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Classification of a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning
(2023-04) Inamanamelluri, Haritha Venkata Sai Lakshmi; Pulipati, Venkateswara Rao; Pradhan, Nrusingha Charan; Chintamaneni, Phanikanth; Manur, Manohar; Vatambeti, Ramesh
A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive.
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Eating behaviours, menstrual history and the athletic career : a retrospective survey from adolescence to adulthood in female endurance athletes
(2023-03-02) Ravi, Suvi; Valtonen, Maarit; Ihalainen, Johanna K.; Holopainen, Elina; Kosola, Silja; Heinonen, Saara; Waller, Ben; Kujala, Urho M.; Parkkari, Jari
Aim To evaluate differences in menstrual and pubertal history and trends in eating behaviours among women with and without a competitive sports background. Additionally, we investigated if menstrual history and eating behaviours are associated with sports career-related factors. Methods This retrospective study was conducted on 100 women with a competitive endurance sports background and their age-matched, gender-matched and municipality-matched controls (n=98). Data were collected using a questionnaire using previously validated instruments. Generalised estimating equations were used to calculate associations of menstrual history and eating behaviours with outcome variables (career length, participation level, injury-related harms and career termination due to injury). Results Athletes reported higher rates of delayed puberty and menstrual dysfunction than controls. No differences between the groups were observed in the Eating Disorder Examination Questionnaire short form (EDE-QS) scores at any age. Previous disordered eating (DE) was associated with current DE in both groups. Among athletes, higher EDE-QS scores during the sports career were associated with a shorter career (B=-0.15, 95% CI -0.26 to -0.05). Secondary amenorrhoea was associated with lower participation level (OR 0.51, 95% CI 0.27 to 0.95), injury-related harms during the career (OR 4.00, 95% CI 1.88 to 8.48) and career termination due to injury (OR 1.89, 95% CI 1.02 to 3.51). Conclusion The findings indicate that DE behaviours and menstrual dysfunction, specifically secondary amenorrhoea, have a disadvantageous relationship with a sports career in women competing in endurance sports. DE during the sports career is associated with DE after the career.
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Sustainable food choices as an impression management strategy
(2023-02) Folwarczny, Michał; Otterbring, Tobias; Ares, Gastón; Department of Business and Economics
Although living standards in certain parts of the world have improved significantly in recent decades, these improvements have often been accompanied by environmental degradation that poses health risks to millions of people. Sustainable food choices play a critical role in addressing these environmental challenges. The current review summarizes how sustainable food choices are used as an impression management action aimed at projecting a desirable image of oneself to others. We show how three distinct factors — status striving, group cooperation, and the desire to attract a partner — influence the use of sustainable food as an impression management tactic. The review concludes by highlighting several future research directions based on recent research on related topics.
Verk
Intermittent Drying of Biomass: A Systematic Reassessment of Energy Efficiency
(University of Iceland, School of Engineering and Natural Sciences, Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, 2026-10-28) Nazemi, Mohiodin; Rúnar Unnþórsson; Faculty of Industrial Engineering, Mechanical Engineering and Computer Science (UI); Iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild (HÍ); School of Engineering and Natural Sciences (UI); Verkfræði- og náttúruvísindasvið (HÍ)
Drying is one of the most energy-intensive steps in the processing of high-moisture biomass, such as seaweed. In research, intermittent drying has often been reported as more energyefficient than continuous drying. However, the results of previous studies have often not been compared under equivalent conditions, making it unclear whether the reported energy savings stem from genuine advantages of intermittent drying or from methodological artefacts. The objective of this thesis is to systematically reassess the energy efficiency of intermittent drying. The research originated from studies on seaweed pelletization, where it was demonstrated that the majority of the energy in the preprocessing chain was used for drying. Therefore, a new comparison framework was developed to evaluate intermittent and continuous drying under identical operating conditions. A systematic literature review and meta-analysis of published studies were conducted. The reassessment of the results showed that many of the advantages previously attributed to intermittent drying diminish or disappear when the comparison is made on an equivalent basis. Furthermore, the results revealed that material thickness, initial moisture content, and drying temperature are the main parameters influencing the relative performance of the two drying methods. The effects of material thickness and initial moisture content were further investigated through controlled experiments using a porous sponge model, while tests with Bladderwrack were used to evaluate the framework using a representative high-moisture biomass. The results demonstrate that intermittent drying is not inherently energy-efficient; rather, its performance depends on material properties and operating conditions. The benefits of intermittent drying are most likely to occur when internal moisture transport limits the drying process. The thesis establishes a framework for the fair evaluation of intermittent drying and demonstrates that the feasibility of high-moisture biomass utilization depends strongly on drying performance, which is influenced more by material properties and operating conditions than by intermittency itself.
Verk
Hauntography: Algorithmic Presence and the Out of Joint in Music AI
(University of Iceland, School of Humanities, Faculty of Icelandic and Comparative Cultural Studies, 2026-10) privato, nicola; Magnússon, Þórhallur; Einarsson, Einar Torfi; Faculty of Icelandic and Comparative Cultural Studies (UI); Íslensku og menningardeild (HÍ); School of Humanities (UI); Hugvísindasvið
Artificial Intelligence (AI) has developed at a rapid pace over the last decade. Accessible computing power and increasingly large datasets, coupled with recent advancements in machine learning techniques, have led to its extensive application in creative domains, including music and the arts. Among AI architectures, the deep learning subset has recently gained prominence. Deep learning models feature multiple processing layers that make them capable of identifying complex statistical patterns in large amounts of data. This architectural complexity makes these systems akin to black boxes, obscuring the processes that lead to the generation of their outputs. For this reason, explainable AI (XAI) has emerged as a prominent field of investigation. While XAI research often focuses on breaking open the AI black box, this dissertation takes a different stance and investigates the creative and epistemological value of AI’s opacity, using it as a theoretical probe to query AI itself. This dissertation asks (i) how the inherent opacity of deep learning systems can come to represent a creative resource, (ii) how this perspective can contribute to reassessing shifting notions of agency, authorship and intentionality in human-AI co-creation, and (iii) how insights from artistic practice may be used to understand the general impact of AI technologies on our society. The dissertation introduces two new interfaces for musical expression (NIMEs) designed to perform with real-time neural audio synthesis (NAS), a deep learning synthesis technique that has recently come to the fore due to its high fidelity and low latency. Three original artistic works based on NAS are then offered and discussed. This corpus, together with works from other artists, is used to answer the first research question, illustrating how artists often leverage AI’s opacity as a creative resource, and increasingly project presences of different kinds onto the hidden layers of AI models. To mobilise the insights from practice and answer the second research question, the dissertation draws on hauntology theory, and through a practice-informed comparative reading of Jacques Derrida and Mark Fisher, develops a framework I term hauntography. Central to this framework are Fisher’s notions of the weird and the eerie, which through Derrida I frame as two co-constitutive modes of phenomena viewed as spectral. Hauntography thus provides an extended vocabulary applicable to creative practice and critical analysis of phenomena from the perspective of epistemological and ontological liminality. To answer the third research question, the theoretical framework is operationalised through thematic analysis of interviews with artists whose practices involve generative AI, and with whom I collaborated during the research. This analysis offers three interrelated insights. First, I observe how AI musical interfaces are often valued by artists insofar as they introduce a weird and eerie perspective on the sonic world represented by the training data. I then develop the notion of spectrophonia to describe how AI outputs bear the deferring traces of the source material, pulverised and reconfigured into something other, yet eerily familiar. Finally, I extend spectrophonia beyond music AI to reflect on AI more broadly. Through a hauntographic reading of Marx, supported by the interviews and by practice, I describe AI as a spectral commodity. While commodities typically conceal the social relations they reify, AI is structurally unable to do so, since these, inscribed in its training data, have to be re-enacted through use.

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