THE IMPACT OF TEXT REPRESENTATION AND PREPROCESSING ON AUTHOR IDENTIFICATION

Author identification, one of the popular topics in text classification and natural language processing, basically aims to determine the author of a given text through various analyses. In the literature, different text representation approaches and use of preprocessing steps are considered for author identification problem. This paper aims to comprehensively examine the impact of text representation and preprocessing steps on author identification specifically for Turkish language. For this purpose, the contributions of all possible combinations of different text representation approaches, namely unigram and bigram, together with the preprocessing tasks, including stemming and stop-word removal, to the performance of author identification are investigated. For the experimental evaluation, a brand new dataset is constituted. Also, two different classification algorithms, namely Multinomial Naive Bayes and Sequential Minimal Optimization, are employed. The results of the experimental analysis reveal that using bigram features alone should be avoided. Besides, it is shown that stop-words should be kept inside the text while stemming can be preferred depending on the classification algorithm so that higher performance can be achieved for author identification

An F-Score-Weighted Indoor Positioning Algorithm Integrating WiFi and Magnetic Field Fingerprints

Indoor positioning systems have attracted much attention with the recent development of location-based services. Although global positioning system (GPS) is a widely accepted and accurate outdoor localization system, there is no such a solution for indoor areas. Therefore, various systems are proposed for the indoor positioning problem. Fingerprint-based positioning is one of the widely used methods in this area. WiFi-received signal strength (RSS) is a frequently used signal type for the fingerprint-based positioning system. Since WiFi signal distribution is nonstationary, accuracy is insufficient. Therefore, the performance of indoor positioning systems can be enhanced using multiple signal types. However, the positioning performance of each signal type varies depending on the characteristics of the environment. Considering the variability of the performances of different signal types, an F-score-weighted indoor positioning algorithm, which integrates WiFi-RSS and MF fingerprints, is proposed in this study. In the proposed approach, the positioning is first performed by maximum likelihood estimation for both WiFi-RSS and magnetic field signal values to calculate the F-score of each signal type. Then, each signal type is combined using F-score values as a weight to estimate a position. The experiments are performed using a publicly available dataset that contains real-world data. Experimental results reveal that the proposed algorithm is efficient in achieving accurate indoor positioning and consolidates the system performance compared to using a single type of signal

Circular traffic sign recognition empowered by circle detection algorithm

21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUS WOS: 000325005300272 Automatic traffic sign detection and recognition is one of the most important components of advanced driver assistance systems. In this paper, a novel method utilizing histograms of oriented gradients based features together with a recently developed and successful circle detection algorithm is proposed for circular traffic sign recognition. In the proposed method, irrelevant backgrounds of traffic signs, whose locations on images are identified within rectangular boundaries, are filtered by detecting their actual circular boundaries. In this way, features representing the traffic signs better can be extracted. The results of the experimental study conducted on a considerably large database demonstrate that the proposed method offers a higher classification performance than the case in which the circle detection is not applied

Effects of Total Knee Arthroplasty on Body Weight and Functional Outcome

[Purpose] We prospectively investigated the effects of arthroplasty surgery on body weight and the effects of obesity on functional outcomes and complications in patients with total knee arthroplasty (TKA). [Subjects] One hundred eighty-six TKA patients were divided into two groups [non-obese body mass index (BMI) = 30 kg/m(2) (n=116)] according to preoperative BMI values. [Methods] Patients were evaluated preoperatively and at 12 months postoperatively with regard to BMI, knee function score, pain, range of knee flexion, and complications. There were no significant postoperative differences between the groups in pain, knee function score, range of knee flexion, and complications. [Results] A statistically significant postoperative improvement was found in all parameters (p<0.05) except for BMI. In the non-obese group, BMI was unchanged whereas in the obese group there was an increase in BMI after surgery. (Conclusion] The results of this Study indicate that patients do not lose weight after arthroplasty surgery. However, arthroplasty surgery offers significant improvements in knee flexion degree, knee function scores, physical function and relief of pain for patients regardless of weight. Increased mobility and decreased pain will not automatically lead to weight reduction. Therefore obesity must be addressed as an independent disease

Physical performance parameters during standing up in patients with unilateral and bilateral total knee arthroplasty

Objective: The aim of this study was to compare quadriceps femoris muscle performance parameters of patients who underwent unilateral and bilateral total knee arthroplasty (TKA)

A comparison of static and dynamic balance in patients with unilateral and bilateral total knee arthroplasty

Objectives: Unilateral and bilateral total knee arthroplasty (TKA) patients were compared with respect to static and dynamic balance in the postoperative sixth and 12(th) months

COVID-19: vaccination vs. hospitalization

Objective Vaccination is the most efficient way to control the coronavirus disease 2019 (COVID-19) pandemic, but vaccination rates remain below the target level in most countries. This multicenter study aimed to evaluate the vaccination status of hospitalized patients and compare two different booster vaccine protocols. Setting Inoculation in Turkey began in mid-January 2021. Sinovac was the only available vaccine until April 2021, when BioNTech was added. At the beginning of July 2021, the government offered a third booster dose to healthcare workers and people aged > 50 years who had received the two doses of Sinovac. Of the participants who received a booster, most chose BioNTech as the third dose. Methods We collected data from 25 hospitals in 16 cities. Patients hospitalized between August 1 and 10, 2021, were included and categorized into eight groups according to their vaccination status. Results We identified 1401 patients, of which 529 (37.7%) were admitted to intensive care units. Nearly half (47.8%) of the patients were not vaccinated, and those with two doses of Sinovac formed the second largest group (32.9%). Hospitalizations were lower in the group which received 2 doses of Sinovac and a booster dose of BioNTech than in the group which received 3 doses of Sinovac. Conclusion Effective vaccinations decreased COVID-19-related hospitalizations. The efficacy after two doses of Sinovac may decrease over time; however, it may be enhanced by adding a booster dose. Moreover, unvaccinated patients may be persuaded to undergo vaccination