Energy conservation and decarbonization of the building stock is a way to achieve sustainable development goals. Visualizing and monitoring energy consumption with a help of Decision Support Systems (DSS) can help to inform and support making decisions to conserve energy, reduce emissions, save costs and improve overall quality of life. However, there are no clear guidelines to how such tools should be designed, and which demands from the different stakeholders they should meet. This literature review presents an overview of existing DSSs that calculate, optimize, visualize and monitor energy usage in buildings. A total of 22 studies have been selected through an in-depth literature search and analysed in a study matrix split into four categories describing relevant features that are vital for each DSS. The study has identified that main functions of analysed DSSs are: 1) to compare costs for CO2 emission reduction or energy saving for various actions; and 2) to compare current energy performance of buildings. Finally, it has shown a variety of needs for different stakeholders that affect the choice of methods and data used by DSS. Hence it is crucial to ensure early alignment of the needs and functions for the developed tools, in order to be efficient in decision-support for better energy efficiency and climate mitigation. / Energibesparing och koldioxidminskning av befintliga byggnader är ett sätt att uppnå de globala målen. Att visualisera och övervaka energiförbrukningen i byggnader med hjälp av ett beslutsstödsystem kan hjälpa till att informera och stödja beslut för att spara energi, minska utsläppen, minska kostnaderna och överlag öka livskvalitén. Det finns dock inga tydliga riktlinjer för hur ett verktyg kan utformas och vilka behov som behöver tillfredsställas från de olika intressenterna. Denna litteraturstudie presenterar en översikt över befintliga beslutsstödsystem som beräknar, optimerar, visualiserar och övervakar energianvändningen i byggnader. Genom en ingående litteratursökning valdes 22 olika studier för en djupare analys med hjälp av en studiematris delad inom fyra olika kategorier som beskriver relevanta funktioner som är viktiga för varje beslutsstödsystem. Studien har identifierat att huvuduppgifterna för varje analyserad beslutsstödsystem är: 1) att jämföra kostnader för CO2 minskning och energibesparing för olika åtgärder; och 2) att jämföra aktuell energiprestanda av byggnader. Slutligen har det visat att olika behov från intressenter styrt de val av metoder och data som används i respektive beslutsstödsystem. Därför är det viktigt att säkerställa en tidig anpassning av behov och huvuduppgifter för de utvecklade verktygen för att vara effektiva i beslutsstöd kring energieffektivisering och klimatpåverkan.

The increasing demand for healthcare services, coupled with the challenges of managing budgets and navigating complex regulations, has underscored the need for sustainable and efficient healthcare delivery. In response to this pressing issue, this thesis aims to optimize hospital efficiency using Artificial Intelligence (AI) techniques. The focus extends beyond improving surgical intraoperative time to encompass preoperative and postoperative periods as well. The research presents a novel Prescriptive Analytics System (PAS) designed to enhance the Surgical Success Rate (SSR) in surgeries and specifically in high volume arthroplasty. The SSR is a critical metric that reflects the successful completion of 4-surgeries during an 8-hour timeframe. By leveraging AI, the developed PAS has the potential to significantly improve the SSR from its current rate of 39% at The Ottawa Hospital to a remarkable 100%. The research is structured around five peer-reviewed journal papers, each addressing a specific aspect of the optimization of surgical efficiency. The first paper employs descriptive analytics to examine the factors influencing delays and overtime pay during surgeries. By identifying and analyzing these factors, insights are gained into the underlying causes of surgery inefficiencies. The second paper proposes three frameworks aimed at improving Operating Room (OR) throughput. These frameworks provide structured guidelines and strategies to enhance the overall efficiency of surgeries, encompassing preoperative, intraoperative, and postoperative stages. By streamlining the workflow and minimizing bottlenecks, the proposed frameworks have the potential to significantly optimize surgical operations. The third paper outlines a set of actions required to transform a selected predictive system into a prescriptive one. By integrating AI algorithms with decision support mechanisms, the system can offer actionable recommendations to surgeons during surgeries. This transformative step holds tremendous potential in enhancing surgical outcomes while reducing time. The fourth paper introduces a benchmarking and monitoring system for the selected framework that predicts SSR. Leveraging historical data, this system utilizes supervised machine learning algorithms to forecast the likelihood of successful outcomes based on various surgical team and procedural parameters. By providing real-time monitoring and predictive insights, surgeons can proactively address potential risks and improve decision-making during surgeries. Lastly, an application paper demonstrates the practical implementation of the prescriptive analytics system. The case study highlights how the system optimizes the allocation of resources and enables the scheduling of additional surgeries on days with a high predicted SSR. By leveraging the system's capabilities, hospitals can maximize their surgical capacity and improve overall patient care.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system. / Applied Science University (ASU) of Jordan

Pediatric obesity is skyrocketing nowadays worldwide. Therefore, helping and supporting clinicians in curing children is needed. As Health IT is soaring thanks to the emergence of a cutting-edge technology, treatments can now be followed closely and daily to give a personalized therapy. Thus, this thesis investigates how to build a clinical decision support tool to address this issue. The study was carried with the company Evira by following the user-centered design thinking method. The user research, carried out with semi-structured interviews at Martina Barnsjukhuset and Capio Vårdcentral Zinkensdamm, provided the users needs. A prototype was developed with considering this user research and requirements. Next, it was tested by experts during task-based and semi-structured interviews. This evaluation phase concluded that the prototype was intuitive and effective. By using different metrics, it allowed clinicians to see the activity of the patients, the irregularities and variation of their weight, their weighting habits, and the gender distribution of the patients. The final tool corrected some of the requests obtained during the evaluation. Thus, the proposed interface could support the decision of clinicians to improve treatment effectiveness and enable better resource planning. Further research can still be carried out in order to fine-tune this tool. / Fetma hos barn ökar kraftigt i dag över hela världen. Därför behövs det hjälp och stöd till kliniker för att behandla barn. Eftersom hälso- och sjukvårdsinformatik är på frammarsch tack vare framväxten av en banbrytande teknik kan behandlingar nu följas noga och dagligen för att ge en personlig behandling. I denna avhandling undersöks värdet av ett kliniskt beslutsstöd för att lösa detta problem. Studien genomfördes tillsammans med företaget Evira genom att följa metoden för användarcentrerat designtänkande. Användar- forskningen, som genomfördes med semistrukturerade intervjuer på Martina Barnsjukhuset och Capio Vårdcentral Zinkensdamm, gav användarnas behov. En prototyp utvecklades med hänsyn till denna användarundersökning och dessa krav. Därefter testades den av experter under uppgiftsbaserade och semistrukturerade intervjuer. I denna utvärderingsfas konstaterades att prototypen var intuitiv och effektiv. Den gjorde det möjligt för kliniker att se patienternas aktivitet och effektiviteten i behandlingen. Det slutliga verktyget korrigerade några av de önskemål som framkom under utvärderingen. Det föreslagna gränssnittet ökade således behandlingseffektiviteten och möjliggjorde bättre resursplanering. Ytterligare forskning kan fortfarande utföras för att finslipa detta verktyg.

<p dir="ltr">Climate change hazards are becoming more frequent and severe and their impact on Department of Defense installations has become a matter of national security. This thesis investigates the intricate relationship between climate change hazards and the Department of Defense (DoD) by examining the multifaceted impacts of environmental shifts on military operations, infrastructure, and strategic planning. As the global climate continues to undergo unpredictable changes, the DoD faces evolving challenges that extend beyond traditional security concerns.</p><p dir="ltr">The research employs a multidisciplinary approach, integrating environmental science and analysis with military strategy to assess the current and anticipated hazards posed by climate change. As the beginning of a multi-year project, this thesis examines extreme weather events in relation to their potential to disrupt critical military assets and installations in Yuma County Arizona.</p><p dir="ltr">Additionally, decision support systems were created and analyzed as part of this thesis in order to provide Department of Defense decision-makers with a tool to create personalized and up to date visuals and data. This support tool could have positive implications for force readiness, mission effectiveness, and strategic planning, recognizing climate change as a pervasive and dynamic threat.</p><p dir="ltr">The study also delves into the strategic response of the DoD to climate change hazards, evaluating adaptation measures, resilience-building initiatives, and the integration of climate considerations into defense planning processes. By examining historical and future conditions, the research identifies areas where these installations could implement changes in order to enhance climate resilience and efficiency within the defense framework.</p><p dir="ltr">In conclusion, this thesis provides an understanding of the intricate interplay between climate change hazards and national security, focusing on their direct and indirect impacts on two military installations in Arizona. By shedding light on the complexities of this relationship, it contributes to the growing body of knowledge essential for developing adaptive strategies and policies that ensure the readiness and effectiveness of the military in the face of a changing climate.</p>

Dagens samhälle genomsyras av olika teknologier som påverkar människan, ekonomin och samhället. Numera besitter verksamheter stora datamängder som behöver registreras och struktureras och detta kan göras med hjälp av ett beslutstödsystem som ger underlag till beslutsfattare. I och med den stora och fortsatt ökade datamängden som behöver registreras och analyseras begränsas människans kognitiva förmåga till att hantera all denna data på egen hand. Som en lösning till denna problematik kan Artificial Intelligence (AI) användas. Syftet med denna forskningsstudie är att undersöka hur AI kan påverka beslutstödsystem inom verksamhetsstyrning. Fokuset grundar sig inom tre aspekter- planering, analys och uppföljning. För att besvara forskningsmålet har en djupare analys genomförts i form av en kvalitativ ansats, där sex deltagare intervjuats. Dessa intervjuer var semistrukturerade och enbart personer med välinsatta kunskaper inom beslutsstödsystem och AI har deltagit. Litteraturstudien, som baseras på tidigare forskning om AI och beslutsstödsystem och hur dessa två fungerar tillsammans, gav en grund för analysering, förklaring och diskussion tillsammans med den empiriska insamlade datan. Med hjälp av den insamlade datan kunde sedan intervjuerna analyseras tematiskt. Resultatet visade att AI:ns förmågor medför fördelar hos verksamheter genom ökadprestanda, effektivisering av strukturerad och ostrukturerad data samt frigör tid hosbeslutsfattare. Baserat på resultatet belystes det att beslutstödsystem kan enbart göra relativt enkla analyser på strukturerad data medan den är begränsad på ostruktureraddata. I samband med detta framhäver resultatet att verksamheter behöver även bearbeta extern information såsom omvärldsbevakningen. På så sätt ger teknologin användarna bästa möjliga beslutsunderlag samt då ett bredare beslutsunderlag tas fram och vidare förbättra planering, analys och uppföljning genom att AI kan analysera och samla in data från olika datakällor. Däremot behöver människan alltid vara närvarande i beslutsfattandet då AI besitter också begränsningar vilket kankompletteras med människan. / In today's society there are various technologies which affect people, the economy and society. Nowadays, businesses have large amounts of data that need to be registered and structured and this can be done with the help of a decision support system that provides information to decision makers. With the large amount of data being established even today, the human cognitive ability is limited to handling allthis data on their own. As a solution to this problem, AI can be used. The purpose of this research study is to investigate how AI can affect decision support systems in operations management. The focus is based on three aspects -planning, analysis and follow-up. In order to answer the research aim, a qualitative approach has been used where six participants were interviewed. These interviews were semi-structured and only people with well-versed knowledge in decision support systems and AI have participated. The literature study, which is based on previous research on AI and decision support systems and how the two work together, provided a basis for analysis, explanation and discussion along with the empirical data collected. As a result of the collected data, the interviews were analyzed thematically. The result showed that AI's capabilities bring benefits to businesses through increased performance, efficiency of structured and unstructured data and frees uptime for decision makers. Based on the results, it was highlighted that decision support systems can only perform relatively simple analyzes on structured data while it is limited on unstructured data. In connection with this, the result highlights that businesses also need to process external information such as monitoring of the environment. In this way, the technology provides users with the best possible basis for decision-making. This is because a broader basis for decision-making is produced and therefore improves planning, analysis and follow-up as AI can analyze and collect data from different data sources. However, the human always needs to be present in the decision-making as AI also has limitations which can be supplemented with the human.

No description available.

No description available.

No description available.