Search results for: decision tree classifier

Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: Seyedvahid Najafi, Viliam Makis

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In recent years, maintenance optimization has attracted special attention due to the growth of industrial systems complexity. Maintenance costs are high for many systems, and preventive maintenance is effective when it increases operations' reliability and safety at a reduced cost. The novelty of this research is to consider general repair in the modeling of multi-unit series systems and solve the maintenance problem for such systems using the semi-Markov decision process (SMDP) framework. We propose an opportunistic maintenance policy for a series system composed of two main units. Unit 1, which is more expensive than unit 2, is subjected to condition monitoring, and its deterioration is modeled using a gamma process. Unit 1 hazard rate is estimated by the proportional hazards model (PHM), and two hazard rate control limits are considered as the thresholds of maintenance interventions for unit 1. Maintenance is performed on unit 2, considering an age control limit. The objective is to find the optimal control limits and minimize the long-run expected average cost per unit time. The proposed algorithm is applied to a numerical example to compare the effectiveness of the proposed policy (policy Ⅰ) with policy Ⅱ, which is similar to policy Ⅰ, but instead of general repair, replacement is performed. Results show that policy Ⅰ leads to lower average cost compared with policy Ⅱ. 

Keywords: condition-based maintenance, proportional hazards model, semi-Markov decision process, two-unit series systems

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Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

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Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Mbuzeni Mathenjwa

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Upon their independence, most African countries adopted constitutions that proclaim respect for the rule of law. The decision to constitutionalise the rule of law is basically informed by the countries’ experience during the colonial era which was characterised by discrimination on various grounds including race, gender and religion. Despite the promise to be bound by and adhere to the rule of law, disrespect for the rule of law has become a norm in the African continent. This is evident from the reported incidence of abuse of power, failure to perform obligations imposed by law and flagrant disregard of the law by the Executive including the heads of states in the continent. In some African countries including South Africa, the courts of law have been approached to rule on the legality of the decisions of the executives, taken contrary to the prescripts of the law. South African Courts have laid down a number of decisions wherein they found that the conduct of the executive contravenes the rule of law. Consequently decisions of the executive have been declared invalid by courts. In this regard courts have become a safety net in preserving the rule of law in. Accordingly, this paper discusses the role of the courts in preserving the rule of law in Africa. This it does by explaining the notion of judicial independence and the doctrine of the rule of law. The explanation on the notion of judicial independence is relevant because only an independent judiciary can effectively review and set aside the decision of the executive including the president of a country. Furthermore, a comparative overview of the enforcement of the rule of law in African countries is done. The methods used for this research is literature review, and study of legislation and case law in selected African countries relating to the independence of the judiciary and the rule of law. Finally, a conclusion is drawn on the role of the independent judiciary to preserve the rule of law in Africa.

Keywords: Africa, constitutions, independence, judiciary

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

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While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Te Kīpa Kēpa B. Morgan, Oliver W. Mcmillan, Dylan N. Taute, Tumanako N. Fa'aui

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Epistemologies are ways of knowing. Indigenous Peoples are aware that they do not perceive and experience the world in the same way as others. So it is important when empowering Indigenous epistemologies, such as that of the New Zealand Māori, to also be able to represent a scientific understanding within the same analysis. A geothermal development assessment tool has been developed by adapting the Mauri Model Decision Making Framework. Mauri is a metric that is capable of representing the change in the life-supporting capacity of things and collections of things. The Mauri Model is a method of grouping mauri indicators as dimension averages in order to allow holistic assessment and also to conduct sensitivity analyses for the effect of worldview bias. R-shiny is the coding platform used for this Vision Mātauranga research which has created an expert decision support tool (DST) that combines a stakeholder assessment of worldview bias with an impact assessment of mauri-based indicators to determine the sustainability of proposed geothermal development. The initial intention was to develop guidelines for quantifying mātauranga Māori impacts related to geothermal resources. To do this, three typical scenarios were considered: a resource owner wishing to assess the potential for new geothermal development; another party wishing to assess the environmental and cultural impacts of the proposed development; an assessment that focuses on the holistic sustainability of the resource, including its surface features. Indicator sets and measurement thresholds were developed that are considered necessary considerations for each assessment context and these have been grouped to represent four mauri dimensions that mirror the four well-being criteria used for resource management in Aotearoa, New Zealand. Two case studies have been conducted to test the DST suitability for quantifying mātauranga Māori and other biophysical factors related to a geothermal system. This involved estimating mauri0meter values for physical features such as temperature, flow rate, frequency, colour, and developing indicators to also quantify qualitative observations about the geothermal system made by Māori. A retrospective analysis has then been conducted to verify different understandings of the geothermal system. The case studies found that the expert DST is useful for geothermal development assessment, especially where hapū (indigenous sub-tribal grouping) are conflicted regarding the benefits and disadvantages of their’ and others’ geothermal developments. These results have been supplemented with evaluations for the cumulative impacts of geothermal developments experienced by different parties using integration techniques applied to the time history curve of the expert DST worldview bias weighted plotted against the mauri0meter score. Cumulative impacts represent the change in resilience or potential of geothermal systems, which directly assists with the holistic interpretation of change from an Indigenous Peoples’ perspective.

Keywords: decision support tool, holistic geothermal assessment, indigenous knowledge, mauri model decision-making framework

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Authors: Yasser M. Saad, Heba El-Sebaie Abd El-Sadek

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Some Metapenaeus monoceros cox1 gene fragments were isolated, purified, sequenced, and comparatively analyzed with some other Crustacean Cox1 gene sequences (obtained from National Center for Biotechnology Information). This work was designed for testing the efficiency of this system in reconstruction of phylogenetic relations among some Crustacean species belonging to four genera (Metapenaeus, Artemia, Daphnia and Calanus). The single nucleotide polymorphism and haplotype diversity were calculated for all estimated mt-DNA fragments. The genetic distance values were 0.292, 0.015, 0.151, and 0.09 within Metapenaeus species, Calanus species, Artemia species, and Daphnia species, respectively. The reconstructed phylogenetic tree is clustered into some unique clades. Cytochrome oxidase subunit 1 gene (cox1) was a powerful system in reconstruction of phylogenetic relations among evaluated crustacean species.

Keywords: crustaceans, genetics, Cox1, phylogeny

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Authors: Muzaffar Shaikh, Shoaib Shaikh, Mark Moyou, Gaby Hawat

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This paper presents the use of Analytic Hierarchy Process (AHP) in evaluating the site selection of a new plant by a corporation. Due to intense competition at a global level, multinational corporations are continuously striving to minimize production and shipping costs of their products. One key factor that plays significant role in cost minimization is where the production plant is located. In the U.S. for example, labor and land costs continue to be very high while they are much cheaper in countries such as India, China, Indonesia, etc. This is why many multinational U.S. corporations (e.g. General Electric, Caterpillar Inc., Ford, General Motors, etc.), have shifted their manufacturing plants outside. The continued expansion of the Internet and its availability along with technological advances in computer hardware and software all around the globe have facilitated U.S. corporations to expand abroad as they seek to reduce production cost. In particular, management of multinational corporations is constantly engaged in concentrating on countries at a broad level, or cities within specific countries where certain or all parts of their end products or the end products themselves can be manufactured cheaper than in the U.S. AHP is based on preference ratings of a specific decision maker who can be the Chief Operating Officer of a company or his/her designated data analytics engineer. It serves as a tool to first evaluate the plant site selection criteria and second, alternate plant sites themselves against these criteria in a systematic manner. Examples of site selection criteria are: Transportation Modes, Taxes, Energy Modes, Labor Force Availability, Labor Rates, Raw Material Availability, Political Stability, Land Costs, etc. As a necessary first step under AHP, evaluation criteria and alternate plant site countries are identified. Depending upon the fidelity of analysis, specific cities within a country can also be chosen as alternative facility locations. AHP experience in this type of analysis indicates that the initial analysis can be performed at the Country-level. Once a specific country is chosen via AHP, secondary analyses can be performed by selecting specific cities or counties within a country. AHP analysis is usually based on preferred ratings of a decision-maker (e.g., 1 to 5, 1 to 7, or 1 to 9, etc., where 1 means least preferred and a 5 means most preferred). The decision-maker assigns preferred ratings first, criterion vs. criterion and creates a Criteria Matrix. Next, he/she assigns preference ratings by alternative vs. alternative against each criterion. Once this data is collected, AHP is applied to first get the rank-ordering of criteria. Next, rank-ordering of alternatives is done against each criterion resulting in an Alternative Matrix. Finally, overall rank ordering of alternative facility locations is obtained by matrix multiplication of Alternative Matrix and Criteria Matrix. The most practical aspect of AHP is the ‘what if’ analysis that the decision-maker can conduct after the initial results to provide valuable sensitivity information of specific criteria to other criteria and alternatives.

Keywords: analytic hierarchy process, multinational corporations, plant site selection, preference ratings

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Authors: David Calderon Villegas, Thomas Kaltizky

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Run-of-river (RoR) hydropower projects represent a viable, clean, and cost-effective alternative to dam-based plants and provide decentralized power production. However, RoR schemes cost-effectiveness depends on the proper selection of site and design flow, which is a challenging task because it requires multivariate analysis. In this respect, this study presents the development of an investment decision support tool for assessing the optimal size of an RoR scheme considering the technical, environmental, and cost constraints. The net present value (NPV) from a project perspective is used as an objective function for supporting the investment decision. The tool has been tested by applying it to an actual RoR project recently proposed in Colombia. The obtained results show that the optimum point in financial terms does not match the flow that maximizes energy generation from exploiting the river's available flow. For the case study, the flow that maximizes energy corresponds to a value of 5.1 m3/s. In comparison, an amount of 2.1 m3/s maximizes the investors NPV. Finally, a sensitivity analysis is performed to determine the NPV as a function of the debt rate changes and the electricity prices and the CapEx. Even for the worst-case scenario, the optimal size represents a positive business case with an NPV of 2.2 USD million and an IRR 1.5 times higher than the discount rate.

Keywords: small hydropower, renewable energy, RoR schemes, optimal sizing, objective function

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Authors: Laila Akbarali

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In the small scale study, an attempt is made to explore the structure and administrative relationships adopted by Multi Campus Universities [MCU] in UK and Pakistan and how these universities deal with some selected issues with respect to student related functions. For this study, literature on multi-site, divisionalized and other complex organizations related to business and Industry was consulted and an attempt was made to empirically test the normative models in the literature with respect to centralized , deconcentrated and decentralized structures. A questionnaire was used to gather data for this study. Purposive sampling was used. The findings of this study are somewhat different for UK and Pakistan. Contrary to a substantial body of organization theory, the results show that deconcentrated and decentralized universities in the UK are prone to delays in decision making and tend not to sensitive to local needs. In Pakistan on the other hand, deconcentrated and decentralized universities are more sensitive to local needs and there are less delays in decision making. The findings suggest that distance and reporting relationships could perhaps be responsible for the contradiction. The results also suggest that there is better coordination when the subsidiary campus sub-registrar reports to the registrar. The findings also highlight, that in both contexts, leadership at the campus level remains an issue. The results suggest that there may be factors other than structure that allow universities to keep their identity intact. The study highlights that MCU are inclined to use Information Technology and develop broad policies within which they allow their campuses to operate.

Keywords: administrative relationships, Multi-Campus, organization structure, registrar

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Authors: Shavindrie Cooray

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It is vital that students are supported in having balanced conversations about topics that might be controversial. This process is crucial to the development of critical thinking skills. This can be difficult to attain in e-learning environments, with some research finding students report a perceived loss in the quality of knowledge exchange and performance. This research investigated if Systems Theory could be applied to structure the discussion, improve information sharing, and reduce conflicts when students are working in online environments. This research involved 160 participants across four categories of student groups at a college in the Northeastern US. Each group was provided with a shared problem, and each group was expected to make a proposal for a solution. Two groups worked face-to-face; the first face to face group engaged with the problem and each other with no intervention from a facilitator; a second face to face group worked on the problem using Systems tools to facilitate problem structuring, group discussion, and decision-making. There were two types of virtual teams. The first virtual group also used Systems tools to facilitate problem structuring and group discussion. However, all interactions were conducted in a synchronous virtual environment. The second type of virtual team also met in real time but worked with no intervention. Findings from the study demonstrated that the teams (both virtual and face-to-face) using Systems tools shared more information with each other than the other teams; additionally, these teams reported an increased level of disagreement amongst their members, but also expressed more confidence and satisfaction with the experience and resulting decision compared to the other groups.

Keywords: e-learning, virtual teams, systems approach, conflicts

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Authors: Tayyab Hassan, Talha Rehman, Qasim Abdul Aziz, Ahmad Salman

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The constant monitoring of blood glucose level is necessary for maintaining health of patients and to alert medical specialists to take preemptive measures before the onset of any complication as a result of diabetes. The current clinical monitoring of blood glucose uses invasive methods repeatedly which are uncomfortable and may result in infections in diabetic patients. Several attempts have been made to develop non-invasive techniques for blood glucose measurement. In this regard, the existing methods are not reliable and are less accurate. Other approaches claiming high accuracy have not been tested on extended dataset, and thus, results are not statistically significant. It is a well-known fact that acetone concentration in breath has a direct relation with blood glucose level. In this paper, we have developed the first of its kind, reliable and high accuracy breath analyzer for non-invasive blood glucose measurement. The acetone concentration in breath was measured using MQ 138 sensor in the samples collected from local hospitals in Pakistan involving one hundred patients. The blood glucose levels of these patients are determined using conventional invasive clinical method. We propose a linear regression classifier that is trained to map breath acetone level to the collected blood glucose level achieving high accuracy.

Keywords: blood glucose level, breath acetone concentration, diabetes, linear regression

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Authors: Ling-Lang Huang

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Background: The holistic health care should ideally cover physical, mental, spiritual, and social aspects of a patient. With very constrained time in current clinical practice system, medical decisions often tip the balance in favor of evidence-based medicine (EBM) in comparison to patient's personal values. Even in the era of competence-based medical education (CBME), when scrutinizing the items of entrustable professional activities (EPAs), we found that EPAs of establishing doctor-patient relationship remained incomplete or even missing. This phenomenon prompted us to raise this project aiming at advocating value-based medicine (VBM), which emphasizes the importance of patient’s values in medical decisions. A true and effective doctor-patient communication and relationship should be a well-balanced harmony of EBM and VBM. By constructing VBM into current EPAs, we can further promote genuine shared decision making (SDM) and fix the missing link in holistic health care. Methods: In this project, we are going to find out EPA elements crucial for establishing an ideal doctor-patient relationship through three distinct pairs of doctor-patient relationships: patients with pulmonary arterial hypertension (relatively young but with grave disease), patients undergoing surgery (facing critical medical decisions), and patients with terminal diseases (facing forthcoming death). We’ll search for important EPA elements through the following steps: 1. Narrative approach to delineate patients’ values among 2. distinct groups. 3.Hermeneutics-based interview: semi-structured interview will be conducted for both patients and physicians, followed by qualitative analysis of collected information by compiling, disassembling, reassembling, interpreting, and concluding. 4. Preliminarily construct those VBM elements into EPAs for doctor-patient relationships in 3 groups. Expected Outcomes: The results of this project are going to give us invaluable information regarding the impact of patients’ values, while facing different medical situations, on the final medical decision. The competence of well-blending and -balanced both values from patients and evidence from clinical sciences is the missing link in holistic health care and should be established in future EPAs to enhance an effective SDM.

Keywords: value-based medicine, shared decision making, entrustable professional activities, holistic health care

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Authors: Aya Belal, Ahmed Maher Eltair, Maggie Ahmed Mashaly

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Geopolymer concrete is gaining recognition as a sustainable alternative to conventional Portland Cement concrete due to its environmentally friendly nature, which is a key goal for Smart City initiatives. It has demonstrated its potential as a reliable material for the design of structural elements. However, the production of Geopolymer concrete is hindered by batch-to-batch variations, which presents a significant challenge to the widespread adoption of Geopolymer concrete. To date, Machine learning has had a profound impact on various fields by enabling models to learn from large datasets and predict outputs accurately. This paper proposes an integration between the current drift to Artificial Intelligence and the composition of Geopolymer mixtures to predict their mechanical properties. This study employs Python software to develop machine learning model in specific Decision Trees. The research uses the percentage oxides and the chemical composition of the Alkali Solution along with the curing conditions as the input independent parameters, irrespective of the waste products used in the mixture yielding the compressive strength of the mix as the output parameter. The results showed 90 % agreement of the predicted values to the actual values having the ratio of the Sodium Silicate to the Sodium Hydroxide solution being the dominant parameter in the mixture.

Keywords: decision trees, geopolymer concrete, machine learning, smart cities, sustainability

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Authors: Maryam Ghoreishi, Christian Larsen

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In this paper, we investigate the effect of stochastic inputs on problem of joint optimal pricing and lot-sizing decisions where the inventory cycle is divided into advance and spot sales periods. During the advance sales period, customer can make reservations while customer with reservations can cancel their order. However, during the spot sales period customers receive the order as soon as the order is placed, but they cannot make any reservation or cancellation during that period. We assume that the inter arrival times during the advance sales and spot sales period are exponentially distributed where the arrival rate is decreasing function of price. Moreover, we assume that the number of cancelled reservations is binomially distributed. In addition, we assume that deterioration process follows an exponential distribution. We investigate two cases. First, we consider two-state case where we find the optimal price during the spot sales period and the optimal price during the advance sales period. Next, we develop a generalized case where we extend two-state case also to allow dynamic prices during the spot sales period. We apply the Markov decision theory in order to find the optimal solutions. In addition, for the generalized case, we apply the policy iteration algorithm in order to find the optimal prices, the optimal lot-size and maximum advance sales amount.

Keywords: inventory control, pricing, Markov decision theory, advance sales system

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Authors: C. M. Balaceanu, G. Suciu, C. S. Bosoc, O. Orza, C. Fernandez, Z. Viniczay

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The Internet of Things (IoT) represents the best option for smart vineyard applications, even if it is necessary to integrate the technologies required for the development. This article is based on the research and the results obtained in the DISAVIT project. For Smart Agriculture, the project aims to provide a trustworthy, intelligent, integrated vineyard management solution that is based on the IoT. To have interoperability through the use of a multiprotocol technology (being the future connected wireless IoT) it is necessary to adopt an agnostic approach, providing a reliable environment to address cyber security, IoT-based threats and traceability through blockchain-based design, but also creating a concept for long-term implementations (modular, scalable). The ones described above represent the main innovative technical aspects of this project. The DISAVIT project studies and promotes the incorporation of better management tools based on objective data-based decisions, which are necessary for agriculture adapted and more resistant to climate change. It also exploits the opportunities generated by the digital services market for smart agriculture management stakeholders. The project's final result aims to improve decision-making, performance, and viticulturally infrastructure and increase real-time data accuracy and interoperability. Innovative aspects such as end-to-end solutions, adaptability, scalability, security and traceability, place our product in a favorable situation over competitors. None of the solutions in the market meet every one of these requirements by a unique product being innovative.

Keywords: blockchain, IoT, smart agriculture, vineyard

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

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Authors: Mariann Veresne Somosi, Zoltan Nagy, Krisztina Varga

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Innovation is the key element of economic development and a key factor in social processes. Technical innovations can be identified as prerequisites and causes of social change and cannot be created without the renewal of society. The study of social innovation can be characterised as one of the significant research areas of our day. The study’s aim is to identify the process of social innovation, which can be defined by input, transformation, and output factors. This approach divides the social innovation process into three parts: situation analysis, implementation, follow-up. The methods associated with each stage of the process are illustrated by the chronological line of social innovation. In this study, we have sought to present methodologies that support long- and short-term decision-making that is easy to apply, have different complementary content, and are well visualised for different user groups. When applying the methods, the reference objects are different: county, district, settlement, specific organisation. The solution proposed by the study supports the development of a methodological combination adapted to different situations. Having reviewed metric and conceptualisation issues, we wanted to develop a methodological combination along with a change management logic suitable for structured support to the generation of social innovation in the case of a locality or a specific organisation. In addition to a theoretical summary, in the second part of the study, we want to give a non-exhaustive picture of the two counties located in the north-eastern part of Hungary through specific analyses and case descriptions.

Keywords: factors of social innovation, methodological combination, social innovation process, supporting decision-making

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Mohd Khairezan Rahmat

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Since the impetus of becoming a develop country by the year 2020, the Malaysian government have been proactive in strengthening the integration of ICT into the national educational system. Teacher-education programs have the responsibility to prepare the nation future teachers by instilling in them the desire, confidence, and ability to fully utilized the potential of ICT into their instruction process. In an effort to fulfill this responsibility, teacher-education program are beginning to create alternatives means for preparing cutting-edge teachers. One of the alternatives is the student’s learning portal. In line with this mission, this study investigates the Faculty of Education, University Teknologi MARA (UiTM) pre-service teachers’ perception of usefulness, attitude, and ability toward the usage of the university learning portal, known as iLearn. The study also aimed to predict factors that might hinder the pre-service teachers’ decision to used iLearn as their platform in learning. The Structural Equation Modeling (SEM), was employed in analyzed the survey data. The suggested findings informed that pre-service teacher’s successful integration of the iLearn was highly influenced by their perception of usefulness of the system. The findings also suggested that the more familiar the pre-service teacher with the iLearn, the more possibility they will use the system. In light of similar study, the present findings hope to highlight the important to understand the user’s perception toward any proposed technology.

Keywords: e-learning, prediction factors, pre-service teacher, structural equation modeling (SEM)

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Authors: S. Aswath, Chinmaya Krishna Tilak, Amal Suresh, Ganesh Udupa

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There are technologies to control a humanoid robot in many ways. But the use of Electromyogram (EMG) electrodes has its own importance in setting up the control system. The EMG based control system helps to control robotic devices with more fidelity and precision. In this paper, development of an electromyogram based interface for human gesture recognition for the control of a humanoid robot is presented. To recognize control signs in the gestures, a single channel EMG sensor is positioned on the muscles of the human body. Instead of using a remote control unit, the humanoid robot is controlled by various gestures performed by the human. The EMG electrodes attached to the muscles generates an analog signal due to the effect of nerve impulses generated on moving muscles of the human being. The analog signals taken up from the muscles are supplied to a differential muscle sensor that processes the given signal to generate a signal suitable for the microcontroller to get the control over a humanoid robot. The signal from the differential muscle sensor is converted to a digital form using the ADC of the microcontroller and outputs its decision to the CM-530 humanoid robot controller through a Zigbee wireless interface. The output decision of the CM-530 processor is sent to a motor driver in order to control the servo motors in required direction for human like actions. This method for gaining control of a humanoid robot could be used for performing actions with more accuracy and ease. In addition, a study has been conducted to investigate the controllability and ease of use of the interface and the employed gestures.

Keywords: electromyogram, gesture, muscle sensor, humanoid robot, microcontroller, Zigbee

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Authors: Ummeh Saika, Toshio Kikuchi

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Population growth and urbanization is a global phenomenon. As the rapid progress of technology, many cities in the international community are facing serious problems of urbanization. There is no doubt that the urbanization will proceed to have significant impact on the ecology, economy and society at local, regional, and global levels. The inhabitants of Dhaka city suffer from lack of proper urban facilities. The green spaces are needed for different functional and leisure activities of the urban dwellers. Again growing densification, a number of green space are transferred into open space in the Dhaka city. As a result greenery of the city's decreases gradually. Moreover, the existing green space is frequently threatened by encroachment. The role of green space, both at community and city level, is important to improve the natural environment and social ties for future generations. Therefore, it seems that the green space needs to be more effective for public interaction. The main objective of this study is to address the effectiveness of urban green space (Urban Park) of Dhaka City. Two approaches are selected to fulfill the study. Firstly, analyze the long-term spatial changes of urban green space using GIS and secondly, investigate the relationship of urban park network with physical and social environment. The case study site covers eight urban parks of Dhaka metropolitan area of Bangladesh. Two aspects (Physical and Social) are applied for this study. For physical aspect, satellite images and aerial photos of different years are used to find out the changes of urban parks. And for social aspect, methods are used as questionnaire survey, interview, observation, photographs, sketch and previous information of parks to analyze about the social environment of parks. After calculation of all data by descriptive statistics, result is shown by maps using GIS. According to physical size, parks of Dhaka city are classified into four types: Small, Medium, Large and Extra Large parks. The observed result showed that the physical and social environment of urban parks varies with their size. In small size parks physical environment is moderate by newly tree plantation and area expansion. However, in medium size parks physical environment are poor, example- tree decrease, exposed soil increase. On the other hand, physical environment of large size and extra large size parks are in good condition, because of plenty of vegetation and well management. Again based on social environment, in small size parks people mainly come from surroundings area and mainly used as waiting place. In medium-size parks, people come to attend various occasion from different places. In large size and extra large size parks, people come from every part of the city area for tourism purpose. Urban parks are important source of green space. Its influence both physical and social environment of urban area. Nowadays green space area gradually decreases and transfer into open space. The consequence of this research reveals that changes of urban parks influence both physical and social environment and also impact on urban life.

Keywords: physical environment, social environment, urban life, urban parks

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Authors: Ellen Bryant, Virginia Behmer, Rebecca Garbed, Jeanette O’Quin, Dana Howard

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As it currently stands, veterinary care in the United States is not accessible to everyone, and veterinarians regularly face cases of clients who are unable to provide necessary care to their animals regardless of the client’s desire to do so. There is currently limited research into how veterinarians address these issues of access to care. It is apparent that veterinarians regularly utilize funding or offer discounted services to treat cases that otherwise would go without care. With need currently exceeding the amount of funds and services available, veterinarians are tasked with deciding which cases are most deserving of assistance. This mixed methods study distributed a survey to companion animal veterinarians practicing in the United States to identify current trends in how these professionals apply principles of distributive justice in the scope of veterinary medicine. Ethical frameworks identified in human bioethics research into distributive justice were presented, along with demographic questions, to identify relationships between veterinarian priorities and the scope of their practice/respective roles/geographic region. By surveying veterinarians across a wide range of specialties, practice types, and clientele this study was able to assess how priorities and opinions shift based on external factors as well as among the respondents themselves. Participants were asked not only to choose how to distribute aid between different clients and case scenarios, but also asked directly which is the best way to distribute aid when need exceeds the resources available.

Keywords: access to veterinary care, bioethics, decision-making, distributive justice, subsidized care

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Authors: Seyedmirsajad Mokhtarimousavi, Danial Talebi, Hamidreza Asgari

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Assigning aircrafts’ activities to appropriate gates is one the most challenging issues in airport authorities’ multiple criteria decision making. The potential financial loss due to imbalances of demand and supply in congested airports, higher occupation rates of gates, and the existing restrictions to expand facilities provide further evidence for the need for an optimal supply allocation. Passengers walking distance, towing movements, extra fuel consumption (as a result of awaiting longer to taxi when taxi conflicts happen at the apron area), etc. are the major traditional components involved in GAP models. In particular, the total cost associated with gate assignment problem highly depends on the airport terminal layout. The study herein presents a well-elaborated literature review on the topic focusing on major concerns, applicable variables and objectives, as well as proposing a three-objective mathematical model for the gate assignment problem. The model has been tested under different concourse layouts in order to check its performance in different scenarios. Results revealed that terminal layout pattern is a significant parameter in airport and that the proposed model is capable of dealing with key constraints and objectives, which supports its practical usability for future decision making tools. Potential solution techniques were also suggested in this study for future works.

Keywords: airport management, terminal layout, gate assignment problem, mathematical modeling

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Authors: Huei-Wen Lin, Huei-Fu Lu

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Event sport tourism (EST) has become an especially important economic sector around the world. As the magnitude continues to grow, attracting more tourists, media, and investment for the host community, and many local areas/regions and states have identified the expenditures by visitors as a potential source of economic or employment growth. The main purposes of this study are to investigate stakeholders’ insights into the feature of hosting EST and using them as a regional development strategy. Continuing the focus of previous literature on the regional development and economic benefits by hosting EST, a total of fıve semi-structured interview questions are designed and a thematic analysis is employed to conduct with eight key sport and tourism decision makers in Atlanta during July to August 2016. Through the depth interviews, the study will contribute to a better understanding of stakeholders’ decision-making, identifying benefits and constraints as well as leveraging the impacts of hosting EST. These findings have provided stakeholders’ perspectives of hosting EST and using them as a reference of regional development in emerging sport tourism markets in the US. Additionally, this study examines key considerations and issues that affect and are critical to reliable understanding of the economic impacts of hosting EST on the regional development, and it will be able to benefit future management authorities (i.e. governments and communities) in their sport tourism development endeavors in defining and hosting successful EST. Furthermore, the insights gained from the qualitative analysis could help other cities/regions analyzing the economic impacts of hosting EST and using it as an instrument of city development strategy.

Keywords: economic impacts, event sport tourism, regional economic development, longitudinal analysis

Procedia PDF Downloads 304

Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

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Authors: Claudineia Brazil, Elison Eduardo Bierhals, Francisco Pereira, José Leandro Néris, Matheus Rippel, Luciane Salvi

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The global concern about climate change has been increasing, since the emission of gases from human activities contributes to the greenhouse effect in the atmosphere, indicating significant impacts to the planet in the coming years. The study of precipitation regime is fundamental for the development of research in several areas. Among them are hydrology, agriculture, and electric sector. Using the climatic projections of the models belonging to the CMIP5, the main objective of the paper was to present an analysis of the impacts of climate change without rainfall in the Uruguay River basin. After an analysis of the results, it can be observed that for the future climate, there is a tendency, in relation to the present climate, for larger numbers of dry events, mainly in the winter months, changing the pluviometric regime for wet summers and drier winters. Given this projected framework, it is important to note the importance of adequate management of the existing water sources in the river basin, since the value of rainfall is reduced for the next years, it may compromise the dynamics of the ecosystems in the region. Facing climate change is fundamental issue for regions and cities all around the world. Society must improve its resilience to phenomenon impacts, and spreading the knowledge among decision makers and citizens is also essential. So, these research results can be subsidies for the decision-making in planning and management of mitigation measures and/or adaptation in south Brazil.

Keywords: climate change, hydrological potential, precipitation, mitigation

Procedia PDF Downloads 339

Authors: Watheq Mansour, Waleed Bin Owais, Mohammad Basheer Kotit, Khaled Khan

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Efficient query processing is one of the utmost requisites in any business environment to satisfy consumer needs. This paper investigates the various types of indexing models, viz. primary, secondary, and multi-level. The investigation is done under the ambit of various types of queries to which each indexing model performs with efficacy. This study also discusses the inherent advantages and disadvantages of each indexing model and how indexing models can be chosen based on a particular environment. This paper also draws parallels between various indexing models and provides recommendations that would help a Database administrator to zero-in on a particular indexing model attributed to the needs and requirements of the production environment. In addition, to satisfy industry and consumer needs attributed to the colossal data generation nowadays, this study has proposed two novel indexing techniques that can be used to index highly unstructured and structured Big Data with efficacy. The study also briefly discusses some best practices that the industry should follow in order to choose an indexing model that is apposite to their prerequisites and requirements.

Keywords: indexing, hashing, latent semantic indexing, B-tree

Procedia PDF Downloads 153

Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

Procedia PDF Downloads 323