Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today’s rapidly changing and complex market environment. This paper examines the impact of ...incorporating Long Short-term Memory (LSTM) models into traditional trading strategies. The core investigation revolves around whether strategies enhanced with LSTM technology perform better than traditional methods alone. Traditional trading strategies typically depend on analyzing current closing prices and various technical indicators to take trading action. However, by applying LSTM models, this study aims to forecast closing prices with greater accuracy, thereby improving trading performance. Our findings indicate that trading strategies that utilize LSTM models outperform traditional strategies. This improvement suggests a significant advantage in using LSTM models for market prediction and trading decision making. Acknowledging that no one-size-fits-all strategy works for every market condition or stock is crucial. As such, traders are encouraged to select and tailor their strategies based on thorough testing and analysis to best suit their needs and market conditions. This study contributes to a better understanding of how integrating LSTM models can enhance traditional trading strategies, offering a path toward more effective decision making in the unpredictable stock market.
To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In ...this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset.
This study compares the effectiveness of fine-tuning Transformer models, specifically BERT, RoBERTa, DeBERTa, and GPT-2, against using prompt engineering in LLMs like ChatGPT and GPT-4 for ...multi-class classification of hotel reviews. As the hospitality industry increasingly relies on online customer feedback to improve services and strategize marketing, accurately analyzing this feedback is crucial. Our research employs a multi-task learning framework to simultaneously conduct sentiment analysis and categorize reviews into aspects such as service quality, ambiance, and food. We assess the capabilities of fine-tuned Transformer models and LLMs with prompt engineering in processing and understanding the complex user-generated content prevalent in the hospitality industry. The results show that fine-tuned models, particularly RoBERTa, are more adept at classification tasks due to their deep contextual processing abilities and faster execution times. In contrast, while ChatGPT and GPT-4 excel in sentiment analysis by better capturing the nuances of human emotions, they require more computational power and longer processing times. Our findings support the hypothesis that fine-tuning models can achieve better results and faster execution than using prompt engineering in LLMs for multi-class classification in hospitality reviews. This study suggests that selecting the appropriate NLP model depends on the task’s specific needs, balancing computational efficiency and the depth of sentiment analysis required for actionable insights in hospitality management.
Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality ...indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar–acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch “Spring Belle”) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.
Collocations have been the subject of much scientific research over the years. The focus of this research is on a subset of collocations, namely metaphorical collocations. In metaphorical ...collocations, a semantic shift has taken place in one of the components, i.e., one of the components takes on a transferred meaning. The main goal of this paper is to review the existing literature and provide a systematic overview of the existing research on collocation extraction, as well as the overview of existing methods, measures, and resources. The existing research is classified according to the approach (statistical, hybrid, and distributional semantics) and presented in three separate sections. The insights gained from existing research serve as a first step in exploring the possibility of developing a method for automatic extraction of metaphorical collocations. The methods, tools, and resources that may prove useful for future work are highlighted.
Kolokacije su već dugi niz godina tema mnogih znanstvenih istraživanja. U fokusu ovoga istraživanja podskupina je kolokacija koju čine metaforičke kolokacije. Kod metaforičkih je kolokacija kod jedne od sastavnica došlo do semantičkoga pomaka, tj. jedna od sastavnica poprima preneseno značenje. Glavni su ciljevi ovoga rada istražiti postojeću literaturu te dati sustavan pregled postojećih istraživanja na temu izlučivanja kolokacija i postojećih metoda, mjera i resursa. Postojeća istraživanja opisana su i klasificirana prema različitim pristupima (statistički, hibridni i zasnovani na distribucijskoj semantici). Također su opisane različite asocijativne mjere i postojeći načini procjene rezultata automatskoga izlučivanja kolokacija. Metode, alati i resursi koji su korišteni u prethodnim istraživanjima, a mogli bi biti korisni za naš budući rad posebno su istaknuti. Stečeni uvidi u postojeća istraživanja čine prvi korak u razmatranju mogućnosti razvijanja postupka za automatsko izlučivanje metaforičkih kolokacija.
Fresh peaches and nectarines are very popular for their high nutritional and therapeutic value. Unfortunately, they are prone to rapid deterioration after harvest, especially if the cold chain is not ...well maintained. The objective of this work is to study the environmental fluctuation and the quality change of fresh peaches and nectarines in cold chain. The temperature, relative humidity, and CO2 level were real-time monitored by sensor nodes with a wireless sensor network (WSN). The cold chain lasted for 16.8 h and consisted of six segments. The dynamic change of temperature, relative humidity, and CO2 level were real-time monitored and analyzed in detail in each of the six stages. The fruit quality index (fruit weight, fruit firmness, and soluble solids concentration (SSC)) were detected and analyzed immediately before the first stage (S1) and at the beginning of the last stage (S6). The results show that without good temperature control fruit softening is the most significant problem, even in a short chain; the WSN node can provide complete and accurate temperature, humidity, and gas monitoring information for cold chains, and can be used to further improve quality and safety assurance for peach fruit cold chains.
This paper deals with the analysis of data retrieved from a web page for booking accommodation. The main idea of the research is to analyze the relationship between accommodation factors and customer ...reviews in order to determine the factors that have the greatest influence on customer reviews. Machine learning methods are applied to the collected data and models that can predict the review category for those accommodations that are not evaluated by users are trained. The relationship between certain accommodation factors and classification accuracy of the models is examined in order to get detailed insight into the data used for model training, as well as to make the models more interpretable. The classification accuracy of each model is tested and the precision and recall of the models are examined and compared.
Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. ...The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.
Translation error analysis in Treat Majcunić, Suzana; Matetić, Maja; Brkić Bakarić, Marija
Zbornik Veleučilišta u Rijeci,
05/2019, Letnik:
7, Številka:
1
Journal Article
Recenzirano
Odprti dostop
The aim of this research paper is to conduct a thorough analysis of inter-annotator agreement in the process of error analysis, which is well-known for its subjectivity and low level of agreement. ...Since the process is tiresome in its nature and the available user interfaces are pretty distinct from what the average annotator is accustomed to, a user-friendly Windows 10 application offering a more attractive user interface is developed with the aim to simplify the process of error analysis. Translations are performed with Google Translate engine and English-Croatian is selected as the language pair. Since there has been a
lot of dispute on inter-annotator agreement and the need for guidelines
has been often been emphasized as crucial, the annotators are given a very detailed introduction into the process of error analysis itself. They are given a presentation with a list of the MQM guidelines enriched
with tricky cases. All annotators are native speakers of Croatian as the target language and have a linguistic background. The results demonstrate that a stronger agreement indicates more similar backgrounds
and that the task of selecting annotators should be conducted more carefully. Furthermore, a training phase on a similar test set is deemed
necessary in order to gain a stronger agreement.
Cilj rada je izvršiti temeljitu analizu slaganja među označivačima u postupku analize pogrešaka koji je poznat po svojoj subjektivnosti i niskoj razini slaganja. Budući da je sam postupak po prirodi zamoran, a sučelja dostupnih alata i usluga poprilično se razlikuju od onog na što je prosječni označivač naviknut, u svrhu pojednostavljenja samog postupka analize pogrešaka razvijena je Windows 10 aplikacija s poznatim
i atraktivnim korisničkim sučeljem. Englesko-hrvatski prijevodi preuzeti su s usluge Google Translate. Budući da je slaganje među označivačima čest predmet rasprave i da je od neospornog značaja istaknuta potreba za smjernicama, označivačima je dan vrlo detaljan uvid
u postupak analize pogrešaka. Također, popis MQM smjernica uz primjere potencijalnih pogrešaka uobličen je u prezentaciju i dan označivačima na
raspolaganje. Označivačima je ciljni, tj. hrvatski jezik materinski, a svi imaju određenu razinu lingvističke pozadine. Rezultati otkrivaju da veća razina slaganja ukazuje na sličnije formalno obrazovanje i da proces odabira označivača treba biti pažljivo osmišljen. Štoviše, testiranje na sličnom skupu podataka trebalo bi prethoditi odabiru označivača kako bi se postigla veća razina slaganja.
Predicting fruit ripeness allows us to choose the optimal time to harvest. The parameter by which peach ripeness is commonly represented is its firmness. As traditional methods for determining ...firmness of peaches are destructive, this paper uses an alternative method for determining peach ripeness which is based on peach impedance, as recommended by the domain expert. The data set on which the data analysis is performed contains measurements obtained from a couple of hundred fruit measurements, which also include peach impedance. In our data analysis, we use one of the high accuracy machine learning models, which are called black box models and which are characterized by low interpretability. The paper presents the results of applying a black box type machine learning method, as well as methods for interpreting black box models which facilitate understanding of the model behavior for domain experts, i.e. Variable importance, Tree Surrogate, Local Interpretable Model-Agnostic Explanations and Break Down. Keywords: interpretability; explainable machine learning; predicting fruit ripeness; peach impedance
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