M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / This research was initiated because of a global increase in congestion on roads and the consequent increase in the rate of fatalities on both national and international roads. Annually, 1.3 million people are killed on the roads globally, and millions are injured. It was estimated that 2.4 million people will be killed in road traffic accidents annually by 2030, and in South Africa, over 14 000 deaths were reported in 2016. A study undertaken by the American Automobile Association Foundation for Traffic Safety (AAAFTS), established in 1947 to conduct research and address growing highway safety issues, found that motorcar accidents, on average, cost the United States $300 billion per annum. In the same vain, the World Health Organisation (WHO) asserted in their 2013 Global Status Safety Report on Road Safety that by 2020, traffic accidents would become the third leading cause of death globally. In this organisation’s 2015 report, South Africa was listed as having one of the highest road fatality rates in the world, averaging 27 out of 100 000 people. Cognisance of these statistics that describe wanton loss of life and serious economic implications, among other reasons, led to the development of autonomous vehicles (AVs), such as Google and Uber’s driverless taxis and Tesla’s autonomous vehicle. Companies have invested in self-driving prototypes, and they bolster this investment with continuous research to rectify imperfections in the technologies and to enable the implementation of AVs on conventional roads. This research aimed to address issues surrounding the systems communication concept, and focused on a novel method of the routing facet of AVs by exploring the mechanisms of the virtual system of packet switching and by applying these same principles to route autonomous vehicles. This implies that automated vehicles depart from a source address and arrive at a pre-determined destination address in a manner analogous to packet switching technology in computer networking, where a data packet is allotted a source and destination address as it traverses the Open Systems Interconnection (OSI) model for open system interconnection prior to dissemination through the network. This research aimed to develop an IoT model that reduces road congestion by means of a cost effective and reliable method of routing AVs and lessen dependency on vehicle-to-vehicle (V2V) communication with their heavy and costly sensor equipment and GPS, all of which under certain conditions malfunction. At the same time, as safety remains the foremost concern, the concept aimed to reduce the human factor to a considerable degree. The researcher demonstrated this by designing a computer-simulated Internet of Things (IoT) model of the concept. Experimental research in the form of a computer simulation was adopted as the most appropriate research approach. A prototype was developed containing the algorithms that simulated the theoretical model of IoT vehicular technology. The merits of the constructed prototype were analysed and discussed, and the results obtained from the implementation exercise were shared. Analysis was conducted to verify arguments on assumptions to clarify the theory, and the outcome of the research (an IoT model encompassing vehicular wireless technologies) shows how the basic concept of packet switching can be assimilated as an effective mechanism to route large-scale autonomous vehicles within the IoT milieu, culminating in an effective commuter operating system. Controlled routing will invariably save the traveller time, provide independence to those who cannot drive, and decrease the greenhouse effect, whilst the packet switching characteristic offers greater overall security. In addition, the implications of this research will require a workforce to supplement new growth opportunities.

Background. Game developers are continuously searching for new ways of populating their vast game worlds with competent and engaging Non-Player Characters (NPCs), and researchers believe Deep Reinforcement Learning (DRL) might be the solution for emergent behavior. Consequently, fusing NPCs with DRL practices has surged in recent years, however, proposed solutions rarely outperform traditional script-based NPCs. Objectives. This thesis explores a novel method of developing an adversarial DRL NPC by combining Reinforcement Learning (RL) algorithms. Our goal is to produce an agent that surpasses its script-based opponents by first mimicking their actions. Methods. The experiment commences with Imitation Learning (IL) before proceeding with supplementary DRL training where the agent is expected to improve its strategies. Lastly, we make all agents participate in 100-deathmatch tournaments to statistically evaluate and differentiate their deathmatch performances. Results. Statistical tests reveal that the agents reliably differ from one another and that our learning agent performed poorly in comparison to its script-based opponents. Conclusions. Based on our computed statistics, we can conclude that our solution was unsuccessful in developing a talented hostile DRL agent as it was unable to convey any form of proficiency in deathmatches. No further improvements could be applied to our ML agent due to the time constraints. However, we believe our outcome can be used as a stepping-stone for future experiments within this branch of research.

Abstract  Date: 2023-05-30 Level: Master thesis in Business Administration, 15 cr  Institution: School of Business, Society and Engineering, Mälardalen University  Authors: Ramak Amyari Khamneh (84/01/29), Aymen Safi (00/03/27) Title: Artificial Intelligence as a Catalyst for Supply Chain Resilience: A Qualitative Study Comparing Scania and Volvo in the Construction Equipment Industry Supervisor: Emre Yildiz Keywords: Artificial Intelligence (AI), Supply Chain Resilience, Construction Equipment Industry, Disruptions, Agility, Redundancy Research question: How do Scania and Volvo interpret and implement Artificial Intelligence (AI) technologies to enhance supply chain resilience and mitigate disruptions in the construction equipment industry? Purpose: The purpose of this master thesis is to investigate how Scania and Volvo interpret and implement AI technologies to enhance supply chain resilience and mitigate disruptions in the construction equipment industry. Method: Qualitative Conclusion: The conclusion of the master thesis is that Scania and Volvo have successfully implemented AI technologies to enhance supply chain resilience in the construction equipment industry, despite challenges, and see AI as a critical component for future supply chain strategies.

Date: 2023-05-30 Level: Bachelor thesis in Business Administration, 15 cr Institution: School of Business, Society & Engineering, Mälardalen University Authors: Belinda Sundqvist         Jerar Ohanisian                 Shaafi Osman Ali                        00/09/08                        97/09/03                               99/02/05 Title: Utilization & impact of AI in digital marketing Examinator: Magnus Linderström  Supervisor: Stylianos Papaioannou Keywords: Artificial intelligence (AI), Digital marketing, Search engine optimization (SEO), Customer relationship management (CRM), Gathered data, Ethics, Effectiveness, Regulation Research question: How do businesses utilize AI and how does it impact digital marketing? Purpose: The purpose of this study is to examine how organizations use artificial intelligence (AI) technology in their digital marketing, and how this affects their efforts to communicate digitally. This in conjunction with any repercussions regarding ethical and legal concerns, that may occur through the utilization of AI. Method: Qualitative method  Conclusion: AI tools, especially text generators like ChatGPT, are being widely used in digital marketing, especially by small to medium-sized enterprises (SMEs). Larger businesses consider ethical and legal aspects like GDPR in AI implementation. The utilization and choice of AI tools depend on a business's needs and resources. While AI helps improve margins, reduce costs, and enhance quality, it also includes challenges like potential skill gaps. Business size matters: large businesses can invest more but struggle with implementation, whereas SMEs adapt quicker and can achieve growth through AI. The study confirms AI's profitability in business, specifically in digital marketing.

ChatGPT is a newly launched Artificial Intelligence (AI) powered model with several functions, providing the user with human-like responses. Recently, ChatGPT have gain a lot of recognition and popularity. The aim of this research is to examine the perceptions of ChatGPT from a Swedish professional service workers (PSW) perspective. More precisely, the study explores how the usage of ChatGPT in regard to supporting various work tasks is perceived. Additionally, the aim is to examine what factors could influence the perceptions regarding the model, and how the information provided is viewed by PSWs. The research uses a qualitative approach, and the data is collected through semi-structured interviews. Moreover, the study uses a thematic analysis for the analysis of data gathered. Additionally, the study uses Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the factors influencing PSWs perception of ChatGPT. The findings show that PSWs believed that ChatGPT could be used to support some of their work tasks. The model was seen as easy to use and had its benefits, such as perceived increased productivity and efficiency. However, the findings also indicate that there are several challenges that could influence the overall usage of ChatGPT. Overall, both performance expectancy and effort expectancy showed to be important factors of the evaluation of ChatGPT usage in this study. Moreover, the findings indicate that the functions and information provided by ChatGPT could influence the perceptions. For example, lack of references, lack of human touch, and security issues were found to influence the interviewed PSWs. Additionally, the study concludes that there are several perceived areas of improvements regarding ChatGPT. This research contributes with knowledge about ChatGPT from a PSWs perspective and how it could be used for work related tasks.

This dissertation reports five experiments exploring the use of AI-based smart agents to support physician-patient interactions. In each experiment, a sample of female participants evaluates video tapes of simulated physician-patient interactions in a setting involving early stage breast cancer diagnosis. Experiment 1 manipulates communication style (empathetic/impassive) for both a human physician (played by an actor) and an avatar that mimics the human. Empathetic styles elicit more liking and trust from patients and are also more persuasive. The avatar loses less than the human physician on desirable patient outcomes when communication style changes from empathetic to impassive. A mediation analysis shows that the communication style and physician type effects flow serially through liking and trust to persuasion. Experiment 2 reports an extended replication, adding a new avatar with less resemblance to the human physician. The findings match those of Experiment 1: both avatars have similar effects on liking, trust, and persuasion and are similarly anthropomorphized. Experiment 3 examines whether the patient's mindset (hope/fear about the cancer prognosis) influences likely patient outcomes. The mindset manipulation does not influence patient outcomes, but we find support for the core serial mediation model (from liking to trust to persuasion). Experiment 4 explores whether it matters how the avatar is deployed. Introducing the avatar as the physician's assistant lowers its evaluations perhaps because the patients feel deprioritized. The human physician is evaluated significantly higher on all outcome dimensions. Experiments 1-4 focused on the first phase of a standard three-phased physician-patient interaction protocol. Experiment 5 examines communication style (empathetic/ impassive) and physician type (human/avatar) effects across the three sequential phases. Patient outcomes improve monotonically over the three interaction phases across all study conditions. Overall, our studies show that an empathetic communication style is more effective in eliciting higher levels of liking, trust, and persuasion. The human physician and the avatar elicit similar levels of these desirable patient interaction outcomes. The avatar loses less when communication style changes from empathetic to impassive, suggesting that patients may have lower expectations of empathy from avatars. Thus, if carefully deployed, smart agents acting as physicians' avatars may effectively support physician-patient interactions. / Doctor of Philosophy / Healthcare professionals often have the difficult task of breaking bad news to patients. Research has shown that physician's communication style influences patient outcomes (liking, trust, persuasion, and compliance). Some physicians may adopt an impassive communication style to avoid emotional involvement with patients and some others may be overly empathetic and are prone to be perceived as inauthentic. These deficiencies persist despite an emphasis on developing physician communication skills. As in other service domains, a new generation of humanoid service robots (HSRs) offers potential for supporting physician-patient interactions. The effectiveness of such Artificial Intelligence (AI)/smart agent supported physician-patient interactions will rest, in part, on the communication style designed into the smart agents. A patient interacting with a smart agent emulating a human physician may assess different cognitive capabilities (knowledge and expertise), attribute different motivations, and make different socio-cultural appraisals than when they interact with the physician in-person. This research examines whether communication style (empathetic versus impassive) implemented via facial expression and vocal delivery elicits different patient responses when interacting with a smart agent (a physician' avatar) versus the physician in person. Findings suggest that, an empathetic (vs impassive) communication style elicits more positive patient responses, avatar physicians fare at par or better than the human physician and lose less on the patient outcomes when the communication style changes from empathetic to impassive. The avatars' appearance does not play a role in persuasion. Avatars were similarly anthropomorphized and participants' mindset (Hope/Fear) did not influence the outcomes. However, if the avatars are introduced as assistants (versus standalone physicians) there is a possibility that patients may feel downgraded/deprioritized, leading to lower evaluations for the avatars than the human physician. The contrast created when the human physician introduces the avatar may have unintended consequences that lower the avatar's evaluation. Without a direct contrast, patients may be more receptive to avatar interactions, particularly as they become more familiar in service environments. Our findings suggest that, if carefully deployed, smart agents acting as physicians' avatars may effectively support physician-patient interactions. Indeed, patients may have lower expectations of empathy from an avatar versus a human physician. This can facilitate more effective physician-patient interactions and elicit positive downstream effects on patient liking, trust and compliance.

Businesses nowadays are increasingly adopting new technologies to obtain competitive advantages. Artificial Intelligence (AI) stands out as an advanced, novel technology that has potential benefits across industries. The fast-food industry is one such industry that is highly competitive, evolving, and requires advanced technologies to cater to modern customers who increasingly demand fast, digitized services. Increased customer engagement has also become a main driving force to adopt technologies since these consumers demand quick, personalized, digitized services. The fast-food industry, compared to other industries, produces food that is perishable, and quick, which demands proper handling before, during, and after preparation, for instant consumption. Services should be quick, fast, and accessible, where adopting advanced technologies has become a necessity for the industry players' survival. Larger organizations have successfully adopted AI and have harnessed a competitive advantage. Conversely, Smaller and Medium Enterprises (SMEs) have successfully adopted digital technologies, assuming it as AI. They have not yet translated to adopt AI, which could threaten their survival and competitiveness in a highly evolving, dynamic industry. On the other hand, AI is a novel technology that has much potential, yet many are unaware of where the technology is heading, specifically, SMEs have a limited understanding and exposure to this technology, demanding more research.  The main purpose of this study is to gain a comprehensive understanding of how fast-food SMEs in Sweden perceive AI, the reasons for non-adoption, and the reasons influencing the behavioral intention to utilize AI for customer engagement within the organization. The study utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to analyze how performance expectancy, effort expectancy, social influence, and facilitating conditions influence individual SMEs' behavioral intentions towards AI adoption on customer engagement by studying it from an individual, organizational context. Through qualitative interviews with fast-food SME owners, IT managers, and marketing managers, the research explored a nuanced understanding of how AI is being perceived by SMEs, challenges, barriers, and factors influencing their adoption behavior.  The research findings indicated that AI technology itself is immature and the immediate business use case is not apparent for SMEs. It was also revealed that SMEs have a misconception between AI and digital technologies. Though there is enthusiasm and willingness to adopt AI within SMEs, significant challenges remain, such as a lack of understanding about AI, resource constraints, complexity, skills, and influences from competitors and stakeholders. The research identified factors specific to SMEs that contribute to extending the UTAUT framework, such as customized payment plans, establishing technology associations, and new business models suiting SMEs. It was further evidenced that customer engagement is not an impactful mediator that influences AI adoption within SMEs. It was concluded that though SMEs have the potential to improve performance, their adoption is limited due to the immaturity of AI and due to identified challenges.

<p dir="ltr">In network security, the exponential growth of intrusions stimulates research toward developing advanced artificial intelligence (AI) techniques for intrusion detection systems (IDS). However, the reliance on AI for IDS presents challenges, including the performance variability of different AI models and the lack of explainability of their decisions, hindering the comprehension of outputs by human security analysts. Hence, this thesis proposes end-to-end explainable AI (XAI) frameworks tailored to enhance the understandability and performance of AI models in this context.</p><p><br></p><p dir="ltr">The first chapter benchmarks seven black-box AI models across one real-world and two benchmark network intrusion datasets, laying the foundation for subsequent analyses. Subsequent chapters delve into feature selection methods, recognizing their crucial role in enhancing IDS performance by extracting the most significant features for identifying anomalies in network security. Leveraging XAI techniques, novel feature selection methods are proposed, showcasing superior performance compared to traditional approaches.</p><p><br></p><p dir="ltr">Also, this thesis introduces an in-depth evaluation framework for black-box XAI-IDS, encompassing global and local scopes. Six evaluation metrics are analyzed, including descrip tive accuracy, sparsity, stability, efficiency, robustness, and completeness, providing insights into the limitations and strengths of current XAI methods.</p><p><br></p><p dir="ltr">Finally, the thesis addresses the potential of ensemble learning techniques in improving AI-based network intrusion detection by proposing a two-level ensemble learning framework comprising base learners and ensemble methods trained on input datasets to generate evalua tion metrics and new datasets for subsequent analysis. Feature selection is integrated into both levels, leveraging XAI-based and Information Gain-based techniques.</p><p><br></p><p dir="ltr">Holistically, this thesis offers a comprehensive approach to enhancing network intrusion detection through the synergy of AI, XAI, and ensemble learning techniques by providing open-source codes and insights into model performances. Therefore, it contributes to the security advancement of interpretable AI models for network security, empowering security analysts to make informed decisions in safeguarding networked systems.<br></p>

As generative AI threatens creatives worldwide, this thesis applies Speculative Design through a Participatory Design process to speculate about the futures of AI in art by critically involving creatives. Conducting field research, interviews, and two co-design workshops, hosted at the Malmö City Library, the project resulted in an exhibition of four speculative scenarios in the form of short stories and AI-generated visualisations, sparking critical discourse and reflection about generative AI tools in art. The main insights from said discourse were that AI tools need to be investigated and critiqued through use by creatives, that designers working on AI tools have a responsibility to design for transparency, and that Speculative Design is the appropriate methodology to address AI in art, especially grounded in a Participatory Design process.

Denna studie utforskar möjligheterna att integrera Artificiell Intelligens (AI) i AstraZenecas Sweden Operations kvalitetssystem för att effektivisera processer och beslutsfattande. Genom en litteraturgenomgång analyseras hur AI kan automatisera uppgifter, förutsäga avvikelser och optimera kvalitetsledningssystem. Specifika områden inom AstraZenecas kvalitetssystem identifieras som potentiella mottagare av AI-implementering. Studien lyfter fram fördelarna men pekar också på utmaningar som datatillförlitlighet, säkerhet och etiska överväganden. Föreslagna strategier för att övervinna dessa utmaningar inkluderar investeringar i robusta säkerhetsåtgärder, etablering av tydliga etiska riktlinjer och löpande användarutbildning. Studien har också använt intervjuer och observationer med processägare inom AstraZenecas kvalitetssystem för att säkerställa ett omfattande resultat. Genom att samla in insikter och perspektiv från dem som är direkt involverade i kvalitetsprocesser, ger studien en djupare förståelse för både utmaningar och möjligheter med AI-integrationen. Denna metod stärker studiens trovärdighet och användbarhet av slutsatser och rekommendationer. Sammanfattningsvis lovar en framgångsrik AI-implementering att förbättra effektiviteten i AstraZenecas kvalitetssystem i Sverige. Ansvarsfull integrering av AI-teknologier har potential att höja kvalitetsstandarder, förbättra beslutsfattande processer och främja innovation, vilket positionerar AstraZeneca som en föregångare inom farmaceutisk excellens och framsteg. / This project investigates the potential integration of Artificial Intelligence (AI) into AstraZeneca's Sweden Operation quality system to streamline processes and decision-making. Drawing from a review of relevant literature, the analysis examines how AI can automate tasks, predict deviations, and optimize quality management processes. Specific areas within AstraZeneca's quality system are identified as potential beneficiaries of AI implementation. While acknowledging the benefits, the study also highlights challenges such as data integrity, security, and ethical considerations. Proposed strategies for overcoming these challenges include investment in robust security measures, establishment of clear ethical guidelines, and ongoing user education. Additionally, this study has utilized interviews and observations with process owners within the quality system at AstraZeneca to ensure a comprehensive result. By gathering insights and perspectives from those directly involved in quality processes, the study provides a deeper understanding of both challenges and opportunities associated with the integration of AI. This approach strengthens the credibility and usability of the study's conclusions and recommendations. In conclusion, successful AI implementation holds the promise of enhancing AstraZeneca's Sweden Operation quality system’s efficiency. Responsible integration of AI technologies has the potential to elevate quality standards, improve decision-making processes, and foster innovation, positioning AstraZeneca as a frontrunner in pharmaceutical excellence and advancement.