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  • 1.
    Ozkan-Okay, Merve
    et al.
    Ankara Univ, Dept Comp Engn, TR-06100 Golbasi, Ankara, Turkiye..
    Akin, Erdal
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Bitlis Eren Univ, Dept Comp Engn, TR-13100 Merkez, Bitlis, Turkiye..
    Aslan, Omer
    Bandirma Onyedi Eylul Univ, Dept Software Engn, TR-10250 Bandirma, Balikesir, Turkiye..
    Kosunalp, Selahattin
    Bandirma Onyedi Eylul Univ, Gonen Vocat Sch, Dept Comp Technol, TR-10250 Bandirma, Balikesir, Turkiye..
    Iliev, Teodor
    Univ Ruse, Dept Telecommun, Ruse 7017, Bulgaria..
    Stoyanov, Ivaylo
    Univ Ruse, Dept Elect Power Engn, Ruse 7017, Bulgaria..
    Beloev, Ivan
    Univ Ruse, Dept Transport, Ruse 7017, Bulgaria..
    A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions2024Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 12, s. 12229-12256Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large datasets, enabling security analysts to detect previously unknown threats. Deep learning, a subfield of ML, has shown great potential in improving the accuracy and efficiency of cybersecurity systems, particularly in image and speech recognition. On the other hand, RL is again a subfield of machine learning that trains algorithms to learn through trial and error, making it particularly effective in dynamic environments. We also evaluated the usage of ChatGPT-like AI tools in cyber-related problem domains on both sides, positive and negative. This article provides an overview of how ML, DL, and RL are applied in cybersecurity, including their usage in malware detection, intrusion detection, vulnerability assessment, and other areas. The paper also specifies several research questions to provide a more comprehensive framework to investigate the efficiency of AI and ML models in the cybersecurity domain. The state-of-the-art studies using ML, DL, and RL models are evaluated in each Section based on the main idea, techniques, and important findings. It also discusses these techniques' challenges and limitations, including data quality, interpretability, and adversarial attacks. Overall, the use of ML, DL, and RL in cybersecurity holds great promise for improving the effectiveness of security systems and enhancing our ability to protect against cyberattacks. Therefore, it is essential to continue developing and refining these techniques to address the ever-evolving nature of cyber threats. Besides, some promising solutions that rely on machine learning, deep learning, and reinforcement learning are susceptible to adversarial attacks, underscoring the importance of factoring in this vulnerability when devising countermeasures against sophisticated cyber threats. We also concluded that ChatGPT can be a valuable tool for cybersecurity, but it should be noted that ChatGPT-like tools can also be manipulated to threaten the integrity, confidentiality, and availability of data.

  • 2.
    Perez-Borrego, Yolanda A.
    et al.
    SESCAM, Hosp Nacl Paraplejicos, FENNSI Grp, Toledo 45071, Spain..
    Soto-Leon, Vanesa
    SESCAM, Hosp Nacl Paraplejicos, FENNSI Grp, Toledo 45071, Spain..
    Brocalero-Camacho, Angela
    SESCAM, Hosp Nacl Paraplejicos, FENNSI Grp, Toledo 45071, Spain.;SESCAM, Hosp Paraplejicos, Unidad Neurol, Toledo 45071, Spain..
    Oliviero, Antonio
    SESCAM, Hosp Nacl Paraplejicos, FENNSI Grp, Toledo 45071, Spain.;SESCAM, Hosp Paraplejicos, Unidad Neurol, Toledo 45071, Spain.;Hosp Los Madronos, Brunete 28690, Spain..
    Carrasco-Lopez, Carmen
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Univ Seville, Dept Anat, Seville 41009, Spain..
    A Retrospective Study on tDCS Treatment in Patients with Drug-Resistant Chronic Pain2024Ingår i: Biomedicines, E-ISSN 2227-9059, Vol. 12, nr 1, artikel-id 115Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background. Transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) has an analgesic effect superior to a placebo in chronic pain. Some years ago, tDCS was implemented at the Hospital Nacional of Paraplegics (Toledo, Spain) to treat patients with pharmacological resistance to chronic pain. Objective. The main objectives of this study with tDCS were (1) to confirm the safety of one-year treatment; (2) to estimate the number of patients after one year in treatment; (3) to describe the effects of tDCS on the pain intensity during one-year treatment; and (4) to identify factors related to treatment success. Methods. This was a retrospective study conducted at the National Hospital for Paraplegics with 155 patients with pharmacologically resistant chronic pain. Anodal tDCS was applied over the M1 for 20 min at 1.5 mA for 10 treatment sessions from Monday to Friday (Induction phase), followed by 2-3 sessions per month (Maintenance phase). Pain intensity was assessed using a Visual Analogue Scale (VAS). Results. Anodal tDCS on M1 confirmed the reduction in the pain intensity. Moreover, 58% of outpatients completed one year of treatment. Only the VAS values obtained during the baseline influenced the response to treatment. Patients with a very high VAS at the baseline were more likely to not respond adequately to tDCS treatment. Conclusions. Anodal tDCS over M1 is an adequate therapy (safe and efficient) to treat drug-resistant chronic pain. Moreover, pain intensity at the start of treatment could be a predictor of patients' continuity with tDCS for at least one year.

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  • 3.
    Khoshkangini, Reza
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..
    Tajgardan, Mohsen
    Qom Univ Technol, Fac Elect & Comp Engn, Qom, Iran..
    Mashhadi, Peyman
    Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..
    Rognvaldsson, Thorsteinn
    Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..
    Tegnered, Daniel
    Volvo Grp Connected Solut, Gothenburg, Sweden..
    Optimal Task Grouping Approach in Multitask Learning2024Ingår i: Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VI / [ed] Luo, B Wu, ZG Cheng, C Li, H Li, C, Springer, 2024, Vol. 14452, s. 206-225Konferensbidrag (Refereegranskat)
    Abstract [en]

    Multi-task learning has become a powerful solution in which multiple tasks are trained together to leverage the knowledge learned from one task to improve the performance of the other tasks. However, the tasks are not always constructive on each other in the multi-task formulation and might play negatively during the training process leading to poor results. Thus, this study focuses on finding the optimal group of tasks that should be trained together for multi-task learning in an automotive context. We proposed a multi-task learning approach to model multiple vehicle long-term behaviors using low-resolution data and utilized gradient descent to efficiently discover the optimal group of tasks/vehicle behaviors that can increase the performance of the predictive models in a single training process. In this study, we also quantified the contribution of individual tasks in their groups and to the other groups' performance. The experimental evaluation of the data collected from thousands of heavy-duty trucks shows that the proposed approach is promising.

  • 4.
    Tucker, Jason
    et al.
    Malmö universitet, Fakulteten för kultur och samhälle (KS), Institutionen för globala politiska studier (GPS).
    Lorig, Fabian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Agent-based social simulations for health crises response: utilising the everyday digital health perspective2024Ingår i: Frontiers In Public Health, ISSN 2296-2565, Vol. 11, s. 1-6, artikel-id 1337151Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There is increasing recognition of the role that artificial intelligence (AI) systems can play in managing health crises. One such approach, which allows for analysing the potential consequences of different policy interventions is agent-based social simulations (ABSS). Here, the actions and interactions of autonomous agents are modelled to generate virtual societies that can serve as a “testbed” for investigating and comparing different interventions and scenarios. This piece focuses on two key challenges of ABSS in collaborative policy interventions during the COVID-19 pandemic. These were defining valuable scenarios to simulate and the availability of appropriate data. This paper posits that drawing on the research on the “everyday” digital health perspective in designing ABSS before or during health crises, can overcome aspects of these challenges. The focus on digital health interventions reflects a rapid shift in the adoption of such technologies during and after the COVID-19 pandemic, and the new challenges this poses for policy makers. It is argued that by accounting for the everyday digital health in modelling, ABSS would be a more powerful tool in future health crisis management.

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  • 5.
    Madhusudhanan, Sheema
    et al.
    Department of Computer Science, Indian Institute of Information Technology Kottayam (IIITK), Kottayam, Kerala, India.
    Jose, Arun Cyril
    Department of Computer Science, Indian Institute of Information Technology Kottayam (IIITK), Kottayam, Kerala, India.
    Sahoo, Jayakrushna
    Department of Computer Science, Indian Institute of Information Technology Kottayam (IIITK), Kottayam, Kerala, India.
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    PRIMϵ: Novel Privacy-preservation Model with Pattern Mining and Genetic Algorithm2024Ingår i: IEEE Transactions on Information Forensics and Security, ISSN 1556-6013, E-ISSN 1556-6021, Vol. 19, s. 571-585Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper proposes a novel agglomerated privacy-preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIMϵ) delivers minimum privacy budget (ϵ) value to protect personal or sensitive data during data mining and publication. In this work, the proposed Pattern identification in the Locale of Users with Mining (PLUM) algorithm, identifies frequent patterns from dataset containing users’ sensitive data. ϵ-allocation by Differential Privacy (DP) is achieved in PRIMϵ with GA PRIMϵ , yielding a quantitative measure of privacy loss (ϵ) ranging from 0.0001 to 0.045. The proposed model maintains the trade-off between privacy and data utility with an average relative error of 0.109 on numerical data and an Earth Mover’s Distance (EMD) metric in the range between [0.2,1.3] on textual data. PRIMϵ model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when ϵ ≤ 0.5. The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIMϵ is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of O(n log n) .

  • 6.
    Ymeri, Gent
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Salvi, Dario
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Carl Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Wassenburg, Myrthe Vivianne
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Center for Neurology, Academic Specialist Center Torsplan, Region Stockholm, Sweden.
    Tsanas, Athanasios
    Usher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK; Alan Turing Institute, London, UK.
    Svenningsson, Per
    Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Center for Neurology, Academic Specialist Center Torsplan, Region Stockholm, Sweden.
    Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol2023Ingår i: BMJ Open, E-ISSN 2044-6055, Vol. 13, nr 12, artikel-id e077766Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    INTRODUCTION: The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.

    AIM: To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

    METHODS AND ANALYSIS: In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck's Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson's Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson's Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

    ETHICS AND DISSEMINATION: The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

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  • 7.
    Akin, Erdal
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Computer Engineering Department, Bitlis Eren University, Bitlis, Türkiye.
    Deep Reinforcement Learning-Based Multirestricted Dynamic-Request Transportation Framework2023Ingår i: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, s. 1-11Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Unmanned aerial vehicles (UAVs) are used in many areas where their usage is increasing constantly. Their popularity, therefore, maintains its importance in the technology world. Parallel to the development of technology, human standards, and surroundings should also improve equally. This study is developed based on the possibility of timely delivery of urgent medical requests in emergency situations. Using UAVs for delivering urgent medical requests will be very effective due to their flexible maneuverability and low costs. However, off-the-shelf UAVs suffer from limited payload capacity and battery constraints. In addition, urgent requests may be requested at an uncertain time, and delivering in a short time may be crucial. To address this issue, we proposed a novel framework that considers the limitations of the UAVs and dynamically requested packages. These previously unknown packages have source–destination pairs and delivery time intervals. Furthermore, we utilize deep reinforcement learning (DRL) algorithms, deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor–critic (A2C) to overcome this unknown environment and requests. The comprehensive experimental results demonstrate that the PPO algorithm has a faster and more stable training performance than the other DRL algorithms in two different environmental setups. Also, we implemented an extension version of a Brute-force (BF) algorithm, assuming that all requests and environments are known in advance. The PPO algorithm performs very close to the success rate of the BF algorithm.

  • 8.
    Abid, Muhammad Adil
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Amouzad Mahdiraji, Saeid
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Lorig, Fabian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Holmgren, Johan
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Petersson, Jesper
    Department of Health Care Management, Region Skåne, 21428 Malmö, Sweden; Department of Neurology, Lund University, 22242 Malmö, Sweden.
    A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment2023Ingår i: Procedia Computer Science, ISSN 1877-0509, Vol. 225, s. 3536-3545Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.

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  • 9.
    Jevinger, Åse
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). The Swedish Knowledge Centre for Public Transport, K2, Lund, Sweden.
    Zhao, Chunli
    Faculty of Engineering, Department of Technology and Society, Lund University, Lund, Sweden; The Swedish Knowledge Centre for Public Transport, K2, Lund, Sweden.
    Persson, Jan A.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). The Swedish Knowledge Centre for Public Transport, K2, Lund, Sweden.
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). The Swedish Knowledge Centre for Public Transport, K2, Lund, Sweden.
    Artificial intelligence for improving public transport: a mapping study2023Ingår i: Public Transport, ISSN 1866-749X, E-ISSN 1613-7159, s. 1-60Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

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  • 10.
    Spalazzese, Romina
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    De Sanctis, Martina
    Gran Sasso Science Institute (GSSI), L’Aquila, Italy.
    Alkhabbas, Fahed
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Shaping IoT Systems Together: The User-System Mixed-Initiative Paradigm and Its Challenges2023Ingår i: Software Architecture: 17th European Conference, ECSA 2023, Istanbul, Turkey, September 18–22, 2023, Proceedings / [ed] Bedir Tekinerdogan, Catia Trubiani, Chouki Tibermacine, Patrizia Scandurra, Carlos E. Cuesta, Springer, 2023, s. 221-229Konferensbidrag (Refereegranskat)
    Abstract [en]

    Internet of Things (IoT) systems are often complex and have to deal with many challenges at the same time, both from a human and technical perspective. In this vision paper, we (i) describe IoT-Together , the Mixed-initiative Paradigm that we devise for IoT user-system collaboration and (ii) critically analyze related architectural challenges.

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