Search results for: supervised decision tree

Authors: Jingsi Li, Neil S. Ferguson

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Time pressure could have an influence on the productivity, quality of decision making, and the efficiency of problem-solving. This has been mostly stemmed from cognitive research or psychological literature. However, a salient scarce discussion has been held for transport adjacent fields. It is conceivable that in many activity-travel contexts, time pressure is a potentially important factor since an excessive amount of decision time may incur the risk of late arrival to the next activity. The activity-travel rescheduling behavior is commonly explained by costs and benefits of factors such as activity engagements, personal intentions, social requirements, etc. This paper hypothesizes that an additional factor of perceived time pressure could affect travelers’ rescheduling behavior, thus leading to an impact on travel demand management. Time pressure may arise from different ways and is assumed here to be essentially incurred due to travelers planning their schedules without an expectation of unforeseen elements, e.g., transport disruption. In addition to a linear-additive utility-maximization model, the less computationally compensatory heuristic models are considered as an alternative to simulate travelers’ responses. The paper will contribute to travel behavior modeling research by investigating the following questions: how to measure the time pressure properly in an activity-travel day plan context? How do travelers reschedule their plans to cope with the time pressure? How would the importance of the activity affect travelers’ rescheduling behavior? What will the behavioral model be identified to describe the process of making activity-travel rescheduling decisions? How do these identified coping strategies affect the transport network? In this paper, a Mixed Heuristic Model (MHM) is employed to identify the presence of different choice heuristics through a latent class approach. The data about travelers’ activity-travel rescheduling behavior is collected via a web-based interactive survey where a fictitious scenario is created comprising multiple uncertain events on the activity or travel. The experiments are conducted in order to gain a real picture of activity-travel reschedule, considering the factor of time pressure. The identified behavioral models are then integrated into a multi-agent transport simulation model to investigate the effect of the rescheduling strategy on the transport network. The results show that an increased proportion of travelers use simpler, non-compensatory choice strategies instead of compensatory methods to cope with time pressure. Specifically, satisfying - one of the heuristic decision-making strategies - is adopted commonly since travelers tend to abandon the less important activities and keep the important ones. Furthermore, the importance of the activity is found to increase the weight of negative information when making trip-related decisions, especially route choices. When incorporating the identified non-compensatory decision-making heuristic models into the agent-based transport model, the simulation results imply that neglecting the effect of perceived time pressure may result in an inaccurate forecast of choice probability and overestimate the affectability to the policy changes.

Keywords: activity-travel rescheduling, decision making under uncertainty, mixed heuristic model, perceived time pressure, travel demand management

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: Maryam Mohammadi, Mohammadjavad Mahdavinejad, Mojtaba Ansari

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The main driving force for increasing movement towards the design of High-Performance Buildings (HPB) are building codes and rating systems that address the various components of the building and their impact on the environment and energy conservation through various methods like prescriptive methods or simulation-based approaches. The methods and tools developed to meet these needs, which are often based on building performance simulation tools (BPST), have limitations in terms of compatibility with the integrated design process (IDP) and HPB design, as well as use by architects in the early stages of design (when the most important decisions are made). To overcome these limitations in recent years, efforts have been made to develop Design Decision Support Systems, which are often based on artificial intelligence. Numerous needs and steps for designing and developing a Decision Support System (DSS), which complies with the early stages of energy-efficient architecture design -consisting of combinations of different methods in an integrated package- have been listed in the literature. While various review studies have been conducted in connection with each of these techniques (such as optimizations, sensitivity and uncertainty analysis, etc.) and their integration of them with specific targets; this article is a critical and holistic review of the researches which leads to the development of applicable systems or introduction of a comprehensive framework for developing models complies with the IDP. Information resources such as Science Direct and Google Scholar are searched using specific keywords and the results are divided into two main categories: Simulation-based DSSs and Meta-simulation-based DSSs. The strengths and limitations of different models are highlighted, two general conceptual models are introduced for each category and the degree of compliance of these models with the IDP Framework is discussed. The research shows movement towards Multi-Level of Development (MOD) models, well combined with early stages of integrated design (schematic design stage and design development stage), which are heuristic, hybrid and Meta-simulation-based, relies on Big-real Data (like Building Energy Management Systems Data or Web data). Obtaining, using and combining of these data with simulation data to create models with higher uncertainty, more dynamic and more sensitive to context and culture models, as well as models that can generate economy-energy-efficient design scenarios using local data (to be more harmonized with circular economy principles), are important research areas in this field. The results of this study are a roadmap for researchers and developers of these tools.

Keywords: integrated design process, design decision support system, meta-simulation based, early stage, big data, energy efficiency

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Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

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Authors: Feras Hanandeh, Izzat Alsmadi, Muhammad M. Kwafha

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Prefix trees or tries are data structures that are used to store data or index of data. The goal is to be able to store and retrieve data by executing queries in quick and reliable manners. In principle, the structure of the trie depends on having letters in nodes at the different levels to point to the actual words in the leafs. However, the exact structure of the trie may vary based on several aspects. In this paper, we evaluated different structures for building tries. Using datasets of words of different sizes, we evaluated the different forms of trie structures. Results showed that some characteristics may impact significantly, positively or negatively, the size and the performance of the trie. We investigated different forms and structures for the trie. Results showed that using an array of pointers in each level to represent the different alphabet letters is the best choice.

Keywords: data structures, indexing, tree structure, trie, information retrieval

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Authors: A. Aguezzoul

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The Logistics Services Providers (LSPs) selection and performance is a strategic decision that affects the overall performance of any company as well as its supply chain. It is a complex process, which takes into account various conflicting quantitative and qualitative factors, as well as outsourced logistics activities. This article focuses on the evolution of the weights associated to these factors over the last years in order to better understand the change in the importance that logistics professionals place on them criteria when choosing their LSPs. For that, an analysis of 17 main studies published during 2014-2017 period was carried out and the results are compared to those of a previous literature review on this subject. Our analysis allowed us to deduce the following observations: 1) the LSPs selection is a multi-criteria process; 2) the empirical character of the majority of studies, conducted particularly in Asian countries; 3) the criteria importance has undergone significant changes following the emergence of information technologies that have favored the work in close collaboration and in partnership between the LSPs and their customers, even on a worldwide scale; 4) the cost criterion is relatively less important than in the past; and finally 5) with the development of sustainable supply chains, the factors associated with the logistic activities of return and waste processing (reverse logistics) are becoming increasingly important in this multi-criteria process of selection and evaluation of LSPs performance.

Keywords: logistics outsourcing, logistics providers, multi-criteria decision making, performance

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Authors: Jonathan E. Oghenekohwo

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The return to scale constitutes a significant investment index in the determination of the quantum of resources that is deployed in investment decision on worker’s continuing professional development. Such investment decision is always predicted on the expected outcomes to the individual, institution and the society in context. Several investments in the development of human capacity on the job have been made, but the return to the scale of such seems not to have been correlated positively with the quantum of resources invested in terms of productivity and performance among workers in many universities. This paper thus found out that, despite the commitment and policy instrument to avail workers the right of continuing professional development, the multiplier effects are not evident in diligence, commitment, honesty, dedication, productivity and improved performance on the job among most administrative staff in Nigerian Universities This author, therefore concludes that, given the policy on the right of workers to get trained on-the job, the outcomes of such training must reflect on the overall performance indices, otherwise, institutions should carry out a forensic analysis of the types of continuing professional development programmes that workers participate in, whether or not, they are consistent with the vision and mission of the institutions in terms of economies of scale of workers professional development to the individual, institution and the nation in context.

Keywords: continuing, professional development, economies of scale, worker’s education, administrative staff

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Authors: E. U. Dampage, Ovindi D. Bandara, Vinushi S. Waraketiya, Samitha S. R. De Silva, Yasiru S. Gunarathne

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The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding of how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir, which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka, was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface Temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.

Keywords: convolutional neural network, CNN, inflow, long short-term memory, LSTM, multi-layer perceptron, MLP, neural network

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Authors: Mohsen Dourandish

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Iran has a huge pipeline network in every state of country which is the longest and vastest pipeline network after Russia and USA (360,000 Km high pressure pipelines and 250,000 Km distribution networks). Furthermore in recent years National Iranian Gas Company is planning to develop natural gas network to cover all cities and villages above 20 families, in a way that 97 percent of Iran population will be gas consumer by 2020. In this condition, network resiliency will be the first priority of NIGC and due to that several planning for increasing resiliency of gas network is under construction. The most important strategy of NIGC is converting tree form pattern network to loop gas networks and developing underground gas storage near main gas consuming centers. In this regard NIGC is planning for construction of over 3500 km high-pressure pipeline and also 10 TCM gas storage capacities in UGSs.

Keywords: Iranian gas network, peak shaving, resiliency, underground gas storage

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Authors: Haifeng Shi, Kun Song, Yili Zhao

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In China, with the extensive promotion of the ‘aging in place’ pension policy as the background, most of the elders will choose to remain in their current homes and communities, finding out of preference or necessity that they will need to remodel their homes to fit their changing needs. This generation elder born in the 1960s to 1970s almost live in the same form of housing-condominium built from 1982 to 2012. Based on the survey of existing multi-family housing, especially in Tianjin, it is found that the current ‘double two-thirds principles’ is becoming the threshold for modification to existing house, particularly in the project of elevators addition to existing condominium (built from 1982 to 2016 without elevators below 6 floors according to the previous building code). Firstly, this article concludes the local policies of elevator addition nationwide, most of which has determined the importance and necessity of the community-based self-organization principle in the operation of the elevator addition. Secondly, by comparing the three existing community management systems (owners' congress, property management system and community committee) in instances, find that the community-based ‘two-thirds’ principle is not conducive to implement for multi-owned property renovation in the community or common accessibility modification in the building. However, analysis the property and other community management related laws, pointing out the shortcomings of the existing community-based ‘two-thirds’ decision-making norms. The analyzation showed that the unit-based and ‘100% principle’ method is more capable of common accessibility in the condominium in China. Differing from existing laws, the unit-based principle will be effective for the process of decision-making and ‘100% principle’ will protect closely profit-related householders for condominium modification in the multi-owned area. These three aspects of the analysis suggest that the establishment of the unit-based self-organization mechanism is a preferred and inevitable method to solve the problem of elevators addition to the existing condominium in China.

Keywords: aging in place, condominium, modification, multi own

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Authors: Ryan Vincent Teodoro, Adrian Paul Virador, Jan Christopher Cardenas

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In the field of construction, managerial positions are completely dominated by males. The researchers conducted this study to see if there is a significant difference between the competencies of male and female project managers in the construction field. To see if there is a significant difference, they subdivided the competency of project managers into three components; decision making, organizing skills, and resiliency. The researchers conducted a five-point Likert scale survey of 28 project managers in the construction field, 18 of them are males and 10 are females. The researchers used Cronbach’s alpha to translate the raw scores of the respondents into competency scores. Then, the competency scores are analyzed using the Mann-Whitney U Test to see if there is a significant difference between the male’s and female’s competency scores. A p-value of 0.808 was calculated, which is greater than 0.05, which means that the null hypothesis is accepted. Therefore, the researchers concluded that there is no significant difference between the competencies of male and female project managers in terms of decision making, organizing skills, and resiliency in the construction field in the National Capital Region, Philippines.

Keywords: competency, resiliency, project managers, Mann-Whitney U test

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Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

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In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange

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Authors: D. Augutis, M. Balalaikins, D. Bastyte, R. Ferenca, A. Gintaras, R. Karpuska, G. Svitra, U. Valainis

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Osmoderma species complex (consisting of Osmoderma eremita, O. barnabita, O. lassallei and O. cristinae) is a scarab beetle serving as indicator species in nature conservation. Osmoderma inhabits cavities containing sufficient volume of wood mould usually caused by brown rot in veteran deciduous trees. As the species, having high demands for the habitat quality, they indicate the suitability of the habitat for a number of other specialized saproxylic species. Since typical habitat needed for Osmoderma and other species associated with hollow veteran trees is rapidly declining, the species complex is protected under various legislation, such as Bern Convention, EU Habitats Directive and the Red Lists of many European states. Natura 2000 sites are the main tool for conservation of O. barnabita in Lithuania, currently 17 Natura 2000 sites are designated for the species, where monitoring is implemented once in 3 years according to the approved methodologies. Despite these monitoring efforts in species reports, provided to EU according to the Article 17 of the Habitats Directive, it is defined on the national level, that overall assessment of O. barnabita is inadequate and future prospects are poor. Therefore, research on the distribution and habitat status of O. barnabita was launched on the national level in 2016, which was complemented by preparatory actions of LIFE OSMODERMA project. The research was implemented in the areas equally distributed in the whole area of Lithuania, where O. barnabita was previously not observed, or not observed in the last 10 years. 90 areas, such as Habitats of European importance (9070 Fennoscandian wooded pastures, 9180 Tilio-Acerion forests of slopes, screes, and ravines), Woodland key habitats (B1 broad-leaved forest, K1 single giant tree) and old manor parks, were chosen for the research after review of habitat data from the existing national databases. The first part of field inventory of the habitats was carried out in 2016 and 2017 autumn and winter seasons, when relative abundance of O. barnabita was estimated according to larval faecal pellets in the tree cavities or around the trees. The state of habitats was evaluated according to the density of suitable and potential trees, percentage of not overshadowed trees and amount of undergrowth. The second part of the field inventory was carried out in the summer with pheromone traps baited with (R)-(+)-γ –decalactone. Results of the research show not only occurrence and habitat status of O. barnabita, but also help to clarify O. barnabita habitat requirements in Lithuania, define habitat size, its structure and distribution. Also, it compares habitat needs between the regions in Lithuania and inside and outside Natura 2000 areas designated for the species.

Keywords: habitat status, insect conservation, Osmoderma barnabita, veteran trees

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Authors: Xiaohua Zou, Yongxin Su

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The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

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Authors: Renjith George, Preethy Mary Donald

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Use of internet sources to retrieve health care related information among health care professionals has increased tremendously as the accessibility to internet is made easier through smart phones and tablets. Though there are huge data available at a finger touch, it is doubtful whether all the sources providing health care information adhere to evidence based practice. The objective of this survey was to study the prevalence of use of internet sources to get health care information, to assess the mind-set towards the authenticity of health care information available via internet sources and to study the awareness about evidence based practice in health care among medical and dental students in Melaka-Manipal Medical College. The survey was proposed as there is limited number of studies reported in the literature and this is the first of its kind in Malaysia. A cross sectional survey was conducted among the medical and dental students of Melaka-Manipal Medical College. A total of 521 students including medical and dental students in their clinical years of undergraduate study participated in the survey. A questionnaire consisting of 14 questions were constructed based on data available from the published literature and focused group discussion and was pre-tested for validation. Data analysis was done using SPSS. The statistical analysis of the results of the survey proved that the use of internet resources for health care information are equally preferred over the conventional resources among health care students. Though majority of the participants verify the authenticity of information from internet sources, there was considerable percentage of candidates who feels that all the information from the internet can be utilised for clinical decision making or were not aware about the need of verification of authenticity of such information. 63.7 % of the participants rely on evidence based practice in health care for clinical decision making while 34.2 % were not aware about it. A minority of 2.1% did not agree with the concept of evidence based practice. The observations of the survey reveals the increasing use of internet resources for health care information among health care students. The results warrants the need to move towards evidence based practice in health care as all health care information available online may not be reliable. The health care person should be judicious while utilising the information from such resources for clinical decision making.

Keywords: authenticity, evidence based practice, health care information, internet

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Authors: Kesya Terinda Natalie, Marcellina Natasha, Rosinta Ria Panggabean

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Purpose: Over the last few years, the company has been implementing sustainability practices to ensure business continuity. However, there are pros and cons regarding the impact of financial reports if companies provide non-financial reports. So this paper aims to prove what the effect of Sustainability Reporting (SR) has on company profitability, as well as things that can be considered as the decision-making of SR disclosure. Methodology: This paper uses the literature review method to describe the results of published articles concerning Sustainability Reporting and Profitability. This study links and analyzes the essence of 50 previous studies related to SR on company profitability, most of which were conducted in Asia. Therefore this research is limited to only 23 studies in Asia. Findings: Sustainability Reporting does not have a significant impact on company profitability because the SR quality of each company varies based on Agency & Legitimacy Theory considerations. Stakeholders are required to focus not only on profitability but also on the long-term of the company. Thus, it is found that SR is used by companies as a sustainable investment, which can improve overall company performance by reducing capital costs and generating positive company value in increasing reputation capital. Value: This paper focuses on how sustainability reporting affects company profitability, as well as things that can be considered as the decision-making of SR disclosure.

Keywords: sustainability reporting, profitability, agency theory, legitimacy theory

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Authors: Mustapha Ouardouz, Mina Amharref, Abdessamed Bernoussi

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The future energy, for limited energy resources countries, goes through renewable energies (solar, wind etc.). The renewable energies constitute a major component of the energy strategy to cover a substantial part of the growing needs and contribute to environmental protection by replacing fossil fuels. Indeed, sustainable development involves the promotion of renewable energy and the preservation of the environment by the use of clean energy technologies to limit emissions of greenhouse gases and reducing the pressure exerted on the forest cover. So the impact studies, of the energy use on the environment and farm-related risks are necessary. For that, a global approach integrating all the various sectors involved in such project seems to be the best approach. In this paper we present an approach based on the multi criteria analysis and the realization of one pilot to achieve the development of an innovative geo-intelligent environmental platform. An implementation of this platform will collect, process, analyze and manage environmental data in connection with the nature of used energy in the studied region. As an application we consider a region in the north of Morocco characterized by intense agricultural and industrials activities and using diverse renewable energy. The strategic goals of this platform are; the decision support for better governance, improving the responsiveness of public and private companies connected by providing them in real time with reliable data, modeling and simulation possibilities of energy scenarios, the identification of socio-technical solutions to introduce renewable energies and estimate technical and implantable potential by socio-economic analyzes and the assessment of infrastructure for the region and the communities, the preservation and enhancement of natural resources for better citizenship governance through democratization of access to environmental information, the tool will also perform simulations integrating environmental impacts of natural disasters, particularly those linked to climate change. Indeed extreme cases such as floods, droughts and storms will be no longer rare and therefore should be integrated into such projects.

Keywords: renewable energies, decision aided tool, environment, simulation

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Authors: R. Tlili Yaakoubi, H.Nakouri, O. Blanpain, S. Lallahem

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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.

Keywords: automation, optimization, paradigm, RTC

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Authors: Otto Kakhidze, Hoda Alkhzaimi, Adam Ramey, Nasir Memon

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This paper provides an alternative, cyber security based a conceptual framework for the ethics of automated warfare. The large body of work produced on fully or partially autonomous warfare systems tends to overlook malicious security factors as in the possibility of technical attacks on these systems when it comes to the moral and legal decision-making. The argument provides a risk-oriented justification to why technical malicious risks cannot be dismissed in legal, ethical and policy considerations when warfare models are being implemented and deployed. The assumptions of the paper are supported by providing a broader model that contains the perspective of technological vulnerabilities through the lenses of the Game Theory, Just War Theory as well as standard and non-standard defense ethics. The paper argues that a conventional risk-benefit analysis without considering ethical factors is insufficient for making legal and policy decisions on automated warfare. This approach will provide the substructure for security and defense experts as well as legal scholars, ethicists and decision theorists to work towards common justificatory grounds that will accommodate the technical security concerns that have been overlooked in the current legal and policy models.

Keywords: automated warfare, ethics of automation, inherent hijacking, security vulnerabilities, risk, uncertainty

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Authors: Fausto Morais

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Artificial intelligence has been widely used in law. Programs are able to classify suits, to identify decision-making patterns, to predict outcomes, and to formalize legal arguments as well. In Brazil, the artificial intelligence victor has been classifying cases to supreme court’s standards. When those programs act doing those tasks, they simulate some kind of legal decision and legal arguments, raising doubts about how artificial intelligence can be integrated into legal reasoning. Taking this into account, the following three issues are identified; the problem of hypernormatization, the argument of legal anthropocentrism, and the artificial legal principles. Hypernormatization can be seen in the Brazilian legal context in the Supreme Court’s usage of the Victor program. This program generated efficiency and consistency. On the other hand, there is a feasible risk of over standardizing factual and normative legal features. Then legal clerks and programmers should work together to develop an adequate way to model legal language into computational code. If this is possible, intelligent programs may enact legal decisions in easy cases automatically cases, and, in this picture, the legal anthropocentrism argument takes place. Such an argument argues that just humans beings should enact legal decisions. This is so because human beings have a conscience, free will, and self unity. In spite of that, it is possible to argue against the anthropocentrism argument and to show how intelligent programs may work overcoming human beings' problems like misleading cognition, emotions, and lack of memory. In this way, intelligent machines could be able to pass legal decisions automatically by classification, as Victor in Brazil does, because they are binding by legal patterns and should not deviate from them. Notwithstanding, artificial intelligent programs can be helpful beyond easy cases. In hard cases, they are able to identify legal standards and legal arguments by using machine learning. For that, a dataset of legal decisions regarding a particular matter must be available, which is a reality in Brazilian Judiciary. Doing such procedure, artificial intelligent programs can support a human decision in hard cases, providing legal standards and arguments based on empirical evidence. Those legal features claim an argumentative weight in legal reasoning and should serve as references for judges when they must decide to maintain or overcome a legal standard.

Keywords: artificial intelligence, artificial legal principles, hypernormatization, legal anthropocentrism argument, legal reasoning

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Authors: Murat Arıbaş, Uğur Özcan

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Due to the increasing number of universities and academics, the fund of the universities for research activities and grants/supports given by government institutions have increased number and quality of academic research projects. Although every academic research project has a specific purpose and importance, limited resources (money, time, manpower etc.) require choosing the best ones from all (Amiri, 2010). It is a pretty hard process to compare and determine which project is better such that the projects serve different purposes. In addition, the evaluation process has become complicated since there are more than one evaluator and multiple criteria for the evaluation (Dodangeh, Mojahed and Yusuff, 2009). Mehrez and Sinuany-Stern (1983) determined project selection problem as a Multi Criteria Decision Making (MCDM) problem. If a decision problem involves multiple criteria and objectives, it is called as a Multi Attribute Decision Making problem (Ömürbek & Kınay, 2013). There are many MCDM methods in the literature for the solution of such problems. These methods are AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), UTADIS (Utilities Additives Discriminantes), ELECTRE (Elimination et Choix Traduisant la Realite), MAUT (Multiattribute Utility Theory), GRA (Grey Relational Analysis) etc. Teach method has some advantages compared with others (Ömürbek, Blacksmith & Akalın, 2013). Hence, to decide which MCDM method will be used for solution of the problem, factors like the nature of the problem, types of choices, measurement scales, type of uncertainty, dependency among the attributes, expectations of decision maker, and quantity and quality of the data should be considered (Tavana & Hatami-Marbini, 2011). By this study, it is aimed to develop a systematic decision process for the grant support applications that are expected to be evaluated according to their scientific adequacy by multiple evaluators under certain criteria. In this context, project evaluation process applied by The Scientific and Technological Research Council of Turkey (TÜBİTAK) the leading institutions in our country, was investigated. Firstly in the study, criteria that will be used on the project evaluation were decided. The main criteria were selected among TÜBİTAK evaluation criteria. These criteria were originality of project, methodology, project management/team and research opportunities and extensive impact of project. Moreover, for each main criteria, 2-4 sub criteria were defined, hence it was decided to evaluate projects over 13 sub-criterion in total. Due to superiority of determination criteria weights AHP method and provided opportunity ranking great number of alternatives TOPSIS method, they are used together. AHP method, developed by Saaty (1977), is based on selection by pairwise comparisons. Because of its simple structure and being easy to understand, AHP is the very popular method in the literature for determining criteria weights in MCDM problems. Besides, the TOPSIS method developed by Hwang and Yoon (1981) as a MCDM technique is an alternative to ELECTRE method and it is used in many areas. In the method, distance from each decision point to ideal and to negative ideal solution point was calculated by using Euclidian Distance Approach. In the study, main criteria and sub-criteria were compared on their own merits by using questionnaires that were developed based on an importance scale by four relative groups of people (i.e. TUBITAK specialists, TUBITAK managers, academics and individuals from business world ) After these pairwise comparisons, weight of the each main criteria and sub-criteria were calculated by using AHP method. Then these calculated criteria’ weights used as an input in TOPSİS method, a sample consisting 200 projects were ranked on their own merits. This new system supported to opportunity to get views of the people that take part of project process including preparation, evaluation and implementation on the evaluation of academic research projects. Moreover, instead of using four main criteria in equal weight to evaluate projects, by using weighted 13 sub-criteria and decision point’s distance from the ideal solution, systematic decision making process was developed. By this evaluation process, new approach was created to determine importance of academic research projects.

Keywords: Academic projects, Ahp method, Research projects evaluation, Topsis method.

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Authors: Zanele Ntshidi, Sebinasi Dzikiti, Dominic Mazvimavi

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South Africa is a dry country and yet it is ranked as the 8th largest exporter of fresh apples (Malus Domestica) globally. Prime apple producing regions are in the Eastern and Western Cape Provinces of the country where all the fruit is grown under irrigation. Climate change models predict increasingly drier future conditions in these regions and the frequency and severity of droughts is expected to increase. For the sustainability and growth of the fruit industry it is important to minimize non-beneficial water losses from the orchard floor. The aims of this study were firstly to compare the water use of cover crop species used in South African orchards for which there is currently no information. The second aim was to investigate how orchard water use (evapotranspiration) was partitioned into beneficial (tree transpiration) and non-beneficial (orchard floor evaporation) water uses for micro-sprinkler irrigated orchards with different canopy covers. This information is important in order to explore opportunities to minimize non-beneficial water losses. Six cover crop species (four exotic and two indigenous) were grown in 2 L pots in a greenhouse. Cover crop transpiration was measured using the gravimetric method on clear days. To establish how water use was partitioned in orchards, evapotranspiration (ET) was measured using an open path eddy covariance system, while tree transpiration was measured hourly throughout the season (October to June) on six trees per orchard using the heat ratio sap flow method. On selected clear days, soil evaporation was measured hourly from sunrise to sunset using six micro-lysimeters situated at different wet/dry and sun/shade positions on the orchard floor. Transpiration of cover crops was measured using miniature (2 mm Ø) stem heat balance sap flow gauges. The greenhouse study showed that exotic cover crops had significantly higher (p < 0.01) average transpiration rates (~3.7 L/m2/d) than the indigenous species (~ 2.2 L/m²/d). In young non-bearing orchards, orchard floor evaporative fluxes accounted for more than 60% of orchard ET while this ranged from 10 to 30% in mature orchards with a high canopy cover. While exotic cover crops are preferred by most farmers, this study shows that they use larger quantities of water than indigenous species. This in turn contributes to a larger orchard floor evaporation flux. In young orchards non-beneficial losses can be minimized by adopting drip or short range micro-sprinkler methods that reduce the wetted soil fraction thereby conserving water.

Keywords: evapotranspiration, sap flow, soil evaporation, transpiration

Procedia PDF Downloads 388

Authors: Kanwar Varinder Pal Singh

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Art is one of the five secondary sciences next to the social sciences. As per the single essential concept in garment design, it is the coexistence and co-creation of two aspects of reality: Ultimate reality and apparent or conventional reality. All phenomena possess two natures: That which is revealed by correct perception and that which is induced by deceptive perception. The object of correct perception is the ultimate reality, the object of deceptive perception is conventional reality. The same phenomenon, therefore, may be perceived according to its ultimate nature or its apparent nature. Ultimate reality is also called ‘emptiness’. Emptiness does not mean that all phenomena are nothing but do not exist in themselves. Although phenomena, the universe, thoughts, beings, time, and so on, seem very real in themselves, ultimately, they are not. Each one of us can perceive the changing and unpredictable nature of existence. This transitory nature of phenomena, impermanence, is the first sign of emptiness. Sometimes, the interdependence of phenomena leads to ultimate reality, which is nothing but emptiness, e.g., a rainbow, which is an effect due to the function of ‘sun rays,’ ‘rain,’ and ‘time.’ In light of the above, to achieve decision-making for the global desirability of garment design, the coexistence of artistic and technical creativity must achieve an object of correct perception, i.e., ultimate reality. This paper mentions the decision-making technique as semiotic engineering, both subjective and objective.

Keywords: global desirability, social desirability, comfort desirability, handle desirability, overall desirability

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Authors: Karam Al-Assaf, Israa Al Khaffaf, Ryan Al Tayeb, Ayman Alzaatreh

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A significant, manageable hazard to public health, happiness, and the achievement of sustainable development is air pollution. Outdoor air pollution has increased globally over the previous years, exposing billions of people worldwide to hazardous air. With UAE being no different, there are a variety of pollution-related problems that need to be addressed. Therefore, to gain insights from the government and decision makers, this study aims to analyze the annual trends of the five major air pollutants (NO2, SO2, O3, CO, and PM10) across five emirates in the UAE (Abu Dhabi, Dubai, Sharjah, Ajman, and Ras AL Khaimah) from 2013 to 2020. The results of the analysis revealed that air pollutants NO2, SO2, and PM10 were significantly different across the years and states. Moreover, it was found that the levels of NO2 are significantly different in Dubai across the years. Moreover, the levels of SO2 are significantly different in Sharjah across the years. Furthermore, it was found that PM10 was significantly different in Ajman across the years. Moreover, the analysis of the significant difference in the pollutants in the three areas (Downtown, Residential, Industrial) revealed that there is no significant difference in the pollutant levels across the years in the three different areas. This tool has shown its effectiveness in monitoring pollutant trends, providing valuable data for government investigations and control measures across the UAE. Additionally, it serves as a valuable resource for decision-makers to develop and implement policies aimed at improving pollutant levels.

Keywords: air pollution, air pollutant, repeated measures, MANOVA, UAE

Procedia PDF Downloads 77

Authors: Tesfaye Mengistu

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This thesis aims to investigate the effectiveness of Reinforcement Learning (RL) for Autonomous Energy Management solutions. The study explores the potential of Model Free RL approaches, such as Monte Carlo RL and Q-learning, to improve energy management by autonomously adjusting energy management strategies to maximize efficiency. The research investigates the implementation of RL algorithms for optimizing energy consumption in a single-agent environment. The focus is on developing a framework for the implementation of RL algorithms, highlighting the importance of RL for enabling autonomous systems to adapt quickly to changing conditions and make decisions based on previous experiences. Moreover, the paper proposes RL as a novel energy management solution to address nations' CO2 emission goals. Reinforcement learning algorithms are well-suited to solving problems with sequential decision-making patterns and can provide accurate and immediate outputs to ease the planning and decision-making process. This research provides insights into the challenges and opportunities of using RL for energy management solutions and recommends further studies to explore its full potential. In conclusion, this study provides valuable insights into how RL can be used to improve the efficiency of energy management systems and supports the use of RL as a promising approach for developing autonomous energy management solutions in residential buildings.

Keywords: artificial intelligence, reinforcement learning, monte carlo, energy management, CO2 emission

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Authors: Apu Kumar Saha, Mrinmoy Majumder

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The increase in the concentration of Green House gases all over the World has induced global warming phenomena whereby the average temperature of the world has aggravated to impact the pattern of climate in different regions. The frequency of extreme event has increased, early onset of season and change in an average amount of rainfall all are engrossing the conclusion that normal pattern of climate is changing. Sophisticated and complex models are prepared to estimate the future situation of the climate in different zones of the Earth. As hydro-energy is directly related to climatic parameters like rainfall and evaporation such energy resources will have to sustain the onset of the climatic abnormalities. The present investigation has tried to assess the impact of climatic abnormalities upon hydropower potential of different regions of the World. In this regard multi-criteria, decision making, and the neural network is used to predict the impact of the change cognitively by an index. The results from the study show that hydro-energy potential of Asian region is mostly vulnerable with respect to other regions of the world. The model results also encourage further application of the index to analyze the impact of climate change on the potential of hydro-energy.

Keywords: hydro-energy potential, neural networks, multi criteria decision analysis, environmental and ecological engineering

Procedia PDF Downloads 549

Authors: Andrew Kipp

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Student absenteeism is harmful to both the school and the absentee student. One approach to improving student absenteeism is targeting contextual factors within the students’ learning environment. However, contemporary literature has not taken an ecological agency approach to understanding student absenteeism. Ecological agency is a theoretical framework that magnifies the interplay between the environment and the actions of people within the environment. To elaborate, the person’s personal history and aspirations and the environmental conditions provide potential outlets or restrictions to their intended action. The framework provides the unique perspective of understanding absentee students’ decision-making through the affordances and constraints found in their learning environment. To that effect, the study was guided by the question, “Why do absentee students decide to engage in absenteeism in a suburban Kansas school?” A case study methodology was used to answer the research question. Four suburban, Kansas high school absentee students in the 2020-2021 school year were selected for the study. The fall 2020 semester was in a remote learning setting, and the spring 2021 semester was in an in-person learning setting. The study captured their decision-making with respect to school attendance throughsemi-structured interviews, prolonged observations, drawings, and concept maps. The data was analyzed through thematic analysis. The findings revealed that peer socialization opportunities, methods of instruction, shifts in cultural beliefs due to COVID-19, manifestations of anxiety and lack of space to escape their anxiety, social media bullying, and the inability to receive academic tutoring motivated the participants’ daily decision to either attend or miss school. The findings provided a basis to improve several institutional and classroom practices. These practices included more student-led instruction and less teacher-led instruction in both in-person and remote learning environments, promoting socialization through classroom collaboration and clubs based on emerging student interests, reducing instances of bullying through prosocial education, safe spaces for students to escape the classroom to manage their anxiety, and more opportunities for one-on-one tutoring to improve grades. The study provides an example of using the ecological agency approach to better understand the personal and environmental factors that lead to absenteeism. The study also informs educational policies and classroom practices to better promote student attendance. Further research should investigate other school contexts using the ecological agency theoretical framework to better understand the influence of the school environment on student absenteeism.

Keywords: student absenteeism, ecological agency, classroom practices, educational policy, student decision-making

Procedia PDF Downloads 143

Authors: Sekkal Nawel, Mahammed Nadir

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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.

Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network

Procedia PDF Downloads 67

Authors: O. Adjoul, A. Feugier, K. Benfriha, A. Aoussat

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In general, issues related to design and maintenance are considered in an independent manner. However, the decisions made in these two sets influence each other. The design for maintenance is considered an opportunity to optimize the life cycle cost of a product, particularly in the nuclear or aeronautical field, where maintenance expenses represent more than 60% of life cycle costs. The design of large-scale systems starts with product architecture, a choice of components in terms of cost, reliability, weight and other attributes, corresponding to the specifications. On the other hand, the design must take into account maintenance by improving, in particular, real-time monitoring of equipment through the integration of new technologies such as connected sensors and intelligent actuators. We noticed that different approaches used in the Design For Maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and maintainability of a multi-component system. This article proposes a method of DFM that assists designers to propose dynamic maintenance for multi-component industrial systems. The term "dynamic" refers to the ability to integrate available monitoring data to adapt the maintenance decision in real time. The goal is to maximize the availability of the system at a given life cycle cost. This paper presents an approach for simultaneous optimization of the design and maintenance of multi-component systems. Here the design is characterized by four decision variables for each component (reliability level, maintainability level, redundancy level, and level of monitoring data). The maintenance is characterized by two decision variables (the dates of the maintenance stops and the maintenance operations to be performed on the system during these stops). The DFM model helps the designers choose technical solutions for the large-scale industrial products. Large-scale refers to the complex multi-component industrial systems and long life-cycle, such as trains, aircraft, etc. The method is based on a two-level hybrid algorithm for simultaneous optimization of design and maintenance, using genetic algorithms. The first level is to select a design solution for a given system that considers the life cycle cost and the reliability. The second level consists of determining a dynamic and optimal maintenance plan to be deployed for a design solution. This level is based on the Maintenance Free Operating Period (MFOP) concept, which takes into account the decision criteria such as, total reliability, maintenance cost and maintenance time. Depending on the life cycle duration, the desired availability, and the desired business model (sales or rental), this tool provides visibility of overall costs and optimal product architecture.

Keywords: availability, design for maintenance (DFM), dynamic maintenance, life cycle cost (LCC), maintenance free operating period (MFOP), simultaneous optimization

Procedia PDF Downloads 118

Authors: Ibrahim Aliyu Isah, Mohd Mansor Ismail, Salim Hassan, Norsida Man, Oluwatoyin Olagunju

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Stingless beekeeping is not only a profitable activity for Malaysian beekeepers but also for the Malaysian economy. However, natural honey has faced some difficulties, which resulted in low production due to a lack of information on improved technology as well as the capacity and potential building of stingless beekeeping farmers, which depend mostly on information received from the extension agents. Hence, it is the responsibility of the extension agents to give useful information on the available technology and develop the capacity of the farmers to make the right decision that will improve their level of production. This study assessed how technology transfer and human resource development skills influence the work performance of the extension agents toward sustainable beekeeping production among beekeepers. The study sought to establish the role of relevant technology transfer and human resource development skills in effective performance. The research design was a descriptive and quantitative survey of stingless beekeepers on technology transfer and human resource development by the extension agent. Data was obtained from 54 beekeeping farmers and was analyzed using descriptive and inferential statistics. The results revealed that technology skill, technology dissemination skill, technology evaluation skill, Decision-making process skill, Leadership development skill and work performance were rated moderate by stingless beekeeping farmers, while Social skill was rated high. A significant and positive correlation (P<0.01) existed between all variables and performance. Regression results showed that leadership development skills, Decision-making process skills, and social skills are significant (P=.05), while technology skills, technology dissemination skills, and technology evaluation skills are not significant. The highest contributing factor is social skill (β=.446). Beekeeping is a profitable project in Malaysia and can be sustained if the extension services and programs are well carried out by competent extension agents and relevant agricultural government agencies.

Keywords: beekeeping, extension agents, human resource development, sustainable, technology transfer, work performance

Procedia PDF Downloads 63