A Neural Network Approach for Assessing the Relationship Between Grip Strength and Hand Anthropometry

Dağdeviren

2021

Educ Inf Technol

Self Cite

In recent years, there has been an increase in the demand for higher education in Turkey, where the demand, as in most other countries, exceeds what is available. The main purpose of this research is to develop machine learning algorithms for predicting the percentage of student placement based on the data related to the university's academic reputation, opportunities of the city where the university is located, facilities and cultural opportunities of the university. When the model accuracy was evaluated on the basis of performance metrics, the Extreme Gradient Boosting (XGBoost) algorithm showed greater predictive accuracy than other machine learning approaches. A sensitivity analysis was performed using the extreme gradient boosting machines algorithm to identify the degree to which the input variables contribute to the determination of the output variable. Five input variables, namely the percentage of student placement at year t-1, the university scientific document score, university Phd programme score, university faculty member/student score, and the percentage of student placement at year t-2 were found to be the most effective parameters. Prediction and sensitivity analysis results obtained in this study can be used in many different ways, such as determining the quotas for universities, allocating resources, and making new regulations.

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Predicting the percentage of student placement: A comparative study of machine learning algorithms

Dağdeviren

2021

Educ Inf Technol

Self Cite

“…Regresyon analizi, olarak sembolize edilen bir bağımlı değişken ve olarak sembolize edilen bir veya daha fazla bağımsız değişkenden oluşan nicel değerler arasındaki ilişkiyi incelemek ve tahmin etmek için kullanılan istatiksel bir yöntemdir [42]. Regresyon analizi bir gözeticili öğrenme Şekil 4.…”

Section: Lineer Regresyon Algoritması (Lineer Regression Algorithm)unclassified

“…Elde edilen öğrenmenin başarımını ölçmek içini eğitim kümesinden bağımsız ve ağın karşılaşmadığı değerler verilerek ağ test edilir. Bu veri kümesi test kümesi ya da test seti olarak adlandırılır [42]. Bunlara ek olarak, ağın eğitimi sırasında genellemenin kötüleştiğini yani aşırı eğitmeyi tespit edebilmek adına doğrulama ya da sınama kümesi adı veriler bir veri seti eğitim seti içerisinden belli bir oranda seçilebilir.…”

Section: Lineer Regresyon Algoritması (Lineer Regression Algorithm)unclassified

Değiştirilebilir konum süresine sahip takip cihazlarında kümeleme parametrelerinin tahmini ve anomali tespiti

Datlica¹,

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

2020

In this study, it was aimed to detect anomalies in the location behavior of objects followed by a tracking device.Theory and Methods: ST-DBSCAN (Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise) density-based clustering algorithm was applied on the data obtained, and weekly patterns were determined for the subject to be located at which time intervals. The input parameters of the ST-DBSCAN algorithm vary according to the frequency of the data from the tracker and the total number of data packets. In this context, the parameters used in the St-DBSCAN algorithm, as well as the frequency of sending data and the number of data packets, are labeled according to the behavior of the object being followed. On these tagged data, linear regression and artificial neural networks methods were compared and a model was proposed that could predict clustering parameters. Results:Weekly patterns were determined by methods developed using information about the object being followed, and these patterns were considered to be normal behaviors of the object being tracked. The instantaneous position is defined as an anomaly if the data obtained is contrary to the pattern. Conclusion:In this study, the temporal data of the object, called time and coordinate data, were grouped with similar ones with the help of clustering algorithms, and were defined as anomalies when data other than sets that were normally considered to be able to predict with these sets.

“…The main aim of MLR model is to allow predicting the relationship among variables including several independent or predictive variables and a dependent or criterion variable (Çakıt, Durgun, & Cetik, 2015).…”

Section: Multiple Linear Regression Modelmentioning

confidence: 99%

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

Karwowski

2017

Hum Ftrs & Erg Mfg Svc

Self Cite

This study aimed to develop an adaptive neuro‐fuzzy inference system (ANFIS) approach to estimate the normalized electromyography (NEMG) responses, where the independent variables are demographic variables including population, gender, ethnicity, age, height, weight, posture, and muscle groups. The study groups comprised 75 US‐based (54 males and 21 females) and 10 Japan‐based (all males) automobile assembly workers. A total of 65 inputs and 1 output reflecting the NEMG values were considered at the beginning. After correlating analysis results, a total of 35 significant predictors were considered for both ANFIS and regression models. The data were partitioned into two datasets, one for training (70% of all data) and one for validation (30% of all data). In addition to a soft‐computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANFIS approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, ANFIS had better predictive accuracy than MLR, as demonstrated by the experimental results. Overall, this study demonstrates that the ANFIS approach can predict normalized EMG responses according to subjects’ demographic variables, posture, and muscle groups.