Browsing by Subject "machine learning"

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  • Koivisto, Maria (Helsingin yliopisto, 2020)
    Immunohistochemistry (IHC) is a widely used research tool for detecting antigens and can be used in medical and biochemical research. The co-localization of two separate proteins is sometimes crucial for analysis, requiring a double staining. This comes with a number of challenges since staining results depend on the pre-treatment of samples, host-species where the antibody was raised and spectral differentiation of the two proteins. In this study, the proteins GABAR-α2 and CAMKII were stained simultaneously to study the expression of the GABA receptor in hippocampal pyramidal cells. This was performed in PGC-1α transgenic mice, possibly expressing GABAR-α2 excessively compared to wildtype mice. Staining optimization was performed regarding primary and secondary antibody concentration, section thickness, antigen retrieval and detergent. Double staining was performed successfully and proteins of interest were visualized using a confocal microscope after which image analyses were performed using two different methods: 1) a traditional image analysis based on intensity and density of stained dots and 2) a novel convolutional neural network (CNN) machine learning approach. The traditional image analysis did not detect any differences in the stained brain slices, whereas the CNN model showed an accuracy of 72% in categorizing the images correctly as transgenic/wildtype brain slices. The results from the CNN model imply that GABAR-α2 is expressed differently in PGC-1α transgenic mice, which might impact other factors such as behaviour and learning. This protocol and the novel method of using CNN as an image analysis tool can be of future help when performing IHC analysis on brain neuronal studies.
  • Tolonen, Mikko; Lahti, Leo; Ilomäki, Niko (2015)
    This article analyses publication trends in the field of history in early modern Britain and North America in 1470–1800, based on English Short- Title Catalogue (ESTC) data. Its major contribution is to demonstrate the potential of digitized library catalogues as an essential scholastic tool and part of reproducible research. We also introduce a novel way of quantitatively analysing a particular trend in book production, namely the publishing of works in the field of history. The study is also our first experimental analysis of paper consumption in early modern book production, and dem- onstrates in practice the importance of open-science principles for library and information science. Three main research questions are addressed: 1) who wrote history; 2) where history was published; and 3) how publishing changed over time in early modern Britain and North America. In terms of our main findings we demonstrate that the average book size of history publications decreased over time, and that the octavo-sized book was the rising star in the eighteenth century, which is a true indication of expand- ing audiences. The article also compares different aspects of the most popu- lar writers on history, such as Edmund Burke and David Hume. Although focusing on history, these findings may reflect more widespread publishing trends in the early modern era. We show how some of the key questions in this field can be addressed through the quantitative analysis of large-scale bibliographic data collections.
  • Huertas, Andres (Helsingin yliopisto, 2020)
    Investment funds are continuously looking for new technologies and ideas to enhance their results. Lately, with the success observed in other fields, wealth managers are taking a closes look at machine learning methods. Even if the use of ML is not entirely new in finance, leveraging new techniques has proved to be challenging and few funds succeed in doing so. The present work explores de usage of reinforcement learning algorithms for portfolio management for the stock market. It is well known the stochastic nature of stock and aiming to predict the market is unrealistic; nevertheless, the question of how to use machine learning to find useful patterns in the data that enable small market edges, remains open. Based on the ideas of reinforcement learning, a portfolio optimization approach is proposed. RL agents are trained to trade in a stock exchange, using portfolio returns as rewards for their RL optimization problem, thus seeking optimal resource allocation. For this purpose, a set of 68 stock tickers in the Frankfurt exchange market was selected, and two RL methods applied, namely Advantage Actor-Critic(A2C) and Proximal Policy Optimization (PPO). Their performance was compared against three commonly traded ETFs (exchange-traded funds) to asses the algorithm's ability to generate returns compared to real-life investments. Both algorithms were able to achieve positive returns in a year of testing( 5.4\% and 9.3\% for A2C and PPO respectively, a European ETF (VGK, Vanguard FTSE Europe Index Fund) for the same period, reported 9.0\% returns) as well as healthy risk-to-returns ratios. The results do not aim to be financial advice or trading strategies, but rather explore the potential of RL for studying small to medium size stock portfolios.
  • Nygren, Saara (Helsingin yliopisto, 2020)
    A relational database management system’s configuration is essential while optimizing database performance. Finding the optimal knob configuration for the database requires tuning of multiple interdependent knobs. Over the past few years, relational database vendors have added machine learning models to their products and Oracle announced the first autonomous (i.e self-driving) database in 2017. This thesis clarifies the autonomous database concept and surveys the latest research on machine learning methods for relational database knob tuning. The study aimed to find solutions that can tune multiple database knobs and be applied to any relational database. The survey found three machine learning implementations that tune multiple knobs at a time. These are called OtterTune, CDBTune, and QTune. Ottertune uses traditional machine learning techniques, while CDBTune and QTune rely on deep reinforcement learning. These implementations are presented in this thesis, along with a discussion of the features they offer. The thesis also presents an autonomic system’s basic concepts like self-CHOP and MAPE-K feedback loop and a knowledge model to define the knowledge needed to implement them. These can be used in the autonomous database contexts along with Intelligent Machine Design and Five Levels of AI-Native Database to present requirements for the autonomous database.
  • Mouchlis, Varnavas D.; Afantitis, Antreas; Serra, Angela; Fratello, Michele; Papadiamantis, Anastasios G.; Aidinis, Vassilis; Lynch, Iseult; Greco, Dario; Melagraki, Georgia (2021)
    De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.
  • Lohilahti, Jonne Antti Kristian (Helsingin yliopisto, 2022)
    Tavoitteet. Tämän tutkimuksen tavoitteena on arvioida tunteiden havaitsemisen mahdollisuutta arkielämässä puettavien laitteiden ja koneoppimismallien avulla. Tunnetiloilla on tärkeä rooli päätöksenteossa, havaitsemisessa ja käyttäytymisessä, mikä tekee objektiivisesta tunnetilojen havaitsemisesta arvokkaan tavoitteen, sekä mahdollisten sovellusten että tunnetiloja koskevan ymmärryksen syventämisen kannalta. Tunnetiloihin usein liittyy mitattavissa olevia fysiologisia ja käyttäymisen muutoksia, mikä mahdollistaa koneoppimismallien kouluttamisen muutoksia aiheuttaneen tunnetilan havaitsemiseksi. Suurin osa tunteiden havaitsemiseen liittyvästä tutkimuksesta on toteutettu laboratorio-olosuhteissa käyttämällä tunteita herättäviä ärsykkeitä tai tehtäviä, mikä herättää kysymyksen siitä että yleistyvätkö näissä olosuhteissa saadut tulokset arkielämään. Vaikka puettavien laitteiden ja kännykkäkyselyiden kehittyminen on helpottanut aiheen tutkimista arkielämässä, tutkimusta tässä ympäristössä on vielä niukasti. Tässä tutkimuksessa itseraportoituja tunnetiloja ennustetaan koneoppimismallien avulla arkielämässä havaittavissa olevien tunnetilojen selvittämiseksi. Lisäksi tutkimuksessa käytetään mallintulkintamenetelmiä mallien hyödyntämien yhteyksien tunnistamiseksi. Metodit. Aineisto tätä tutkielmaa varten on peräisin tutkimuksesta joka suoritettiin osana Helsingin Yliopiston ja VTT:n Sisu at Work projektia, missä 82:ta tietotyöläistä neljästä suomalaisesta organisaatiosta tutkittiin kolmen viikon ajan. Osallistujilla oli jakson aikana käytettävissään mittalaitteet jotka mittasivat fotoplethysmografiaa (PPG), ihon sähkönjohtavuutta (EDA) ja kiihtyvyysanturi (ACC) signaaleita, lisäksi heille esitettiin kysymyksiä koetuista tunnetiloista kolmesti päivässä puhelinsovelluksen avulla. Signaalinkäsittelymenetelmiä sovellettiin signaaleissa esiintyvien liikeartefaktien ja muiden ongelmien korjaamiseksi. Sykettä (HR) ja sykevälinvaihtelua (HRV) kuvaavia piirteitä irroitettiin PPG signaalista, fysiologista aktivaatiota kuvaavia piirteitä EDA signaalista, sekä liikettä kuvaavia piirteitä ACC signaalista. Seuraavaksi koneoppimismalleja koulutettiin ennustamaan raportoituja tunnetiloja irroitetujen piirteiden avulla. Mallien suoriutumista vertailtiin suhteessa odotusarvoihin havaittavissa olevien tunnetilojen määrittämiseksi. Lisäksi permutaatiotärkeyttä sekä Shapley additive explanations (SHAP) arvoja hyödynnettiin malleille tärkeiden yhteyksien selvittämiseksi. Tulokset ja johtopäätökset. Mallit tunnetiloille virkeä, keskittynyt ja innostunut paransivat suoriutumistaan yli odotusarvon, joista mallit tunnetilalle virkeä paransivat suoriutumista tilastollisesti merkitsevästi. Permutaatiotärkeys korosti liike- ja HRV-piirteiden merkitystä, kun SHAP arvojen tarkastelu nosti esiin matalan liikkeen, matalan EDA:n, sekä korkean HRV:n merkityksen mallien ennusteille. Nämä tulokset ovat lupaavia korkean aktivaation positiivisten tunnetilojen havaitsemiselle arkielämässä, sekä nostavat esiin mahdollisia yhteyksiä jatkotutkimusta varten.
  • Niemi, Hannele (2021)
    This special issue raises two thematic questions: (1) How will AI change learning in the future and what role will human beings play in the interaction with machine learning, and (2), What can we learn from the articles in this special issue for future research? These questions are reflected in the frame of the recent discussion of human and machine learning. AI for learning provides many applications and multimodal channels for supporting people in cognitive and non-cognitive task domains. The articles in this special issue evidence that agency, engagement, self-efficacy, and collaboration are needed in learning and working with intelligent tools and environments. The importance of social elements is also clear in the articles. The articles also point out that the teacher's role in digital pedagogy primarily involves facilitating and coaching. AI in learning has a high potential, but it also has many limitations. Many worries are linked with ethical issues, such as biases in algorithms, privacy, transparency, and data ownership. This special issue also highlights the concepts of explainability and explicability in the context of human learning. We need much more research and research-based discussion for making AI more trustworthy for users in learning environments and to prevent misconceptions.
  • Kibble, Milla; Khan, Suleiman A.; Ammad-ud-din, Muhammad; Bollepalli, Sailalitha; Palviainen, Teemu; Kaprio, Jaakko; Pietiläinen, Kirsi H.; Ollikainen, Miina (2020)
    We combined clinical, cytokine, genomic, methylation and dietary data from 43 young adult monozygotic twin pairs (aged 22-36 years, 53% female), where 25 of the twin pairs were substantially weight discordant (delta body mass index > 3 kg m(-2)). These measurements were originally taken as part of the TwinFat study, a substudy of The Finnish Twin Cohort study. These five large multivariate datasets (comprising 42, 71, 1587, 1605 and 63 variables, respectively) were jointly analysed using an integrative machine learning method called group factor analysis (GFA) to offer new hypotheses into the multi-molecular-level interactions associated with the development of obesity. New potential links between cytokines and weight gain are identified, as well as associations between dietary, inflammatory and epigenetic factors. This encouraging case study aims to enthuse the research community to boldly attempt new machine learning approaches which have the potential to yield novel and unintuitive hypotheses. The source code of the GFA method is publically available as the R package GFA.
  • Itkonen, Sami (Helsingin yliopisto, 2020)
    Sanayhdistelmät ovat useamman sanan kombinaatioita, jotka ovat jollakin tavalla jähmeitä ja/tai idiomaattisia. Tutkimuksessa tarkastellaan suomen kielen verbaalisia idiomeja sanaupotusmenetelmän (word2vec) avulla. Työn aineistona käytetään Gutenberg-projektista haettuja suomenkielisiä kirjoja. Työssä tutkitaan pääosin erityisesti idiomeja, joissa esiintyy suomen kielen sana ‘silmä’. Niiden idiomaattisuutta mitataan komposiittisuuden (kuinka hyvin sanayhdistelmän merkitys vastaa sen komponenttien merkitysten kombinaatiota) ja jähmeyttä leksikaalisen korvaustestin avulla. Vastaavat testit tehdään myös sanojen sisäisen rakenteen huomioonottavan fastText-algoritmin avulla. Työssä on myös luotu Gutenberg-korpuksen perusteella pienehkö luokiteltu lausejoukko, jota lajitellaan neuroverkkopohjaisen luokittelijan avulla. Tämä lisäksi työssä tunnustellaan eri ominaisuuksien kuten sijamuodon vaikutusta idiomin merkitykseen. Mittausmenetelmien tulokset ovat yleisesti ottaen varsin kirjavia. fastText-algoritmin suorituskyky on yleisesti ottaen hieman parempi kuin perusmenetelmän; sen lisäksi sanaupotusten laatu on parempi. Leksikaalinen korvaustesti antaa parhaimmat tulokset, kun vain lähin naapuri otetaan huomioon. Sijamuodon todettiin olevan varsin tärkeä idiomin merkityksen määrittämiseen. Mittauksien heikot tulokset voivat johtua monesta tekijästä, kuten siitä, että idiomien semanttisen läpinäkyvyyden aste voi vaihdella. Sanaupotusmenetelmä ei myöskään normaalisti ota huomioon sitä, että myös sanayhdistelmillä voi olla useita merkityksiä (kirjaimellinen ja idiomaattinen/kuvaannollinen). Suomen kielen rikas morfologia asettaa menetelmälle myös ylimääräisiä haasteita. Tuloksena voidaan sanoa, että sanaupotusmenetelmä on jokseenkin hyödyllinen suomen kielen idiomien tutkimiseen. Testattujen mittausmenetelmien käyttökelpoisuus yksin käytettynä on rajallinen, mutta ne saattaisivat toimia paremmin osana laajempaa tutkimusmekanismia.
  • Lehtonen, Leevi (Helsingin yliopisto, 2021)
    Quantum computing has an enormous potential in machine learning, where problems can quickly scale to be intractable for classical computation. A Boltzmann machine is a well-known energy-based graphical model suitable for various machine learning tasks. Plenty of work has already been conducted for realizing Boltzmann machines in quantum computing, all of which have somewhat different characteristics. In this thesis, we conduct a survey of the state-of-the-art in quantum Boltzmann machines and their training approaches. Primarily, we examine variational quantum Boltzmann machine, a specific variant of quantum Boltzmann machine suitable for the near-term quantum hardware. Moreover, as variational quantum Boltzmann machine heavily relies on variational quantum imaginary time evolution, we effectively analyze variational quantum imaginary time evolution to a great extent. Compared to the previous work, we evaluate the execution of variational quantum imaginary time evolution with a more comprehensive collection of hyperparameters. Furthermore, we train variational quantum Boltzmann machines using a toy problem of bars and stripes, representing more multimodal probability distribution than the Bell states and the Greenberger-Horne-Zeilinger states considered in the earlier studies.
  • Ng, Wai Tong; But, Barton; Choi, Horace C. W.; de Bree, Remco; Lee, Anne W. M.; Lee, Victor H. F.; Lopez, Fernando; Mäkitie, Antti A.; Rodrigo, Juan P.; Saba, Nabil F.; Tsang, Raymond K. Y.; Ferlito, Alfio (2022)
    Introduction: Nasopharyngeal carcinoma (NPC) is endemic to Eastern and South-Eastern Asia, and, in 2020, 77% of global cases were diagnosed in these regions. Apart from its distinct epidemiology, the natural behavior, treatment, and prognosis are different from other head and neck cancers. With the growing trend of artificial intelligence (AI), especially deep learning (DL), in head and neck cancer care, we sought to explore the unique clinical application and implementation direction of AI in the management of NPC. Methods: The search protocol was performed to collect publications using AI, machine learning (ML) and DL in NPC management from PubMed, Scopus and Embase. The articles were filtered using inclusion and exclusion criteria, and the quality of the papers was assessed. Data were extracted from the finalized articles. Results: A total of 78 articles were reviewed after removing duplicates and papers that did not meet the inclusion and exclusion criteria. After quality assessment, 60 papers were included in the current study. There were four main types of applications, which were auto-contouring, diagnosis, prognosis, and miscellaneous applications (especially on radiotherapy planning). The different forms of convolutional neural networks (CNNs) accounted for the majority of DL algorithms used, while the artificial neural network (ANN) was the most frequent ML model implemented. Conclusion: There is an overall positive impact identified from AI implementation in the management of NPC. With improving AI algorithms, we envisage AI will be available as a routine application in a clinical setting soon.
  • Hämäläinen, Kreetta (Helsingin yliopisto, 2021)
    Personalized medicine tailors therapies for the patient based on predicted risk factors. Some tools used for making predictions on the safety and efficacy of drugs are genetics and metabolomics. This thesis focuses on identifying biomarkers for the activity level of the drug transporter organic anion transporting polypep-tide 1B1 (OATP1B1) from data acquired from untargeted metabolite profiling. OATP1B1 transports various drugs, such as statins, from portal blood into the hepatocytes. OATP1B1 is a genetically polymorphic influx transporter, which is expressed in human hepatocytes. Statins are low-density lipoprotein cholesterol-lowering drugs, and decreased or poor OATP1B1 function has been shown to be associated with statin-induced myopathy. Based on genetic variability, individuals can be classified to those with normal, decreased or poor OATP1B1 function. These activity classes were employed to identify metabolomic biomarkers for OATP1B1. To find the most efficient way to predict the activity level and find the biomarkers that associate with the activity level, 5 different machine learning models were tested with a dataset that consisted of 356 fasting blood samples with 9152 metabolite features. The models included both a Random Forest regressor and a classifier, Gradient Boosted Decision Tree regressor and classifier, and a Deep Neural Network regressor. Hindrances specific for this type of data was the collinearity between the features and the large amount of features compared to the number of samples, which lead to issues in determining the important features of the neural network model. To adjust to this, the data was clustered according to their Spearman’s rank-order correlation ranks. Feature importances were calculated using two methods. In the case of neural network, the feature importances were calculated with permutation feature importance using mean squared error, and random forest and gradient boosted decision trees used gini impurity. The performance of each model was measured, and all classifiers had a poor ability to predict decreasead and poor function classes. All regressors performed very similarly to each other. Gradient boosted decision tree regressor performed the best by a slight margin, but random forest regressor and neural network regressor performed nearly as well. The best features from all three models were cross-referenced with the features found from y-aware PCA analysis. The y-aware PCA analysis indicated that 14 best features cover 95% of the explained variance, so 14 features were picked from each model and cross-referenced with each other. Cross-referencing highest scoring features reported by the best models found multiple features that showed up as important in many models.Taken together, machine learning methods provide powerful tools to identify potential biomarkers from untargeted metabolomics data.
  • Garcia Moreno-Esteva, Enrique; White, Sonia L. J.; Wood, Joanne M.; Black, Alex A. (2018)
    In this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements were explored using machine learning approaches. Two different techniques of machine-learning were used (Bayesian and K-Means) to obtain separate model sequences or average scanpaths for those children who responded either correctly or incorrectly to the graph task. Application of these machine-learning approaches indicated distinct differences in the resulting scanpaths for children who completed the graph task correctly or incorrectly: children who responded correctly accessed information that was mostly categorised as critical, whereas children responding incorrectly did not. There was also evidence that the children who were correct accessed the graph information in a different, more logical order, compared to the children who were incorrect. The visual behaviours aligned with different aspects of graph comprehension, such as initial understanding and orienting to the graph, and later interpretation and use of relevant information on the graph. The findings are discussed in terms of the implications for early mathematics teaching and learning, particularly in the development of graph comprehension, as well as the application of machine learning techniques to investigations of other visual-cognitive behaviours.
  • Xu, Yongjun; Liu, Xin; Cao, Xin; Huang, Changping; Liu, Enke; Qian, Sen; Liu, Xingchen; Wu, Yanjun; Dong, Fengliang; Qiu, Cheng-Wei; Qiu, Junjun; Hua, Keqin; Su, Wentao; Wu, Jian; Xu, Huiyu; Han, Yong; Fu, Chenguang; Yin, Zhigang; Liu, Miao; Roepman, Ronald; Dietmann, Sabine; Virta, Marko; Kengara, Fredrick; Zhang, Ze; Zhang, Lifu; Zhao, Taolan; Dai, Ji; Yang, Jialiang; Lan, Liang; Luo, Ming; Liu, Zhaofeng; An, Tao; Zhang, Bin; He, Xiao; Cong, Shan; Liu, Xiaohong; Zhang, Wei; Lewis, James P.; Tiedje, James M.; Wang, Qi; An, Zhulin; Wang, Fei; Zhang, Libo; Huang, Tao; Lu, Chuan; Cai, Zhipeng; Wang, Fang; Zhang, Jiabao (2021)
    Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.
  • Tanoli, Ziaurrehman; Vähä-Koskela, Markus; Aittokallio, Tero (2021)
    Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means. Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication. Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.
  • Romppainen, Jonna (Helsingin yliopisto, 2020)
    Surface diffusion in metals can be simulated with the atomistic kinetic Monte Carlo (KMC) method, where the evolution of a system is modeled by successive atomic jumps. The parametrisation of the method requires calculating the energy barriers of the different jumps that can occur in the system, which poses a limitation to its use. A promising solution to this are machine learning methods, such as artificial neural networks, which can be trained to predict barriers based on a set of pre-calculated ones. In this work, an existing neural network based parametrisation scheme is enhanced by expanding the atomic environment of the jump to include more atoms. A set of surface diffusion jumps was selected and their barriers were calculated with the nudged elastic band method. Artificial neural networks were then trained on the calculated barriers. Finally, KMC simulations of nanotip flattening were run using barriers which were predicted by the neural networks. The simulations were compared to the KMC results obtained with the existing scheme. The additional atoms in the jump environment caused significant changes to the barriers, which cannot be described by the existing model. The trained networks also showed a good prediction accuracy. However, the KMC results were in some cases more realistic or as realistic as the previous results, but often worse. The quality of the results also depended strongly on the selection of training barriers. We suggest that, for example, active learning methods can be used in the future to select the training data optimally.
  • Mukhtar, Usama (Helsingin yliopisto, 2020)
    Sales forecasting is crucial for run any retail business efficiently. Profits are maximized if popular products are available to fulfill the demand. It is also important to minimize the loss caused by unsold stock. Fashion retailers face certain challenges which make sales forecasting difficult for the products. Some of these challenges are the short life cycle of products and introduction of new products all around the year. The goal of this thesis is to study forecasting methods for fashion. We use the product attributes for products in a season to build a model that can forecast sales for all the products in the next season. Sales for different attributes are analysed for three years. Sales for different variables vary for values which indicate that a model fitted on product attributes may be used for forecasting sales. A series of experiments are conducted with multiple variants of the datasets. We implemented multiple machine learning models and compared them against each other. Empirical results are reported along with the baseline comparisons to answer research questions. Results from first experiment indicate that machine learning models are almost doing as good as the baseline model that uses mean values as predictions. The results may improve in the upcoming years when more data is available for training. The second experiment shows that models built for specific product groups are better than the generic models that are used to predict sales for all kinds of products. Since we observed a heavy tail in the data, a third experiment was conducted to use logarithmic sales for predictions, and the results do not improve much as compared to results from previous methods. The conclusion of the thesis is that machine learning methods can be used for attribute-based sales forecasting in fashion industry but more data is needed, and modeling specific groups of products bring better results.
  • Kumar, Mukkesh; Ang, Li Ting; Png, Hang; Ng, Maisie; Tan, Karen; Loy, See Ling; Tan, Kok Hian; Chan, Jerry Kok Yen; Godfrey, Keith M.; Chan, Shiao-yng; Chong, Yap Seng; Eriksson, Johan G.; Feng, Mengling; Karnani, Neerja (2022)
    The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A(1c) (HbA(1c)), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA(1c) was positively associated with increased risks of GDM (p = 0.001, odds ratio (95% CI) 1.34 (1.13-1.60)) and preterm birth (p = 0.011, odds ratio 1.63 (1.12-2.38)). Optimal control of preconception HbA(1c) may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception.
  • Kulkarni, Ritwik; Di Minin, Enrico (2021)
    1. As resources for conservation are limited, gathering and analysing information from digital platforms can help investigate the global biodiversity crisis in a cost-efficient manner. Development and application of methods for automated content analysis of digital data sources are especially important in the context of investigating human-nature interactions. 2. In this study, we introduce novel application methods to automatically collect and analyse textual data on species of conservation concern from digital platforms. An end-to-end pipeline is constructed that begins from searching and downloading news articles about species listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) along with news articles from specific Twitter handles and proceeds with implementing natural language processing and machine learning methods to filter and retain only relevant articles. A crucial aspect here is the automatic annotation of training data, which can be challenging in many machine learning applications. A Named Entity Recognition model is then used to extract additional relevant information for each article. 3. The data collected over a 1-month period included 15,088 articles focusing on 585 species listed in Appendix I of CITES. The accuracy of the neural network to detect relevant articles was 95.91% while the Named Entity recognition model helped extract information on prices, location and quantities of traded animals and plants. A regularly updated database, which can be queried and analysed for various research purposes and to inform conservation decision making, is generated by the system. 4. The results demonstrate that natural language processing can be used successfully to extract information from digital text content. The proposed methods can be applied to multiple digital data platforms at the same time and used to investigate human-nature interactions in conservation science and practice.
  • Ärje, Johanna; Melvad, Claus; Jeppesen, Mads Rosenhoj; Madsen, Sigurd Agerskov; Raitoharju, Jenni; Rasmussen, Maria Strandgård; Iosifidis, Alexandros; Tirronen, Ville; Gabbouj, Moncef; Meissner, Kristian; Hoye, Toke Thomas (British Ecological Society, 2020)
    Methods in Ecology and Evolution 11 8 (2020)
    1. Understanding how biological communities respond to environmental changes is a key challenge in ecology and ecosystem management. The apparent decline of insect populations necessitates more biomonitoring but the time-consuming sorting and expert-based identification of taxa pose strong limitations on how many insect samples can be processed. In turn, this affects the scale of efforts to map and monitor invertebrate diversity altogether. Given recent advances in computer vision, we propose to enhance the standard human expert-based identification approach involving manual sorting and identification with an automatic image-based technology. 2. We describe a robot-enabled image-based identification machine, which can automate the process of invertebrate sample sorting, specimen identification and biomass estimation. We use the imaging device to generate a comprehensive image database of terrestrial arthropod species which is then used to test classification accuracy, that is, how well the species identity of a specimen can be predicted from images taken by the machine. We also test sensitivity of the classification accuracy to the camera settings (aperture and exposure time) to move forward with the best possible image quality. We use state-of-the-art Resnet-50 and InceptionV3 convolutional neural networks for the classification task. 3. The results for the initial dataset are very promising as we achieved an average classification accuracy of 0.980. While classification accuracy is high for most species, it is lower for species represented by less than 50 specimens. We found significant positive relationships between mean area of specimens derived from images and their dry weight for three species of Diptera. 4. The system is general and can easily be used for other groups of invertebrates as well. As such, our results pave the way for generating more data on spatial and temporal variation in invertebrate abundance, diversity and biomass.