Search results for: multi-layers decision engine

Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

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Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

Procedia PDF Downloads 519

Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Tarek Dandashi

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WSNs may provide a fast and reliable solution for the early detection of environment events like forest fires. This is crucial for alerting and calling for fire brigade intervention. Sensor nodes communicate sensor data to a host station, which enables a global analysis and the generation of a reliable decision on a potential fire and its location. A WSN with TinyOS and nesC for the capturing and transmission of a variety of sensor information with controlled source, data rates, duration, and the records/displaying activity traces is presented. We propose a similarity distance (SD) between the distribution of currently sensed data and that of a reference. At any given time, a fire causes diverging opinions in the reported data, which alters the usual data distribution. Basically, SD consists of a metric on the Cumulative Distribution Function (CDF). SD is designed to be invariant versus day-to-day changes of temperature, changes due to the surrounding environment, and normal changes in weather, which preserve the data locality. Evaluation shows that SD sensitivity is quadratic versus an increase in sensor node temperature for a group of sensors of different sizes and neighborhood. Simulation of fire spreading when ignition is placed at random locations with some wind speed shows that SD takes a few minutes to reliably detect fires and locate them. We also discuss the case of false negative and false positive and their impact on the decision reliability.

Keywords: forest fire, WSN, wireless sensor network, algortihm

Procedia PDF Downloads 262

Authors: Martin Doran, Roy Sterritt, George Wilkie

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NASA, ESA, and NSSC space agencies have plans to put planetary rovers on Mars in 2020. For these future planetary rovers to succeed, they will heavily depend on sensors to detect obstacles. This will also become of vital importance in the future, if rovers become less dependent on commands received from earth-based control and more dependent on self-configuration and self-decision making. These planetary rovers will face harsh environments and the possibility of hardware failure is high, as seen in missions from the past. In this paper, we focus on using Autonomic principles where self-healing, self-optimization, and self-adaption are explored using the MAPE-K model and expanding this model to encapsulate the attributes such as Awareness, Analysis, and Adjustment (AAA-3). In the experimentation, a Pioneer P3-DX research robot is used to simulate a planetary rover. The sonar sensors on the P3-DX robot are used to simulate the sensors on a planetary rover (even though in reality, sonar sensors cannot operate in a vacuum). Experiments using the P3-DX robot focus on how our software system can be adapted with the loss of sonar sensor functionality. The autonomic manager system is responsible for the decision making on how to make use of remaining ‘enabled’ sonars sensors to compensate for those sonar sensors that are ‘disabled’. The key to this research is that the robot can still detect objects even with reduced sonar sensor capability.

Keywords: autonomic, self-adaption, self-healing, self-optimization

Procedia PDF Downloads 350

Authors: Pushkar Umaranikar, Chris Pollett

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An online advertisement system and its implementation for the Yioop open source search engine are presented. This system supports both selling advertisements and displaying them within search results. The selling of advertisements is done using a system to auction off daily impressions for keyword searches. This is an open, ascending price auction system in which all accepted bids will receive a fraction of the auctioned day’s impressions. New bids in our system are required to be at least one half of the sum of all previous bids ensuring the number of accepted bids is logarithmic in the total ad spend on a keyword for a day. The mechanics of creating an advertisement, attaching keywords to it, and adding it to an advertisement inventory are described. The algorithm used to go from accepted bids for a keyword to which ads are displayed at search time is also presented. We discuss properties of our system and compare it to existing auction systems and systems for selling online advertisements.

Keywords: online markets, online ad system, online auctions, search engines

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Authors: Chuanqiao Zheng, Wei Zeng, Haozhen Lin

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As of March 2020, the number of Chinese netizens has reached 904 million. The proportion of Internet users accessing the Internet through mobile phones is as high as 99.3%. Under the background of 'Internet +', tourists have a stronger sense of independence in the choice of tourism destinations and tourism products. Tourists are more inclined to learn about the relevant information on tourism destinations and other tourists' evaluations of tourist products through the Internet. The search engine, as an integrated platform that contains a wealth of information, is highly valuable to the analysis of the characteristics of the Internet attention given to various tourism destinations, through big data mining and analysis. This article uses the Baidu Index as the data source, which is one of the products of Baidu Search. The Baidu Index is based on big data, which collects and shares the search results of a large number of Internet users on the Baidu search engine. The big data used in this article includes search index, demand map, population profile, etc. The main research methods used are: (1) based on the search index, analyzing the Internet attention given to the tourism in five cities in Northeast Sichuan at different times, so as to obtain the overall trend and individual characteristics of tourism development in the region; (2) based on the demand map and the population profile, analyzing the demographic characteristics and market positioning of the tourist groups in these cities to understand the characteristics and needs of the target groups; (3) correlating the Internet attention data with the permanent population of each province in China in the corresponding to construct the Boston matrix of the Internet attention rate of the Northeast Sichuan tourism, obtain the tourism target markets, and then propose development strategies for different markets. The study has found that: a) the Internet attention given to the tourism in the region can be categorized into tourist off-season and peak season; the Internet attention given to tourism in different cities is quite different. b) tourists look for information including tour guide information, ticket information, traffic information, weather information, and information on the competing tourism cities; with regard to the population profile, the main group of potential tourists searching for the keywords of tourism in the five prefecture-level cities in Northeast Sichuan are youth. The male to female ratio is about 6 to 4, with males being predominant. c) through the construction of the Boston matrix, it is concluded that the star market for tourism in the Northeast Sichuan Economic Zone includes Sichuan and Shaanxi; the cash cows market includes Hainan and Ningxia; the question market includes Jiangsu and Shanghai; the dog market includes Hubei and Jiangxi. The study concludes with the following planning strategies and recommendations: i) creating a diversified business format that integrates cultural and tourism; ii) creating a brand image of niche tourism; iii) focusing on the development of tourism products; iv) innovating composite three-dimensional marketing channels.

Keywords: Baidu Index, big data, internet attention, tourism

Procedia PDF Downloads 123

Authors: Mikiale Gebreslase Gebremariam, Chai Huaqi, Tesfay Gebretsdkan Gebremichael, Dawit Nega Bekele

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In recent years, brownfield redevelopment projects (BRPs) have contributed to the overarching paradigm of the United Nations 2030 agendas. In the present circumstance, most developed nations adopted BRPs, an efficacious urban policy tool. However, in developing and some advanced countries, BRPs are lacking due to limitations of awareness, policy tools, and financial capability for cleaning up brownfield sites. For example, the growth and development of Ethiopian cities were achieved at the cost of poor urban planning, including no community consultations and excessive urbanization for future growth. The demand for land resources is more and more urgent as the result of an intermigration to major cities and towns for socio-economic reasons and population growth. In the past, the development mode of spreading major cities has made horizontal urbanizations stretching outwards. Expansion in search of more land resources, while the outer cities are growing, the inner cities are polluted by environmental pollution. It is noteworthy that the rapid development of cities has not brought about an increase in people's happiness index. Thus, the proposed management framework for managing brownfields in Ethiopia as a lesson to the developing nation facing similar challenges and growth will add immense value in solving the problems and give insights into brownfield land utilization. Under the umbrella of the grey incidence decision-making model and with the consideration of multiple stakeholders and tight environmental and economic constraints, the proposed management framework integrates different criteria from economic, social, environmental, technical, and risk aspects into the grey incidence decision-making model and gives useful guidance to manage brownfields in Ethiopia. Furthermore, it will contribute to the future development of the social economy and the missions of the 2030 UN sustainable development goals.

Keywords: Brownfields, environmental sustainability, Ethiopia, grey-incidence decision-making, sustainable urban development

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Authors: Zohra Mekranfar, Ahmed Saidi, Abdellah Mebrek

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This work aims to develop multi-criteria decision making (MCDM) and spatial data warehouse (SDW) methods, which will be integrated into a GIS according to a ‘GIS dominant’ approach. The GIS operating tools will be operational to operate the SDW. The MCDM methods can provide many solutions to a set of problems with various and multiple criteria. When the problem is so complex, integrating spatial dimension, it makes sense to combine the MCDM process with other approaches like data mining, ascending analyses, we present in this paper an experiment showing a geo-decisional methodology of SWD construction, On-line analytical processing (OLAP) technology which combines both basic multidimensional analysis and the concepts of data mining provides powerful tools to highlight inductions and information not obvious by traditional tools. However, these OLAP tools become more complex in the presence of the spatial dimension. The integration of OLAP with a GIS is the future geographic and spatial information solution. GIS offers advanced functions for the acquisition, storage, analysis, and display of geographic information. However, their effectiveness for complex spatial analysis is questionable due to their determinism and their decisional rigor. A prerequisite for the implementation of any analysis or exploration of spatial data requires the construction and structuring of a spatial data warehouse (SDW). This SDW must be easily usable by the GIS and by the tools offered by an OLAP system.

Keywords: data warehouse, GIS, MCDM, SOLAP

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Authors: Hyungsuk Han, Soohong Jeon, Chungwon Lee, YongHoon Kim

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When a rubber mount and flexible coupling are installed on the main engine, high torsional vibration can occur. The root cause of this high torsional vibration can be attributed to the lateral-torsional coupled vibration of the shaft system. Therefore, the lateral-torsional coupled vibration is investigated numerically after approximating the shaft system to a three-degrees-of-freedom Jeffcott rotor. To verify that the high torsional vibration is caused by the lateral-torsional coupled vibration, a test unit that can simulate this lateral-torsional coupled vibration occurring in the propulsion shaft is developed. Performing a vibration test with the test unit, it can be experimentally verified that the high torsional vibration occurring in the propulsion shaft of the particular ship was caused by the lateral-torsional coupled vibration.

Keywords: Jeffcott rotor, lateral-torsional coupled vibration, propulsion shaft, stability

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Authors: Ahmed Al-Adaileh

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Renewable energy sources, domestic storage systems, controllable loads and machine learning technologies will be key components of future smart homes management systems. An energy management scheme that uses a Deep Learning (DL) approach to support the smart home management systems, which consist of a standalone photovoltaic system, storage unit, heating ventilation air-conditioning system and a set of conventional and smart appliances, is presented. The objective of the proposed scheme is to apply DL-based machine learning to predict various running parameters within a smart home's environment to achieve maximum comfort levels for occupants, reduced electricity bills, and less dependency on the public grid. The problem is using Reinforcement learning, where decisions are taken based on applying the Continuous-time Markov Decision Process. The main contribution of this research is the proposed framework that applies DL to enhance the system's supervised dataset to offer unlimited chances to effectively support smart home systems. A case study involving a set of conventional and smart appliances with dedicated processing units in an inhabited building can demonstrate the validity of the proposed framework. A visualization graph can show "before" and "after" results.

Keywords: smart homes systems, machine learning, deep learning, Markov Decision Process

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Authors: A. Luft, S. Bremen, N. Balc

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The task of flexibility planning and design, just like factory planning, for example, is to create the long-term systemic framework that constitutes the restriction for short-term operational management. This is a strategic challenge since, due to the decision defect character of the underlying flexibility problem, multiple types of flexibility need to be considered over the course of various scenarios, production programs, and production system configurations. In this context, an evaluation model has been developed that integrates both conventional and additive resources on a basic task level and allows the quantification of flexibility enhancement in terms of mix and volume flexibility, complexity reduction, and machine capacity. The model helps companies to decide in early decision-making processes about the potential gains of implementing additive manufacturing technologies on a strategic level. For companies, it is essential to consider both additive and conventional manufacturing beyond pure unit costs. It is necessary to achieve an integrative view of manufacturing that incorporates both additive and conventional manufacturing resources and quantifies their potential with regard to flexibility and manufacturing complexity. This also requires a structured process for the strategic production systems design that spans the design of various scenarios and allows for multi-dimensional and comparative analysis. A respective guideline for the planning of additive resources on a strategic level is being laid out in this paper.

Keywords: additive manufacturing, production system design, flexibility enhancement, strategic guideline

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Authors: Ebtissem Sassi, Abdellatif Benabdelhafid, Sami Hammami

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Congestion, pollution and space misuse are the major risks in the hinterland. Management of these risks is a major issue for all the actors intervening in territory management. A good mastery of these risks is based on the consideration of environmental and physical constraints since the implementation of a policy integrates simultaneously an efficient use, territorial resources, and financial resources which become increasingly rare. Yet, this balance can be difficult to establish simultaneously by all the actors. Indeed, every actor has often the tendency to favor these objectives in detriment to others. In this framework, we have fixed the objective of designing and achieving a model which will centralize multidisciplinary data and serve the analysis tool as well as a decision support tool. In this article, we will elaborate some methodological axes allowing the good management of the territory system through (i) determination of the structural factors of the decision support system, (ii) integration of methods tools favoring the territorial decisional process. Logistics territory geographic information system is a model dealing with this issue. The objective of this model is to facilitate the exchanges between the actors around a common question which was the research subject of human sciences researchers (geography, economy), nature sciences (ecology) as well as finding an optimal solution for simultaneous responses to all these objectives.

Keywords: complexity, territory, logistics, territory planning, conceptual model, GIS, MCA

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Authors: Edoba Bright Omoregie

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Nigeria became a federation in 1954 six years before it gained independence away from British colonial rule. The country has remained a federation since then despite the challenging circumstances of military rule and civil strife which have tasked its federal credentials. Since 1961, when it first decided a federalism dispute, cases over vertical and horizontal powers have inundated the country’s Supreme Court. In its current practice of federalism after democratic rule was resumed in 1999, the country has witnessed a spell of intergovernmental disputes over a good number of federalism issues. Such conflicts have eventually found their way to the Supreme Court for resolution, not as a final appellate court (which it is in other non-federal matters) but as a court of first and final instance following the constitutional provision granting the court such power. However, in April 2014 one of such disputes was denied hearing by the court when it declined original jurisdiction to determine the matter. The suit was instituted by one state of the federation against the federal government and the other 35 states challenging the collection of value added tax (a consumption tax)on certain goods and services within the state. The paper appraises the rationale of the court’s decision and reason that its decision to decline jurisdiction is the result of an avoidable misunderstanding of the dual sovereignty instituted by the federal system of Nigeria as well as a misconception of the role which the court is constitutionally assigned to play in resolving intergovernmental schisms in the federal system.

Keywords: dual sovereignty, federalism, intergovernmental conflict, Supreme Court

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Authors: Yu Zhao, Shinsuke Kato, Jianing Zhao

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Particles exhausted from cars have an adverse impacts on human health. The study developed a three-dimensional particle dispersion numerical model including particle coagulation to simulate the particle concentration distribution under idling conditions in a residential underground garage. The simulation results demonstrate that particle disperses much faster in the vertical direction than that in horizontal direction. The enhancement of particle dispersion in the vertical direction due to the increase of cars with engine running is much stronger than that in the car exhaust direction. Particle dispersion from each pair of adjacent cars has little influence on each other in the study. Average particle concentration after 120 seconds exhaust is 1.8-4.5 times higher than the initial total particles at ambient environment. Particle pollution in the residential underground garage is severe.

Keywords: dispersion, idling conditions, particle concentration, residential underground garage

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Authors: Panpan Feng

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The concept of green infrastructure originated in Europe and the United States. It aims to ensure smart growth of urban and rural ecosystems and achieve sustainable urban and rural ecological, social, and economic development by combining it with gray infrastructure in traditional planning. Based on the literature review of the theoretical origin, value connotation, and measurement methods of green infrastructure, this study summarizes the research content of green infrastructure at different scales from the three spatial levels of region, city, and block and divides it into functional dimensions, spatial dimension, and strategic dimension. The results show that in the functional dimension, from region-city-block, the research on green infrastructure gradually shifts from ecological function to social function. In the spatial dimension, from region-city-block, the research on the spatial form of green infrastructure has shifted from two-dimensional to three-dimensional, and the spatial structure of green infrastructure has shifted from single ecological elements to multiple composite elements. From a strategic perspective, green infrastructure research is more of a spatial planning tool based on land management, environmental livability and ecological psychology, providing certain decision-making support.

Keywords: green infrastructure, multi-scale, social and ecological functions, spatial strategic decision-making tools

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Authors: Nuno Biga

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Artificial Intelligence (AI) has the potential to transform management into several impactful ways. It allows machines to interpret information to find patterns in big data and learn from context analysis, optimize operations, make predictions sensitive to each specific situation and support data-driven decision making. The introduction of an 'artificial brain' in organization also enables learning through complex information and data provided by those who train it, namely its users. The "Assisted-BIGAMES" version of the Accident & Emergency (A&E) simulator introduces the concept of a "Virtual Assistant" (VA) sensitive to context, that provides users useful suggestions to pursue the following operations such as: a) to relocate workstations in order to shorten travelled distances and minimize the stress of those involved; b) to identify in real time existing bottleneck(s) in the operations system so that it is possible to quickly act upon them; c) to identify resources that should be polyvalent so that the system can be more efficient; d) to identify in which specific processes it may be advantageous to establish partnership with other teams; and e) to assess possible solutions based on the suggested KPIs allowing action monitoring to guide the (re)definition of future strategies. This paper is built on the BIGAMES© simulator and presents the conceptual AI model developed and demonstrated through a pilot project (BIG-AI). Each Virtual Assisted BIGAME is a management simulator developed by the author that guides operational and strategic decision making, providing users with useful information in the form of management recommendations that make it possible to predict the actual outcome of different alternative management strategic actions. The pilot project developed incorporates results from 12 editions of the BIGAME A&E that took place between 2017 and 2022 at AESE Business School, based on the compilation of data that allows establishing causal relationships between decisions taken and results obtained. The systemic analysis and interpretation of data is powered in the Assisted-BIGAMES through a computer application called "BIGAMES Virtual Assistant" (VA) that players can use during the Game. Each participant in the VA permanently asks himself about the decisions he should make during the game to win the competition. To this end, the role of the VA of each team consists in guiding the players to be more effective in their decision making, through presenting recommendations based on AI methods. It is important to note that the VA's suggestions for action can be accepted or rejected by the managers of each team, as they gain a better understanding of the issues along time, reflect on good practice and rely on their own experience, capability and knowledge to support their own decisions. Preliminary results show that the introduction of the VA provides a faster learning of the decision-making process. The facilitator designated as “Serious Game Controller” (SGC) is responsible for supporting the players with further analysis. The recommended actions by the SGC may differ or be similar to the ones previously provided by the VA, ensuring a higher degree of robustness in decision-making. Additionally, all the information should be jointly analyzed and assessed by each player, who are expected to add “Emotional Intelligence”, an essential component absent from the machine learning process.

Keywords: artificial intelligence, gamification, key performance indicators, machine learning, management simulators, serious games, virtual assistant

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Authors: Robin C. Ladwig

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The future of work becomes less predictable, which requires increasing the adaptability of organizations to social and work changes. Society is transforming regarding gender identity in the sense that more people come forward to identify as trans and gender diverse (TGD). Organizations are ill-equipped to provide a safe and encouraging work environment by lacking inclusive organizational structures. The qualitative multi-method research about TGD inclusivity in the workplace explores the enablers and barriers for TGD individuals to satisfactory engage in the work environment and organizational culture. Furthermore, these TGD insights are analyzed about their organizational implications and awareness from a leadership and management perspective. The semi-structured online interviews with TGD individuals and the photo-elicit open-ended questionnaire addressed to leadership and management in diversity, career development, and human resources have been analyzed with a critical grounded theory approach. Findings demonstrated the significance of TGD voices, the support of leadership and management, as well as the synergy between voices and leadership. Hence, it indicates practical implications such as the revision of exclusive language used in policies, data collection, or communication and reconsideration of organizational decision-making by leaders to include TGD voices.

Keywords: future of work, occupational identity, organisational decision-making, trans and gender diverse identity

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Authors: Htet Khaing Lin

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This study explores the relationship between a diverse set of variables, encompassing both environmental and socio-economic factors, and poverty levels in the Philippines for the years 2012, 2015, and 2018. Employing Ordinary Least Squares (OLS), Spatial Lag Models (SLM), and Spatial Error Models (SEM), this study delves into the dynamics of key indicators, including daytime and nighttime land surface temperature, cropland surface, urban land surface, rainfall, population size, normalized difference water, vegetation, and drought indices. The findings reveal consistent patterns and unexpected correlations, highlighting the need for nuanced policies that address the multifaceted challenges arising from the interplay of environmental and socio-economic factors.

Keywords: poverty analysis, OLS, spatial lag models, spatial error models, Philippines, google earth engine, satellite data, environmental dynamics, socio-economic factors

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Klaus Peter Scherer, Daniel Knöll, Constantin Rieder

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Problem and Purpose: Intelligent systems are available and helpful to support the human being decision process, especially when complex surgical eye interventions are necessary and must be performed. Normally, such a decision support system consists of a knowledge-based module, which is responsible for the real assistance power, given by an explanation and logical reasoning processes. The interview based acquisition and generation of the complex knowledge itself is very crucial, because there are different correlations between the complex parameters. So, in this project (semi)automated self-learning methods are researched and developed for an enhancement of the quality of such a decision support system. Methods: For ophthalmic data sets of real patients in a hospital, advanced data mining procedures seem to be very helpful. Especially subgroup analysis methods are developed, extended and used to analyze and find out the correlations and conditional dependencies between the structured patient data. After finding causal dependencies, a ranking must be performed for the generation of rule-based representations. For this, anonymous patient data are transformed into a special machine language format. The imported data are used as input for algorithms of conditioned probability methods to calculate the parameter distributions concerning a special given goal parameter. Results: In the field of knowledge discovery advanced methods and applications could be performed to produce operation and patient related correlations. So, new knowledge was generated by finding causal relations between the operational equipment, the medical instances and patient specific history by a dependency ranking process. After transformation in association rules logically based representations were available for the clinical experts to evaluate the new knowledge. The structured data sets take account of about 80 parameters as special characteristic features per patient. For different extended patient groups (100, 300, 500), as well one target value as well multi-target values were set for the subgroup analysis. So the newly generated hypotheses could be interpreted regarding the dependency or independency of patient number. Conclusions: The aim and the advantage of such a semi-automatically self-learning process are the extensions of the knowledge base by finding new parameter correlations. The discovered knowledge is transformed into association rules and serves as rule-based representation of the knowledge in the knowledge base. Even more, than one goal parameter of interest can be considered by the semi-automated learning process. With ranking procedures, the most strong premises and also conjunctive associated conditions can be found to conclude the interested goal parameter. So the knowledge, hidden in structured tables or lists can be extracted as rule-based representation. This is a real assistance power for the communication with the clinical experts.

Keywords: an expert system, knowledge-based support, ophthalmic decision support, self-learning methods

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Authors: Phan Nghiem Vu, Le Tuan Vu, Ta Quang Tai

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The optimal construction design used during the execution of a construction project is a key factor in determining high productivity and customer satisfaction, however, this management process sometimes is carried out without care and the systematic method that it deserves, bringing negative consequences. This study proposes a knowledge management (KM) approach that will enable the intelligent use of experienced and acknowledged engineers to improve the management of construction design works for a project. Then a knowledge-based application to support this decision-making process is proposed and described. To define and design the system for the application, semi-structured interviews were conducted within five construction consulting organizations with the purpose of studying the way that the method’ optimizing process is implemented in practice and the knowledge supported with it. A system of an optimizing construction design works (OCDW) based on knowledge was developed then validated with construction experts. The OCDW was liked as a valuable tool for construction design works’ optimization, by supporting organizations to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The benefits are described as provided by the performance support system, reducing costs and time, improving product design quality, satisfying customer requirements, expanding the brand organization.

Keywords: optimizing construction design work, construction consultant organization, knowledge management, knowledge-based application

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Authors: Yuguang Gao, Mingtao Deng

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The integration of independently operating hospitals into a unified healthcare service system has become a strategic imperative in the pursuit of hospitals’ high-quality development. Central to the concept of group governance over such transformation, exemplified by a collaborative medical alliance, is the delineation of shared value, vision, and goals. Given the inherent disparity in capabilities among hospitals within the alliance, particularly in the treatment of different diseases characterized by Disease Related Groups (DRG) in terms of effectiveness, efficiency and resource utilization, this study aims to address the centralized decision-making of intra-hospital patient referral within the medical alliance to enhance the overall production and quality of service provided. We first introduce the notion of production utility, where a higher production utility for a hospital implies better performance in treating patients diagnosed with that specific DRG group of diseases. Then, a Discrete-Event Simulation (DES) framework is established for patient referral among hospitals, where patient flow modeling incorporates a queueing system with fixed capacities for each hospital. The simulation study begins with a two-member alliance. The pivotal strategy examined is a "whether-to-refer" decision triggered when the bed usage rate surpasses a predefined threshold for either hospital. Then, the decision encompasses referring patients to the other hospital based on DRG groups’ production utility differentials as well as bed availability. The objective is to maximize the total production utility of the alliance while minimizing patients’ average length of stay and turnover rate. Thus the parameter under scrutiny is the bed usage rate threshold, influencing the efficacy of the referral strategy. Extending the study to a three-member alliance, which could readily be generalized to multi-member alliances, we maintain the core setup while introducing an additional “which-to-refer" decision that involves referring patients with specific DRG groups to the member hospital according to their respective production utility rankings. The overarching goal remains consistent, for which the bed usage rate threshold is once again a focal point for analysis. For the two-member alliance scenario, our simulation results indicate that the optimal bed usage rate threshold hinges on the discrepancy in the number of beds between member hospitals, the distribution of DRG groups among incoming patients, and variations in production utilities across hospitals. Transitioning to the three-member alliance, we observe similar dependencies on these parameters. Additionally, it becomes evident that an imbalanced distribution of DRG diagnoses and further disparity in production utilities among member hospitals may lead to an increase in the turnover rate. In general, it was found that the intra-hospital referral mechanism enhances the overall production utility of the medical alliance compared to individual hospitals without partnership. Patients’ average length of stay is also reduced, showcasing the positive impact of the collaborative approach. However, the turnover rate exhibits variability based on parameter setups, particularly when patients are redirected within the alliance. In conclusion, the re-structuring of diagnostic disease groups within the medical alliance proves instrumental in improving overall healthcare service outcomes, providing a compelling rationale for the government's promotion of patient referrals within collaborative medical alliances.

Keywords: collaborative medical alliance, disease related group, patient referral, simulation

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Authors: Nora Alkharji, Wafa Fallatah

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Introduction: One of the major issues to consider when is deciding on what to feed a baby is the quality of the food itself. Whilst commercially prepared infant formulas are a nutritious alternative to breast milk, and even contain some vitamins and nutrients, most major medical organizations consider breastfeeding the best nutritional option for babies. Choosing whether to breastfeed or formula feed your baby is one of the first decisions expectant parents will make. The American Academy of Pediatrics (AAP) is in agreement with other organizations such as the American Medical Association (AMA), the American Dietetic Association (ADA), and the World Health Organization (WHO) in recommending breastfeeding as the best nutrition for babies and best suited for a baby's digestive system. In addition, breastfeeding helps in the combatting of infections, prevention of allergies, and protection against various chronic conditions. The decision to breastfeed or formula feed one’s baby is a very personal one. However, certain points need to be clarified regarding the nutritional value of breastfeeding versus formula feeding to allow for informed decision-making. Methodology: -A formal debate about whether to breastfeed or formula feed babies as the better choice. -There will be two debaters, both lactation consultants -Arguments will be based on evidence-based medicine -Duration period of debated: 45 min Result: Clarification and heightened awareness of the benefits of breastfeeding. Conclusion: This debate will make the choice between breastfeeding or formula feeding a relatively easy one to make by both health worker and parents.

Keywords: breastmilk, formula milk, nutritional, comparison

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Authors: Suarez-Puello Alejandro, Baquero-Ruiz Andrés F, Suarez-Puello Rodrigo

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This paper discusses the results of a foresight exercise which analyzes Bogota’s fruit, vegetable and tuber supply chain strategy- described at the Food Supply and Security Master Plan (FSSMP)-to provide the inhabitants of Bogotá, Colombia, with basic food products at a fair price. The methodology consisted of using quantitative and qualitative foresight tools such as system dynamics and variable selection methods to better represent interactions among stakeholders and obtain more integral results that could shed light on this complex situation. At first, the Master Plan is an input to establish the objectives and scope of the exercise. Then, stakeholders and their relationships are identified. Later, system dynamics is used to model product, information and money flow along the fruit, vegetable and tuber supply chain. Two scenarios are presented, discussing actions by the public sector and the reactions that could be expected from the whole food supply system. Finally, these impacts are compared to the Food Supply and Security Master Plan’s objectives suggesting recommendations that could improve its execution. This foresight exercise performed at a governmental level is intended to promote the widen the use of foresight as an anticipatory, decision-making tool that offers solutions to complex problems.

Keywords: decision making, foresight, public policies, supply chain, system dynamics

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Authors: Elmo Thiago Lins Cöuras Ford, Valentina Alessandra Carvalho do Vale

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A swimming pool heating system is presented, composed of two alternative collectors with serial PVC absorber tubes that work in regimen of forced stream that is gotten through a bomb. A 500 liters reservoir was used, simulating the swimming pool, being raised some data that show the viability of the considered system. The chosen outflow was corresponding to 100 l/h. In function of the low outflow it was necessary the use of a not popular bomb, choosing the use of a low outflow alternative pumping system, using an air conditioner engine with three different rotations for the desired end. The thermal data related to each collector and their developed system will be presented. The UV and thermal degradations of the PVC exposed to solar radiation will be also boarded, demonstrating the viability of using tubes of this material as absorber elements of radiation in water heating solar collectors.

Keywords: solar energy, solar swimming pool, water heating, PVC tubes, alternative system

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Authors: Rene Hellmuth

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The requirements for the factory planning and the building concerned have changed in the last years. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring gains more importance in order to maintain the competitiveness of a factory. Restrictions regarding new areas, shorter life cycles of product and production technology as well as a VUCA (volatility, uncertainty, complexity and ambiguity) world cause more frequently occurring rebuilding measures within a factory. Restructuring of factories is the most common planning case today. Restructuring is more common than new construction, revitalization and dismantling of factories. The increasing importance of restructuring processes shows that the ability to change was and is a promising concept for the reaction of companies to permanently changing conditions. The factory building is the basis for most changes within a factory. If an adaptation of a construction project (factory) is necessary, the inventory documents must be checked and often time-consuming planning of the adaptation must take place to define the relevant components to be adapted, in order to be able to finally evaluate them. The different requirements of the planning participants from the disciplines of factory planning (production planner, logistics planner, automation planner) and industrial construction planning (architect, civil engineer) come together during reconstruction and must be structured. This raises the research question: Which requirements do the disciplines involved in the reconstruction planning place on a digital factory model? A subordinate research question is: How can model-based decision support be provided for a more efficient design of the conversion within a factory? Because of the high adaptation rate of factories and its building described above, a methodology for rescheduling factories based on the requirements engineering method from software development is conceived and designed for practical application in factory restructuring projects. The explorative research procedure according to Kubicek is applied. Explorative research is suitable if the practical usability of the research results has priority. Furthermore, it will be shown how to best use a digital factory model in practice. The focus will be on mobile applications to meet the needs of factory planners on site. An augmented reality (AR) application will be designed and created to provide decision support for planning variants. The aim is to contribute to a shortening of the planning process and model-based decision support for more efficient change management. This requires the application of a methodology that reduces the deficits of the existing approaches. The time and cost expenditure are represented in the AR tablet solution based on a building information model (BIM). Overall, the requirements of those involved in the planning process for a digital factory model in the case of restructuring within a factory are thus first determined in a structured manner. The results are then applied and transferred to a construction site solution based on augmented reality.

Keywords: augmented reality, digital factory model, factory planning, restructuring

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Zeynep Aytaç, Nuri Yücel

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Today, as the need for high efficiency and fuel-efficient engines have increased, centrifugal compressor designs are expected to be high-efficient and have high-pressure ratios than ever. The present study represents a design methodology of centrifugal compressor placed in a mini jet engine for the design and off-design points with the utilization of computational fluid dynamics (CFD) and compares the performance characteristics at the mentioned two points. Although the compressor is expected to provide the required specifications at the design point, it is known that it is important for the design to deliver the required parameters at the off-design point also as it will not operate at the design point always. It was observed that the obtained mass flow rate, pressure ratio, and efficiency values are within the limits of the design specifications for the design and off-design points. Despite having different design inputs for the mentioned two points, they reveal similar flow characteristics in the general frame.

Keywords: centrifugal compressor, computational fluid dynamics, design point, off-design point

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Authors: Siti Azrina B. A. Aziz, Siti Hafizah A. Hamid

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New approaches to analyze and visualize data stream in real-time basis is important in making a prompt decision by the decision maker. Financial market trading and surveillance, large-scale emergency response and crowd control are some example scenarios that require real-time analytic and data visualization. This situation has led to the development of techniques and tools that support humans in analyzing the source data. With the emergence of Big Data and social media, new techniques and tools are required in order to process the streaming data. Today, ranges of tools which implement some of these functionalities are available. In this paper, we present chronological evolution evaluation of technologies for supporting of real-time analytic and visualization of the data stream. Based on the past research papers published from 2002 to 2014, we gathered the general information, main techniques, challenges and open issues. The techniques for streaming text visualization are identified based on Text Visualization Browser in chronological order. This paper aims to review the evolution of streaming text visualization techniques and tools, as well as to discuss the problems and challenges for each of identified tools.

Keywords: information visualization, visual analytics, text mining, visual text analytics tools, big data visualization

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Authors: Siti N. I. Mat Kamal, Othman Ibrahim, Mehrbakhsh Nilashi, Jafalizan M. Jali

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The use of digital forensics (DF) is nowadays essential for law enforcement agencies to identify analysis and interpret the digital information derived from digital sources. In Malaysia, the engagement of Malaysian Law Enforcement Agencies (MLEA) with this new technology is not evenly distributed. To investigate the factors influencing the adoption of DF in Malaysia law enforcement agencies’ operational environment, this study proposed the initial theoretical framework based on the integration of technology organization environment (TOE), institutional theory, and human organization technology (HOT) fit model. A questionnaire survey was conducted on selected law enforcement agencies in Malaysia to verify the validity of the initial integrated framework. Relative advantage, compatibility, coercive pressure, normative pressure, vendor support and perceived technical competence of technical staff were found as the influential factors on digital forensics adoption. In addition to the only moderator of this study (agency size), any significant moderating effect on the perceived technical competence and the decision to adopt digital forensics by Malaysian law enforcement agencies was found insignificant. Thus, these results indicated that the developed integrated framework provides an effective prediction of the digital forensics adoption by Malaysian law enforcement agencies.

Keywords: digital forensics, digital forensics adoption, digital information, law enforcement agency

Procedia PDF Downloads 151