Search results for: uncertainty and decision making

Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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Authors: Dionne Coley B. A.

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The role of a lawyer as a mediator is to be impartial in assisting parties to arrive at a decision. This decision should be made in a voluntary and mutually acceptable manner where the mediator encourages the parties to communicate, identify their interests, assess risks and consider settlement options. One of the key components to mediation is impartiality where mediators are to have a duty to remain impartial throughout the course of mediation and uphold an “objective” demeanor with both parties. The question is whether a mediator should take on evaluative role while encouraging the parties to come to a decision. This means that the mediator would not only encourage dialogue and responses between the parties but also assess and provide an opinion on the matter. This paper submits the argument that the role of a mediator should not be one of evaluation as this does not encourage the dialogue, process or desired outcomes associated with mediation.

Keywords: evaluation, lawyer, mediation, reform

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Authors: Dogan Can Hatunoglu, Cengiz Hakan Gurkanlı, Hatice Merve Demirci

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Today, the maker movement is regarded as a socio-cultural movement that represents designing and building objects for innovations. In these creativity-based activities of the movement, individuals from different backgrounds such as; inventors, programmers, craftspeople, DIY’ers, tinkerers, engineers, designers, and hackers, form a community and work collaboratively for mutual, open-source innovations. Today, with the accessibility of recently emerged technologies and digital fabrication tools, the Maker Movement is continuously expanding its scope and has evolved into a new experience, and for many, it is now considered as new kind of industrial revolution. In this new experience, makers create new things within their community by using new digital tools and technologies in spots called makerspaces. In these makerspaces, activities of learning, experience sharing, and mentoring are evolved into maker events. Makers who share common interests in making benefit from makerspaces as meeting and working spots. In literature, there are many sources on Maker Movement, maker communities, and their activities, especially in the field of business administration. However, there is a gap in the literature about the maker communities in Turkey. This research aims to be an information source on the dynamics and process design of “making” activities in Turkish maker communities and also aims to provide insights to sustain and enhance local maker communities in the future. Within this aim, semi-structured interviews were conducted with founders and facilitators from selected Turkish maker communities. (1) The perception towards Maker Movement, makers, activity of making, and current situation of maker communities, (2) motivations of individuals who participate the maker communities, and (3) key drivers (collaboration and decision-making in design processes) of maker activities from the perspectives of main actors (founders, facilitators) are all examined deeply with question on personal experiences and perspectives. After a qualitative approached data analysis concerning the maker communities in Turkey, this research reveals that there are two main conclusions regarding (1) the foundation of the Turkish maker mindset and (2) emergence of self-sustaining communities.

Keywords: Maker Movement, maker community, makerspaces, open-source design, sustainability

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Authors: Paria Tajabor, Azin Azarbani

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The purpose of this research study is exploring two kind of semantic repositories with regards to various factors to find the best approaches that an artificial manager can use to produce ontology in a system based on their interaction, association and research. To this end, as the best way to evaluate each system and comparing with others is analysis, several benchmarking over these two repositories were examined. These two semantic repositories: OWLIM and AllegroGraph will be the main core of this study. The general objective of this study is to be able to create an efficient and cost-effective manner reports which is required to support decision making in any large enterprise.

Keywords: OWLIM, allegrograph, RDF, reasoning, semantic repository, semantic-web, SPARQL, ontology, query

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Authors: Samira Alvandi

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In the context of the circular economy, production planning aims to eliminate waste and emissions and maximize resource efficiency. Historically production planning is challenged through arrays of uncertainty and complexity arising from the interdependence and variability of products, processes, and systems. Manufacturers worldwide are facing new challenges in tackling various environmental issues such as climate change, resource depletion, and land degradation. In managing the inherited complexity and uncertainty and yet maintaining profitability, the manufacturing sector is in need of a holistic framework that supports energy efficiency and carbon emission reduction schemes. The proposed framework addresses the current challenges and integrates simulation modeling with optimization for finding optimal machine-job allocation to maximize throughput and total energy consumption while minimizing lead time. The aluminium refinery facility in western Sydney, Australia, is used as an exemplar to validate the proposed framework.

Keywords: smart production planning, simulation-optimisation, energy aware capacity planning, energy intensive industries

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Authors: Hao Zhang, Hongyang Yu

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Scale uncertainty is a well-known challenging problem in visual SLAM. Because RGB-D sensor provides depth information, RGB-D SLAM improves this scale uncertainty problem. However, due to the limitation of physical hardware, the depth map output by RGB-D sensor usually contains a large area of missing depth values. These missing depth information affect the accuracy and robustness of RGB-D SLAM. In order to reduce these effects, this paper completes the missing area of the depth map output by RGB-D sensor and then fuses the completed dense depth map into ORB SLAM2. By adding the process of obtaining pixel-level dense depth maps, a better RGB-D visual SLAM algorithm is finally obtained. In the process of obtaining dense depth maps, a deep learning model of indoor scenes is adopted. Experiments are conducted on public datasets and real-world environments of indoor scenes. Experimental results show that the proposed SLAM algorithm has better robustness than ORB SLAM2.

Keywords: RGB-D, SLAM, dense depth, depth map

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Authors: Nelson F. F. Ebecken, Gilberto C. Pereira, Lucio P. de Andrade

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Bays, estuaries and coastal ecosystems are some of the most used and threatened natural systems globally. Its deterioration is due to intense and increasing human activities. This paper aims to monitor the socio-ecological in Brazil, model and simulate it through a multilayer network representing a DPSIR structure (Drivers, Pressures, States-Impacts-Responses) considering the concept of Management based on Ecosystems to support decision-making under the National/State/Municipal Coastal Management policy. This approach considers several interferences and can represent a significant advance in several scientific aspects. The main objective of this paper is the coupling of three different types of complex networks, the first being an ecological network, the second a social network, and the third a network of economic activities, in order to model the marine ecosystem. Multilayer networks comprise two or more "layers", which may represent different types of interactions, different communities, different points in time, and so on. The dependency between layers results from processes that affect the various layers. For example, the dispersion of individuals between two patches affects the network structure of both samples. A multilayer network consists of (i) a set of physical nodes representing entities (e.g., species, people, companies); (ii) a set of layers, which may include multiple layering aspects (e.g., time dependency and multiple types of relationships); (iii) a set of state nodes, each of which corresponds to the manifestation of a given physical node in a layer-specific; and (iv) a set of edges (weighted or not) to connect the state nodes among themselves. The edge set includes the intralayer edges familiar and interlayer ones, which connect state nodes between layers. The applied methodology in an existent case uses the Flow cytometry process and the modeling of ecological relationships (trophic and non-trophic) following fuzzy theory concepts and graph visualization. The identification of subnetworks in the fuzzy graphs is carried out using a specific computational method. This methodology allows considering the influence of different factors and helps their contributions to the decision-making process.

Keywords: marine ecosystems, complex systems, multilayer network, ecosystems management

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Authors: Hema Bhardwaj, D. Srinivasa Rao

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Technology for analysing, processing, and extracting meaningful data from enormous and complicated datasets can be termed as "big data." The technique of big data mining and big data analysis is extremely helpful for business movements such as making decisions, building organisational plans, researching the market efficiently, improving sales, etc., because typical management tools cannot handle such complicated datasets. Special computational and statistical issues, such as measurement errors, noise accumulation, spurious correlation, and storage and scalability limitations, are brought on by big data. These unique problems call for new computational and statistical paradigms. This research paper offers an overview of the literature on big data mining, its process, along with problems and difficulties, with a focus on the unique characteristics of big data. Organizations have several difficulties when undertaking data mining, which has an impact on their decision-making. Every day, terabytes of data are produced, yet only around 1% of that data is really analyzed. The idea of the mining and analysis of data and knowledge discovery techniques that have recently been created with practical application systems is presented in this study. This article's conclusion also includes a list of issues and difficulties for further research in the area. The report discusses the management's main big data and data mining challenges.

Keywords: big data, data mining, data analysis, knowledge discovery techniques, data mining challenges

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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

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Authors: Kemi Olalekan Oduntan

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This paper was carried out in order to empirical, and descriptively analysis how interest rate and foreign exchange rate liberalization influence investment decision in Nigeria. The study spanned through the period of 1985 – 2014, secondary data were restricted to relevant variables such as investment (Proxy by Gross Fixed Capital Formation) saving rate, interest rate and foreign exchange rate. Theories and empirical literature from various scholars were reviews in the paper. Ordinary Least Square regression method was used for the analysis of data collection. The result of the regression was critically interpreted and discussed. It was discovered for empirical finding that tax investment decision in Nigeria is highly at sensitive rate. Hence, all the alternative hypotheses were accepted while the respective null hypotheses were rejected as a result of interest rate and foreign exchange has significant effect on investment in Nigeria. Therefore, impact of interest rate and foreign exchange rate on the state of investment in the economy cannot be over emphasized.

Keywords: interest rate, foreign exchange liberalization, investment decision, economic growth

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Authors: Irina Canco, Amela Malaj

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It is evident that there exists a relationship between supply chain management and business performance. Surveys have showed that in many cases the manager's beliefs and expectations on supply chain management do not match the reality of the business. In this context, the study of supply chain issues is of particular importance and interest considering specifically the current period. The economic problems of this period, are present in Albania as well. The complexity of the supply chain focuses on order fulfilment. Therefore, in this paper, attention will be paid to the impact of supply chain management on business performance. The objective of the paper is to find a relationship between the good supply chain management and business performance. This research is based on the results of surveys referring to the experience of successful businesses on issues related to sustainable supply chain management and its synchronization with the provision of products and services required by the final customers. This study clearly evidenced the impact of the speed of meeting customer requirements on AMAZONA performance. This was also confirmed mathematically through one of the decision criteria in conditions of uncertainty—Laplace criterion.

Keywords: supply chain management, AMAZONA, business performance, Laplace criteria

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Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Sihem Oujdi, Hafida Belbachir

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Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed.

Keywords: C4.5 algorithm, decision trees, S-CART, spatial data mining

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Authors: Abdel Rahman Khider Hassan

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This paper describes a multi-criteria geospatial model for prediction of landslide hazard zonation (LHZ) for Lake Naivasha catchment (Kenya), based on spatial analysis of integrated datasets of location intrinsic parameters (slope stability factors) and external landslides triggering factors (natural and man-made factors). The intrinsic dataset included: lithology, geometry of slope (slope inclination, aspect, elevation, and curvature) and land use/land cover. The landslides triggering factors included: rainfall as the climatic factor, in addition to the destructive effects reflected by proximity of roads and drainage network to areas that are susceptible to landslides. No published study on landslides has been obtained for this area. Thus, digital datasets of the above spatial parameters were conveniently acquired, stored, manipulated and analyzed in a Geographical Information System (GIS) using a multi-criteria grid overlay technique (in ArcGIS 10.2.2 environment). Deduction of landslide hazard zonation is done by applying weights based on relative contribution of each parameter to the slope instability, and finally, the weighted parameters grids were overlaid together to generate a map of the potential landslide hazard zonation (LHZ) for the lake catchment. From the total surface of 3200 km² of the lake catchment, most of the region (78.7 %; 2518.4 km²) is susceptible to moderate landslide hazards, whilst about 13% (416 km²) is occurring under high hazards. Only 1.0% (32 km²) of the catchment is displaying very high landslide hazards, and the remaining area (7.3 %; 233.6 km²) displays low probability of landslide hazards. This result confirms the importance of steep slope angles, lithology, vegetation land cover and slope orientation (aspect) as the major determining factors of slope failures. The information provided by the produced map of landslide hazard zonation (LHZ) could lay the basis for decision making as well as mitigation and applications in avoiding potential losses caused by landslides in the Lake Naivasha catchment in the Kenya Highlands.

Keywords: decision making, geospatial, landslide, multi-criteria, Naivasha

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Authors: Fernando Martín-Espejo, Christophe Crombez

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This paper presents a political-economic analysis of the reforms introduced during the coronavirus crisis at the EU level with a special emphasis on the recovery fund Next Generation EU (NGEU). It also introduces a spatial model to evaluate whether the governmental features of the recovery fund can be framed inside the community method. Particularly, by evaluating the brake clause in the NGEU legislation, this paper analyses theoretically the political and legislative implications of the introduction of flexibility clauses in the EU decision-making process.

Keywords: EU, legislative procedures, spatial model, coronavirus

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Authors: Sima Aznavi, Poria Fajri, Hanif Livani

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Smart homes are small energy systems which may be equipped with renewable energy sources, storage devices, and loads. Energy management strategy plays a main role in the efficient operation of smart homes. Effective energy scheduling of the renewable energy sources and storage devices guarantees efficient energy management in households while reducing the energy imports from the grid. Nevertheless, despite such strategies, independently day ahead energy schedules for multiple households can cause undesired effects such as high power exchange with the grid at certain times of the day. Therefore, the interactions between multiple smart home day ahead energy projections is a challenging issue in a smart grid system and if not managed appropriately, the imported energy from the power network can impose additional burden on the distribution grid. In this paper, a central energy management strategy for a network consisting of multiple households each equipped with renewable energy sources, storage devices, and Plug-in Electric Vehicles (PEV) is proposed. The decision-making strategy alongside the smart home energy management system, minimizes the energy purchase cost of the end users, while at the same time reducing the stress on the utility grid. In this approach, the smart home energy management system determines different operating scenarios based on the forecasted household daily load and the components connected to the household with the objective of minimizing the end user overall cost. Then, selected projections for each household that are within the same cost range are sent to the central decision-making system. The central controller then organizes the schedules to reduce the overall peak to average ratio of the total imported energy from the grid. To validate this approach simulations are carried out for a network of five smart homes with different load requirements and the results confirm that by applying the proposed central energy management strategy, the overall power demand from the grid can be significantly flattened. This is an effective approach to alleviate the stress on the network by distributing its energy to a network of multiple households over a 24- hour period.

Keywords: energy management, renewable energy sources, smart grid, smart home

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Authors: Ghada Elshafei, Abdelazim Negm

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Life cycle assessment is a technique to assess the environmental aspects and potential impacts associated with a product, process, or service, by compiling an inventory of relevant energy and material inputs and environmental releases; evaluating the potential environmental impacts associated with identified inputs and releases; and interpreting the results to help you make a more informed decision. In this paper, the life cycle assessment of aluminum and beech wood as two commonly used materials in Egypt for window frames are heading, highlighting their benefits and weaknesses. Window frames of the two materials have been assessed on the basis of their production, energy consumption and environmental impacts. It has been found that the climate change of the windows made of aluminum and beech wood window, for a reference window (1.2m × 1.2m), are 81.7 mPt and - 52.5 mPt impacts respectively. Among the most important results are: fossil fuel consumption, potential contributions to the green building effect and quantities of solid waste tend to be minor for wood products compared to aluminum products; incineration of wood products can cause higher impacts of acidification and eutrophication than aluminum, whereas thermal energy can be recovered.

Keywords: aluminum window, beech wood window, green building, life cycle assessment, life cycle analysis, SimaPro software, window frame

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Authors: Khalid Kaddoura, Tarek Zayed

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Infrastructure assets are essential in urban cities; their purpose is to facilitate the public needs. As a result, their conditions and states shall always be monitored to avoid any sudden malfunction. Sewer systems, one of the assets, are an essential part of the underground infrastructure as they transfer sewer medium to designated areas. However, their conditions are subject to deterioration due to ageing. Therefore, it is of great significance to assess the conditions of pipelines to avoid sudden collapses. Current practices of sewer pipeline assessment rely on industrial protocols that consider distinct defects and grades to conclude the limited average or peak score of the assessed assets. This research aims to enhance the evaluation by integrating the Quality Function Deployment (QFD) and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods in assessing the condition of sewer pipelines. The methodology shall study the cause and effect relationship of the systems’ defects to deduce the relative influence weights of each defect. Subsequently, the overall grade is calculated by aggregating the WHAT’s and HOW’s of the House of Quality (HOQ) using the computed relative weights. Thus, this study shall enhance the evaluation of the assets to conclude informative rehabilitation and maintenance plans for decision makers.

Keywords: condition assessment, DEMATEL, QFD, sewer pipelines

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Authors: Ameen Alshugaa, Abul Mansur Masih

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This paper sheds light on the economic impacts of political uncertainty caused by the civil uprisings that swept the Arab World and have been collectively known as the Arab Spring. Measuring documented effects of political uncertainty on regional stock market indices, we examine the impact of the Arab Spring on the volatility of stock markets in eight countries in the Middle East and North Africa (MENA) region: Egypt, Lebanon, Jordon, United Arab Emirate, Qatar, Bahrain, Oman and Kuwait. This analysis also permits testing the existence of financial contagion among equity markets in the MENA region during the Arab Spring. To capture the time-varying and multi-horizon nature of the evidence of volatility and contagion in the eight MENA stock markets, we apply two robust methodologies on consecutive data from November 2008 to March 2014: MGARCH-DCC, Continuous Wavelet Transforms (CWT). Our results indicate two key findings. First, the discrepancies between volatile stock markets of countries directly impacted by the Arab Spring and countries that were not directly impacted indicate that international investors may still enjoy portfolio diversification and investment in MENA markets. Second, the lack of financial contagion during the Arab Spring suggests that there is little evidence of cointegration among MENA markets. Providing a general analysis of the economic situation and the investment climate in the MENA region during and after the Arab Spring, this study bear significant importance for policy makers, local and international investors, and market regulators.

Keywords: Portfolio Diversification , MENA Region , Stock Market Indices, MGARCH-DCC, Wavelet Analysis, CWT

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Authors: Desmond Agbolade Ademola

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This work aimed to introduce a theory, called Guided Energy Theory of a particle that answered questions that arise from quantum foundation, quantum mechanics theory, and interpretation such as: what is nature of wavefunction? Is mathematical formalism of wavefunction correct? Does wavefunction collapse during measurement? Do quantum physical entanglement and many world interpretations really exist? In addition, is there uncertainty in the physical reality of our nature as being concluded in the Quantum theory? We have been able to show by the fundamental analysis presented in this work that the way quantum mechanics theory, and interpretation describes nature is not correlated with physical reality. Because, we discovered amongst others that, (1) Guided energy theory of a particle fundamentally provides complete physical observable series of quantized measurement of a particle momentum, force, energy e.t.c. in a given distance and time.In contrast, quantum mechanics wavefunction describes that nature has inherited probabilistic and indeterministic physical quantities, resulting in unobservable physical quantities that lead to many worldinterpretation.(2) Guided energy theory of a particle fundamentally predicts that it is mathematically possible to determine precise quantized measurementof position and momentum of a particle simultaneously. Because, there is no uncertainty in nature; nature however naturally guides itself against uncertainty. Contrary to the conclusion in quantum mechanics theory that, it is mathematically impossible to determine the position and the momentum of a particle simultaneously. Furthermore, we have been able to show by this theory that, it is mathematically possible to determine quantized measurement of force acting on a particle simultaneously, which is not possible on the premise of quantum mechanics theory. (3) It is evidently shown by our theory that, guided energy does not collapse, only describes the lopsided nature of a particle behavior in motion. This pretty offers us insight on gradual process of engagement - convergence and disengagement – divergence of guided energy holders which further highlight the picture how wave – like behavior return to particle-like behavior and how particle – like behavior return to wave – like behavior respectively. This further proves that the particles’ behavior in motion is oscillatory in nature. The mathematical formalism of Guided energy theory shows that nature is certainty whereas the mathematical formalism of Quantum mechanics theory shows that nature is absolutely probabilistics. In addition, the nature of wavefunction is the guided energy of the wave. In conclusion, the fundamental mathematical formalism of Quantum mechanics theory is wrong.

Keywords: momentum, physical entanglement, wavefunction, uncertainty

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Authors: V. S. S. Kumar, B. Vikram

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The development of effective decision support tools that adopted in the construction industry is vital in the world we live in today, since it can lead to substantial cost reduction and efficient resource consumption. Solving the time-cost trade off problems and its related variants is at the heart of scientific research for optimizing construction planning problems. In general, the classical optimization techniques have difficulties in dealing with TCT problems. One of the main reasons of their failure is that they can easily be entrapped in local minima. This paper presents an investigation on the application of meta-heuristic techniques to two particular variants of the time-cost trade of analysis, the time-cost trade off problem (TCT), and time-cost trade off optimization problem (TCO). In first problem, the total project cost should be minimized, and in the second problem, the total project cost and total project duration should be minimized simultaneously. Finally it is expected that, the optimization models developed in this paper will contribute significantly for efficient planning and management of construction project.

Keywords: fuzzy sets, uncertainty, optimization, time cost trade off problems

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Authors: Khalid Javed

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This study examines a feature of the budget process called the tax expenditure budget. The tax expenditure concept relies heavily on a normative notion that shielding certain. Taxpayer income from taxation deprives government of its rightful revenues. This view is inconsistent with the proposition that income belongs to the taxpayers and that tax liability is determined through the democratic process, not through arbitrary, bureaucratic Assumptions. Furthermore, the methodology of the tax expenditure budget is problematic as its expansive tax base treats the multiple taxation of saving as the norm. By using an expansive view of income as the underlying assumption of the tax expenditure concept, this viewpoint institutionalizes a particular bias into the decision-making process.

Keywords: revenue, expenditure, tax budget, propostion

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Authors: Noor Azwa Azreen Binti Abd. Aziz, Muhamad Zaim Bin Mohd Rozi

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Moving towards the 4th Industrial Revolution, artificial intelligence (AI) is now more popular than ever. This subject is gaining significance every day and is continually expanding, often merging with other fields. Instead of merely being passive observers, there are benefits to understanding modern technology by delving into its inner workings. However, in a world teeming with digital information, the impact of AI on the spread of disinformation has garnered significant attention. The dissemination of inaccurate or misleading information is referred to as misinformation, posing a serious threat to democratic society, public debate, and individual decision-making. This article delves deep into the connection between AI and the dissemination of false information, exploring its potential, risks, and ethical issues as AI technology advances. The rise of AI has ushered in a new era in the dissemination of misinformation as AI-driven technologies are increasingly responsible for curating, recommending, and amplifying information on online platforms. While AI holds the potential to enhance the detection and mitigation of misinformation through natural language processing and machine learning, it also raises concerns about the amplification and propagation of false information. AI-powered deepfake technology, for instance, can generate hyper-realistic videos and audio recordings, making it increasingly challenging to discern fact from fiction.

Keywords: artificial intelligence, digital information, disinformation, ethical issues, misinformation

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Authors: Meera Tandan, Mohan Timilsina, Martin Cormican, Akke Vellinga

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Background: In general practice, many infections are treated empirically without microbiological confirmation. Understanding susceptibility of antimicrobials during empirical prescribing can be helpful to reduce inappropriate prescribing. This study aims to apply a prediction model using a decision tree approach to predict the antimicrobial resistance (AMR) of urinary tract infections (UTI) based on non-clinical features of patients over 65 years. Decision tree models are a novel idea to predict the outcome of AMR at an initial stage. Method: Data was extracted from the database of the microbiological laboratory of the University Hospitals Galway on all antimicrobial susceptibility testing (AST) of urine specimens from patients over the age of 65 from January 2011 to December 2014. The primary endpoint was resistance to common antimicrobials (Nitrofurantoin, trimethoprim, ciprofloxacin, co-amoxiclav and amoxicillin) used to treat UTI. A classification and regression tree (CART) model was generated with the outcome ‘resistant infection’. The importance of each predictor (the number of previous samples, age, gender, location (nursing home, hospital, community) and causative agent) on antimicrobial resistance was estimated. Sensitivity, specificity, negative predictive (NPV) and positive predictive (PPV) values were used to evaluate the performance of the model. Seventy-five percent (75%) of the data were used as a training set and validation of the model was performed with the remaining 25% of the dataset. Results: A total of 9805 UTI patients over 65 years had their urine sample submitted for AST at least once over the four years. E.coli, Klebsiella, Proteus species were the most commonly identified pathogens among the UTI patients without catheter whereas Sertia, Staphylococcus aureus; Enterobacter was common with the catheter. The validated CART model shows slight differences in the sensitivity, specificity, PPV and NPV in between the models with and without the causative organisms. The sensitivity, specificity, PPV and NPV for the model with non-clinical predictors was between 74% and 88% depending on the antimicrobial. Conclusion: The CART models developed using non-clinical predictors have good performance when predicting antimicrobial resistance. These models predict which antimicrobial may be the most appropriate based on non-clinical factors. Other CART models, prospective data collection and validation and an increasing number of non-clinical factors will improve model performance. The presented model provides an alternative approach to decision making on antimicrobial prescribing for UTIs in older patients.

Keywords: antimicrobial resistance, urinary tract infection, prediction, decision tree

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Authors: Majid Shahbazi

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Subclavian artery stenosis (SAS) is a common vascular disease that can cause a range of symptoms, from arm fatigue and weakness to ischemic stroke. Revascularization procedures, such as percutaneous transluminal angioplasty and stenting, are widely used to treat SAS and improve blood flow to the affected arm. However, the optimal management of patients with SAS is still unclear, and further research is needed to evaluate the safety and efficacy of different treatment options. This study aims to investigate the characteristics of patients with SAS who underwent revascularization procedures in Latvia (Specifically RAKUS). The research part of this paper aims to describe and analyze the demographics, comorbidities, diagnostic methods, types of revascularization procedures, and antiaggregant therapy used. The goal of this study is to provide insights into the current clinical practice in Latvia and help future treatment decision-makers. To achieve this aim, a retrospective study of 76 patients with SAS who underwent revascularization procedures was performed. After statistical analysis of the data, the study provided insights into the characteristics and management of patients with SAS in Latvia, highlighting the most observed comorbidities in these patients, the preferred diagnostic methods, and the most performed procedures. These findings can inform clinical decision-making and may have implications for the management of patients with subclavian artery stenosis in Latvia.

Keywords: subclavian artery stenosis, revascularization, characteristics of patients, comorbidities, retrospective analysis

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Authors: Kristina Johansen

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Participation is a universal right that all children and young people are entitled to, according to the Convention on the Rights of the Child. Social work and action research share participation as a core value. However, we have limited knowledge of how children and young people of refugee background experience taking part in participatory research. The point of departure of this presentation is a qualitative study involving young, unaccompanied refugees, addressing the issues of psychosocial health and participation. The research design included participatory methods and action research. The presentation highlights the perspectives of young, unaccompanied refugees on what made participating in the research process valuable, what created challenges for participation and what created challenges for the action part in the research process. Feedback from participants indicated that taking part in enjoyable experiences, being listened to, sharing experiences, and learning from each other contributed to making the participation valuable. At the same time, participants addressed challenges related to communication, sensitive topics, participation in decision-making and powerlessness. The presentation will end with implications for social work research and practice involving young refugees.

Keywords: participatory research, power, young unaccompanied refugeees, relationships, participation

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

Abstract:

The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Dolapo Adeyemo

Abstract:

Technology can be designed specifically for geriatrics and persons with disabilities or ICT accessibility solutions. Both solutions stand to benefit from advances in Artificial intelligence, which are computer systems that perform tasks that require human intelligence. Tasks such as decision making, visual perception, speech recognition, and even language translation are useful in both situation and will provide significant benefits to people with temporarily or permanent disabilities. This research’s goal is to review innovations focused on the use of artificial intelligence that bridges the accessibility gap in technology from a user-centered perspective. A mixed method approach that utilized a comprehensive review of academic literature on the subject combined with semi structure interviews of users, developers, and technology product owners. The internet of things and artificial intelligence technology is creating new opportunities in the assistive technology space and proving accessibility to existing technology. Device now more adaptable to the needs of the user by learning the behavior of users as they interact with the internet. Accessibility to devices have witnessed significant enhancements that continue to benefit people with disabilities. Examples of other advances identified are prosthetic limbs like robotic arms supported by artificial intelligence, route planning software for the visually impaired, and decision support tools for people with disabilities and even clinicians that provide care.

Keywords: ICT, IOT, accessibility solutions, universal design

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Authors: Nilamadhab Mohanty

Abstract:

It is essential to use message formats that make communication understandable and correct. It is because; the information format can influence consumer decision on the purchase of a product. This study combines information from qualitative inquiry, media trend analysis, eye tracking experiment, and questionnaire data to examine the impact of specific message format and consumer perceived risk on attention to the information and risk retention. We investigated the influence of message framing (goal framing, attribute framing, and mix framing) on consumer memory, study time, and decisional uncertainty while deciding on the purchase of drugs. Furthermore, we explored the impact of consumer perceived risk (associated with the use of the drug, i.e., RISK-AB and perceived risk associated with the non-use of the drug, i.e., RISK-EB) on message format preference. The study used eye-tracking methods to understand the differences in message processing. Findings of the study suggest that the message format influences information processing, and participants' risk perception impacts message format preference. Eye tracking can be used to understand the format differences and design effective advertisements.

Keywords: message framing, consumer perceived risk, advertising, eye tracking

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Authors: Corey F. Fitzgerald

Abstract:

This study provides a comprehensive examination of the biomechanical sequencing inherent in pitching motions, coupled with an advanced methodology for interpreting gathered data to inform programming strategies. The analysis is conducted utilizing state-of-the-art biomechanical laboratory equipment capable of detecting subtle changes and deviations, facilitating highly informed decision-making processes. Through this presentation, the intricate dynamics of pitching sequences are meticulously discussed to highlight the complex movement patterns accessible and actionable for performance enhancement purposes in the weight room.

Keywords: sport science, applied biomechanics, strength and conditioning, applied research

Procedia PDF Downloads 60