has better performance for detection of moving objects?” Three color spaces including RGB, YCbCr, and Y are applied and compared, in terms of detection rate. Through simulation, we prove that RGB space is more suitable for moving object detection based on background subtraction.

Keywords: gaussian mixture model, background subtraction, moving object detection, color space, morphological filtering

Procedia PDF Downloads 598

Authors: Arnab Majumdar, Sanjoy Sadhukhan

Abstract:

Although gun barrel steels are an important variety from defense view point, available literatures are very limited. In the present work, an IF grade Ni-Cr-Mo-V high strength low alloy steel is produced in Electric Earth Furnace-ESR Route. Ingot was hot forged to desired dimension with a reduction ratio of 70-75% followed by homogenization, hardening and tempering treatment. Sample chemistry, NMIR, macro and micro structural analyses were done. Mechanical properties which include tensile, impact, and fracture toughness were studied. Ultrasonic testing was done to identify internal flaws. The existing high strength low alloy Ni-Cr-Mo-V steel shows improved properties in modified processing route and heat treatment schedule in comparison to properties noted earlier for manufacturing of gun barrels. The improvement in properties seems to withstand higher explosive loads with the same amount of steel in gun barrel application.

Keywords: gun barrel steels, IF grade, chemistry, physical properties, thermal and mechanical processing, mechanical properties, ultrasonic testing

Procedia PDF Downloads 351

Authors: Farhan A. Alenizi

Abstract:

Digital watermarking has evolved in the past years as an important means for data authentication and ownership protection. The images and video watermarking was well known in the field of multimedia processing; however, 3D objects' watermarking techniques have emerged as an important means for the same purposes, as 3D mesh models are in increasing use in different areas of scientific, industrial, and medical applications. Like the image watermarking techniques, 3D watermarking can take place in either space or transform domains. Unlike images and video watermarking, where the frames have regular structures in both space and temporal domains, 3D objects are represented in different ways as meshes that are basically irregular samplings of surfaces; moreover, meshes can undergo a large variety of alterations which may be hard to tackle. This makes the watermarking process more challenging. While the transform domain watermarking is preferable in images and videos, they are still difficult to implement in 3d meshes due to the huge number of vertices involved and the complicated topology and geometry, and hence the difficulty to perform the spectral decomposition, even though significant work was done in the field. Spatial domain watermarking has attracted significant attention in the past years; they can either act on the topology or on the geometry of the model. Exploiting the statistical characteristics in the 3D mesh models from both geometrical and topological aspects was useful in hiding data. However, doing that with minimal surface distortions to the mesh attracted significant research in the field. A 3D mesh blind watermarking technique is proposed in this research. The watermarking method depends on modifying the vertices' positions with respect to the center of the object. An optimal method will be developed to reduce the errors, minimizing the distortions that the 3d object may experience due to the watermarking process, and reducing the computational complexity due to the iterations and other factors. The technique relies on the displacement process of the vertices' locations depending on the modification of the variances of the vertices’ norms. Statistical analyses were performed to establish the proper distributions that best fit each mesh, and hence establishing the bins sizes. Several optimizing approaches were introduced in the realms of mesh local roughness, the statistical distributions of the norms, and the displacements in the mesh centers. To evaluate the algorithm's robustness against other common geometry and connectivity attacks, the watermarked objects were subjected to uniform noise, Laplacian smoothing, vertices quantization, simplification, and cropping. Experimental results showed that the approach is robust in terms of both perceptual and quantitative qualities. It was also robust against both geometry and connectivity attacks. Moreover, the probability of true positive detection versus the probability of false-positive detection was evaluated. To validate the accuracy of the test cases, the receiver operating characteristics (ROC) curves were drawn, and they’ve shown robustness from this aspect. 3D watermarking is still a new field but still a promising one.

Keywords: watermarking, mesh objects, local roughness, Laplacian Smoothing

Procedia PDF Downloads 146

Authors: Faisal Alshammari, Abdulmajid Addali, Mosab Alrashed, Taihiret Alhashan

Abstract:

The application of acoustic emission techniques is gaining popularity, as it can monitor the condition of gears and bearings and detect early symptoms of a defect in the form of pitting, wear, and flaking of surfaces. Early detection of these defects is essential as it helps to avoid major failures and the associated catastrophic consequences. Signal processing techniques are required for early defect detection – in this article, a time domain technique called the Energy Index (EI) is used. This article presents an investigation into the Energy Index’s effectiveness to detect early-stage defect initiation and deterioration, and compares it with the common r.m.s. index, Kurtosis, and the Kolmogorov-Smirnov statistical test. It is concluded that EI is a more effective technique for monitoring defect initiation and development than other statistical parameters.

Keywords: acoustic emission, signal processing, kurtosis, Kolmogorov-Smirnov test

Procedia PDF Downloads 344

Authors: Chandu Rathnayake, Isuri Anuradha

Abstract:

Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

Procedia PDF Downloads 64

Authors: Yongxin Chen, Min Zhang

Abstract:

As industrial enterprises are the primary source of national pollution, environmental information disclosure is a crucial way to demonstrate to stakeholders the work they have done in fulfilling their environmental responsibilities and accepting social supervision. In the era of the digital economy, many companies, actively embracing the opportunities that come with digital transformation, have begun to apply digital technology to information collection and disclosure within the enterprise. However, less is known about the relationship between digital transformation and environmental disclosure. This study investigates how enterprise digital transformation affects environmental disclosure in 643 Chinese industrial companies, according to information processing theory. What is intriguing is that the depth (size) and breadth (diversity) of environmental disclosure linearly increase with the rise in the collection, processing, and analytical capabilities in the digital transformation process. However, the volume of data will grow exponentially, leading to a marginal increase in the economic and environmental costs of utilizing, storing, and managing data. In our empirical findings, linearly increasing benefits and marginal costs create a unique inverted U-shaped relationship between the degree of digital transformation and environmental disclosure in the Chinese industrial sector. Besides, based on the upper echelons theory, we also propose that the top management team with high stability and managerial capabilities will invest more effort and expense into improving environmental disclosure quality, lowering the carbon footprint caused by digital technology, maintaining data security etc. In both these contexts, the increasing marginal cost curves would become steeper, weakening the inverted U-shaped slope between DT and ED.

Keywords: digital transformation, environmental disclosure, the top management team, information processing theory, upper echelon theory

Procedia PDF Downloads 117

Authors: Amandeep Singh, Niraj Bala

Abstract:

In the last decades, light-weight materials such as magnesium matrix composites have become hot topic for material research due to their excellent mechanical and physical properties. However, relatively very less work has been done related to the wear behavior of these composites. Magnesium matrix composites have wide applications in automobile and aerospace sector. In this review, attempt has been done to collect the literature related to wear behavior of magnesium matrix composites fabricated through various processing techniques such as stir casting, powder metallurgy, friction stir processing etc. Effect of different reinforcements, reinforcement content, reinforcement size, wear load, sliding speed and time have been studied by different researchers in detail. Wear mechanism under different experimental condition has been reviewed in detail. The wear resistance of magnesium and its alloys can be enhanced with the addition of different reinforcements. Wear resistance can further be enhanced by increasing the percentage of added reinforcements. Increase in applied load during wear test leads to increase in wear rate of magnesium composites.

Keywords: hardness, magnesium matrix composites, reinforcement, wear

Procedia PDF Downloads 312

Authors: Anaam H. Al-ShaikhAli, Mansour A. Al-Herz

Abstract:

The research leverages the proven HS-FCC technology to directly crack crude oils into petrochemical building blocks. Crude oils were subjected to an optimized hydro-processing process where metal contaminants and sulfur were reduced to an acceptable level for feeding the crudes into the HS-FCC technology. The hydro-processing is achieved through a fixed-bed reactor which is composed of 3 layers of catalysts. The crude oil is passed through a dementalization catalyst followed by a desulfurization catalyst and finally a de-aromatization catalyst. The hydroprocessing was conducted at an optimized liquid hourly space velocity (LHSV), temperature, and pressure for an optimal reduction of metals and sulfur from the crudes. The hydro-processed crudes were then fed into a micro activity testing (MAT) unit to simulate the HS-FCC technology. The catalytic cracking of crude oils was conducted over tailored catalyst formulations under an optimized catalyst/oil ratio and cracking temperature for optimal production of total light olefins.

Keywords: petrochemical, catalytic cracking, catalyst synthesis, HS-FCC technology

Procedia PDF Downloads 77

Authors: Chia-Chi Chang, Chuan-Bi Lin, Chia-Min Chan

Abstract:

Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system.

Keywords: ZigBee, Li-ion battery, solar panel, CC2530

Procedia PDF Downloads 360