1,721,043 research outputs found
Novel random models of entity mobility models and performance analysis of random entity mobility models
No full textIt has become possible to collect data from geographically large areas with smart devices that are prevalently used today. Sensors that are integrated into smart devices make it possible for these devices to receive and transmit data wirelessly. The most important problem of this model that is known as mobile crowd sensing and that allows inferences on the data obtained from its users is lack of data. The main reason for this problem is the lack of sufficient usage of the sensors on devices by the user. To increase the amount of data collected, while users may be incentivized in various ways, the amount and accuracy of the collected data may be increased by developing random entity mobility models (REMMs). In this study, two new models (random point and random journey) were proposed as alternatives to existing REMMs. In the experiment environment that was created to measure the performances of the proposed models, their performances were compared to those that are currently used prevalently (random waypoint (RWP), random walk (RW), and random direction (RD)). In the experiment environment, the performances were compared in terms of three different metrics (visiting rates of nodes, rates of reaching the basis, and the number of messages they carried to the basis). The greatest increase in differently sized areas and at different numbers of nodes in the RP model in terms of rates of reaching the basis was 2.6% compared to RWP, 7% compared to RW, and 46.34% compared to RD, while these values for the number of nodes that were visited were 3% compared to RWP, 1.5% compared to RW, and 17.67% compared to RD. In the same conditions in terms of the metric on the number of messages, the model collected 1465.4, 2933.46, and 7260.12 more messages than those in respectively RWP, RW, and RD. The greatest increase in differently sized areas and at different numbers of nodes in the RJ model in terms of reaching the basis was 1% compared to RWP, 3.5% compared to RW, and 25% compared to RD, while these values for the number of nodes that were visited were 0.75% compared to RWP, 2% compared to RW, and 21.4% compared to RD. In the same conditions in terms of the metric on the number of messages, the model collected 1109.56, 1534.26, and 4488.5 more messages than those in RWP, RW, and RD, respectively
Research on behavior of two new random entity mobility models in 3-D space
No full textMobile data collectors that can be used without a central control mechanism currently have common use in many fields. Because they do not need a central unit, each node in a network can move independently. The field literature offers various group- or entity-based models to define the functioning of mobile data collectors. In this study, a random entity mobility model (REMM) research was performed. The study was based on the models random walk (RW) and random waypoint (RWP), used in several former studies mentioned in the literature. Furthermore, the models random point (RP) and random journey (RJ) proposed by Bilgin [1] for two-dimensional (2D) space were transferred to three-dimensional (3D-cubic) to be used in the study. Study findings obtained by defining a various number of fixed nodes in areas of various sizes were analyzed using 4 different metrics. It was observed that 4 different metric values decreased for 4 REMMs when the cubic area was enlarged by increasing the edge lengths (150-200-250 pixel) of the cubic. When the cubic's edge length is 150-200-250 pixel, respectively, connected node ratio (CNR) metric value is 98.04%-95.8%-91.34% for RP and 96.83%-83.23%-70% for RJ. Provided that the cubic area remains constant, the increases in the number of nodes generally tend to increase, although there are slight fluctuations on the results. When the cubic edge is 200 and the node numbers are 4-64-10, the message delay is 13.345-16.566-27.386-40.050 seconds for RW and 6.579-9.124-11.431-13.456 seconds for RWP. In the comparisons made by taking the average of the values obtained according to the size of the cubic area and the number of nodes, the RP model reached the highest values for all metrics. For example, the visited node ratio (VNR) metric average for the cubic edge 200 pixels is 98.76% for RP and 94.68%-87.38%-94.78% for RW-RWP-RJ. The VNR metric for the cubic edge 250 is 96.55%-93.7%-87.45%-51.27% for the RP-RW-RWP-RJ. Similarly, the average values obtained for other metrics prove this situation. In addition, when the results of the study are examined, it has been measured that the RP model can deliver the message to the base with less delay than other models. The average delay for the cubic edge 150 is 2.933-27.667-23.236-5.698 second for the RP-RW-RWP-RJ and 2.846-24.337-10.148-4.293 second when the edge is 200. When the average results obtained were examined, the success ranking in the delay metric was RP-RJ-RWP and RW, while the other metrics were formed as RP-RJ-RW-RWP. Considering all the obtained results, it was seen that the proposed two models achieved better results than the existing models in 3D after 2D
Gated transformer network based EEG emotion recognition
No full textMulti-channel Electroencephalogram (EEG) based emotion recognition is focused on several analysis of frequency bands of the acquired signals. In this paper, spectral properties appeared on five EEG bands (delta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document}, theta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document}, alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document}, beta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document}, gamma\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document}) and gated transformer network (GTN) based emotion recognition using EEG signal are proposed. Spectral energies and differential entropies of 62-channel signals are converted to 3D (sequence-channel-trial) form to feed the GTN. The GTN with enhanced gated two tower based transformer architecture is fed by 3D sequences extracted from SEED and SEED-IV emotional datasets. 15 participants' states in session 1-3 are evaluated using the proposed GTN based sequence classification, and the results are repeated by 3x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document} shuffling. Totally, 135 times training and testing are performed on each dataset, and the results are presented. The proposed GTN model achieves mean accuracy rates of 98.82% on the SEED dataset and 96.77% on the SEED-IV dataset for three and four emotional state recognition tasks, respectively. The proposed emotion recognition model can be employed as a promising approach for EEG emotion recognition
Classification of Turkish tweets by document vectors and investigation of the effects of parameter changes on classification success
No full textNatural language processing is an artificial intelligence field which is gaining in popularity in recent years. To make an emotional deduction from texts related to an issue, or classify documents are of great importance considering the increasing data size in today's world. Understanding and interpreting written texts is a feature that pertains to people. But, it is possible to deduce from texts or classify texts using natural language processing which is a sub-branch of machine learning and artificial intelligence. In this study, both text classification was made on Turkish tweets, and text classification success of method parameter changes was investigated using two different methods of the algorithm mentioned as document vectors in the literature. It was found in the study that as well as higher accuracy values were obtained by the DBoW (Distributed Bag of Words) method than DM (Distributed Memory) method; higher accuracy values were also obtained by DBoW-NS (Negative Sampling) architecture than others
Schema retrieval with embeddings and vector stores using retrieval-augmented generation and llm-based sql query generation
No full textIn today's world, where the volume and variety of data are increasing at an extraordinary rate, extracting meaningful insights from data is of critical importance; however, the complexity of standard database query languages makes it difficult for users without technical expertise to access information. This study proposes an innovative Retrieval-Augmented Generation (RAG) architecture that analyzes natural language queries, identifies related database schemas, and automatically converts them to SQL. Unlike fixed schema selection (fixed-k) methods, a unique hierarchical clustering mechanism is introduced to dynamically determine the number of relevant schemas, minimizing noise. Furthermore, the architecture incorporates an iterative repair mechanism, data enrichment with sample rows, and a hybrid query strategy (Turkish + English) to overcome cross-lingual barriers. Performance evaluations on 15 databases demonstrate that the proposed method improved the schema retrieval F1 score from 0.79 to 0.88. In the SQL generation phase, the execution accuracy (EX) of the GPT-4o model increased from 0.70 to 0.78 with the proposed optimizations, representing an approximate 11% improvement relative to the baseline configuration without requiring fine-tuning.Bilimsel Araştırma Projeleri Birimi, İstanbul Teknik Üniversitesi FGA-2026-253
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A novel method proposal to ıncrease the classification success of Turkish text
No full textBu çalışma, yazarı bilinmeyen bir dokümanının yazarını tahmin etmeyi amaçlamaktadır. Bunun için 6 farklı köşe yazarına ait 6 köşe yazısı öncelikle ön-işlem aşamasına sokulmuştur. Ardından bu metinler üzerinden n-gram (2-3) ile özellikler çıkarılmıştır. Çıkarılan özellikler üzerinden sistem 6 farklı makine öğrenmesi üzerinde çapraz geçerleme (10) ile test edilmiştir. Buraya kadar olan kısım literatürde şimdiye kadar uygulanmış olan yöntemdir. Bizim önerimiz ön işlem aşamasının ardından eldeki metinleri LZW algoritması ile kayıpsız sıkıştırarak özellik sayısını azaltmak ve bunun sistemin başarısı üzerindeki etkileri araştırmak üzerinedir. Ön-işlemden geçmiş olan metinler LZW algoritması ile binary (ikili) ve decimal (onlu) olarak sıkıştırılır. Sıkıştırmanın ardından n-gram (2-3) ile çıkarılan özellikler ile sistem 6 farklı makine öğrenmesi yönteminde test edilmiş ve çalışma sonuçları 5 farklı metrik için incelenmiştir. Yapılan çalışma sonucunda ikili olarak sıkıştırılmış metinler hem 2-gram hem de 3-gramda, 6 farklı makine öğrenmesi algoritmasında da daha iyi sonuçlar elde etmiştir. Random Tree ve Naïve bayes algoritmasında onlu sıkıştırma, ham verinin gerisinde kalsa da diğer 4 algoritmada daha iyi elde sonuçlar elde etmiş ama ortalama başarı değerlerinde geride kalmıştır. Yapılan çalışma sonucunda ikili sıkıştırma tüm metriklerinde diğer iki yönteme göre daha başarılıdır. Yapılan çalışmada yazar tanıma işlemi yapılmış olsa da önerilen bu yöntemin tüm metin sınıflandırma işlemlerinde kullanılabileceği düşünülmektedir.This study aims to estimate the author of an unknown document. For this purpose, first of all, six different columns of 6 different columnists were pre-processed. Then with n-grams (2-3) features were extracted from these texts. The system has been tested with 10-fold cross-validation on 6 different machine learning algorithms. This part of the study is the method that has been applied so far in the literature. Our suggestion is to reduce the number of features with the LZW algorithm and to investigate the effects on the success of the system. The pre-processed texts are compressed binary and decimal with the LZW algorithm. After compression, the system has been tested with 6 different machine learning algorithms, and the study results has been analyzed for 5 different metrics. As a result of the study, the compressed binary text has obtained better results in both 2-gram and 3-gram, for 6 different machine learning algorithms. In the Random-Tree and Naïve Bayes algorithm, decimal compression is behind the raw data. In the other four algorithms, it achieved better results but remained behind the average success values. As a result of the study, binary compression is more successful in all metrics than the other two methods. In the study, although the author recognition process has been done, it can be thought that the proposed method can be used in all text classification procedures
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