Forecasting Workforce Requirement for State Transportation Agencies: A Machine Learning Approach

A decline in the number of construction engineers and inspectors available at State Transportation Agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in this sector. One of the crucial aspects of workforce planning involves forecasting the required workforce for any industry or agency. This thesis developed machine learning models to estimate the person-hour requirements of STAs at the agency and project levels. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee data between 2012 and 2021. At the project level, machine learning regressors ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. At the agency level, a classic time series modeling approach, as well as neural networks-based models, were developed to forecast the monthly person-hour requirements of the agency. Parametric and non-parametric tests were employed in comparing the models across both levels. The results indicated a high performance from the random forest regressor, a tree ensemble with bagging, which recorded an average R-squared value of 0.91. The one-dimensional convolutional neural network model was the most effective model for forecasting the monthly person requirements at the agency level. It recorded an average RMSE of 4,500 person-hours monthly over short-range forecasting and an average of 5,000 person-hours monthly over long-range forecasting. These findings underscore the capability of machine learning models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management

Forecasting Workforce Requirement for State Transportation Agencies: A Machine Learning Approach

A decline in the number of construction engineers and inspectors available at State Transportation Agencies (STAs) to manage the ever-increasing lane miles has emphasized the importance of workforce planning in this sector. One of the crucial aspects of workforce planning involves forecasting the required workforce for any industry or agency. This thesis developed machine learning models to estimate the person-hour requirements of STAs at the agency and project levels. The Arkansas Department of Transportation (ARDOT) was used as a case study, using its employee data between 2012 and 2021. At the project level, machine learning regressors ranging from linear, tree ensembles, kernel-based, and neural network-based models were developed. At the agency level, a classic time series modeling approach, as well as neural networks-based models, were developed to forecast the monthly person-hour requirements of the agency. Parametric and non-parametric tests were employed in comparing the models across both levels. The results indicated a high performance from the random forest regressor, a tree ensemble with bagging, which recorded an average R-squared value of 0.91. The one-dimensional convolutional neural network model was the most effective model for forecasting the monthly person requirements at the agency level. It recorded an average RMSE of 4,500 person-hours monthly over short-range forecasting and an average of 5,000 person-hours monthly over long-range forecasting. These findings underscore the capability of machine learning models to provide more accurate workforce demand forecasts for STAs and the construction industry. This enhanced accuracy in workforce planning will contribute to improved resource allocation and management

Evolving forecasting classifications and applications in health forecasting

Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation

Supervised Learning Algorithms in Educational Data Mining: A Systematic Review

The academic institutions always looking for tools that improve their performance and enhance individuals outcomes. Due to the huge ability of data mining to explore hidden patterns and trends in the data, many researchers paid attention to Educational Data Mining (EDM) in the last decade. This field explores different types of data using different algorithms to extract knowledge that supports decision-making and academic sector development. The researchers in the field of EDM have proposed and adopted different algorithms in various directions. In this review, we have explored the published papers between 2010-2020 in the libraries (IEEE, ACM, Science Direct, and Springer) in the field of EDM are to answer review questions. We aimed to find the most used algorithm by researchers in the field of supervised machine learning in the period of 2010-2020. Additionally, we explored the most direction in the EDM and the interest of the researchers. During our research and analysis, many limitations have been examined and in addition to answering the review questions, some future works have been presented

Methods for anticipating governance breakdown and violent conflict

In this paper, authors Sarah Bressan, Håvard Mokleiv Nygård, and Dominic Seefeldt present the evolution and state of the art of both quantitative forecasting and scenario-based foresight methods that can be applied to help prevent governance breakdown and violent conflict in Europe’s neighbourhood. In the quantitative section, they describe the different phases of conflict forecasting in political science and outline which methodological gaps EU-LISTCO’s quantitative sub-national prediction tool will address to forecast tipping points for violent conflict and governance breakdown. The qualitative section explains EU-LISTCO’s scenario-based foresight methodology for identifying potential tipping points. After comparing both approaches, the authors discuss opportunities for methodological advancements across the boundaries of quantitative forecasting and scenario-based foresight, as well as how they can inform the design of strategic policy options

Land valuation using an innovative model combining machine learning and spatial context

Valuation predictions are used by buyers, sellers, regulators, and authorities to assess the fairness of the value being asked. Urbanization demands a modern and efficient land valuation system since the conventional approach is costly, slow, and relatively subjective towards locational factors. This necessitates the development of alternative methods that are faster, user-friendly, and digitally based. These approaches should use geographic information systems and strong analytical tools to produce reliable and accurate valuations. Location information in the form of spatial data is crucial because the price can vary significantly based on the neighborhood and context of where the parcel is located. In this thesis, a model has been proposed that combines machine learning and spatial context. It integrates raster information derived from remote sensing as well as vector information from geospatial analytics to predict land values, in the City of Springfield. These are used to investigate whether a joint model can improve the value estimation. The study also identifies the factors that are most influential in driving these models. A geodatabase was created by calculating proximity and accessibility to key locations as well as integrating socio-economic variables, and by adding statistics related to green space density and vegetation index utilizing Sentinel-2 -satellite data. The model has been trained using Greene County government data as truth appraisal land values through supervised machine learning models and the impact of each data type on price prediction was explored. Two types of modeling were conducted. Initially, only spatial context data were used to assess their predictive capability. Subsequently, socio-economic variables were added to the dataset to compare the performance of the models. The results showed that there was a slight difference in performance between the random forest and gradient boosting algorithm as well as using distance measures data derived from GIS and adding socioeconomic variables to them. Furthermore, spatial autocorrelation analysis was conducted to investigate how the distribution of similar attributes related to the location of the land affects its value. This analysis also aimed to identify the disparities that exist in terms of socio-economic structure and to measure their magnitude.Includes bibliographical references

Exploring Machine Learning to Improve Procurement and Purchasing Processes

Machine learning is an area of artificial intelligence that enables systems to improve their per-formance by learning from data without being purposefully programmed for the task. Learning occurs by training algorithms to identify correlations and patterns in large amounts of data, which can be then utilized to make predictions and conclusions. Machine learning has grown in popularity in recent years for a variety of commercial applications, and the purchasing process is no exception. The continuously improving computing power and data management capabili-ties of computers have enabled more sophisticated machine learning applications, which has also expanded the research work around machine learning. However, when looking at studies on the development of purchasing and purchasing processes using machine learning applica-tions, the number of publications is limited, especially for studies that have used empirical data from interviews. Thus, the aim of this research was to provide a current overview of the opportunities and po-tential challenges of implementing machine learning applications in procurement and purchas-ing processes. In addition, interviews were conducted with the aim of finding out the reasons that are preventing efficient procurement and purchasing processes, the work tasks that inter-viewees would most like to see assist from information systems, and whether machine learn-ing could offer help with perceived problems. The research methods used in this thesis were both theoretical and empirical. The theoretical part consisted of an introduction to different aspects of machine learning and procurement and purchasing processes, using available aca-demic and industry material as sources. The aim was to keep the source material as up to date as possible. Interviews were conducted with one company, involving eight participants in total. Based on the results of the research, machine learning applications that provide assist with pricing, cost analysis and forecasting of material requirements were seen especially useful. Challenges were perceived, in particular due to the poor quality of the used data, the large amount of data and the traceability of the data. Based on the interviews the low level of au-tomatization of processes, data reliability problems, forecasting material requirements, and pricing and analysing of materials were seen as challenges. The results suggested that machine learning can be used to improve purchasing and procurement processes, and the empirical research supports this conclusion.Koneoppimisella tarkoitetaan tekoälyn osa-aluetta, joka mahdollistaa järjestelmien suorituskyvyn parantamisen oppimalla datasta ilman, että sitä on tarkoituksenmukaisesti ohjelmoitu kyseisestä tehtävää varten. Oppiminen tapahtuu kouluttamalla algoritmeja tunnistamaan suurista tietomääristä korrelaatioita ja malleja, joiden perusteella pystytään luoman ennusteita sekä tekemään johtopäätöksiä. Koneoppiminen on kasvattanut viime vuosina suosiotaan erilaisten kaupallisten sovelluskohteiden muodossa, eikä myöskään hankinta- ja ostoprosessit ole tässä asissa poikkeus. Jatkuvasti parantuva tietokoneiden laskentakyky ja tiedonhallinta ovat mahdollistaneet entistä kehittyneempiä koneoppimista hyödyntäviä sovelluksia, mikä on myös laajentanut koneoppimisen ympärillä tapahtuvaa tutkimustyötä. Kuitenkin kun tarkastellaan tutkimuksia, joissa käsitellään hankinta - ja ostoprosessien kehittämistä koneoppimisen avulla on julkaisumäärä rajallista, erityisesti sellaisten tutkimusten osalta, joissa on hyödynnetty kokemusperäistä, haastatteluista saatua tietoa. Täten tämän työn tavoitteena oli tarjota ajankohtainen katsaus koneoppimista hyödyntävien sovellusten tarjoamista mahdollisuuksista ja potentiaalisista haasteista niitä hankinta ja osto prosseihin käyttöönotettaessa. Lisäksi suoritettiin haastatteluita, joiden tavoitteena oli saada selville syyt, jotka ovat esteinä tehokkaan hankinnan- ja ostoprosessien tapahtumiselle sekä mihin työtehtäviin haastateltavat toivoisivat erityisesti apua tietojärjestelmien kautta ja voisiko koneoppiminen tarjota apua koettuihin ongelmiin. Työn tutkimusmetodit olivat teoreettisia sekä empiirisiä. Teoreettinen osio koostui koneoppimisen sekä hankinnta- ja ostoprosessien eri osa-aluiden esittelystä, joiden lähdemateriaalina hyödynnettin saatavilla olevia akateemisia sekä koneoppimisen ja hankinnan ja oston alojen julkaisuja. Lähdemateriaali pyrittiin pitämään mahdollisimman ajankohtaisena. Haastattelut suoritettiin yhden yrityksen kanssa, johon otti osaa kahdeksan henkilöä. Tutkimuksen tulosten perusteella koneoppimisen sovellukset, jotka auttavat hinnoittelussa, kustannusten analysoinnissa sekä materiaalitarpeiden ennustamisessa nähtiin erityisen hyödyllisinä. Haasteina koettin ongelmat, jotka johtuivat erityisesti käytetyn datan heikosta laadusta, datan suuresta määrästä sekä datan jäljitettävyydestä. Haastatteluiden perusteella haasteina koettiin prosessien vähäinen automatisointi, datan luotettavuussongelmat, materiaalitarpeiden ennustaminen sekä materiaalien hinnoitteluun ja analysointiin liittyvät haasteet. Tuloksista voitiin tulkita, että hankinnan ja oston prosesseja voidaan kehittää koneoppimisen avulla, ja empiirinen tutkimusosio myös tukee tätä johtopäätöstä

Artificial intelligence based system for human resources appraisal

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCurrent organisations face many challenges and changes that have resulted from human and technological developments in modern society. Human capital is an active component of an organization's economic value. To benefit from this resource, an effective performance appraisal system must be designed. Such a system should provide job descriptions for all the employees. Additionally, it should contain comprehensive data about all employees in the past and present and determine their capabilities, skills, experience, and level of competence to identify the extent of their contribution to achieving business goals. Knowing the employee’s performance level enables management to identify deficiencies in their performance and work. That can help the management to improve performance by identifying appropriate training courses so they can perform their tasks efficiently and effectively. It is important for the organisation to obtain a comprehensive and clear picture of the duties and responsibilities of all its employees by having job descriptions for all its functions and having all data related to employees, their abilities, qualifications, and previous experience. This research focuses on the urgent need for human resources management systems to take advantage of technological developments using artificial intelligence as a means to achieve the goals of human resource management in accordance with the vision and mission of the organisation and, more specifically, in a fast, accurate, and more objective way. The study has been organised into six phases with regard to developing a performance appraisal system. The study began by providing job descriptions for all organisational jobs, followed by using computer programmes to enter all data related to the employees in all roles they previously held and departments they worked in within the organization. Subsequently, a questionnaire was designed and distributed to employees in many universities and companies to understand the employees' opinions about (1) the importance of job descriptions and the performance evaluation form, and (2) the importance of using Artificial intelligence in the appraisal system. The researcher inserted the artificial neural network into the applicable performance appraisal system in order to develop it. The new system has been implemented in the College of Business Administration in Jeddah. The researcher then conducted personal interviews to explore the views and experiences of managers after using the advanced system. Most of the opinions, about 80%, were in favour of using the newly developed human resources management system instead of the previous system because it showed many added benefits, such as speed, accuracy, and comprehensiveness of objective information about the employee, in addition to the ease of making the right decisions

An ensemble model for predictive energy performance:Closing the gap between actual and predicted energy use in residential buildings

The design stage of a building plays a pivotal role in influencing its life cycle and overall performance. Accurate predictions of a building's performance are crucial for informed decision-making, particularly in terms of energy performance, given the escalating global awareness of climate change and the imperative to enhance energy efficiency in buildings. However, a well-documented energy performance gap persists between actual and predicted energy consumption, primarily attributed to the unpredictable nature of occupant behavior.Existing methodologies for predicting and simulating occupant behavior in buildings frequently neglect or exclusively concentrate on particular behaviors, resulting in uncertainties in energy performance predictions. Machine learning approaches have exhibited increased accuracy in predicting occupant energy behavior, yet the majority of extant studies focus on specific behavior types rather than investigating the interactions among all contributing factors. This dissertation delves into the building energy performance gap, with a particular emphasis on the influence of occupants on energy performance. A comprehensive literature review scrutinizes machine learning models employed for predicting occupants' behavior in buildings and assesses their performance. The review uncovers knowledge gaps, as most studies are case-specific and lack a consolidated database to examine diverse behaviors across various building types.An ensemble model integrating occupant behavior parameters is devised to enhance the accuracy of energy performance predictions in residential buildings. Multiple algorithms are examined, with the selection of algorithms contingent upon evaluation metrics. The ensemble model is validated through a case study that compares actual energy consumption with the predictions of the ensemble model and an EnergyPlus simulation that takes occupant behavior factors into account.The findings demonstrate that the ensemble model provides considerably more accurate predictions of actual energy consumption compared to the EnergyPlus simulation. This dissertation also addresses the research limitations, including the reusability of the model and the requirement for additional datasets to bolster confidence in the model's applicability across diverse building types and occupant behavior patterns.In summary, this dissertation presents an ensemble model that endeavors to bridge the gap between actual and predicted energy usage in residential buildings by incorporating occupant behavior parameters, leading to more precise energy performance predictions and promoting superior energy management strategies