Search results for: semantic decision

Authors: Paulino V. Tavares, Ana L. Romao

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Public governance represents an articulated arrangement, dynamic and interactive, present in the exercise of authority aimed at strengthening the decision-making procedure in public administration with transparency, accountability, responsiveness and capable of to emerge control and social empowerment, to pursue and achieve the objectives efficiently and with the effectiveness desired by the collective, respecting laws and providing social, institutional and economic equility in society. With this, using a multidimensional approach with the application of three questionnaires to a universe of twenty Counselors of the Courts of Auditors (Brazil), twenty professionals of public administration (Brazil), twenty Government/Provincial Counselors (South Africa), and twenty South African professionals of public administration, the present work aims to capture what is the perception about the efficiency and equility of public policies in Brazil and South Africa. With this, up until now, 30 responses have been obtained, and the results indicate that, in Brazil, 65% affirm due to the inefficiency of public policies, 70% point out that they do not believe in the equility of these same policies. In South Africa, the results indicate that 45% believe in government efficiency, and, with regard to the equility of public policies, 65% do not believe. In Brazil, the research reveals at least three reasons for this result, that is, lack of planning, lack of clear objectives of public policies, and lack of information on the part of society, while in South Africa, so far, research has not identified a specific reason for this result.

Keywords: efficiency, equility, governance, public policy

Procedia PDF Downloads 103

Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Naragain Phumchusri, Kittitach Pongpairoj

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Overbooking is a very useful revenue management technique that could help reduce costs caused by either undersales or oversales. In this paper, we propose an overbooking model for two types of cars that can minimize the total cost for car rental service. With two types of cars, there is an upgrade possibility for lower type to upper type. This makes the model more complex than one type of cars scenario. We have found that convexity can be proved in this case. Sensitivity analysis of the parameters is conducted to observe the effects of relevant parameters on the optimal solution. Model simplification is proposed using multiple linear regression analysis, which can help estimate the optimal overbooking level using appropriate independent variables. The results show that the overbooking level from multiple linear regression model is relatively close to the optimal solution (with the adjusted R-squared value of at least 72.8%). To evaluate the performance of the proposed model, the total cost was compared with the case where the decision maker uses a naïve method for the overbooking level. It was found that the total cost from optimal solution is only 0.5 to 1 percent (on average) lower than the cost from regression model, while it is approximately 67% lower than the cost obtained by the naïve method. It indicates that our proposed simplification method using regression analysis can effectively perform in estimating the overbooking level.

Keywords: overbooking, car rental industry, revenue management, stochastic model

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Authors: Deborah Garcia-Magna

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The use of new technologies has become widespread in the last decade, giving rise to various risks associated with the transfer of personal data and the publication of sensitive material on social media. There are already several supranational instruments that seek to protect the citizens involved in this growing traffic of personal information and, especially, the most vulnerable people, such as minors, who are also the ones who make the most intense use of these new means of communication. In this sense, the configuration of the concept of privacy as a legal right has necessarily been influenced by these new social uses and supranational instruments. The researcher considers correct the decision to introduce sexting as a new criminal behaviour in the Penal Code in 2015, but questions the concrete manner in which it has been made. To this end, an updated review of the various options that our legal system already offered is made, assessing whether these legal options adequately addressed the new social needs and guidelines from jurisprudence and other supranational instruments. Some important issues emerge as to whether the principles of fragmentarity and subsidiarity may be violated since the new article 197.7 of the Spanish Penal Code could refer to very varied behaviours and protect not only particularly vulnerable persons. In this sense, the research focuses on issues such as the concept of 'seriousness' of the infringement of privacy, the possible reckless conduct of the victim, who hang over its own private material to third parties, the affection to other legal rights such as freedom and sexual indemnity, the possible problems of concurrent offences, etc.

Keywords: criminal law reform, ECHR jurisprudence, right to privacy, sexting

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Authors: Samatha Ss Sutton, Kaouther Kooli

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In recent years social media influences (SMI) have become integrated into many companies marketing strategies to create buzz, target new and younger markets and further expand social media coverage in business (Lim et al 2017). SMI’s can be defined as online personalities with a substantial number of followers, across one or more social media platforms, with influence on their followers (Lou and Yuan 2018). Recently expenditure on influencer marketing has increased exponentially becoming an important area for marketing opportunities and strategies in the future (Lou and Yuan 2018). In order to market products and brands effectively through SMI’s it is important for business to understand the attributes of SMI that effect purchase intention (Lim et al 2017) of their followers and whether or not these attributes vary across generations so to market effectively to their specific segment or target market. The present study involves quantitative research to understand the attributes by which influence differs across generations namely Generation X and Millennials and its effects on purchase intentions of these generational groups. A survey will be conducted using an online questionnaire. Structural Equation Modelling and Multi group analysis will be applied. The study provides insight to marketers/decision makers on how to use influencers accordingly with their target consumer.

Keywords: social media marketing, social media influencers, attitude towards social media influencers, intention to purchase

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Authors: Ali R. Alshawawreh, Francisco Liébana-Cabanillas, Francisco J. Blanco-Encomienda

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Collaboration between marketing and business intelligence (BI) is crucial in today's ever-evolving business landscape. These two domains play pivotal roles in molding customers' experiential knowledge. Marketing insights offer valuable information regarding customer needs, preferences, and behaviors, thus refining marketing strategies and enhancing overall customer experiences. Conversely, BI facilitates data-driven decision-making, leading to heightened operational efficiency, product quality, and customer satisfaction. The analysis of customer data through BI unveils patterns and trends, informing product development, marketing campaigns, and customer service initiatives aimed at enriching experiences and knowledge. Customer experiential knowledge (CEK) encompasses customers' implicit comprehension of consumption experiences influenced by diverse factors, including social and cultural influences. This study primarily focuses on telecommunications companies in Jordan, scrutinizing how experiential customer knowledge mediates the relationship between marketing intelligence, business intelligence, and innovation in product and service offerings. Drawing on theoretical frameworks such as the resource-based view (RBV) and service-dominant logic (SDL), the research aims to comprehend how organizations utilize their resources, particularly knowledge, to foster innovation. Employing a quantitative research approach, the study collected and analyzed primary data to explore hypotheses. The chosen method was justified for its efficacy in handling large sample sizes. Structural equation modeling (SEM) facilitated by Smart PLS software evaluated the relationships between the constructs, followed by mediation analysis to assess the indirect associations in the model. The study findings offer insights into the intricate dynamics of organizational innovation, uncovering the interconnected relationships between business intelligence, customer experiential knowledge-based innovation (CEK-DI), marketing intelligence (MI), and product and service innovation (PSI), underscoring the pivotal role of advanced intelligence capabilities in developing innovative practices rooted in a profound understanding of customer experiences. Organizations equipped with cutting-edge BI tools are better positioned to devise strategies informed by precise insights into customer needs and behaviors. Furthermore, the positive impact of BI on PSI reaffirms the significance of data-driven decision-making in shaping the innovation landscape. Companies leveraging BI demonstrate adeptness in identifying market opportunities guiding the development of novel products and services. The substantial impact of CEK-DI on PSI highlights the crucial role of customer experiences in driving organizational innovation. Firms actively integrating customer insights into their innovation processes are more likely to create offerings aligned with customer expectations, fostering higher levels of product and service innovation. Additionally, the positive and significant effect of MI on CEK-DI underscores the critical role of market insights in shaping innovative strategies. While the relationship between MI and PSI is positive, a slightly weaker significance level indicates a nuanced association, suggesting that while MI contributes to innovation, other factors may also influence the innovation landscape, warranting further exploration. In conclusion, the study underscores the essential role of intelligence capabilities, particularly artificial intelligence, in driving innovation, emphasizing the necessity for organizations to leverage market and customer intelligence for effective and competitive innovation practices. Collaborative efforts between marketing and business intelligence serve as pivotal drivers of innovation, influencing experiential customer knowledge and shaping organizational strategies and practices, ultimately enhancing overall customer experiences and organizational performance.

Keywords: marketing intelligence, business intelligence, product, customer experiential knowledge-driven innovation

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Authors: Khalid Kaddoura, Ahmed Assad, Tarek Zayed

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Sewer pipelines are prone to deterioration over-time. In fact, their deterioration does not follow a fixed downward pattern. This is in fact due to the defects that propagate through their service life. Sewer pipeline defects are categorized into distinct groups. However, the main two groups are the structural and operational defects. By definition, the structural defects influence the structural integrity of the sewer pipelines such as deformation, cracks, fractures, holes, etc. However, the operational defects are the ones that affect the flow of the sewer medium in the pipelines such as: roots, debris, attached deposits, infiltration, etc. Yet, the process for each defect to emerge follows a cause and effect relationship. Deformation, which is the change of the sewer pipeline geometry, is one type of an influencing defect that could be found in many sewer pipelines due to many surrounding factors. This defect could lead to collapse if the percentage exceeds 15%. Therefore, it is essential to predict the deformation percentage before confronting such a situation. Accordingly, this study will predict the percentage of the deformation defect in sewer pipelines adopting the multiple regression analysis. Several factors will be considered in establishing the model, which are expected to influence the defamation defect severity. Besides, this study will construct a time-based curve to understand how the defect would evolve overtime. Thus, this study is expected to be an asset for decision-makers as it will provide informative conclusions about the deformation defect severity. As a result, inspections will be minimized and so the budgets.

Keywords: deformation, prediction, regression analysis, sewer pipelines

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Authors: Prakash Singh

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This study focused on the importance of collegiality to improve the effectiveness of a poorly resourced school (PRS). In an effective school that embraces collegiality as its culture, one can expect to find a teaching staff and a management team that shares responsibilities and accountabilities through the development of a common purpose and vision, regardless of whether the school is considered to be poorly resourced or not. Working together in collegial teams is a more effective way to accomplish tasks and to create a climate for effective learning, even for learners in PRSs from poor communities. The main aim of this study was therefore to determine whether collegiality as a leadership strategy could extract the best from people in a PRS, and consequently create the most effective and efficient educational climate possible. The responses received from the teachers and the principal at the PRS supports the notion that collegiality does have a positive influence on learning, as demonstrated by the improved academic achievement of the learners. The teachers were now more involved in the school. They agreed that this was a positive development. Their descriptions of increased involvement, shared accountability and shared decision-making identified important aspects of collegiality that transformed the school from being dysfunctional. Hence, it is abundantly clear that a collegial leadership style can help extract the best from people because the most effective and efficient educational climate can be created at a school when collegiality is employed. Collegial leadership demonstrates that even in PRSs, there are boundless opportunities to improve teaching and learning.

Keywords: collegiality, collegial leadership, effective educational climate, poorly resourced school

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Authors: Hesham A. Khalek, Shafik S. Khoury, Remon F. Aziz, Mohamed A. Hakam

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Slipforming operation’s linearity is a source of planning complications, and operation is usually subjected to bottlenecks at any point, so careful planning is required in order to achieve success. On the other hand, Discrete-event simulation concepts can be applied to simulate and analyze construction operations and to efficiently support construction scheduling. Nevertheless, preparation of input data for construction simulation is very challenging, time-consuming and human prone-error source. Therefore, to enhance the benefits of using DES in construction scheduling, this study proposes an integrated module to establish a framework for automating the generation of time schedules and decision support for Slipform construction projects, particularly through the project feasibility study phase by using data exchange between project data stored in an Intermediate database, DES and Scheduling software. Using the stored information, proposed system creates construction tasks attribute [e.g. activities durations, material quantities and resources amount], then DES uses all the given information to create a proposal for the construction schedule automatically. This research is considered a demonstration of a flexible Slipform project modeling, rapid scenario-based planning and schedule generation approach that may be of interest to both practitioners and researchers.

Keywords: discrete-event simulation, modeling, construction planning, data exchange, scheduling generation, EZstrobe

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Authors: Shahrokh Barati, Reza Ramezani

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Due to the countries growing attention to the renewable energy producing, the demand for energy from renewable energy has gone up among the renewable energy sources; wind energy is the fastest growth in recent years. In this regard, in order to increase the availability of wind turbines, using of Fault Detection and Isolation (FDI) system is necessary. Wind turbines include of various faults such as sensors fault, actuator faults, network connection fault, mechanical faults and faults in the generator subsystem. Although, sensors and actuators have a large number of faults in wind turbine but have discussed fewer in the literature. Therefore, in this work, we focus our attention to design a sensor and actuator fault detection and isolation algorithm and Fault-tolerant control systems (FTCS) for Wind Turbine. The aim of this research is to propose a comprehensive fault detection and isolation system for sensors and actuators of wind turbine based on data-driven approaches. To achieve this goal, the features of measurable signals in real wind turbine extract in any condition. The next step is the feature selection among the extract in any condition. The next step is the feature selection among the extracted features. Features are selected that led to maximum separation networks that implemented in parallel and results of classifiers fused together. In order to maximize the reliability of decision on fault, the property of fault repeatability is used.

Keywords: FDI, wind turbines, sensors and actuators faults, renewable energy

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Karan Vishavjit, Aakash Lakra, Shafaq Khan

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The COVID-19 pandemic has pushed healthcare professionals to use big data analytics as a vital tool for tracking and evaluating the effects of contagious viruses. To effectively analyze huge datasets, efficient NoSQL databases are needed. The analysis of post-COVID-19 health and well-being outcomes and the evaluation of the effectiveness of government efforts during the pandemic is made possible by this research’s integration of several datasets, which cuts down on query processing time and creates predictive visual artifacts. We recommend applying sharding and indexing technologies to improve query effectiveness and scalability as the dataset expands. Effective data retrieval and analysis are made possible by spreading the datasets into a sharded database and doing indexing on individual shards. Analysis of connections between governmental activities, poverty levels, and post-pandemic well being is the key goal. We want to evaluate the effectiveness of governmental initiatives to improve health and lower poverty levels. We will do this by utilising advanced data analysis and visualisations. The findings provide relevant data that supports the advancement of UN sustainable objectives, future pandemic preparation, and evidence-based decision-making. This study shows how Big Data and NoSQL databases may be used to address problems with global health.

Keywords: big data, COVID-19, health, indexing, NoSQL, sharding, scalability, well being

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Authors: Manirath Wongsim, Ekkachai Naenudorn, Nipotepat Muangkote

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Information Technology (IT), play an important role in business management strategies and can provide assistance in all phases of decision making. Thus, many organizations need to be seen as adopting IT, which is critical for a company to organize, manage and operate its processes. In order to implement IT successfully, it is important to understand the underlying factors that influence agricultural system's adoption success. Therefore, this research intends to study this perspective of factors that influence and impact successful IT adoption and related agricultural performance. Case study and survey methodology were adopted for this research. Case studies in two Thai- organizations were carried out. The results of the two main case studies suggested 21 factors that may have an impact on IT adoption in agriculture in Thailand, which led to the development of the preliminary framework. Next, a survey instrument was developed based on the findings from case studies. Survey questionnaires were gathered from 217 respondents from two large-scale surveys were sent to selected members of Thailand farmer, and Thailand computer to test the research framework. The results indicate that the top five critical factors for ensuring IT adoption in agricultural were: 1) network and communication facilities; 2) software; 3) hardware; 4) farmer’s IT knowledge, and; 5) training and education. Therefore, it is now clear which factors are influencing IT adoption and which of those factors are critical success factors for ensuring IT adoption in agricultural organization.

Keywords: agricultural systems adoption, factors influencing IT adoption, factors affecting in agricultural adoption

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Authors: Sufrin Hannan, Budi Suharjo, Rita Nurmalina, Kirbrandoko

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The challenge for independent surveyor service companies now is growing with increasing uncertainty in business. Protection from the government for domestic independent surveyor industry from competitor attack, such as entering the global surveyors to Indonesia also no longer exists. Therefore, building customer loyalty becomes very important to create a long-term relationship between an independent surveyor with its customers. This study aims to develop a model that can be used to build customer loyalty by looking at various factors that determine customer loyalty, especially on independent surveyors for coal inspection in Indonesia. The development of this model uses the relationship marketing approach. Testing of the hypothesis is done by testing the variables that determine customer loyalty, either directly or indirectly, which amounted to 10 variables. The data were collected from 200 questionnaires filled by independent surveyor company decision makers from 51 exporting companies and coal trading companies in Indonesia and analyzed using Structural Equation Model (SEM). The results show that customer loyalty of independent surveyors is influenced by customer satisfaction, trust, switching-barrier, and relationship-bond. Research on customer satisfaction shows that customer satisfaction is influenced by the perceived quality and perceived value, while perceived quality is influenced by reliability, assurance, responsiveness, and empathy.

Keywords: relationship marketing, customer loyalty, customer satisfaction, switching barriers, relationship bonds

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Authors: Joelle Hawa, Michael Gilding

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The role of interest as a driver of action has been an on-going debate in the sociological sciences. This paper shows evidence as to how economic actors frame their environment in terms of interests and goals to take action. It introduces the concept of 'dynamic actor compass', a cognitive tool that is socially contingent and allows economic actors to navigate their environment, evaluate the level of alignment of interests and goals with other players, and decide whether or not they are willing to rely on, collaborate or partner with others in the field. The paper builds on Kaplan’s model of framing contests and integrates Max Weber’s interests, and ideas construct as well as Beckert’s concept of fictional expectations. The author illustrates this conceptual framework in the case of MedTech entrepreneurs’ capital raising activities in Australia. The study adopts a grounded theory methodology, running in-depth interviews with 24 MedTech entrepreneurs in order to examine their decision-making processes and actions to finance their innovation trajectory. The findings show that participants take into account material and ideal interests and goals that they impose adapt or negotiate with other actors in their environment. These interactions affect the way MedTech entrepreneurs perceive other funders in the field, influencing their capital raising strategies.

Keywords: expectations, financing innovation, frames, goals, interest-oriented action, managerial cognition

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Authors: L. A. Toro, N. Prieto, J. J. Vargas

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There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit.

Keywords: control charts, multivariate exponentially moving average (MEWMA), exponentially moving average (EWMA), industrial control process

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Authors: X. L. Nguyen, T. Y. Chou, F. Y. Min, F. C. Lin, T. V. Hoang, Y. M. Huang

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In Vietnam, land use planning is utilized as an efficient tool for the local government to adjust land use. However, planned locations are facing disapproval from people who live near these planned sites because of environmental problems. The selection of these locations is normally based on the subjective opinion of decision-makers and is not supported by any scientific methods. Many researchers have applied Multi-Criteria Analysis (MCA) methods in which Analytic Hierarchy Process (AHP) is the most popular techniques in combination with Fuzzy set theory for the subject of rationality assessment of land use planning locations. In this research, the Fuzzy set theory and Analytic Network Process (ANP) multi-criteria-based technique were used for the assessment process. The Fuzzy Analytic Hierarchy Process was also utilized, and the output results from two methods were compared to extract the differences. The 20 planned landfills in Hung Ha district, Thai Binh province, Vietnam was selected as a case study. The comparison results indicate that there are different between weights computed by AHP and ANP methods and the assessment outputs produced from these two methods also slight differences. After evaluation of existing planned sites, some potential locations were suggested to the local government for possibility of land use planning adjusts.

Keywords: Analytic Hierarchy Process, Analytic Network Process, Fuzzy set theory, land use planning

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Authors: Arnab Adhikari, Adrija Majumdar, Gaurav Gupta, Arnab Bisi

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Team formation plays an instrumental role in the sports like cricket. Existing literature reveals that most of the works on player selection focus only on the players’ efficiency and ignore the consistency. It motivates us to design an improved player selection method based on both player’s efficiency and consistency. To measure the players’ efficiency measurement, we employ a modified data envelopment analysis (DEA) technique namely ‘super-efficient DEA model’. We design a modified consistency index based on semi variance approach. Here, we introduce a new parameter called ‘fitness index’ for consistency computation to assess a player’s fitness level. Finally, we devise a single performance score using both efficiency score and consistency score with the help of a linear programming model. To test the robustness of our method, we perform a rigorous numerical analysis to determine the all-time best One Day International (ODI) Cricket XI. Next, we conduct extensive comparative studies regarding efficiency scores, consistency scores, selected team between the existing methods and the proposed method and explain the rationale behind the improvement.

Keywords: decision support systems, sports, super-efficient data envelopment analysis, semi variance approach

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Authors: Saeed Sayyad Hagh Shomar

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Traffic congestion pricing in urban streets is one of the most suitable options for solving the traffic problems and environment pollutions in the cities of the country. Unlike its acceptable outcomes, there are problems concerning the necessity to pay by the mass. Regarding the fact that public response in order to succeed in this strategy is so influential, studying their response and behavior to get the feedback and improve the strategies is of great importance. In this study, a questionnaire was used to examine the public reactions to the traffic congestion pricing schemes at the center of Tehran metropolis and the factors involved in people’s decision making in accepting or rejecting the congestion pricing schemes were assessed based on the data obtained from the questionnaire as well as the international experiences. Then, by analyzing and comparing the schemes, guidelines to reduce public objections to them are discussed. The results of reviewing and evaluating the public reactions show that all the pros and cons must be considered to guarantee the success of these projects. Consequently, with targeted public education and consciousness-raising advertisements, prior to initiating a scheme and ensuring the mechanism of the implementation after the start of the project, the initial opposition is reduced and, with the gradual emergence of the real and tangible benefits of its implementation, users’ satisfaction will increase.

Keywords: demand management, international experiences, traffic congestion pricing, public acceptance, public reactions, public objection

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Authors: Oshin Anand, Atanu Rakshit

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The vast increment in the e-commerce ventures makes this area a prominent research stream. Besides several quantified parameters, the textual content of reviews is a storehouse of many information that can educate companies and help them earn profit. This study is an attempt in this direction. The article attempts to categorize data based on a computed metric that quantifies the influencing capacity of reviews rendering two categories of high and low influential reviews. Further, each of these document is studied to conclude several product feature categories. Each of these categories along with the computed metric is converted to linguistic identifiers and are used in an association mining model. The article makes a novel attempt to combine feature attraction with quantified metric to categorize review text and finally provide frequent patterns that depict customer preferences. Frequent mentions in a highly influential score depict customer likes or preferred features in the product whereas prominent pattern in low influencing reviews highlights what is not important for customers. This is achieved using a hybrid approach of text mining for feature and term extraction, sentiment analysis, multicriteria decision-making technique and association mining model.

Keywords: association mining, customer preference, frequent pattern, online reviews, text mining

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Authors: Eric Wilson Santos Cabral, Marta Monteiro Da Costa Cruz, Fabio Luis Maciel Machado, Henrique Andrade, Rodrigo Pirola Pestana, Vivian Andrea Parreira

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The large variation in the price of diesel in Brazil directly affects the variable cost of companies operating in the transportation sector. In rail transport, the great challenge is to overcome the annual budget, cargo and ore transported with cost reduction in relation to previous years, becoming more efficient every year. Some effective measures are necessary to achieve the reduction of the liter ratio consumed by KTKB (Gross Ton per Kilometer multiplied by thousand). This acronym represents the indicator of energy efficiency of some railroads in the world. This study is divided into two parts: the first, to identify using statistical tools, part of the controlled variables in the railways, which have a correlation with the energy efficiency indicator, seeking to aid decision-making. The second, with the use of the train dynamics simulator, within scenarios defined in the operational reality of a railroad, seeks to optimize the train formations and the train stop model for the change of train drivers. With the completion of the study, companies in the rail sector are expected to be able to reduce some of their transportation costs.

Keywords: railway transport, railway simulation, energy efficiency, fuel consumption

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Authors: Francois Brambati, Daniele Ruscio, Federica Biassoni, Rebecca Hueting, Alessandra Tedeschi

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This research, in the frame of social acceptance of renewable energies and community-based production and consumption models, aims at (1) supporting a data-driven approachable to dealing with climate change and (2) identifying & quantifying the psycho-sociological dimensions and factors that could support the transition from a technology-driven approach to a consumer-driven approach throughout the emerging “prosumer business models.” In addition to the existing Social Acceptance dimensions, this research tries to identify a purely individual psychological fourth dimension to understand processes and factors underling individual acceptance and adoption of renewable energy business models, realizing a Prosumer Acceptance Index. Questionnaire data collection has been performed throughout an online survey platform, combining standardized and ad-hoc questions adapted for the research purposes. To identify the main factors (individual/social) influencing the relation with renewable energy technology (RET) adoption, a Factorial Analysis has been conducted to identify the latent variables that are related to each other, revealing 5 latent psychological factors: Factor 1. Concern about environmental issues: global environmental issues awareness, strong beliefs and pro-environmental attitudes rising concern on environmental issues. Factor 2. Interest in energy sharing: attentiveness to solutions for local community’s collective consumption, to reduce individual environmental impact, sustainably improve the local community, and sell extra energy to the general electricity grid. Factor 3. Concern on climate change: environmental issues consequences on climate change awareness, especially on a global scale level, developing pro-environmental attitudes on global climate change course and sensitivity about behaviours aimed at mitigating such human impact. Factor 4. Social influence: social support seeking from peers. With RET, advice from significant others is looked for internalizing common perceived social norms of the national/geographical region. Factor 5. Impact on bill cost: inclination to adopt a RET when economic incentives from the behaviour perception affect the decision-making process could result in less expensive or unvaried bills. Linear regression has been conducted to identify and quantify the factors that could better predict behavioural intention to become a prosumer. An overall scale measuring “acceptance of a renewable energy solution” was used as the dependent variable, allowing us to quantify the five factors that contribute to measuring: awareness of environmental issues and climate change; environmental attitudes; social influence; and environmental risk perception. Three variables can significantly measure and predict the scores of the “Acceptance in becoming a prosumer” ad hoc scale. Variable 1. Attitude: the agreement to specific environmental issues and global climate change issues of concerns and evaluations towards a behavioural intention. Variable 2. Economic incentive: the perceived behavioural control and its related environmental risk perception, in terms of perceived short-term benefits and long-term costs, both part of the decision-making process as expected outcomes of the behaviour itself. Variable 3. Age: despite fewer economic possibilities, younger adults seem to be more sensitive to environmental dimensions and issues as opposed to older adults. This research can facilitate policymakers and relevant stakeholders to better understand which relevant psycho-sociological factors are intervening in these processes and what and how specifically target when proposing change towards sustainable energy production and consumption.

Keywords: behavioural intention, environmental risk perception, prosumer, renewable energy technology, social acceptance

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Authors: Pavel Shcherban, Viktoria Ivanova, Alexander Neprokin, Vladislav Golovanov

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Currently, LLC «Lukoil-Kaliningradmorneft» is implementing a comprehensive program for the development of offshore fields of the Kaliningrad region. This is largely associated with the depletion of the resource base of land in the region, as well as the positive results of geological investigation surrounding the Baltic Sea area and the data on the volume of hydrocarbon recovery from a single offshore field are working on the Kaliningrad region – D-6 «Kravtsovskoye».The article analyzes the main stages of the LLC «Lukoil-Kaliningradmorneft»’s development program for the development of the hydrocarbon resources of the region's shelf and suggests an optimization algorithm that allows managing a multi-criteria process of development of shelf deposits. The algorithm is formed on the basis of the problem of sequential decision making, which is a section of dynamic programming. Application of the algorithm during the consolidation of the initial data, the elaboration of project documentation, the further exploration and development of offshore fields will allow to optimize the complex of technical and technological solutions and increase the economic efficiency of the field development project implemented by LLC «Lukoil-Kaliningradmorneft».

Keywords: offshore fields of hydrocarbons of the Baltic Sea, development of offshore oil and gas fields, optimization of the field development scheme, solution of multicriteria tasks in oil and gas complex, quality management in oil and gas complex

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Authors: Afrin Jaman Bonny, Mehrin Jahan, Zannatul Ferdhoush, Mumenunnessa Keya, Md. Shihab Mahmud, Sharun Akter Khushbu, Sheak Rashed Haider Noori, Sheikh Abujar

Abstract:

In 2020, the SARS-CoV-2 pandemic had led all the educational institutions to move to online learning platforms to ensure safety as well as the continuation of learning without any disruption to students’ academic life. But after the reopening of those educational institutions suddenly in Bangladesh, it became a vital demand to observe students take on this decision and how much they are comfortable with the new habits. When all educational institutions were ordered to re-open after more than a year, data was collected from students of all educational levels. A Google Form was used to conduct this online survey, and a total of 565 students participated without being pressured. The survey reveals the students' preferences for online and offline education systems, as well as their mental health at the time including their behavior to get back to offline classes depending on getting vaccinated or not. After evaluating the findings, it is clear that respondents' choices vary depending on gender and educational level, with female and male participants experiencing various mental health difficulties and attitudes toward returning to offline classes. As a result of this study, the student’s overall perspective on the sudden reopening of their educational institutions has been analyzed.

Keywords: covid-19 epidemic, educational proceeding, university students, school/college students, physical activity, online platforms, mental health, psychological distress

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Majid Bahramian, Kaan Yetilmezsoy

Abstract:

Environmental performance of residential buildings been a hotspot for the research community, however, the indoor environmental quality significantly overlooked in the literature. The paper is motivated by the understanding of the occupants from the indoor environmental qualities and seeks to find the satisfaction level in two high-rise green-certified residential buildings. Views of more than 250 respondents in each building were solicited on 15 Indoor Environmental Qualities (IEQ) parameters. Findings suggest that occupants are generally satisfied with five critical aspects of IEQ, but some unsatisfaction exists during operation phase. The results also indicate that the green build certification systems for new buildings have some deficiencies which affect the actual environmental performance of green buildings during operation. Some reasons were suggested by the occupants of which the design-focus construction and lack of monitoring after certification were the most critical factors. Among the crucial criteria for environmental performance assessment of green buildings, energy saving, reduction of Greenhouse Gases (GHG) emissions, environmental impacts on neighborhood area, waste reduction and IEQs, were the most critical factors dominating the performance, in a descending order. This study provides valuable information on the performance of IEQ parameters of green building and gives a deeper understanding for stakeholders and companies involved in construction sector with the relevant feedback for their decision-making on current and future projects.

Keywords: indoor environmental qualities, green buildings, occupant satisfaction, environmental performance

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Authors: Khomkrit Mongkhuntod, Chatchawal Chaichana, Atipoang Nuntaphan

Abstract:

Feed Water Heater is the important equipment for thermal power plant. The heating temperature from feed heating process is an impact to power plant efficiency or heat rate. Normally, the degradation of feed water heater that operated for a long time is effect to decrease plant efficiency or increase plant heat rate. For Mae Moh power plant, each unit operated more than 20 years. The degradation of the main equipment is effect of planting efficiency or heat rate. From the efficiency and heat rate analysis, Mae Moh power plant operated in high heat rate more than the commissioning period. Some of the equipment were replaced for improving plant efficiency and plant heat rates such as HP turbine and LP turbine that the result is increased plant efficiency by 5% and decrease plant heat rate by 1%. For the target of power generation plan that Mae Moh power plant must be operated more than 10 years. These work is focus on thermal efficiency analysis of feed water heater to compare with the commissioning data for find the way to improve the feed water heater efficiency that may effect to increase plant efficiency or decrease plant heat rate by use heat balance model simulation and economic value add (EVA) method to study the investment for replacing the new feed water heater and analyze how this project can stay above the break-even point to make the project decision.

Keywords: feed water heater, power plant efficiency, plant heat rate, thermal efficiency analysis

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Authors: B. Z. Abubakar, J. P. Voh, B. F. Umar, S. Khalid, A. A. Barau, J. Aigbe

Abstract:

The study was conducted to identify and assess the various community development programmes designed and executed by Sokoto Agricultural and Community Development Project (SACDP) with the assistance of International Funds for Agricultural Development (IFAD) among women beneficiaries in Kwacciyar-lalle and Mogonho communities of Sokoto state. A simple random sampling technique was employed to select 20 project beneficiaries in each of the selected communities, making a total of 40 beneficiaries. Structured questionnaire, descriptive statistics such as frequencies and percentages and also participatory methodologies such as focus group discussion and pair wise ranking were used to analyze the data. Results showed that majority of the beneficiaries (75%) were married and undertook animal rearing as their major occupation. Results further showed that (85%) of the beneficiaries were involved in decision making, which enhanced their participation. Pair-wise ranking showed dug well as the most preferred activity, followed by construction of Islamic school in Kwacciyar-lalle while well construction followed by provision of improved animal species were most preferred in Mogonho. Recommendations made in the light of achieving people’s participation include provision of more infrastructural facilities and working materials.

Keywords: community development, focus group, pair-wise ranking, infrastructure

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Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen

Abstract:

Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.

Keywords: hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

Abstract:

There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

Procedia PDF Downloads 93