2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)最新文献

{"title":"Prediction of Photovoltaic Energy Production Using Machine Learning Methods in the RapidMiner Application","authors":"D. Sepideh Hassankhani, I. Budinská, Z. Balogh, Ján Moižiš, D. Saeid Hassankhani","doi":"10.1109/INES56734.2022.9922608","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922608","url":null,"abstract":"As the penetration of using clean energy in government plans and companies is rising, many researchers are seeking the influence of multiple factors on the processes leading to producing renewable energy. Electricity via photovoltaic (PV) cells, quickly became popular in all countries due to fewer restrictions compared to other energies. In this study, we compared different machine learning methods based on the classification and prediction of solar energy output. by analyzing a specific case study in Slovakia, Finally, this model was implementedin the RapidMiner platform and the effective factors in predicting by comparing evaluation were identified.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121834339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causes and possibilities of reducing student drop-out in Hungarian technical higher education","authors":"Tamás Kersánszki, Ildikó Holik, István Dániel Sanda","doi":"10.1109/INES56734.2022.9922627","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922627","url":null,"abstract":"Student dropout is a severe problem in higher education worldwide. In technical higher education, this issue is particularly relevant, as a significant proportion of students cannot meet the requirements of basic subjects, especially in science and technology, and drop out of higher education due to failures. The reduction of student dropout in higher education institutions is becoming an increasingly urgent task due to the deteriorating demographic indicators. One such possible solution is student mentoring, in which university students with good academic results mentor their peers in need. By analyzing the results of institutional research, the study focuses on the problem of student dropout, its causes and one of the ways to reduce dropout, contemporary mentoring.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116917831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time to start using checklists for reporting artificial intelligence in health care and biomedical research: a rapid review of available tools","authors":"Z. Zrubka, L. Gulácsi, M. Péntek","doi":"10.1109/INES56734.2022.9922639","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922639","url":null,"abstract":"While the volume of using artificial intelligence (AI) and machine learning (ML) in medical research has grown considerable over the past years, the reporting quality for the majority of such studies has been poor, raising concerns about the replicability, biasedness, validity and overall value for a vast amount of research. This rapid review aims to summarize reporting guidelines for medical AI studies. Following a systematic search in the PubMed database up to May 2022 and the reference lists of previously published reviews in the field, we identified 22 reporting checklists published or under development for a variety of study designs and clinical fields or general use. The main aims, the target audience and specific focus of the identified checklists has been summarized. Given the documented positive impact of checklists on the reporting quality of medical research, we encourage researchers using AI or ML in medicine to start using them.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125149795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying the areas affected by the pandemic based on the opinions of Hungarian SME sector","authors":"J. Varga, Mónika Garai-Fodor, Agnes Csiszárik- Kocsir","doi":"10.1109/INES56734.2022.9922644","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922644","url":null,"abstract":"The coronavirus pandemic that escalated in 2020 brought changes in all areas of life, putting all sectors of the economy, including households and businesses, on a new footing. Changed working conditions and closures have put many businesses in a difficult situation. Many businesses managed to survive the restrictions at considerable cost, but there were also some entities that did not survive the difficult months following the outbreak of the coronavirus epidemic. New ways of working and changed circumstances have rewritten previous management and practical principles and experience. The aim of this paper is to describe the impact of the coronavirus epidemic and the areas most affected by the subsequent crisis, based on the results of a questionnaire survey of 161 enterprises in Hungary. The aim of the study is to identify the areas which are the most vulnerable in the life of SMEs and which should be given the most attention in the next possible future crises.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"449 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123456315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reasoning Mechanism for the Implementation of Computational Design Synthesis","authors":"Patrik Müller, P. Gembarski, R. Lachmayer","doi":"10.1109/INES56734.2022.9922623","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922623","url":null,"abstract":"Expert systems enable the automated solution synthesis for complex product classes. Particularly to be highlighted in this area is the Computational Design Synthesis (CDS), which automatically designs, evaluates and guides solutions. Thus, individualized osseointegrative implants can be generated in one single step on the basis of CT scans. This paper provides a framework to implement CDS in different architectures for Computer-Aided Engineering Environment (CAEE) and help to estimate potentials for different use cases to maximize the potential of the method.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125594198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Associations Between HPV-infections Associated Risk Factors and Cervical Cancer Associated Risk Factors Using Chi-square Method","authors":"Ogbolu Melvin Omone, M. Kozlovszky","doi":"10.1109/INES56734.2022.9922618","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922618","url":null,"abstract":"Human Papillomaviruses (HPV) is one of the most common Sexually Transmitted Infections (STIs) among females and males in our society; causing persistent genital HPV-infections that can further develop into cervical cancer in females. The main kind of HPV-genotypes causing cervical cancer (which is categorized as human carcinogens) are the HPV-genotype 16 and 18. However, our society does not have relevant information about these HPV-genotypes. Hence, to mitigate and prevent the spread of HPV-infections and cervical cancer mortality rate in our society, more energy should be accelerated into awareness creation on HPV and cervical cancer globally. This study focuses on the risk factors causing HPV-infections and cervical cancer disease; and how associated each risk factor is with the other. The purpose of this research is to study the risk factors of HPV-infections and cervical cancer risk factors, and further reveal the associations between the associated risk factors of both diseases using chi-square method. Statistical results would help readers and researchers to understand the risk factors of HPV and cervical cancer, and they can develop restrains in practicing high-risk sexual behaviors and aim to practice safe sexual intercourse.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126947299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Limitations of PSO in Cooperation with FPI-based Adaptive Control for Nonlinear Systems","authors":"H. Issa, J. Tar","doi":"10.1109/INES56734.2022.9922654","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922654","url":null,"abstract":"Essentially two kinds of adaptive controllers exist. To first-class belong those that commence their work with an available analytically known approximate system model and try to refine its parameters by using real-time observations. The elements of the second class do not wish to amend this initial model: rather they apply casual, not permanently maintained model corrections that rather depend on the observed situations that occur during the actual motion of the controlled system. Theoretically, it can be expected that an analytical model in the background can be refined, and placed in use even in this latter case. By the use of the van der Pol oscillator as a benchmark example of nonlinear systems and the Particle Swarm Optimization method for model correction, it is realized that our idea is too optimistic and the correction of the analytical model by learning may have limitations. This statement is substantiated via simulation examples.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114958114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of spectral resolution on the segmentation quality of magnetic resonance imaging data","authors":"Ágnes Győrfi, Szabolcs Csaholczi, Ioan-Marius Lukáts-Pisak, Lehel Dénes-Fazakas, Andrea Koble, O. Shvets, G. Eigner, L. Kovács, L. Szilágyi","doi":"10.1109/INES56734.2022.9922634","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922634","url":null,"abstract":"The majority of the machine learning methods employed for brain tissue or tumor segmentation from multi-spectral MRI data, especially the ensemble learning methods, use hundreds of attributes for the characterization of the pixels, which leads to enormous storage space requirement. Dealing with hundreds of volumetric records under such circumstances also represents a severe computational burden. To facilitate the establishment and deployment of such data processing frameworks, this paper proposes to investigate, which is the best trade-off between segmentation accuracy and necessary storage space, via manipulating with the spectral resolution used for the attributes of the pixels. Three machine learning methods, two multi-spectral brain MRI datasets, and five statistical indicators of segmentation accuracy were involved in the experimental study, which revealed that an 8-bit color depth or spectral resolution of the feature data is sufficient to obtain the finest achievable segmentation accuracy, while allowing for up to 50% reduction of the memory required by the segmentation procedure, compared to the commonly deployed feature encoding techniques.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122287519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autonomous Non-Technical Surgical Skill Assessment and Workload Analysis in Laparoscopic Cholecystectomy Training","authors":"R. Elek, Berta Mach, Kristóf Móga, Alexander Ládi, T. Haidegger","doi":"10.1109/INES56734.2022.9922657","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922657","url":null,"abstract":"Despite the fact that Minimally Invasive Surgery (MIS) offers undeniable clinical benefits, such as less tissue damage, smaller scars and faster recovery, it requires extensive training from the surgeons, including technical and non-technical skills. Coping with stress and distractions, maintinaing situation awareness, prompt decision making, advanced communication, leadership and teamwork are all essential in MIS. Workload-which represents the human effort to perform a task-shows a strong correlation with non-technical skills. In this paper, a MIS training experiment is introduced, developed to autonomously assess non-technical surgical skills based on sensory data (im-age and force). For this, a surgical phantom and adequate workflow were designed to simulate a stressful laparoscopic cholecystectomy tasks, such as peritoneum dissection and cystic artery clipping. The experiment included the simulation of an abrupt situation (cystic artery bleeding). 20 training session were recorded from 7 subjects (3 non-medicals, 2 residents, 1 expert surgeon and 1 expert MIS surgeon). Analysis of the surgical workload and autonomous skill classification based on surgical tool tracking and force measurements were presented. Workload was tested for the two groups (medical and control) with the Surgical Task Load Index (SURG-TLX) workload assessment tool. Unpaired t-tests showed significant differences between the two groups in the case of mental demands, physical demands and situational stress (p <0.0001, 95 % confidence interval (CI)), and also in the case of task complexity (p <0.05). There were no significant differences in temporal demands and distraction levels. Learning curve in workload was studied with paired t-tests; only task complexity resulted significant difference between the first and the second trials. Autonomous non-technical skill classification was done based on image data with tracked instruments based on Channel and Spatial Reliability Tracker (CSRT) and force data. Time series classification was done by a Fully Convolutional Neural Network (FCN), which resulted high accuracy on temporal demands classification based on the $z$ component of the used forces (85 %) and 75 % accuracy for classifying mental demands/situational stress with the $x$ component of the used forces validated with Leave One Out Cross-Validation (LOOCV). It suggests there are non-technical skills and workload components which can be classified autonomously with objectively measured data.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126272663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-dominated Set of Public Service System Designs using Simulated Annealing Approach","authors":"Marek Kvet, J. Janáček","doi":"10.1109/INES56734.2022.9922632","DOIUrl":"https://doi.org/10.1109/INES56734.2022.9922632","url":null,"abstract":"Public service system designing is often an important subject of operations researchers' interest due to many applicable solving and modelling approaches. This paper focuses on special subclass of location problems, solving of which leads to construction of a special small set of non-dominated solutions. The necessity of searching for a Pareto front or its approximation arises whenever several quality criteria of the design need to be followed at the same time. The scientific content of this contribution consists in introducing a separate heuristic method producing a good approximation of the Pareto front. The suggested algorithm is based on simulated annealing probabilistic metaheuristic approach. The quality of obtained set of non-dominated solutions is compared to the exact Pareto front making use of real-world benchmarks. The main goal of reported research is to enrich the state-of-the-art solving tools by an efficient heuristic tool for bi-criteria public service system designing.","PeriodicalId":253486,"journal":{"name":"2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121969229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}