Topluluk Öğrenmesi ve Bayes Optimizasyonu ile İşaret Dili Tanıma: Derin Öğrenme Temelli Bir Yaklaşım

Year 2025, Volume: 11 Issue: 2, 393 - 409

Andaç Fındıkçı , Musa Balcı , Hüseyin Aydilek , Mustafa Yasin Erten

https://doi.org/10.29132/ijpas.1637971

Abstract

İletişim, insanlık tarihinin en temel ihtiyaçlarından biri olarak sürekli bir evrim geçirmiştir. İlk dönemlerde beden dili ve jestlerle kurulan iletişim, zamanla konuşma dilinin gelişmesiyle daha karmaşık bir yapıya bürünmüştür. Yazının icadı ise iletişimde devrim niteliğinde bir adım olmuştur. Ancak, bu hızlı gelişim iletişim sorunlarını da beraberinde getirmiştir. Günümüz dünyasında, bu problemler üzerine birçok çalışma yapılmakta ve çözüm yolları aranmaktadır. Teknolojik gelişmeler ve yapay zekâ, iletişim sorunlarına çözüm üretme potansiyeli taşımaktadır. Özellikle işitme engelli bireylerle iletişimde yaşanan zorluklar bu alanda öne çıkmaktadır. Bu çalışmada, işaret dili ile iletişimi ko-laylaştırmak amacıyla yapay zekâ algoritmaları kullanılarak Amerikan İşaret Dili'ni tespit eden bir model geliştirilmiştir. InceptionV3, DenseNet169 ve VGG16 gibi derin öğrenme mimarileri kullanılarak oluşturulan model, Kaggle veri seti üzerinde eğitilmiş ve sonuçlar ensemble learning yöntemiyle birleştirilmiştir. Model performansları, Bayes optimizasyonu ile optimize edilmiş ve karmaşıklık matrislerine dayanan metriklerle değerlendirilmiştir. Sonuçlar, ensemble learning modellerinin daha yüksek performans gösterdiğini ortaya koymuş, bu modelin işitme engelli bireylerle iletişimde kullanılabilecek etkili bir araç olabileceği sonucuna ulaşılmıştır.

Keywords

Topluluk öğrenmesi , bayes hiperparametre optimizasyonu , InceptionV3 , DenseNet169 , VGG16

Sign Language Recognition with Ensemble Learning and Bayesian Optimization: A Deep Learning-Based Approach

Abstract

Communication has undergone a continuous evolution as one of the most fundamental needs in human history. Initially, communication was established through body language and gestures, but it became more complex over time with the development of spoken language. The invention of writing marked a revolutionary milestone in the history of communication. However, this rapid advancement also brought about communication challenges. In today’s world, numerous studies focus on addressing these issues and finding effective solutions. Technological advancements and artificial intelligence hold significant potential for solving communication problems. Notably, the difficulties in communicating with individuals who are hearing impaired have become a prominent area of focus. In this study, a model was developed using artificial intelligence algorithms to facilitate communication through sign language, specifically detecting American Sign Language. The model was created using deep learning architectures such as InceptionV3, DenseNet169, and VGG16, and trained on a dataset sourced from Kaggle. The results were combined using the ensemble learning method. The performance of the models was optimized through Bayesian search optimization algorithm and evaluated using metrics derived from confusion matrices. The findings revealed that ensemble learning models demonstrated superior performance, indicating that this model could serve as an effective tool in improving communication with hearing-impaired individuals.

Keywords

Ensemble learning , bayesian hyperparameter optimization , InceptionV3 , Dense-Net169 , VGG16

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