Correlation value determined to increase Salmonella prediction success of deep neural network for agricultural waters

Buyrukoğlu, Selim; Yılmaz, Yıldıran; Topalcengiz, Zeynal

doi:10.1007/s10661-022-10050-7

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…The use of ML methods has witnessed a significant rise in health science, particularly in areas such as diagnosis, predicting mortality and case numbers during pandemics, and more. [18][19][20][21][22][23][24] The application of ML algorithms in the health sector has become imperative for health organizations in terms of patient interventions and public warnings. A growing body of recent research reflects the increasing interest in ML within healthcare.…”

Section: Adoption Of Machine Learning In Health Sciencementioning

confidence: 99%

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

Yilmaz

2024

Concurrency and Computation

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SummaryThe promising results of ML (machine learning) methods in various disciplines have led to the frequent use of these methods in health fields such as disease diagnosis, personalized medicine, medical image‐based diagnosis, and predicting the number of deaths and cases in a pandemic. However, a neglected area in the field of healthcare is the lack of study with ML to predict treatment outcomes for tuberculosis (TB) patients, particularly children experiencing failed treatment. This need has become more apparent as the coronavirus pandemic has reversed the gains of health institutions with TB disease, especially in children. Therefore, this article conducted a study using the stacked ensemble ML method to early predict the risk for children experiencing a failed treatment outcome of TB. To fulfill this need and determine the most appropriate technique, a two‐stage methodology was followed in this work. First, predictions were obtained by combining the information gain feature selection (IGFS) approach with a variety of single‐based ML algorithms, including logistic regression (LR), deep belief neural networks (DBN), random forest (RF), and decision tree (DT). Second, the proposed method, which includes a stacked ensemble ML technique, was used. The latter model uses LR as a meta‐learner and the aforementioned single‐based ML algorithms (DBN, LR, RF, and DT). The performance results of ML models used in the two stages were compared, and the proposed model which is the combination of the stack‐based ensemble learning model and the IGFS technique provided better ROC curves, accuracy, precision, and recall results.

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Section: Adoption Of Machine Learning In Health Sciencementioning

confidence: 99%

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

Yilmaz

2024

Concurrency and Computation

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“…Makine öğrenmesi birçok alanda kullanılmış ve tatmin edici sonuçlar elde edilmiştir. Örneğin çevre alanında bir çalışmada Buyrukoğlu ve arkadaşları, tarımsal yüzey sularında Salmonella varlığının öngörülmesi için belirlenen korelasyon değerinin başarısını araştırmışlardır [25]. Spor alanında bir çalışma da ise araştırmacılar futbolcuların pozisyonlarını belirlemek için yığılmış topluluk öğrenme modeli önermişler ve 83.9% sınıflandırma başarımı elde etmişlerdir [26].…”

Section: Elektroensefalografiunclassified

EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi

Aslan

2022

DÜMF MD

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Alzheimer beyindeki bozulmalardan kaynaklı bilişsel ve davranışsal eksiklikler gibi semptomlarla kendini gösteren önemli bir nörolojik hastalıktır. Alzheimer hastalığının kesin bir tedavi yöntemi bulunmamaktadır. Ancak hastalığın erken teşhisi ile hastalığın ilerlemesinin yavaşlatılması amaçlanmaktadır. Bu durum hastanın yaşam standartlarının korunmasında önem arz etmektedir. Ayrıca hastalığın tam olarak teşhisi deneyimli bir uzman tarafından değerlendirilecek olan maliyetli testler ve yorucu bir teşhis aşaması gerektirmektedir. Bu motivasyonla önerilen yöntemle Alzheimer hastalığının EEG sinyallerinden otomatik olarak gerçekleştirilmesini amaçlayan yeni bir bilgisayar destekli tanı sistemi sunulmaktadır. Sunulan çalışmada öncelikle ham EEG verilerine önişlem uygulanarak var olan gürültüler giderilmiştir. Sonraki aşamada ise her bir kanaldan alınan verilere dalgacık dönüşümü uygulandıktan sonra istatistiksel özellikler hesaplanmıştır. Elde edilen özelliklerin k-en yakın komşu (kNN) sınıflandırıcısı ile sınıflandırılmasıyla sağlıklı katılımcılar ile Alzheimer hastası katılımcılar 91.12% doğrulukla ayırt edilmiştir.

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“…In addition to artificial neural networks (ANNs), machine learning algorithms, ensemble models, and hybrid models could be used to high prediction performance to assess coal gasification and methanation applications. Deep learning algorithms have also been used to predict coal methanation parameters [18]. Due to the high performance of machine learning methods in different disciplines, these methods have been used frequently in various energy fields, especially in the last decade.…”

Section: Introductionmentioning

confidence: 99%

Birleştirilmiş kömür gazlaştırma ve metanlaştırma sisteminin parçacık sürü optimizasyon yöntemiyle performans değerlendirmesi

Herdem

Yalçın

2022

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Carbon dioxide hydrogenation is a promising method of producing alternative fuels in an environmentally friendly way. Researchers in the current literature have mainly investigated the performance of carbon dioxide hydrogenation systems that use carbon dioxide from various sources and hydrogen from water electrolysis units. In the present study, the performance of a combined coal gasification and methanation unit is investigated to produce methane and power. The carbon dioxide and hydrogen for the methanation unit are provided from the coal gasification system. A Particle swarm optimization (PSO) method is applied to optimize the carbon dioxide and hydrogen values here. Therefore, the water electrolysis unit, which needs high amounts of energy is removed from the system, effectively. The results from the studied system showed that it is possible to produce ~225 kilotons of methane annually by using ~946 kilotons of coal per year. In addition, the results revealed that annual carbon dioxide utilization of ~624.3 kilotons is possible. The system efficiency is estimated at around 49%.

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Correlation value determined to increase Salmonella prediction success of deep neural network for agricultural waters

Cited by 9 publications

References 29 publications

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases

EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi

Birleştirilmiş kömür gazlaştırma ve metanlaştırma sisteminin parçacık sürü optimizasyon yöntemiyle performans değerlendirmesi

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