JUCS - Journal of Universal Computer Science最新文献

{"title":"Price Prediction and Determination of the Affecting Variables of the Real Estate by Using X-Means Clustering and CART Decision Trees","authors":"Sait Can Yucebas, S. Yalpir, Levent Genc, Melike Dogan","doi":"10.3897/jucs.98733","DOIUrl":"https://doi.org/10.3897/jucs.98733","url":null,"abstract":"The use of machine learning in real estate is quite new. When the working area is large, the factors affecting the price may vary according to the geographical regions and socioeconomic factors. It is thought that the price prediction performance of a model that will reflect these differences will be more successful than a general model. Unsupervised learning methods can be used both to increase performance and to show the variation of different factors affecting the price according to regions. With this aim, a hybrid model of X-Means clustering and CART decision trees was established in this study.  This model successfully learned the geographical and physical variables that affect the price. The prediction performance of the model was compared with the direct capitalization method, which is the gold standard in the domain. The hybrid model has a superior performance over direct capitalization in terms of mean square error, root mean square error and adjusted R-Squared metrics. The scores were 72.86, 0.0057 and 0.978, respectively. The effect of clustering was also examined. Clustering increased the prediction performance by 36%. ","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"53 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140652132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An SVR-based and Location-aware Method for Mobile QoS Prediction","authors":"Lifang Ren, Jing Li, Wenjian Wang","doi":"10.3897/jucs.106314","DOIUrl":"https://doi.org/10.3897/jucs.106314","url":null,"abstract":"With the rapid development of intelligent mobile communication technology, the num­ber of mobile services and the number of mobile users are both continuously increasing. So, the services used by a user can only account for a very small proportion of the existing services, which results in a sparse user­service quality of service (QoS) matrix. However, QoS is critical for service selection and service recommendation. Therefore, predicting the unknown values of the sparse QoS matrix is essential. However, due to the sparsity of QoS data, the QoS predic­tion accuracy is difficult to improve. Faced with the problem, this paper intends to utilize the outstanding generalization ability and only support vectors dependent property of support vector regression (SVR) to overcome the difficulty brought by the sparsity of data and predict the un­known QoS more accurately. Moreover, it is evident that in the mobile environment, QoS values are closely related to the locations of the invoking users. Therefore, this paper intends to improve the accuracy of QoS prediction by incorporating not only the information of similar users but also the information of nearby users into feature vectors. On the other hand, the known QoS values of nearby users can be used to roughly estimate the unknown QoS values of the cold­start user, so as to alleviate the cold­start problem to some extent. Thus, a location­aware SVR­based method for QoS prediction (SVR4QP) is proposed. Compared with some classical QoS prediction algorithms, the experimental results show that in 1/3 of the cases, SVR4QP is moderate; in 1/6 of the cases, SVR4QP is suboptimal; and in half of the cases, SVR4QP is optimal. Compared with some novel mobile QoS prediction methods, the experimental results show that in 1/4 of the cases, SVR4QP is moderate; in half of the cases, SVR4QP is suboptimal; and in 1/4 of the cases, SVR4QP is op­timal. All these indicate that SVR4QP has comparatively more accurate mobile QoS prediction.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"117 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic Fracterm Calculus","authors":"Jan Bergstra, John V. Tucker","doi":"10.3897/jucs.107082","DOIUrl":"https://doi.org/10.3897/jucs.107082","url":null,"abstract":"Previously, in [Bergstra and Tucker 2023], we provided a systematic description of elementary arithmetic concerning addition, multiplication, subtraction and division as it is practiced. Called the naive fracterm calculus, it captured a consensus on what ideas and options were widely accepted, rejected or varied according to taste. We contrasted this state of the practical art with a plurality of its formal algebraic and logical axiomatisations, some of which were motivated by computer arithmetic. We identified a significant gap between the wide embrace of the naive fracterm calculus and the narrow precisely defined formalisations. In this paper, we introduce a new intermediate and informal axiomatisation of elementary arithmetic to bridge that gap; it is called the synthetic fracterm calculus. Compared with naive fracterm calculus, the synthetic fracterm calculus is more systematic, resolves several ambiguities and prepares for reasoning underpinned by logic; indeed, it admits direct formalisations, which the naive fracterm calculus does not. The methods of these papers may have wider application, wherever formalisations are needed to analyse and standardise practices.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"126 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel LSB Steganography Technique Using Image Segmentation","authors":"Yasir Yakup Demircan, Serhat Ozekes","doi":"10.3897/jucs.105702","DOIUrl":"https://doi.org/10.3897/jucs.105702","url":null,"abstract":"Steganography is a process to hide data inside a cover file mostly used in media files like image, video, and audio files. Least significant bit (LSB) steganography is a technique where the least significant bits of pixels are used for information hiding. The purpose of using only those bits is to minimize the visual impact of the hidden data on the image file. LSB technique of steganography is one of the most popular forms of steganography available today. As a result, various steganalysis techniques are developed for this steganography technique. One of them is the visual analysis of pixels through pixel modifications to expose hidden data in a visual manner. The proposed method achieves resistance to this attack using an image segmentation model and extracting the most texture-complex areas of an image and hiding information in these specific areas as pseudo-randomized least significant bit replacements. As the outcome of the study, an alternative approach to LSB steganography that results competitively with existing methods is provided. ","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"126 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of CNC Vibration Speeds by Heralick Features","authors":"Melih Kuncan, Kaplan Kaplan, Yılmaz Kaya, Mehmet Recep Mínaz, H. M. Ertunç","doi":"10.3897/jucs.106543","DOIUrl":"https://doi.org/10.3897/jucs.106543","url":null,"abstract":"In the contemporary landscape of industrial manufacturing, the concept of computer numerical control (CNC) has emerged due to the optimization of conventional machinery, distinguished by its remarkable precision and expeditious processing capabilities. These inherent advantages have seamlessly paved the way for the pervasive integration of CNC machines across a myriad of industrial manufacturing sectors. The present study embarks upon a comprehensive inquiry, delving into the intricate analysis of a specialized prototype CNC molding machine, encompassing a meticulous assessment of its structural rigidity, robustness, and propensity for vibrational occurrences. Moreover, an insightful exploration is undertaken to discern the intricate interplay between vibrational signals and intricate machining processes, particularly under diverse conditions such as the presence or absence of the cutting tool, and at varying rotational speeds denoted in revolutions per minute (RPM). The trajectory of this research voyage encompasses an extensive array of empirical experiments meticulously conducted on the prototype CNC machine, with synchronous real-time acquisition of vibrational data. This empirical journey starts by generating two distinct datasets, each meticulously designed to encompass an assemblage of seven distinct rotational speeds, spanning the spectrum from 18000 to 30000 RPM, thereby facilitating enhanced diversity within the dataset. In parallel, a secondary dataset is meticulously derived from the CNC machine operating in the absence of the cutting tool, thereby encapsulating an exhaustive range of 20 discrete RPM values. The extraction of pivotal features aimed at discerning between the vibrational signals arising from distinct conditions (i.e., those emanating from situations involving the presence or absence of the cutting tool) and the associated variance in CNC machine speeds is facilitated through an innovative framework grounded in co-occurrence matrices. The culmination of this methodological framework is the identification of discernible co-occurrence matrices, thereby facilitating the subsequent computation of Heralick features. The classification effort was performed systematically using 10-fold cross-validation analysis, covering a number of different machine learning models. The outcomes emanating from this intricate sequence of systematic methodologies underscore remarkable achievements. Specifically, the classification of vibrational signals corresponding to varying CNC machine speeds, contingent upon the presence or absence of the cutting tool, yields commendable accuracy rates of 94.27% and 94.16%, respectively. Notably, an exemplary accuracy rate of 100% is attained when classifying differing conditions (i.e., situations involving the presence or absence of the cutting tool) across specific RPM settings, prominently at 22000  24000  26000  28000  and 30000 RPM. ","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"140 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Data-Driven Attack Method on Machine Learning Models","authors":"Emre Sadıkoğlu, Irfan Kösesoy, Murat Gök","doi":"10.3897/jucs.108445","DOIUrl":"https://doi.org/10.3897/jucs.108445","url":null,"abstract":"With the increasing popularity and usage of artificial intelligence systems, it has become crucial to address their vulnerability to cyber-attacks. In this study, we propose a novel gradient descent-based method to generate fake data that can be accepted as positive by a targeted machine learning model. Our method is designed to generate a large number of positive samples with a minimal number of probes to the model, making it difficult to detect by security systems. Additionally, we develop an alternative model to the attacked model using a reverse engineering approach, trained on a dataset composed of the samples generated by our method. We evaluate the success of our proposed method and the alternative model through a series of experiments. We conducted experiments on six distinct datasets, each of which was trained using three separate machine-learning algorithms. This resulted in a total of eighteen unique models that were evaluated and compared in our analysis. In the evaluation of results, the most commonly used metrics in the literature, including effective attack rate (EAR), accuracy, precision, recall, and F1 score, were employed. Focusing particularly on EAR-oriented assessments, our method demonstrates its effectiveness with a notably high EAR of 97% in the combination of the kNN method and the Cancer dataset. According to the results of our experiments, the proposed method demonstrates high effectiveness as a data-driven attack method.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"58 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Content Modeling in Smart Learning Environments: A systematic literature review","authors":"Alberto Jiménez-Macías, P. Muñoz-Merino, Margarita Ortiz-Rojas, Mario Muñoz-Organero, C. Delgado Kloos","doi":"10.3897/jucs.106023","DOIUrl":"https://doi.org/10.3897/jucs.106023","url":null,"abstract":"Educational content has become a key element for improving the quality and effectiveness of teaching. Many studies have been conducted on user and knowledge modeling using machine-learning algorithms in smart-learning environments. However, few studies have focused on content modeling to estimate content indicators based on student interaction. This study presents a systematic literature review of content modeling using machine learning algorithms in smart learning environments. Two databases were used: Scopus and Web of Science (WoS), with studies conducted until August 2023. In addition, a manual search was performed at conferences and in relevant journals in the area. The results showed that assessment was the most used content in the papers, with difficulty and discrimination as the most common indicators. Item Response Theory (IRT) is the most commonly used technique; however, some studies have used different traditional learning algorithms such as Random Forest, Neural Networks, and Regression. Other indicators, such as time, grade, and number of attempts, were also estimated. Owing to the few studies on content modeling using machine learning algorithms based on interactions, this study presents new lines of research based on the results obtained in the literature review.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"22 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What is the Consumer Attitude toward Healthcare Services? A Transfer Learning Approach for Detecting Emotions from Consumer Feedback","authors":"Bashar Alshouha, J. Serrano-Guerrero, D. Elizondo, Francisco P. Romero, J. A. Olivas","doi":"10.3897/jucs.104093","DOIUrl":"https://doi.org/10.3897/jucs.104093","url":null,"abstract":"The capability of offering patient-centered healthcare services involves knowing the consumer needs. Many of these needs can be conveyed through opinions about services that can be found on social networks. The consumers/patients can express their complains, satisfaction, frustration, etc. in terms of feelings and emotions toward those services; for that reason, it is pivotal to accurately detect them. There are many recent techniques to detect sentiments or emotions, but one of the most promising is transfer learning. This allows adapting a model originally trained for a task to a different one by fine-tuning. Following this idea, the primary objective of this research is to study whether several pre-trained language models can be adapted to a task such as patient emotion detection in an efficient manner. For this purpose, seven clinical and biomedical pre-trained models and four domain-general models have been adapted to detect multiple emotions. These models have been tuned using a dataset consisting of real patient opinions which convey several emotions per opinion. The experiments carried out state the domain-specific pre-trained models outperform the domain-general ones. Particularly, Clinical-Longformer obtained the best scores, 98.18% and 95.82% in terms of accuracy and F1-score, respectively. Analyzing the patient feedback available on social networks may provide valuable knowledge about consumer sentiments and emotions, especially for healthcare managers. This information can be very interesting for purposes such as assessing the quality of healthcare services or designing patient-centered services.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"2 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139592154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Filtering Techniques for Object Recognition in Autonomous Vehicles","authors":"Ngo Le Huy Hien, A. Kor, Mei Choo Ang, É. Rondeau, J. Georges","doi":"10.3897/jucs.102428","DOIUrl":"https://doi.org/10.3897/jucs.102428","url":null,"abstract":"The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, and reduce detection time by up to 43.98% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"1 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139592166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dimensionality Reduction for Hierarchical Multi-Label Classification: A Systematic Mapping Study","authors":"Raimundo Osvaldo Vieira, Helyane Bronoski Borges","doi":"10.3897/jucs.91309","DOIUrl":"https://doi.org/10.3897/jucs.91309","url":null,"abstract":"Hierarchical multi-label classification problems typically deal with datasets with many attributes and labels, which can negatively impact the classifier performance. The application of dimensionality reduction methods can significantly improve the performance of classifiers. Dimensionality reduction can be performed by feature extraction or feature selection, according to the problem domain and datasets characteristics. This work carried out a systematic literature mapping to identify the approaches and techniques of dimensionality reduction that have been used in hierarchical multi-label classification tasks. Searches were performed on 7 important databases for the Computer Science field. From a list of 184 retrieved papers, 12 were selected for analysis, from which it was possible to determine a general overview of studies conducted from 2010 to 2022. It was identified that feature selection was the most frequent reduction method, with filter approach standing out. In addition, it was detected that most of the works used tree hierarchical structure. As its main outcome, this paper presents the state of the art of dimensionality reduction problem for hierarchical multi-label classification, indicating trends and research issues in the field.","PeriodicalId":124602,"journal":{"name":"JUCS - Journal of Universal Computer Science","volume":"1 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139592164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}